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T. Sugimoto1,2, T. Sakurai1,3, H. Akatsu4, T. Doi5, Y. Fujiwara6, A. Hirakawa7, F. Kinoshita8, M. Kuzuya9, S. Lee5, K. Matsuo10, M. Michikawa11, S. Ogawa6, R. Otsuka12, K. Sato13, H. Shimada14, H. Suzuki15, H. Suzuki6, H. Takechi16, S. Takeda17, H. Umegaki9, S. Wakayama18, H. Arai19, On behalf of the J-MINT investigators


1. Center for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Japan; 2. Medical Genome Center, National Center for Geriatrics and Gerontology, Obu, Japan; 3. Department of Cognition and Behavior Science, Nagoya University Graduate School of Medicine, Nagoya, Japan; 4. Department of Community-based Medical Education, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan; 5. Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan; 6. Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan; 7. Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan; 8. Department of Advanced Medicine, Nagoya University Hospital, Nagoya, Japan; 9. Department of Community Healthcare and Geriatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan; 10. Department of Dentistry and Oral-Maxillofacial Surgery, School of Medicine, Fujita Health University, Toyoake, Japan; 11. Department of Biochemistry, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan; 12. Section of the National Institute for Longevity Sciences‒Longitudinal Study of Aging, National Center for Geriatrics and Gerontology, Obu, Japan; 13. Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan; 14. Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan; 15. Sompo Care Inc., Tokyo, Japan; 16. Department of Geriatrics and Cognitive Disorders, School of Medicine, Fujita Health University, Toyoake, Japan; 17. Department of Clinical Psychology, Tottori University Graduate School of Medical Sciences, Yonago, Japan; 18. Sompo Holdings, Inc., Tokyo, Japan; 19. National Center for Geriatrics and Gerontology, Obu, Japan.

Corresponding Author: Takashi Sakurai, 7-430 Morioka, Obu, Aichi, 474-8511, Japan, Tel: +81-562-46-2311, E-mail:

J Prev Alz Dis 2021;
Published online June 9, 2021,



Background/Objectives: The Japan-multimodal intervention trial for prevention of dementia (J-MINT) is intended to verify the effectiveness of multi-domain interventions and to clarify the mechanism of cognitive improvement and deterioration by carrying out assessment of dementia-related biomarkers, omics analysis and brain imaging analysis among older adults at high risk of dementia. Moreover, the J-MINT trial collaborates with partnering private enterprises in the implementation of relevant interventional measures. This manuscript describes the study protocol.
Design/Setting: Eighteen-month, multi-centered, randomized controlled trial.
Participants: We plan to recruit 500 older adults aged 65-85 years with mild cognitive impairment. Subjects will be centrally randomized into intervention and control groups at a 1:1 allocation ratio using the dynamic allocation method with all subjects stratified by age, sex, and cognition.
Intervention: The multi-domain intervention program includes: (1) management of vascular risk factors; (2) group-based physical exercise and self-monitoring of physical activity; (3) nutritional counseling; and (4) cognitive training. Health-related information will be provided to the control group every two months.
Measurements: The primary and secondary outcomes will be assessed at baseline, 6-, 12-, and 18-month follow-up. The primary outcome is the change from baseline to 18 months in a global composite score combining several neuropsychological domains. Secondary outcomes include: cognitive change in each neuropsychological test, incident dementia, changes in blood and dementia-related biomarkers, changes in geriatric assessment including activities of daily living, frailty status and neuroimaging, and number of medications taken.
Conclusions: This trial that enlist the support of private enterprises will lead to the creation of new services for dementia prevention as well as to verify the effectiveness of multi-domain interventions for dementia prevention.

Key words: Cognitive decline, multidomain intervention, physical exercise, nutrition, cognitive training.



The number of people living with dementia is estimated at 46.8 million in 2015 worldwide, and is expected to increase to 131.5 million by 2050 (1). In recent years, several studies have reported a decreasing incidence of dementia in Western countries (2-4), which, however, is not the case in Japan (5). Given the high drug development failure rate in Alzheimer’s disease (AD) (6), especially for disease-modifying therapies, developing successful non-pharmacological strategies to prevent dementia is an urgent priority.
To date, several large multi-domain prevention trials have shown that interventions targeting multiple modifiable risk factors for dementia simultaneously in older adults, especially in those at increased risk of dementia, could slow cognitive decline and reduce incident dementia (7-9). The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) is the first large randomized controlled trial (RCT) which demonstrated that a multi-domain lifestyle intervention (dietary counseling, physical exercise, cognitive training, and vascular and metabolic risk monitoring) could ameliorate cognitive decline in older adults at increased risk of developing dementia, though the effect size (difference between control and intervention groups) was very small (Cohen’s d = 0.13) (7). Sub-group analyses of the other two RCTs, the Prevention of Dementia by Intensive Vascular Care (PreDIVA) (8) and the Multidomain Alzheimer Preventive Trial (MAPT) (9) also showed the effectiveness of multi-domain intervention in older adults at increased risk of dementia, although the primary outcomes were not statistically significant. These studies provide important methodological lessons: selection of at-risk individuals; appropriate timing and intensity of the interventions implemented; and selection of appropriate sensitive tools to detect changes in cognition (10, 11). However, further studies are still needed to verify the superior effectiveness of multi-domain interventions in different settings and populations (10, 11). In addition, the mechanism of effectiveness of such interventions should be further explored. In this context, the World-Wide FINGERS (WW-FINGERS) Network was launched at the 2017 Alzheimer’s Association International Conference (AAIC) in London (12). This network aims to test, adapt, and optimize the FINGER multi-domain lifestyle model for use in various populations and settings. Furthermore, data sharing is an important mission of this network, with about 30 countries around the world participating in the network (12).
In Japan, the Japan-Multimodal Intervention Trial for Prevention of Dementia (J-MINT) was started in 2019 and included in the WW-FINGERS Network (12). The aim of the J-MINT trial is to verify whether multi-domain intervention, which consists of management of vascular risk factors, group-based physical exercise and self-monitoring of physical activity, nutritional counseling, and cognitive training, could prevent the progression of cognitive decline among older adults with mild cognitive impairment. Specifically, assessment of blood-based biomarkers, omics analysis and neuroimaging make up the core of the J-MINT trial toward elucidation of the mechanism of cognitive improvement and deterioration. Additionally, the J-MINT trial collaborates with partnering private enterprises in the implementation of relevant interventional measures. This manuscript describes the study protocol for the J-MINT trial developed in accordance with the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 statement (see Appendix 1).



Study design

The J-MINT trial is designed as an 18-month, open-labeled, randomized, controlled, multicenter trial of multidomain interventions designed to target modifiable risk factors for dementia among older adults with mild cognitive impairment. Being organized by a central coordinating center at the National Center for Geriatrics and Gerontology (NCGG), this study involves the following 4 other centers located in Japan: Nagoya University; Nagoya City University, Fujita Health University, and Tokyo Metropolitan Institute of Gerontology. A total of 500 subjects being recruited at these centers will be randomized into intervention or control groups during the course of the trial. Multi-domain interventions provided by partnering private enterprises will cover the following 4 domains: management of vascular risk factors (diabetes, hypertension, and dyslipidemia), group-based physical exercise and increasing physical activity, nutritional counselling, and cognitive training using Brain HQ (Posit Science Corporation). The control group will be given instructions on the management of vascular risk factors and health-related information in writing. The primary and secondary outcomes will be assessed in both groups at baseline, 6, 12, and 18 months. The study flow diagram is shown in Figure 1.

Figure 1. Study flow of the Japan-multidomain intervention trial for prevention of dementia (J-MINT)


Ethic committee review and approval

All study procedures have been reviewed and approved by the Institutional Review Boards (IRBs) at each study site and the trial has been registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) as number UMIN000038671. The purpose, nature, and potential risks of participation in the trial will be fully explained to the participants, and all participants will provide written informed consent before participating in the trial.

Eligibility criteria (Inclusion/exclusion criteria)

Table 1 shows the inclusion and exclusion criteria of the J-MINT trial. In order to focus on those at increased risk of dementia, the target population for the trial was defined as older adults with mild cognitive impairment. All prospective participants will be evaluated for the presence of mild cognitive impairment by using the National Center for Geriatrics and Gerontology Functional Assessment Tool (NCGG-FAT) which has been established as a screening tool for older adults at high risk of incident dementia (13, 14). Participants are to be deemed eligible for entry in the J-MINT trial (1) if they are 65-85 years old at the time of enrollment; (2) if they have age- and education-adjusted cognitive decline with a standard deviation (SD) of 1.0 or more from the reference threshold for one or more of the four cognitive domains of memory, attention, executive function, and processing speed, as measured with the NCGG-FAT; and (3) if they are able to provide written informed consent for themselves (Table 1).
Participants are to be excluded: (1) if they need to restrict any physical exercise and/or diet due to functional decline, including severe bone or joint disease, renal failure, and unstable ischemic heart disease and cardiopulmonary disorders; (2) if they were diagnosed with dementia; (3) if they have a Mini-Mental State Examination (MMSE) score of less than 24 (15); (4) if they have a care certificate for long-term care facilities; (5) if they are unable to speak Japanese; (6) if they are unable to undergo cognitive tests; (7) if they are deemed ineligible for enrollment by the responsible investigator or co-investigator at each study site (Table 1).

Table 1. Inclusion and exclusion criteria for J-MINT study.

Note. Abbreviations: J-MINT, Japan-multimodal intervention trial for prevention of dementia; MMSE, Mini-mental state examination; NCGG-FAT, National Center for Geriatrics and Gerontology-Functional Assessment Tool; SD, standard deviation.


Enrollment and assessment procedures


The NCGG and 4 other centers are to recruit participants from their hospital and/or community-based cohorts. In some cohorts, the cognitive test results to be obtained at the time of participation are to be used to select participants who are likely to meet the selection criteria, and an invitation is to be directly sent by mail to those who have met the criteria. In addition, prospective participants, i.e., patients as well as community residents, are to be informed of the call for recruitment in this study through posters, newspaper advertisements, and public relations papers. Research staff is to fully explain the purpose, nature, and potential risks of participation in this trial for prospective participants before trial participation. Then, participants who provided written informed consent are to be assessed for their cognitive function (the NCGG-FAT (13, 14), and the MMSE (15)). Participants are to be evaluated for final eligibility by the research staff and clinicians according to the trial’s inclusion and exclusion criteria. Participant recruitment was started in November, 2019 and completed in December, 2020.


Figure 2 shows the timeline in the J-MINT trial for assessments scheduled according to the SPIRIT guidelines. All participants are to complete neuropsychological tests at baseline, as well as at 6-, 12-, and 18-month follow-up and to undergo comprehensive geriatric assessments (CGA) at baseline, as well as 6- and 18-month follow-up. Additionally, the participants are to undergo blood tests, dementia-related biomarker tests, and brain magnetic resonance imaging (MRI) or computed tomography (CT) evaluations at baseline and at 18-month follow-up. The participants are to be evaluated for adherence to and satisfaction with the intervention protocol, and all adverse events experienced by the participants are to be recorded during the trial.

Figure 2. Timeline for scheduled assessments of the J-MINT trial

*: Non-mandatory; †: Dementia-related biomarker testing and whole genome sequencing are to be conducted only for subjects recruited at the National Center for Geriatrics and Gerontology. Abbreviations: CT, computed tomography; J-MINT, Japan-multidomain intervention trial for prevention of dementia; MRI, magnetic resonance imaging; NfL, neurofilament light chain; SPECT, single photon emission computed tomography.


Primary outcome measure: cognitive change at 18-month follow-up (composite score)

The primary outcome measure is the change from baseline at 18 months in a global composite score using several neuropsychological tests, which include tests of global cognitive function (MMSE (15)); memory (Logical memory Ⅰ and Ⅱ subset of the Wechsler Memory Scale-Revised (WMS-R) (16) and the Free and Cued Selective Reminding Test (FCSRT) (17)); attention (Digit Span of the Wechsler Adult Intelligence Scale (WAIS)-III (18)); executive function/processing speed (Trail Making Test (TMT) (19), Digit Symbol Substitution Test (DSST) subset of the WAIS-III (18), Letter word fluency test (19)).
Neuropsychological tests are to be performed at each site by trained and board-certificated psychologists, occupational therapists, and/or speech therapists, who were trained in the implementation and scoring of neuropsychological tests through participation in a training session held at the NCGG. Moreover, the Logical memory Ⅰ and Ⅱ subset of the WMS-R is to be scored by two psychologists from the NCGG to minimize variability in scoring. Then, the composite score is to be generated by averaging the Z scores of each neuropsychological test standardized by the baseline mean and standard deviation (SD) for each test from the full-analysis set population. A Z score of –1, for example, represents a score 1 SD below the baseline mean. A 1-point decrease on the composite score indicates an average decline of 1 SD across the neuropsychological tests.

Secondary outcome measures

Secondary outcome measures of cognitive changes are: change from the baseline to 6 and 12 months in global composite score; change from baseline in score of each neuropsychological test at 6-/12-/18-month follow-up; and incident dementia. At follow-up conducted every 6 months, participants are to be advised to receive primary health care or present to a memory clinic for further evaluation (e.g., neuropsychological tests, blood tests, brain MRI, CT, or single photon emission computed tomography (SPECT)) (1) if they have complaints about cognitive decline; (2) if they have a MMSE score of less than 24 or (3) if they have a decline in MMSE score of 3 points or more from the baseline. They are to be confirmed to have incident dementia, by consensus of two or more physicians, according to the criteria of the National Institute on Aging-Alzheimer’s Association (NIA/AA) workgroups (20), with the diagnosis of incident dementia confirmed, as required, in consultation with the Endpoint (incident dementia) Committee of the J-MINT trial (see Appendix 2).
Other secondary outcome measures are changes in each component of CGA including activities of daily living (ADL) and frailty status, blood markers, dementia-related blood biomarkers, neuroimaging assessed using MRI or CT, and number of medications taken.

a) Comprehensive geriatric assessment

CGA includes the following self-administered questionnaires and physical and cognitive performance:
– ADL: Participants are to be assessed for basic and instrumental ADL. Basic ADL is to be assessed using the Barthel index (21), which assesses basic self-care abilities, such as feeding, transferring from a bed to a chair, bathing, bowel control, and bladder control, with the score ranging from 0 (complete dependence) to 100 (complete independence). Instrumental ADL is to be assessed using the Lawton index (22), which includes 8 items, such as using the telephone, shopping, and handling medications, with the score ranging from 0 (low function) to 8 (high function).
– Frailty status: Participants are to be assessed for physical frailty status based on the frailty phenotype proposed by Fried et al. in the Cardiovascular Health Study (23). The components of the frailty phenotype include shrinking, weakness, slowness, self-reported exhaustion, and low physical activity. Participants are to be assessed for social frailty status based on the following 5 items: living alone, going out less frequently than last year, not visiting friends often, not feeling like helping friends or family, and not talking with anybody every day (24). Participants are also to be assessed for oral frailty status by using the Oral Frailty Index-8 (25), which includes 8 items: subjective difficulties in eating hard food, choking on tea or soup, using denture, caring for dry mouth, going out, chewing hard food such as pickled radish or shredded and dried squid, brushing teeth at least twice a day, and regularly visiting a dental clinic.
– Dietary diversity: The 11-item Food Diversity Score Kyoto (26) has been modified to assess each participant’s dietary diversity by asking about frequency in consumption of 14 foods for the past week (i.e., cereals, fish and shellfish, meat, eggs, milk, dairy products, beans, seaweed, potatoes, fruit, nuts, and oils and fat).
– Nutritional status: Participants are to be assessed for nutritional status using the Mini-Nutritional Assessment Short-Form (MNA-SF) (27) composed of 6 questions, whose score ranges from 0 to 14, with a higher score indicating better nutritional status.
– Appetite: Participants are to be assessed for their appetite using the Council on Nutritional Appetite Questionnaire (28). The total score ranges from 8 to 40, with a higher score indicating a better appetite.
– Depressive symptoms: Participants are to be assessed for depressive symptoms using self-rated 15-item Geriatric Depression Scale (29). This scale ranges from 0 to 15, with a higher score indicating a higher level of depressive symptoms.
– History of falls and fall risk: Participants are to be evaluated for history of falls within the past 12 months and fall risk, with the latter assessed using the Fall Risk Index (30) composed of 21 questions to detect the risk of falls, including the following 3 subcategories: physical function (8 items), geriatric syndrome (8 items) and environmental hazards (5 items). Each item receives a score of 1 (risk present) or 0 (risk absent), with a higher sum for each category indicating a higher risk of falls.
– Social network: Participants are to be assessed for social network using the Lubben Social Network Scale 6 (31), which is composed of 6 questions. The score ranges from 0 to 30, with a higher score indicating better social network.
– Health-related quality of life: Participants are to be assessed for health-related quality of life using a health index of the EQ-5D (32) consisting of 5 dimensions (mobility, self-care, pain/discomfort, usual activities, and anxiety/depression). The scores for these five dimensions are combined to obtain up to 3,125 possible health states, from which a single index (utility) score is computed.
– Sleep quality: Participants are to be assessed for their sleep quality using the Pittsburgh Sleep Quality Index (PSQI) (33), which is intended to assess subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. This index score ranges from 0 to 21 and higher score indicates poor sleep quality.
– Social participation: Participants are to be assessed for social participation using the questionnaire about social engagement in eight types of groups (34): (1) neighborhood associations/senior citizen clubs/fire-fighting teams (Local Community); (2) hobby groups (Hobby); (3) sports groups or clubs (Sports); (4) political organizations or groups (Politics); (5) industrial or trade associations (Industry); (6) religious organizations or groups (Religion); (7) volunteer groups (Volunteer); and (8) Others.
– Handicap from hearing loss: Participants are to be assessed for handicap from hearing loss using Hearing Handicap Inventory for the Elderly (HHIE) (35), which consists of 25 questions, with 13 questions of these evaluating emotional aspects of hearing-related QOL, and the remaining 12 evaluating social haring-related QOL among older adults. The total score ranges from 0 to 100, and a higher total score indicates a more severe subjective hearing-related handicap.
– Anthropometric measurements: Participants are to undergo anthropometric measurements including height, body weight, and calf circumference, with body mass index calculated as body weight in kilograms divided by height in square meters (kg/m2). In addition, optionally, participants are to be evaluated for presence or absence of sarcopenia (36) with appendicular muscle mass (AMM) measured by bioelectrical impedance analysis (BIA), and skeletal muscle mass index calculated as AMM divided by height in square meters (kg/m2).
– Physical performance: Participants are to be evaluated for usual gait speed over a distance of 2.4 m at the middle of the walkway that has both acceleration and deceleration zones each 1 m long, with the gait speed measured twice and the mean value calculated (37). Hand grip strength of both right and left hand are to be measured in the participants using a standard digital hand grip dynamometer (Takei Scientific Instruments Co., Ltd, Japan) in standing position with the shoulder adducted and neutrally rotated and the elbow fully extended (38). Participants are to be assessed for lower extremity muscular strength by the Five-Times-Sit-to-Stand test (39), which measures the time (seconds) required to perform five successive chair stands as quickly as possible from the initial seated position with their arms crossed on their chest and sitting with their back against a chair.
– Visual function and vision-related QOL (optional): Participants are to be assessed for visual acuity (naked- and best corrected-visual acuity), visual refraction, and intra-ocular pressure. Slit lump examination and fundus examination are to be performed. The retinal and peripapillary nerve fiber layer thickness is to be measured by the optical coherence tomography (Model RS-3000, Nidek, Japan) without pupil dilatation. Additionally, the 25-Item National Eye Institute Visual Functioning Questionnaire (NEI-VFQ-25) (40) is to be used to assess their vision-specific health related QOL.
– Cognitive function (optional): Participants are to be assessed for cognitive function by CogEvo (Total Brain Care, Kobe, Japan) (41) and Japanese version of the Montreal Cognitive Assessment (MoCA-J) (42). CogEvo is a computer-aided cognitive function test battery, which allows for evaluation fo cognitive function including orientation, attention, memory, executive function and spatial cognition. MoCA-J is a brief cognitive screening tool for detecting older adults with mild cognitive impairment by assessing nine domains of cognition including attention, concentration, executive functions, memory, language, visuoconstructional skills, conceptual thinking, calculation, and orientation.

b) Blood and urinary tests

Blood markers assessed in all participants at baseline and 18-month follow-up are to include glucose, insulin, hemoglobin A1c, glycoalbumin, total protein, albumin (Alb), aspartate aminotransferase, alanine aminotransferase, γ-glutamyl transpeptidase, total cholesterol, high density lipoprotein-cholesterol, triglyceride, creatinine (Cr), estimated glomerular filtration rate, blood urea nitrogen, sodium, potassium, chloride, calcium, phosphorus, free triiodothyronine, free thyroxine, thyroid stimulating hormone, rapid plasma reagin, treponema pallidum hemagglutination, vitamin B1, vitamin B12, folate, and C-reactive protein. In addition, all participants will be assessed at baseline for their apolipoprotein E (APOE) phenotype.
Optionally, the following are to be measured: brain natriuretic peptide, interleukin-6, 25-hydroxyvitamin D, ghrelin, glucagon-like peptide-1, blood ketone bodies, carnitine, total bile acid, osmotic pressure, catecholamines, fatty acids, leptin, creatine, creatine kinase, nicotinamide phosphoribosyltransferase, and microRNAs. Urine glucose, urine protein, occult blood, urine Alb/Cr (u-Alb/Cr), and 8-Hydroxy-deoxyguanosine are also to be assessed optionally.

c) Blood-based dementia-related biomarkers

Participants recruited from the NCGG are to be assessed for dementia-related biomarkers. To assess amyloid-β (Aβ) deposition in the brain, which is the earliest pathological signature of AD, ratios of plasma Aβ-related peptides, APP669-711/Aβ1-42 and Aβ1-40/Aβ1-42, and their composites are to be measured using the immunoprecipitation-mass spectrometry assay (43). In addition, plasma concentrations of tau phosphorylated at threonine 181 (p-tau181) and neurofilament light chain (NfL) are to be assessed using the single-molecule array (SimoaTM) platform (44, 45).

d) Whole genome sequencing

Whole genome sequencing is to be applied to blood samples from participants recruited from the NCGG, which is being used to conduct a comprehensive search for mutations within genes known to be associated with dementia and to identify novel pathogenic genes.

e) Brain MRI and CT

At baseline and 18-month follow-up, brain MRI or CT will be performed by the scanner available at each site to detect any local lesion, such as cerebral infarction, that could greatly affect cognitive function. At the NCGG, three-dimensional (3D) T1-weighted images, T2-weighted images, T2*-weighted images, 3D fluid attenuation inversion recovery (FLAIR) images, diffusion-weighted images, and diffusion kurtosis images are to be acquired on a Siemens Magnetom Skyra 3T MRI scanner (Siemens Medical Solutions, Erlangen Germany). To elucidate the mechanisms of cognitive improvement and deterioration during the intervention period, brain structural alterations, such as atrophy, cerebral small vessel disease and micro structural change in white matter and gray matter, are to be analyzed.

Adverse events and serious adverse events

To evaluate the safety of the intervention, all adverse events (AEs) and serious AEs are to be monitored during the course of the trial. Information to be collected about AEs is to include their date of onset, severity, associated treatment, consequences, and causation. Serious AEs are to be reported to the principal investigator, the IRB, and the co-investigators immediately.

Interventions procedures

Intervention arm

The intervention group is to receive multi-domain intervention programs including: (1) management of vascular risk factors; (2) group-based physical exercises and self-monitoring of physical activity; (3) nutritional counseling; and (4) cognitive training using Brain HQ. Figure 3 shows the J-MINT trial network, where the NCGG and the 4 other centers are to manage vascular risk factors and supervise the implementation of the multi-domain intervention, while working with Sompo Holdings, Inc. and Sompo Care Inc., care management companies in Japan. Group-based physical exercise programs, nutritional counseling, and cognitive training are to be provided by Konami Sports Co., Ltd., Sompo Health Support Inc., and Posit Science Corporation, respectively.

Figure 3. Network of the Japan-multidomain intervention trial for prevention of dementia


Management of vascular risk factors

Diabetes mellitus, hypertension, and dyslipidemia in the participants are to be treated according to relevant clinical practice guidelines in Japan, with diabetes managed based on the treatment guideline for elderly patients with diabetes mellitus 2017 by the Japan Diabetes Society (JDS)/Japan Geriatric Society (JGS) Joint Committee (46), Hypertension managed based on the Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2019) (47), and dyslipidemia managed based on the Japan Atherosclerosis Society (JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases 2017 (48). Results of blood tests and brain MRI or CT will be mailed to all participants in both the intervention and control arms, together with letters recommending that they contact primary health care as required.

Group-based physical exercise and self- monitoring of physical activity

Participants are to engage in the group-based physical exercise session lasting 90 minutes at each site once a week for a total 78 sessions. Trained instructors are to provide group-based physical exercise sessions, each of which includes muscle stretching, muscle strength training, aerobic exercise, dual-task training combining exercise and cognitive tasks, namely “cognicise” (“cogni” for cognition + “cise” for exercise), and a group meeting to promote behavioral change, consistently with a previous trial (49). Intervention with combined cognitive and physical exercise was shown to improve cognitive and physical performance in older adults with mild cognitive impairment (49).
Two types of exercise session are to be provided: (1) one involving muscle strength training, which consists of 10 minutes of muscle stretching, 15-20 minutes of muscle strength training, 5 minutes of rest, 20-30 minutes of aerobic exercise, 5 minutes of rest, 20-30 minutes of dual-task training; (2) the other involving group meeting, which consist of 10 minutes of stretching, 20-30 minutes of aerobic exercise, 5 minutes of rest, 20-30 minutes of dual-task training, 5 minutes of rest, and 15-20 minutes of group meeting. The sessions involving muscle strength training and group meeting are to be provided at a ratio of 1:1 and 3:1 per month from baseline to 6 months and from 6 months to 18 months, respectively. Aerobic exercise is to be set at moderate intensity and to be progressively intensified from 40% to 80% of maximum heart rate (HR) over the study period. The maximum HR is to be estimated by age-predicted maximal HR formula (207 – 0.7 × age). HR during aerobic exercise is to be monitored by using the wrist-worn device (Fitbit® Inspire HR activity monitor). Instructors are also to monitor their HR and exercise intensity using an iPad Air tablet computer synchronizing with participant’s Fitbit® Inspire HR activity monitor. These devices are to be distributed to all participants in the intervention arm.
In group meetings, health-related information including that on prevention of dementia, frailty, knee and low back pain, malnutrition, sleep disorder, and falls and fall-related fracture, and the beneficial effect of social participation and physical activity is to be provided to promote healthy behaviors. Moreover, participants are also to be assessed for their subjective physical activity, perceived benefit and barrier to exercise, and satisfaction with the intervention programs by using the Global Physical Activity Questionnaire (50), the Exercise Benefits/Barriers Scale (51), and the Client Satisfaction Questionnaire (52), respectively, at the start of the intervention, as well as at 6-, 12-, and 18-month follow-up.
Participants are to receive several exercise videos/messages every week via an iPad Air tablet computer to promote home-based muscle strengthening exercises, aerobic exercise, and dual-task training three or more times a week. Moreover, participants are also to be advised to monitor their daily steps, HR, and sleep using an iPad Air tablet computer synchronizing with their Fitbit® Inspire HR activity monitor.

Nutritional counseling

Nutritional counseling is to be offered individually by qualified health consultants (registered dieticians, nurses, or public health nurses). Participants are to receive face-to-face counseling (60 minutes per session) once and telephone counseling 4 times during a 6-month period, with face-to-face counseling 3 times and twelve times telephone counseling are scheduled to occur 3 and 12 times, respectively, during the course of the18-month intervention period. In face-to-face counseling, health consultants are to assess each participant’s current situation and problems, propose coping methods, and set individual goals together with the participant. In telephone counseling, health consultants are to assess changes in lifestyle and track goal achievement. Participants are to be requested to monitor their body weight and to track goal achievement everyday using a study-provided booklet.
For the first 6 months, nutritional counseling is to focus on improvement of lifestyle (sleep and waking times) and dietary behavior (dietary timing, frequency, and regularity) based on the body’s circadian rhythm (chrono-nutrition). From the next 7 to 18 months, nutritional counseling is to include guidance on dietary intake required to improve cognitive and physical condition, including frailty and sarcopenia and on chewing and swallowing function and oral care (assessment of oral frailty (25), brushing teeth, and regular visits to a dental clinic). Appropriate dietary intakes are to be determined based on the Dietary Reference Intakes for Japanese (2020) (53), which provide the reference intakes of proteins, fats, carbohydrates, vitamins, and minerals and guidance on energy-providing nutrient balance for older adults 65-74 years old and 75 years or older, respectively. Moreover, participants are to be instructed to take a well-balanced diet and increase dietary diversity, which is reported to be associated with better physical and cognitive performance in Japanese older community-dwelling adults (54, 55). Participants are also to be requested to monitor their dietary diversity every day using a study-provided booklet. Additionally, based on the evidence from previous cohort studies and clinical trial (56-60), intake of fish and seafood containing eicosapentaenoic and docosahexaenoic acids, milk and dairy products, fruits, vegetables, and green tea is to be also recommended.

Cognitive training

Participants are to be instructed to engage in cognitive training individually using an iPad Air tablet computer wherein the Brain HQ (Japanese version) has been installed. The Brain HQ (Japanese version) is customized for the J-MINT trial and consists of 13 visual exercises focusing on specific cognitive abilities, such as attention, processing speed, memory, mental flexibility, and visuospatial ability. Exercise difficulty is adjusted based on cognitive abilities of each individual to ensure and sustain engagement of attention and motivation. Brain HQ exercise is reported to be beneficial for several cognitive domains including processing speed and memory in an RCT conducted specifically in older adults (61).
During the 4-6, 10-12, and 16-18 months (intensive training periods) in the 18-month intervention period, participants are to be instructed to engage in intensive training lasting at least 30 minutes per day for 4 or more days per week. Participants are to be monitored for adherence during each intensive training period as well as in the entire 18-month intervention period. Lower adherence may also be expected in cognitive training due to its use of tablet computers involving operational difficulties. Therefore, continuous support is to be provided not only in terms of instructions on the tablet computer operations at the start of the program, but in the form of a free consultation desk to be put in place during the intervention period.

Delivery of intervention during the coronavirus disease-19 (COVID-19) pandemic

The declaration by the Japanese government of a state of emergency due to the COVID-19 pandemic is expected to result in travel restrictions, social distancing, and avoidance of “3Cs” (closed spaces, crowded places, and close-contact settings, particularly their combination) and is likely to lead to part of the J-MINT intervention being restricted, especially group-based physical exercise and face-to-face nutritional counseling. In such situation, the J-MINT trial is to provide on-line intervention programs for physical exercise and nutritional counseling.

Price sensitivity measurement

At the completion of the intervention programs, a price sensitivity measurement approach is to be drawn on to discuss the appropriate price of each intervention program in view of its future implementation in the real-world setting.

Control arm

The control group is to receive health-related information in writing every 2 months on how to prevent/manage dementia, frailty, low back pain, malnutrition, health-related disease, sleep disorder and falls and fall-related fracture, as well as on the beneficial effect of social participation and physical activity and how to manage vascular risk factors according to current clinical practice guidelines.

Statistical considerations

Sample size

Since, to date, no multi-domain intervention trials have detected changes in our global composite score consisting of several neuropsychological tests, sample size calculation was based on the previous randomized controlled trial that evaluated the effect of an exercise program combined with physical and cognitive tasks in 308 older adults with mild cognitive impairment shown to have an age-adjusted cognitive decline (an SD of 1.5 or more from the reference threshold in any of the cognitive domains determined by using the NCGG-FAT (49)). In this trial, the intervention group showed a significantly greater score change on the MMSE than the control group (Intervention group vs. control group, 0.0 ± 2.48 vs. -0.8 ± 2.48 after the 40-week trial. Based on this trial, we hypothesized that the present study is also likely to detect a difference of change in cognitive function between the intervention and control groups. With a two-sided significance level of 5% and a statistical power of 80%, the total sample size required by the t-test was calculated as 302. In addition, the dropout rate at final follow-up is estimated to be 40% at each trial site. Thus, it was assumed that the trial required to enroll a total of 500 patients.

Randomization and blinding

In this trial, participants are to be centrally randomized at a 1:1 allocation ratio into intervention and control groups using the dynamic allocation method through stratification by the following variables:
1) Age at enrollment (65-74 years vs. 75-85 years).
2) Sex (female vs. male).
3) MMSE (24-27 vs. 28-30)
4) Presence of memory impairment as determined by the NCGG-FAT (amnestic vs. non-amnestic)

Participants are to be randomized electronically via a web-based system constructed by a trial statistician (F. K.) and a co-investigator (A. H.). Study participants and research staff including investigators and clinicians at each site are to be blinded to the next assignment in the sequence using electronic assignment and a dynamic allocation algorithm.

Data collection forms and data monitoring

All variables are to be measured by trained research staff at each site, and most of the measured outcome data are to be collected in the form of hard copy forms. Then, these data are to be collected through entry of these data by assessors using an electronic data capture (EDC) system. All hard copy forms are to be retained as back-ups as required at each site.
On-site monitoring is to be conducted at each site to ensure that the patient rights are protected, the reported data are accurate, and the conduct of the trial is compliant with the current approved protocol. The monitor is to ensure that (1) written informed consent is obtained from all participants before their participation in the trial; (2) primary outcome data reported in the EDC are complete and accurate; and (3) all AEs and serious AEs are reported appropriately.

Data analyses

In this trial, we defined the following four analysis sets: (1) the intention-to-treat (ITT) analysis set, which includes all subjects randomized regardless of whether or not they received the intervention programs/health-related information; (2) the full analysis set (FAS), which includes subjects who received the intervention program/health-related information at least once and had at least one post-baseline assessment of neuropsychological tests; (3) the per protocol set (PPS), which includes subjects who received the intervention program/health-related information for 18-month or more; (4) the safety analysis set (SAF), which includes subjects who received the intervention program/health-related information at least once. The primary efficacy analysis is to be performed using the FAS, and the secondary efficacy analysis is to be performed using the ITT and the PPS.
Data are to be presented as means, medians, standard deviations, ranges and interquartile ranges for continuous and ordinal variables, and counts and percentages for categorical variables. Differences in baseline clinical characteristics between the intervention and control groups are to be examined for significance by using the t-test or the Mann-Whitney U test for continuous variables and by the chi-squared test or the Fisher’s exact test for categorical variables, as appropriate.
To evaluate differences from baseline in cognitive changes at 18-month follow-up between the intervention and control groups, the mixed-effects model for repeated measures (MMRM) with an unstructured covariance structure is to be used with groups, time of visit, group by time interaction, age at randomization, sex, presence of memory impairment at randomization (amnestic or non-amnestic), and baseline composite cognitive score as covariates. For the secondary continuous variables, the same analyses are to be performed using MMRM. For the secondary categorical variables, logistic regression analyses or chi-squared tests will be used as appropriate. Frequencies of serious AEs are to be summarized for the intervention and control groups.
All statistical analyses are to be performed using SAS (SAS Institute, Inc., Cary, NC, USA). P-values of < 0.05 are to be considered statistically significant.



The J-MINT trial will be the first not only to demonstrate the effectiveness of multi-domain interventions but to clarify the mechanism of cognitive improvement and deterioration through assessment of dementia-related biomarkers, omics analysis and brain image analysis. Moreover, along with the creation of evidence for prevention of dementia, collaborative research with partnering companies is expected to allow creation of new services and manuals for proposed ideal services. The J-MINT trial raises some concern that high-intensity intervention programs may result in an insufficient level of adherence, which lead to minimal beneficial effect. To address this issue, group-based physical exercise and nutritional counseling have been designed, in light of encouraging results from the FINGER and MAPT trials, to include face-to-face contacts, which may increase adherence (62). Moreover, there is still lack of consensus about the level of sustained adherence deemed acceptable or sufficient for prevention of cognitive decline. Given that all participants are to be monitored for adherence to its intervention programs during the intervention period, the J-MINT may provide insight into the level of sustained adherence deemed acceptable or sufficient for each intervention program among older adults at higher risk of dementia.
Finally, the J-MINT Prime trial incorporating two RCTs, Japan-multimodal Intervention Trial for Prevention of Dementia in Kanagawa (UMIN000041887) and Japan-multimodal Intervention Trial for Prevention of Dementia PRIME Tamba Study (UMIN000041938), was initiated in Japan to clarify the effectiveness of multi-domain intervention in different settings and populations in Japan. Both the J-MINT trial and the J-MINT Prime trial are intended to evaluate cognitive and other outcomes measures, with their combined data-analyses being planned. These results are expected to contribute to the development of a mechanism through which a sustainable dementia prevention service may be made widely available to those in need.


Funding: This work was financially supported by Japan Agency for Medical Research and Development (AMED) under Grant Number JP20de0107002. The sponsors had no role in the design and conduct of the study, in the collection, analysis, and interpretation of data, in the preparation of the manuscript or in the review or approval of the manuscript.

Acknowledgments: We thank all the patients for their participation in the study and all members of the J-MINT study group (see Appendix 2) and on-site study staff for their efforts in the conduct of assessments and interventions.

Conflicts of interest: The authors have no conflict of interest to declare.

Trial registration: UMIN000038671; Registered on the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) 24 November 2019. (






1. Prince M, Wimo A, Guerchet M, Ali GC, Wu YT, Prina M. World Alzheimer Report 2015 -The Global Impact of Dementia: Alzheimer’s Disease International; 2015
2. Matthews FE, Arthur A, Barnes LE, Bond J, Jagger C, Robinson L, Brayne C. A two-decade comparison of prevalence of dementia in individuals aged 65 years and older from three geographical areas of England: results of the Cognitive Function and Ageing Study I and II. Lancet 2013;382, 1405-1412.
3. Satizabal CL, Beiser AS, Chouraki V, Chene G, Dufouil C, Seshadri S. Incidence of Dementia over Three Decades in the Framingham Heart Study. N Engl J Med 2016;374, 523-532.
4. Ding M, Qiu C, Rizzuto D, Grande G, Fratiglioni L. Tracing temporal trends in dementia incidence over 25 years in central Stockholm, Sweden. Alzheimers Dement 2020;16, 770-778.
5. Ohara T, Hata J, Yoshida D, Mukai N, Nagata M, Iwaki T, Kitazono T, Kanba S, Kiyohara Y, Ninomiya T. Trends in dementia prevalence, incidence, and survival rate in a Japanese community. Neurology 2017;88, 1925-1932.
6. Cummings J, Lee G, Ritter A, Sabbagh M, Zhong K. Alzheimer’s disease drug development pipeline: 2019. Alzheimers Dement (N Y) 2019;5, 272-293.
7. Ngandu T, Lehtisalo J, Solomon A, Levalahti E, Ahtiluoto S, Antikainen R, Backman L, Hanninen T, Jula A, Laatikainen T, Lindstrom J, Mangialasche F, Paajanen T, Pajala S, Peltonen M, Rauramaa R, Stigsdotter-Neely A, Strandberg T, Tuomilehto J, Soininen H, Kivipelto M. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet 2015;385, 2255-2263.
8. Moll van Charante EP, Richard E, Eurelings LS, van Dalen JW, Ligthart SA, van Bussel EF, Hoevenaar-Blom MP, Vermeulen M, van Gool WA. Effectiveness of a 6-year multidomain vascular care intervention to prevent dementia (preDIVA): a cluster-randomised controlled trial. Lancet 2016;388, 797-805.
9. Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, Bories L, Cufi MN, Dantoine T, Dartigues JF, Desclaux F, Gabelle A, Gasnier Y, Pesce A, Sudres K, Touchon J, Robert P, Rouaud O, Legrand P, Payoux P, Caubere JP, Weiner M, Carrie I, Ousset PJ, Vellas B. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol 2017;16, 377-389.
10. Andrieu S, Coley N, Lovestone S, Aisen PS, Vellas B. Prevention of sporadic Alzheimer’s disease: lessons learned from clinical trials and future directions. Lancet Neurol 2015;14, 926-944.
11. Rosenberg A, Mangialasche F, Ngandu T, Solomon A, Kivipelto M. Multidomain Interventions to Prevent Cognitive Impairment, Alzheimer’s Disease, and Dementia: From FINGER to World-Wide FINGERS. J Prev Alzheimers Dis 2020;7, 29-36.
12. Kivipelto M, Mangialasche F, Snyder HM, Allegri R, Andrieu S, Arai H, Baker L, Belleville S, Brodaty H, Brucki SM, Calandri I, Caramelli P, Chen C, Chertkow H, Chew E, Choi SH, Chowdhary N, Crivelli L, Torre R, Du Y, Dua T, Espeland M, Feldman HH, Hartmanis M, Hartmann T, Heffernan M, Henry CJ, Hong CH, Håkansson K, Iwatsubo T, Jeong JH, Jimenez-Maggiora G, Koo EH, Launer LJ, Lehtisalo J, Lopera F, Martínez-Lage P, Martins R, Middleton L, Molinuevo JL, Montero-Odasso M, Moon SY, Morales-Pérez K, Nitrini R, Nygaard HB, Park YK, Peltonen M, Qiu C, Quiroz YT, Raman R, Rao N, Ravindranath V, Rosenberg A, Sakurai T, Salinas RM, Scheltens P, Sevlever G, Soininen H, Sosa AL, Suemoto CK, Tainta-Cuezva M, Velilla L, Wang Y, Whitmer R, Xu X, Bain LJ, Solomon A, Ngandu T, Carrillo MC. World-Wide FINGERS Network: A global approach to risk reduction and prevention of dementia. Alzheimers Dement 2020;16, 1078-1094.
13. Makizako H, Shimada H, Park H, Doi T, Yoshida D, Uemura K, Tsutsumimoto K, Suzuki T. Evaluation of multidimensional neurocognitive function using a tablet personal computer: test-retest reliability and validity in community-dwelling older adults. Geriatr Gerontol Int 2013;13, 860-866.
14. Shimada H, Makizako H, Park H, Doi T, Lee S. Validity of the National Center for Geriatrics and Gerontology-Functional Assessment Tool and Mini-Mental State Examination for detecting the incidence of dementia in older Japanese adults. Geriatr Gerontol Int 2017;17, 2383-2388.
15. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12, 189-198.
16. Wechsler D.Wechsler Memory Scale-Revised. San Antonio, TX: Psychological Corporation, 1981.
17. Grober E, Buschke H. Genuine memory deficits in dementia. Developmental Neuropsychology 1987;3, 13-36.
18. Wechsler D. Manual for the Wechsler Adult Intelligence Scale, Psychological Corp., Oxford, England, 1955.
19. Lezak MD, Lezak MD. Neuropsychological assessment, Oxford University Press, Oxford ; New York, 2004.
20. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Jr., Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011;7, 263-269.
21. Mahoney FI, Barthel DW. Functional evaluation: The Barthel Index. Md State Med J 1965;14, 61-65.
22. Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 1969;9, 179-186.
23. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56, M146-156.
24. Makizako H, Shimada H, Tsutsumimoto K, Lee S, Doi T, Nakakubo S, Hotta R, Suzuki T. Social Frailty in Community-Dwelling Older Adults as a Risk Factor for Disability. J Am Med Dir Assoc 2015;56, M146-156.
25. Tanaka T, Hirano H, Ohara Y, Nishimoto M, Iijima K. Oral Frailty Index-8 in the risk assessment of new-onset oral frailty and functional disability among community-dwelling older adults. Arch Gerontol Geriatr 2021;94: 104340.
26. Kimura Y, Wada T, Ishine M, Ishimoto Y, Kasahara Y, Konno A, Nakatsuka M, Sakamoto R, Okumiya K, Fujisawa M, Otsuka K, Matsubayashi K. Food diversity is closely associated with activities of daily living, depression, and quality of life in community-dwelling elderly people. J Am Geriatr Soc 2009;57, 922-924.
27. Rubenstein LZ, Harker JO, Salva A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci 2001;56, M366-372.
28. Wilson MM, Thomas DR, Rubenstein LZ, Chibnall JT, Anderson S, Baxi A, Diebold MR, Morley JE. Appetite assessment: simple appetite questionnaire predicts weight loss in community-dwelling adults and nursing home residents. Am J Clin Nutr 2005;82, 1074-1081.
29. Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, Leirer VO (1982) Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res 1982;17, 37-49.
30. Kikuchi R, Kozaki K, Iwata A, Hasegawa H, Toba K. Evaluation of risk of falls in patients at a memory impairment outpatient clinic. Geriatr Gerontol Int 2009;9, 298-303.
31. Lubben J, Blozik E, Gillmann G, Iliffe S, von Renteln Kruse W, Beck JC, Stuck AE. Performance of an abbreviated version of the Lubben Social Network Scale among three European community-dwelling older adult populations. Gerontologist. 2006;46, 503-513.
32. Kunz S. Psychometric properties of the EQ-5D in a study of people with mild to moderate dementia. Qual Life Res 2010;19, 425-434.
33. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989;28, 193-213.
34. Kanamori S, Kai Y, Aida J, Kondo K, Kawachi I, Hirai H, Shirai K, Ishikawa Y, Suzuki K. Social participation and the prevention of functional disability in older Japanese: the JAGES cohort study. PLoS One 2014;9, e99638.
35. Ventry IM, Weinstein BE. TThe hearing handicap inventory for the elderly: a new tool. Ear Hear 1982;3, 128-134.
36. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S, Kojima T, Kuzuya M, Lee JSW, Lee SY, Lee WJ, Lee Y, Liang CK, Lim JY, Lim WS, Peng LN, Sugimoto K, Tanaka T, Won CW, Yamada M, Zhang T, Akishita M, Arai H. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc 2019;21, 300-307.
37. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, Scherr PA, Wallace RB. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 1994;49, M85-94.
38. Watanabe T, Owashi K, Kanauchi Y, Mura N, Takahara M, Ogino T. The short-term reliability of grip strength measurement and the effects of posture and grip span. J Hand Surg Am 2005;30, 603-609.
39. Whitney SL, Wrisley DM, Marchetti GF, Gee MA, Redfern MS, Furman JM. Clinical measurement of sit-to-stand performance in people with balance disorders: validity of data for the Five-Times-Sit-to-Stand Test. Phys Ther 2005;85, 1034-1045.
40. Mangione CM, Lee PP, Gutierrez PR, Spritzer K, Berry S, Hays RD; National Eye Institute Visual Function Questionnaire Field Test Investigators.Development of the 25-item National Eye Institute Visual Function Questionnaire. Arch Ophthalmol 2001;119, 1050-1058.
41. Ichii S, Nakamura T, Kawarabayashi T, Takatama M, Ohgami T, Ihara K, Shoji M. CogEvo, a cognitive function balancer, is a sensitive and easy psychiatric test battery for age-related cognitive decline. Geriatr Gerontol Int 2020;20, 248-255.
42. Fujiwara Y, Suzuki H, Yasunaga M, Sugiyama M, Ijuin M, Sakuma N, Inagaki H, Iwasa H, Ura C, Yatomi N, Ishii K, Tokumaru AM, Homma A, Nasreddine Z, Shinkai S. Brief screening tool for mild cognitive impairment in older Japanese: validation of the Japanese version of the Montreal Cognitive Assessment. Geriatr Gerontol Int 2010;10, 225-232.
43. Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Doré V, Fowler C, Li QX, Martins R, Rowe C, Tomita T, Matsuzaki K, Ishii K, Ishii K, Arahata Y, Iwamoto S, Ito K, Tanaka K, Masters CL, Yanagisawa K. High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature 2018;554, 249-254.
44. Tatebe H, Kasai T, Ohmichi T, Kishi Y, Kakeya T, Waragai M, Kondo M, Allsop D, Tokuda T. Quantification of plasma phosphorylated tau to use as a biomarker for brain Alzheimer pathology: pilot case-control studies including patients with Alzheimer’s disease and down syndrome. Mol Neurodegener 2017;12, 63.
45. Shinomoto M, Kasai T, Tatebe H, Kondo M, Ohmichi T, Morimoto M, Chiyonobu T, Terada N, Allsop D, Yokota I, Mizuno T, Tokuda T. Plasma neurofilament light chain: A potential prognostic biomarker of dementia in adult Down syndrome patients. PLoS One 2019;14, e0211575.
46. Committee Report: Glycemic targets for elderly patients with diabetes: Japan Diabetes Society (JDS)/Japan Geriatrics Society (JGS) Joint Committee on Improving Care for Elderly Patients with Diabetes. J Diabetes Investig 2017;8, 126-128.
47. Umemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, Horio T, Hoshide S, Ikeda S, Ishimitsu T, Ito M, Ito S, Iwashima Y, Kai H, Kamide K, Kanno Y, Kashihara N, Kawano Y, Kikuchi T, Kitamura K, Kitazono T, Kohara K, Kudo M, Kumagai H, Matsumura K, Matsuura H, Miura K, Mukoyama M, Nakamura S, Ohkubo T, Ohya Y, Okura T, Rakugi H, Saitoh S, Shibata H, Shimosawa T, Suzuki H, Takahashi S, Tamura K, Tomiyama H, Tsuchihashi T, Ueda S, Uehara Y, Urata H, Hirawa N. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2019). Hypertens Res 2019;42, 1235-1481.
48. Kinoshita M, Yokote K, Arai H, Iida M, Ishigaki Y, Ishibashi S, Umemoto S, Egusa G, Ohmura H, Okamura T, Kihara S, Koba S, Saito I, Shoji T, Daida H, Tsukamoto K, Deguchi J, Dohi S, Dobashi K, Hamaguchi H, Hara M, Hiro T, Biro S, Fujioka Y, Maruyama C, Miyamoto Y, Murakami Y, Yokode M, Yoshida H, Rakugi H, Wakatsuki A, Yamashita S; Committee for Epidemiology and Clinical Management of Atherosclerosis (2018) Japan Atherosclerosis Society (JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases. J Atheroscler Thromb 2017;25, 846-984.
49. Shimada H, Makizako H, Doi T, Park H, Tsutsumimoto K, Verghese J, Suzuki T. Effects of Combined Physical and Cognitive Exercises on Cognition and Mobility in Patients With Mild Cognitive Impairment: A Randomized Clinical Trial. J Am Med Dir Assoc 2018;19, 584-591.
50. Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J Phys Act Health. 2009;6, 790-804.
51. Sechrist KR, Walker SN, Pender NJ. Development and psychometric evaluation of the exercise benefits/barriers scale. Res Nurs Health 1987;10, 357-365.
52. Attkisson CC, Zwick R. The client satisfaction questionnaire. Psychometric properties and correlations with service utilization and psychotherapy outcome. Eval Program Plann 1982;5, 233-237.
53. Ministry of Health, Labour and Welfare of Japan, 2020. The dietary reference intakes for Japanese, 2020., Last updated December 24, 2019, Accessed on November 29, 2020.
54. Yokoyama Y, Nishi M, Murayama H, Amano H, Taniguchi Y, Nofuji Y, Narita M, Matsuo E, Seino S, Kawano Y, Shinkai S. Association of Dietary Variety with Body Composition and Physical Function in Community-dwelling Elderly Japanese. J Nutr Health Aging 2016;20, 691-696.
55. Otsuka R, Nishita Y, Tange C, Tomida M, Kato Y, Nakamoto M, Imai T, Ando F, Shimokata H. Dietary diversity decreases the risk of cognitive decline among Japanese older adults. Geriatr Gerontol Int. 2017;17, 937-944.
56. Scarmeas N, Anastasiou CA, Yannakoulia M. Nutrition and prevention of cognitive impairment. Lancet Neurol 2018;17, 1006-1015.
57. Otsuka R, Tange C, Nishita Y, Kato Y, Imai T, Ando F, Shimokata H. Serum docosahexaenoic and eicosapentaenoic acid and risk of cognitive decline over 10 years among elderly Japanese. Eur J Clin Nutr 2014;68, 503-509.
58. Ozawa M, Ohara T, Ninomiya T, Hata J, Yoshida D, Mukai N, Nagata M, Uchida K, Shirota T, Kitazono T, Kiyohara Y. Milk and dairy consumption and risk of dementia in an elderly Japanese population: the Hisayama Study. J Am Geriatr Soc. 2014;62, 1224-1230.
59. Otsuka R, Kato Y, Nishita Y, Tange C, Nakamoto M, Tomida M, Imai T, Ando F, Shimokata H. Cereal Intake Increases and Dairy Products Decrease Risk of Cognitive Decline among Elderly Female Japanese. J Prev Alzheimers Dis. 2014;1, 160-167.
60. Tomata Y, Sugiyama K, Kaiho Y, Honkura K, Watanabe T, Zhang S, Sugawara Y, Tsuji I. Green Tea Consumption and the Risk of Incident Dementia in Elderly Japanese: The Ohsaki Cohort 2006 Study. Am J Geriatr Psychiatry 2016;24, 881-889.
61. Smith GE, Housen P, Yaffe K, Ruff R, Kennison RF, Mahncke HW, Zelinski EM. A cognitive training program based on principles of brain plasticity: results from the Improvement in Memory with Plasticity-based Adaptive Cognitive Training (IMPACT) study. J Am Geriatr Soc 2009;57, 594-603.
62. Coley N, Ngandu T, Lehtisalo J, Soininen H, Vellas B, Richard E, Kivipelto M, Andrieu S; HATICE, FINGER, and MAPT/DSA groups. Adherence to multidomain interventions for dementia prevention: Data from the FINGER and MAPT trials. Alzheimers Dement 2019;15, 729-741.


J. Cartailler1,2,*, C. Loyer3,*, E. Vanderlynden3, R. Nizard4, C. Rabuel3, L. Coblentz Baumann3,5, C. Hourregue6, J. Dumurgier6, C. Paquet6

1. Department of Anesthesiology and Intensive Care, Lariboisière – Saint Louis Hospitals, Paris, France; 2. Paris Diderot University, Paris, France, Inserm, UMRS-942, France; 3. Département de médecine générale, Université de Paris, France; 4. Département de chirurgie orthopédique et traumatologique, APHP, Hôpital Lariboisière-Fernand Widal, Université de Paris, France; 5. Patient-Centered Outcomes Research Unit, UMR 1123, Université Paris-Diderot & Inserm, France; 6. Cognitive Neurology Center, Saint-Louis Lariboisière-Fernand Widal Hospital, APHP, Université de Paris INSERU1144, France; * These authors contributed equally

Corresponding Author: C. Paquet, Cognitive Neurology Center, Saint-Louis Lariboisière-Fernand Widal Hospital, APHP, Université de Paris INSERU1144, France,

J Prev Alz Dis 2021;3(8):322-328
Published online April 7, 2021,



Background: Surgery and anesthesia can result in temporary or permanent deterioration of the cognitive functions, for which causes remain unclear.
Objectives: In this pilot study, we analyzed the determinants of cognitive decline following a non-emergency elective prosthesis implantation surgery for hip or knee.
Design: Prospective single-center study investigating psychomotor response time and changes in MoCA scores between the day before (D-1) and 2 days after (D+2) following surgery at the Lariboisière Hospital (Paris, France).
Participants: 60 patients (71.9±7.1-year-old, 72% women) were included.
Measurements: Collected data consisted in sociodemographic data, treatments, comorbidities and the type of anesthesia (local, general or both). Furthermore, we evaluated pain and well-being before as well as after the surgery using point scales.
Results: Post-operative (D+2) MoCA scores were significantly lower than pre-operative ones (D-1) with a median difference of 2 pts [IQR]=4pts, (p<0.001), we found no significant difference between locoregional and general anesthesia. Pre-operative benzodiazepine or anticholinergic treatments were also associated to a drop in MoCA scores (p=0.006). Finally, the use of ketamine during anesthesia (p=0.043) and the well-being (p=0.006) evaluated before intervention, were both linked to a reduced cognitive impact. Conclusion: In this pilot study, we observed a post-operative short-term cognitive decline following a lower limb surgery. We also identified pre and perioperative independent factors linked to cognitive decline following surgery. In a next stage, a larger cohort should be used to confirm the impact of these factors on cognitive decline.

Key word: Cognitive decline, lower limb surgery, benzodiazepine, ketamine, well-being.

Abbreviations: AC: Anticholinergic; BDZ: Benzodiazepine; MoCA: Montreal Cognitive Assessment; POCD: Post-Operative Cognitive Decline.



Post-Operative Cognitive Decline (POCD) is a major cause of mortality and morbidity costing over $150 billion dollars yearly in health care expenses in the United States (1). POCD includes post-operative delirium, NeuroCognitive Disorder (NCD) and delayed NeuroCognitive Recovery (2). NCD definition from the DSM-V consists in a significant cognitive decline from a previous level of performance, diagnosed at least 30 days after the surgery and assessed by standardized neuropsychological testing. Delayed neurocognitive recovery is tested using the same criteria as for NCD except that the diagnosis window must be less than 30 days after intervention (2). In the elderly (>60 years old (yo)) POCD can result in a loss of autonomy (3, 4). However, clear negative consequences of anesthesia on POCD are still debatable (5, 6). Some papers speculate that POCD in the elderly is attributable to the per-operative use of benzodiazepines (BDZ) and anticholinergic (AC) drugs (7, 8), while others suggest that POCD result from a pre-existing cerebral fragility (4, 6). Taking into account population ageing which drives a growing demand for surgery, the identification of factors increasing the risk of POCD or more severe cognitive dysfunctions is needed.
Diagnosing NCD and delayed neurocognitive recovery requires the collection of preoperative cognitive status along with additional stages into the routine care. Indeed, a minimal setup depends upon establishing a baseline of cognitive functions prior the surgery, then on the administration of at least a second neuropsychometric test in the post-operative period (9). Tools for a quick assessment of POCD are for example the Confusion Assessment Method (CAM), Mini Mental State (MMS) or the Montreal Cognitive Assessment (MoCA) (3, 10).
In particular, the MoCA test evaluates several cognitive functions which impairment is a common mechanism shared by most of POCD, including memory, concentration and verbal abstraction among others. Identifying POCD risk factors (drugs, comorbidity, surgical or anesthesia setups) might help targeting patients that should be followed-up, leading to a personalized care. This prospective pilot study aims to investigate the cognitive decline following a non-emergency elective hip or knee replacement, and to identify independent risk factors associated with it. Additionally, we search for factors linked with persistent cognitive decline evaluated six weeks after the surgery.



Study population

Between February and September 2017, patients from the orthopedic department of Lariboisière Hospital, programmed for a non-emergency hip or knee replacement and who provided consent, were included in this monocentric, prospective study based on the daily clinical practice. We did not include non-French-speaking patients and those refusing to participate to the study. Based on the routine clinical practice, all patients underwent a standardized clinical examination, including medical history and physical examination, laboratory tests were performed in all subjects including chemistry panel and complete blood count. Patients underwent either a general anesthesia (GA), a locoregional anesthesia (LRA) or LRA+ propofol (LRA+P).

Cognitive assessment

To evaluate the cognitive diminution, we administered the MoCA one day before (D-1), then two days (D+2)and six weeks (W+6) after the surgery. The same practitioner administered MoCA tests. Additionally, the same patient never performed twice the same version of the test.


To identify determinants of post-operative decline, we collected during the pre-, per-, and postoperative periods the following covariates:
Preoperative period: Age; Tobacco; Alcohol; Diabetes; Cholesterol; Hypertension (HT); Thyroids; Feeling of defective memory; Previous Neoplasia; Previous GA; Previous LRA; Cognitive Complaints; Anti-hypertensive treatment; Anti-diabetic treatment (per os); AC drugs; Pre-op. Antalgic; Pre-op. NSAID; BDZ; Antidepressants; MoCA (D-1) score; MoCA (D-1) evaluation duration (min); Self-evaluation of pain (D-1) based on a point scale ranging from 0 (no pain) to 10 (extreme pain); Well-being score evaluated using a custom point scale ranging from 0 (no happiness) to 10 (extreme happiness); Instrument Activity Daily Living (iADL) based on the Lawton scale.
Peroperative period (surgery and anesthesia): Type of anesthesia as described above; Ketamine; Sufentanyl; Corticoids; Droperidol, Ephedrine; Atropine; Tranexamic acid; Clonidine chlorhydrate; Surgery duration; Occurrence of complications (see Appendix).
Post-operative period: Antalgic; NSAID; Nefopam chlorhydrate; Pregabalin; AC; BDZ; Peri-operative complications; MoCA (D+2, W+6) score; MoCA (D+2, W+6) evaluation duration (min); Well-being scale; Self-evaluation of pain (D+2, W+6); Well-being (D+2) score.

Statistical analysis

Numerical variables were expressed by the median and interquartile range or mean and standard deviation, categorical variables were expressed as the count and percentage. For all statistical test, we chose a significant level alpha=0.05. Patient characteristic data, duration of surgery, and MoCA scores were compared using chi2, Student’s t-test or Wilcoxon rank-sum test as appropriate. When t-test was used, the distribution normality was assessed with the two-tailed Shapiro-Wilk and Lilliefors tests. Sensitivity of variable of interest was computed from confusion matrices.
Comparison of MoCA (D-1) scores distribution and medians between GA, LRA, and LRA+P categories were analyzed with Kolmogorov-Smirnov and Mann-Whitney tests, both two-tailed.
For binary analysis, we associated for each patient the dummy variable 1 for the loss of at least one MoCA point between (D-1) and (D+2), 0 otherwise. To identify factors associated to a post-operative decline, we first proceeded with a univariate analysis (Chi2 for dummy variables and Logistic Regression (LR) for non-binary variable). We collected associations conditioned to have at least 5 occurrences in the entire population. Time duration of MoCA administration were compared using a two tailed paired t-test, after log-transform.
To investigate the impact of AC and BDZ treatment, we constructed two dummies variables for ‘AC’ AND ‘BDZ’ and for ‘AC’ OR ‘BDZ’ treatments. We searched for an association between these variables and cognitive decline (ΔMoCA≥1) between D-1 and D+2.
We used a multivariate class weight corrected logistic regression model to produce a risk model for loss of at least one MoCA point between D-1 and D+2. Variables with a p-value ≤0.05 in the univariate analysis were introduced in the multivariate model. In the case of incomplete data, patients were excluded from the multivariate analysis. We performed statistical analysis using R-studio software.



Sixty patients (71.9±7.1 yr., 72% women) above 60 year-old were prospectively included in this study (Fig. 1, Tab. 1). Among them, 27 (resp. 33) underwent a non-emergency elective prosthesis hip (resp. knee) implantation surgery. The distributions of age, time of surgery, MoCA, pain and well-being scores were not different between patient from the hip and the knee groups (Kolmogorov-Smirnov test, unsignificant difference, data not shown). Therefore, for the statistical analysis we decided to consider patients that had a hip and knee surgery as a single study group.

Table 1. Comparison of population characteristic data between the patient with ΔMoCA≥ 1pt and patients with no point loss. Binary data are number and percentage, while non binary data are shown in median and interquartile range (IQR). NSAIDs ; Nonsteroidal anti-inflammatory drugs, † includes the following complications: failed LRA – modification of the surgery – Hemorrhage – incisors defect (see Supplementary)

Figure 1. Flowchart


Patients underwent GA, (n=27) or LRA (n=14) or LRA+ P (n = 13). There was no significant difference, neither in mean nor in variance, on the ΔMoCA between D-1 and D+2 between the 3 groups of anesthesia (Fig. 2). We investigated if MoCA scores obtained one day before (D-1), two days after (D+2) and six weeks (W+6) following the surgery were significantly different. MoCA score drop was 2[4] points (median[IQR], p<0.001) between (D-1) and (D+2) (Fig. 3A) including 8, 14 and 19 patients who lost 1, 2 or 3 or more points respectively. We found no significant differences between D-1 and W+6 (MoCA median[IQR]: 24[3.5] versus 24 [4], p-value= 0.831), although among the 31 patients evaluated at 6 weeks, 32% did not recovered their baseline level score. We observed a significant decrease in the evaluation duration of the MOCA between (D-1) and (D+2) (p <0.001) and between (D+2) and (W+6) (p<0.001). The surgery duration did not affect the cognitive functions (median[IQR] = 70[40] min, ranging: 30 to 200min).

Figure 2. Distribution of ΔMoCA between D-1 and D+2 for the three types of anesthesia (GA, LRA, LRA+Propofol) showing no significant difference in (Wilcoxon test)


In the univariate analysis, we found a significant positive relationship between ΔMoCA≥1 and BDZ treatment (p-value = 0.047, sensitivity = 93.8%), AC drugs (p-value = 0.023, sensitivity = 100%), ‘BDZ or AC’ (p-value = 0.006, sensitivity = 93.8%), perioperative complications (p-value = 0.035, sensitivity = 100%) and a negative significant result with the use of ketamine agent (p-value = 0.043, sensitivity = 62.5%). Results are summarized in Tab. 1. Furthermore, among non-binary variables, only well-being (p-value = 0.006, sensitivity = 94.6%) was significantly associated with cognitive decline. Using a multivariate analysis, we found that ‘well-being’, ‘Ketamine’, ‘AC’ or ‘BDZ’, were independent risk factors associated with cognitive decline between D-1 and D+2 (see Tab. 2A).
Similarly, we found that, persistent cognitive decline was associated with BDZ (p-value = <0.001) and perioperative complications (p-value = 0.013). Additionally, we also found that post-operative confusion was also associated with a W+6 cognitive decline (p-value = 0.002). Results are shown in Tab. 2B.

Table 2. Factors associated with cognitive decline at D+2 and w+6



In this study, MoCA scores were significantly impacted by the surgery, with a loss of 2.25 points in average. We described that POCD were independently associated with pre-operative well-being, per-operative use of ketamine, peri-operative complications and pre-existing treatment based on either BDZ or AC drugs. In particular, well-being and ketamine had a protective effect, while complications and BDZ/AC treatments were linked to cognitive decay. Here, the type of anesthesia was not linked to cognitive decline while persistent cognitive decline was linked to pre-existing BDZ medication and per-operative complications. Finally, MoCA administration duration was neither affected by the type of anesthesia nor linked to POCD, however, the duration at W+6 was significantly shorter than D-1 and D+2 reflecting the training effect (Fig. 3B). These results suggest insight to improve perioperative cognitive protection and in particular to identify upstream of the intervention patients that could be affected by surgery or anesthesia.
Ketamine is a non-barbiturate anesthetic inhibiting acetylcholine that could lead to an increased number of delirium (11). Per-operative use of ketamine is still debated, with on one side studies defending its neuroprotective and anti-inflammatory actions (12), and on the other side studies arguing for no beneficial effects (13). Interestingly ketamine was shown to speed up recovery to consciousness after GA, yet possible impact on POCD was unclear (14). In the present study, per-operative ketamine administration was associated to fewer POCD, this support previous findings suggesting that administration of ketamine during a surgery reduces occurrences of POCD.
Chaiwat et al 2019 (15) reported more POCD among patients sedated with propofol, which was also suspected to decrease mean arterial pressure with a risk of brain hypoxia leading to cognitive decline (16). However, ketamine could preserve the hemodynamic stability during propofol-based anesthesia. In our study, we compared three groups with and without propofol. We did not find any impact of the kind of anesthesia on the cognitive decline suggesting that propofol did not impact cognition in this cohort. Furthermore, patients who received ketamine also received propofol supporting the hypothesis of a benefic effect of the ketamine during propofol infusion. However, even if several studies are in favor of ketamine protective effect, further prospective randomized controlled studies would be needed to confirm this hypothesis.
BDZ and AC drugs have long been suspected to be involved in drug-induced cognitive decline (17-19). On one hand, BDZ act as positive allosteric modulators of GABA-A (γ-aminobutyric acid) receptors, which results in an overall increased cortical inhibition, the latter possibly responsible for mental deterioration (20). On the other hand, delirium pathway arises from a dopamine excess and acetylcholine decrease, that could be due or accentuate directly by AC drugs (21). Furthermore, consequences of BDZ or AC treatment on cognition have been extensively described for speed-up age-related cognitive decay and Alzheimer’s disease, not for POCD (22, 23). In the present study, using a homogenous prospective non-emergent population, we observed detrimental effects of BDZ and AC drugs on cognitive functions. This finding supports previous results and hypotheses, however larger prospective and multicentric studies will be needed to confirm these results, allowing recommendations regarding the use of BDZ and AC drugs in non-emergent surgery. Clarifying a possible mechanisms underpinning BDZ/AC deleterious effects on sedated patients will benefit the prevention of temporary delayed NeuroCognitive Recovery as well as permanent POCD (NCD). We already know that the GABA-A agonist role of propofol is potentiated by BDZ, that could in turn promote POCD (24). However, among our 54 patients, 15 had no propofol, yet for this subpopulation the cognitive decline was statistically undistinguishable from the 39 other patients. A regular intake of BDZ was also associated to a reduced cognitive reserve (22), the latter linked to more POCD (25). Nevertheless, we found no correlation between MoCA (D-1) scores or sociocultural levels, and intake of BDZ/AC drugs.
Per-operative complications were associated with ΔMoCA both two days and six weeks after the surgery. These findings are consistent with previous works (26), and even more with recent works about complications following total hip arthroplasties (27). This suggests that an extended period under anesthesia does not affect post-operative cognition while a physiological response to a surgical complication does.Indeed, surgery-related inflammatory response has been associated to POCD (28). While we did not collect inflammatory-related biomarkers, we can note that among patient with peri-operative complications, only two had NSAIDs and three had ketamine.
Finally, depressed mood, in particular among the elderly, was associated with cognitive impairment (29). Several studies outlined the link between POCD and elderly mood deterioration, assessed using Geriatric Depression Scale both for patients receiving a cardiac as well as a non-cardiac surgery (26, 30). In our study, we found no link between cognitive decline and patient treated for depression, which agrees with these studies. Interestingly, we found that well being measured before and after the surgery were not linked to pain scores, in fact pre-operative well-being was here an independent factor associated with POCD, which was not the case for pain. These results might be explained by the fact that pain was carefully managed after the surgery (av. score ≈4), which does mitigate POCD (31). In the end, it emerges from this study that well-being measured before the surgery is a better indicator of cognitive decline than post-operative pain, which suggests that the patient’s state before surgery affects post-operative cognitive trajectory.

Limitations and perspectives

This study has some limitations. The patients included were very homogeneous but the inclusion criteria, the prospective methodology during a limited period led to a small number of patients. Larger multicentric studies would be needed to confirm or not these results and to propose recommendation regarding non-emergency arthroplasty.
In some group the number of patients was too low to provide useful data and/or multivariate analysis. However, the prospective methodology and our statistical approach allowed us to underline interesting insights about POCD. This article is the first result of a pilot study suggesting that cognitive decline occurring after a surgery could be anticipated based on pre-operative factors, yet only partially. We are performing a larger study in order to identify patients who are at risk of both delayed neurocognitive recovery and permanent cognitive decline.



To conclude, we reported a significant cognitive decline two days following a non-emergency lower limb surgery. BDZ or AC drugs were associated with a poor cognitive trajectory, both in short-term, but also in long-term in the case of BDZ drugs. At the same time, use of per-operative ketamine appeared to have a cognitive protective effect. Pre-operative well-being and perioperative complications were the two non-drug factors associated with positive and negative outcome respectively. Findings in the present study suggest that patient’s medical prescriptions and the surgery/anesthesia smooth progress can both independently affect POCD. Nevertheless, further research, based on a larger cohort, should investigate separately factors discussed above, in particular drugs for which neurophysiological mechanisms remain unclear and the role in cognitive decline/protection is still a divisive topic.


– Drop of MoCA after surgery is not associated with the type of anesthesia
– Pre-operative treatment and well-being are associated to MoCA points loss after surgery
– Per-operative complications and ketamine administration affect post-operative MoCA


Statement of Informed Consent: We obtained an authorization by the French data privacy administrative body ‘Commission Nationale de l’Informatique et des Libertés’ (CNIL), under the reference number z2c2680420w. In agreement with the ethics committee for this non-interventional study an oral agreement was obtained from each patient during their first medical visit.

Funding: Assistance Publique – Hôpitaux de Paris (APHP), Hôpital Lariboisière-Fernand Widal, Université de Paris.
Conflict of Interest: Pr. C. PAQUET is member of the International and National Advisory Boards of Lilly, ROCHE, Biogen. She is consultant of Fujiribio, ALZOHIS, NEUROIMMUNE and GILEAD and is involved as investigator in several clinical trials for Roche, Esai, Lilly, Biogen, Astra-Zeneca, Lundbeck, Neuroimmune. Dr. J. DUMURGIER is investigator in several passive anti-amyloid immunotherapies and other clinical trials for Roche, Eisai, Lilly, Biogen, Astra-Zeneca, Lundbeck. Similarly, Dr. C. HOURREGUE is investigator for Roche, Eisai and Biogen. Pr. R. NIZARD, Dr. C. RABUEL, Dr. L. COBLENTZ BAUMANN, Dr. C. LOYER, Dr. J. CARTAILLER and Dr. E. VANDERLYNDEN declare that they have no conflict of interest.

Authors’ contributions: Claire Paquet designed the study and analyzed the data and prepared the manuscript. Remy Nizard, Chirstophe Rabuel and Camille Loyer included the patients. Jerome Cartailler, Julien Dumurgier and Claire Paquet have performed statistical analysis and results interpretation. All authors read and validated the manuscript.



1. E. Braunwald, A. S. Fauci, D. L. Kasper, S. L. Hauser, D. L. Longo and J. L. Jameson, Harrison’s principles of internal medicine, McGraw Hill, 2001.
2. E. Mahanna-Gabrielli, K. J. Schenning, L. I. Eriksson, J. N. Browndyke, C. B. Wright, L. Evered, D. A. Scott, N. Y. Wang, C. H. Brown IV, E. Oh and others, «State of the clinical science of perioperative brain health: report from the American Society of Anesthesiologists Brain Health Initiative Summit 2018,» British Journal of Anaesthesia, 2019.
3. B. A. Fritz, P. L. Kalarickal, H. R. Maybrier, M. R. Muench, D. Dearth, Y. Chen, K. E. Escallier, A. B. Abdallah, N. Lin and M. S. Avidan, «Intraoperative electroencephalogram suppression predicts postoperative delirium,» Anesthesia and analgesia, 2016; vol. 122, p. 234.
4. T. S. Wildes, A. M. Mickle, A. B. Abdallah, H. R. Maybrier, J. Oberhaus, T. P. Budelier, A. Kronzer, S. L. McKinnon, D. Park, B. A. Torres and others, «Effect of electroencephalography-guided anesthetic administration on postoperative delirium among older adults undergoing major surgery: the ENGAGES randomized clinical trial,» Jama, 2019; vol. 321, pp. 473-483.
5. L. S. Rasmussen, A. Steentoft, H. Rasmussen, P. A. Kristensen and J. T. Moller, «Benzodiazepines and postoperative cognitive dysfunction in the elderly. ISPOCD Group. International Study of Postoperative Cognitive Dysfunction,» British journal of anaesthesia, 1999; vol. 83, pp. 585-589.
6. U. Dokkedal, T. G. Hansen, L. S. Rasmussen, J. Mengel-From and K. Christensen, «Cognitive functioning after surgery in middle-aged and elderly Danish twins,» Anesthesiology: The Journal of the American Society of Anesthesiologists, 2016; vol. 124, pp. 312-321.
7. P. Pandharipande, A. Shintani, J. Peterson, B. T. Pun, G. R. Wilkinson, R. S. Dittus, G. R. Bernard and E. W. Ely, «Lorazepam is an independent risk factor for transitioning to delirium in intensive care unit patients,» Anesthesiology: The Journal of the American Society of Anesthesiologists, 2006; vol. 104, pp. 21-26.
8. C. Pratico, D. Quattrone, T. Lucanto, A. Amato, O. Penna, C. Roscitano and V. Fodale, «Drugs of anesthesia acting on central cholinergic system may cause post-operative cognitive dysfunction and delirium,» Medical hypotheses, 2005;vol. 65, pp. 972-982.
9. T. L. Tsai, L. P. Sands and J. M. Leung, «An update on postoperative cognitive dysfunction,» Advances in anesthesia, 2010; vol. 28, pp. 269-284.
10. Z. S. Nasreddine, N. A. Phillips, V. Bédirian, S. Charbonneau, V. Whitehead, I. Collin, J. L. Cummings and H. Chertkow, «The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment,» Journal of the American Geriatrics Society, 2005; vol. 53, pp. 695-699.
11. J. Sleigh, M. Harvey, L. Voss and B. Denny, «Ketamine–More mechanisms of action than just NMDA blockade,» Trends in anaesthesia and critical care, 2014; vol. 4, pp. 76-81.
12. O. Dale, A. A. Somogyi, Y. Li, T. Sullivan and Y. Shavit, «Does intraoperative ketamine attenuate inflammatory reactivity following surgery? A systematic review and meta-analysis,» Anesthesia & Analgesia, 2012; vol. 115, pp. 934-943.
13. M. S. Avidan, H. R. Maybrier, A. B. Abdallah, E. Jacobsohn, P. E. Vlisides, K. O. Pryor, R. A. Veselis, H. P. Grocott, D. A. Emmert, E. M. Rogers and others, «Intraoperative ketamine for prevention of postoperative delirium or pain after major surgery in older adults: an international, multicentre, double-blind, randomised clinical trial,» The Lancet, 2017; vol. 390, pp. 267-275.
14. V. S. Hambrecht-Wiedbusch, D. Li and G. A. Mashour, «Paradoxical EmergenceAdministration of Subanesthetic Ketamine during Isoflurane Anesthesia Induces Burst Suppression but Accelerates Recovery,» Anesthesiology: The Journal of the American Society of Anesthesiologists, 2017; vol. 126, pp. 482-494.
15. O. Chaiwat, M. Chanidnuan, W. Pancharoen, K. Vijitmala, P. Danpornprasert, P. Toadithep and C. Thanakiattiwibun, «Postoperative delirium in critically ill surgical patients: incidence, risk factors, and predictive scores,» BMC anesthesiology, 2019; vol. 19, p. 39.
16. K. Wild, D. Howieson, F. Webbe, A. Seelye and J. Kaye, «Status of computerized cognitive testing in aging: a systematic review,» Alzheimer’s & Dementia,2008; vol. 4, pp. 428-437.
17. J. M. Starr and L. J. Whalley, «Drug-induced dementia,» Drug Safety, 1994;vol. 11, pp. 310-317.
18. R. T. Bartus, R. L. 3. Dean, B. Beer and A. S. Lippa, «The cholinergic hypothesis of geriatric memory dysfunction,» Science, 1982; vol. 217, pp. 408-414.
19. H. Hampel, M. M. Mesulam, A. C. Cuello, A. S. Khachaturian, A. Vergallo, M. R. Farlow, e. al and APMI, «Revisiting the Cholinergic Hypothesis in Alzheimer’s Disease: Emerging Evidence from Translational and Clinical Research,» The journal of prevention of Alzheimer’s disease, 2019; vol. 6, no. 1, pp. 2-15.
20. C. E. Griffin III, A. M. Kaye, F. R. Bueno and A. D. Kaye, «Benzodiazepine pharmacology and central nervous system–mediated effects,» The Ochsner Journal, 2013;vol. 13, pp. 214-223.
21. J. R. Maldonado, «Neuropathogenesis of delirium: review of current etiologic theories and common pathways,» The American Journal of Geriatric Psychiatry, 2013;vol. 21, pp. 1190-1222.
22. S. B. Gage, Y. Moride, T. Ducruet, T. Kurth, H. Verdoux, M. Tournier, A. Pariente and B. Bégaud, «Benzodiazepine use and risk of Alzheimer’s disease: case-control study,» Bmj, 2014; vol. 349, p. g5205.
23. G. Grande, I. Tramacere, D. L. Vetrano, S. Pomati, C. Mariani and G. Filippini, «Use of benzodiazepines and cognitive performance in primary care patients with first cognitive complaints,» International psychogeriatrics, 2018; vol. 30, pp. 597-601.
24. B. A. Orser and D. R. Miller, «Propofol-benzodiazepine interactions: insights from a “bench to bedside” approach,» Canadian Journal of Anesthesia/Journal canadien d’anesthésie, 2001;vol. 48, pp. 431-434.
25. I. Feinkohl, G. Winterer and T. Pischon, «Hypertension and risk of post-operative cognitive dysfunction (POCD): A systematic review and meta-analysis,» Clinical practice and epidemiology in mental health: CP & EMH, 2017; vol. 13, p. 27.
26. N. H. Greene, D. K. Attix, B. C. Weldon, P. J. Smith, D. L. McDonagh and T. G. Monk, «Measures of executive function and depression identify patients at risk for postoperative delirium,» Anesthesiology: The Journal of the American Society of Anesthesiologists, 2009; vol. 110, pp. 788-795.
27. K. T. Aziz, M. J. Best, Z. Naseer, R. L. Skolasky, K. E. Ponnusamy, R. S. Sterling and H. S. Khanuja, «The Association of Delirium with Perioperative Complications in Primary Elective Total Hip Arthroplasty,» Clinics in orthopedic surgery, 2018; vol. 10, pp. 286-291.
28. A. Alam, Z. Hana, Z. Jin, K. C. Suen and D. Ma, «Surgery, neuroinflammation and cognitive impairment,» EBioMedicine, 2018.
29. J. A. Brommelhoff, M. Gatz, B. Johansson, J. J. McArdle, L. Fratiglioni and N. L. Pedersen, «Depression as a risk factor or prodromal feature for dementia? Findings in a population-based sample of Swedish twins.,» Psychology and aging, 2009; vol. 24, p. 373.
30. J. L. Rudolph and E. R. Marcantonio, «Postoperative delirium: acute change with long-term implications,» Anesthesia and analgesia, 2011; vol. 112, p. 1202.
31. S. K. Inouye, T. Robinson, C. Blaum, J. Busby-Whitehead, M. Boustani, A. Chalian, S. Deiner, D. Fick, L. Hutchison, J. Johanning and others, «Postoperative delirium in older adults: best practice statement from the American Geriatrics Society,» Journal of the American College of Surgeons, 2015; vol. 220, pp. 136-148.
32. J. A. Yesavage, T. L. Brink, T. L. Rose, O. Lum, V. Huang, M. Adey and V. O. Leirer, «Development and validation of a geriatric depression screening scale: a preliminary report,» Journal of psychiatric research, 1982;vol. 17, pp. 37-49.
33. P. K. Mistry, G. S. Gaunay and D. M. Hoenig, «Prediction of surgical complications in the elderly: Can we improve outcomes?,» Asian journal of urology, 2017; vol. 4, pp. 44-49.
34. J. A. Hudetz and P. S. Pagel, «Neuroprotection by ketamine: a review of the experimental and clinical evidence,» Journal of cardiothoracic and vascular anesthesia, 2010. vol. 24, pp. 131-142.



P. Daunt1, C.G. Ballard2, B. Creese2, G. Davidson3, J. Hardy4, O. Oshota1, R.J. Pither1, A.M. Gibson1 for the Alzheimer’s Disease Neuroimaging Initiative*


1. Cytox Limited, Manchester, UK; 2. University of Exeter Medical School, Exeter, UK; 3. Ledcourt Associates Limited, Cambridge, UK.; 4. UK Dementia Research Institute, University College London, London, UK.

Corresponding Authors: Alex Gibson, Cytox Ltd., John Eccles House, Robert Robinson Avenue, Oxford Science Park, Oxford, OX4 4GP, United Kingdom. Email: Tel:+44 (0)1865 338018

J Prev Alz Dis 2021;1(8):78-83
Published online November 11, 2020,



BACKGROUND: There is a clear need for simple and effective tests to identify individuals who are most likely to develop Alzheimer’s Disease (AD) both for the purposes of clinical trial recruitment but also for improved management of patients who may be experiencing early pre-clinical symptoms or who have clinical concerns.
OBJECTIVES: To predict individuals at greatest risk of progression of cognitive impairment due to Alzheimer’s Disease in individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) using a polygenic risk scoring algorithm. To compare the performance of a PRS algorithm in predicting cognitive decline against that of using the pTau/Aß1-42 ratio CSF biomarker profile.
DESIGN: A longitudinal analysis of data from the Alzheimer’s Disease Neuroimaging Initiative study conducted across over 50 sites in the US and Canada.
SETTING: Multi-center genetics study.
PARTICPANTS: 515 subjects who upon entry to the study were diagnosed as cognitively normal or with mild cognitive impairment.
MEASUREMENTS: Use of genotyping and/or whole genome sequencing data to calculate polygenic risk scores and assess ability to predict subsequent cognitive decline as measured by CDR-SB and ADAS-Cog13 over 4 years
RESULTS: The overall performance for predicting those individuals who would decline by at least 15 ADAS-Cog13 points from a baseline mild cognitive impairment in 4 years was 72.8% (CI:67.9-77.7) AUC increasing to 79.1% (CI: 75.6-82.6) when also including cognitively normal participants. Assessing mild cognitive impaired subjects only and using a threshold of greater than 0.6, the high genetic risk participant group declined, on average, by 1.4 points (CDR-SB) more than the low risk group over 4 years. The performance of the PRS algorithm tested was similar to that of the pTau/Aß1-42 ratio CSF biomarker profile in predicting cognitive decline.
CONCLUSION: Calculating polygenic risk scores offers a simple and effective way, using DNA extracted from a simple mouth swab, to select mild cognitively impaired patients who are most likely to decline cognitively over the next four years.

Key words: Polygenic risk, cognitive decline, Alzheimer’s disease.



Alzheimer’s disease (AD) is the most common form of dementia with nearly 50 million people affected globally and an estimated economic impact of $818 billion (1).
As well as having a clear heritable component (2), AD is genetically complex. Neuropathologically, the disease is characterized by extracellular senile plaques containing β -amyloid (Aβ) and intracellular neurofibrillary tangles containing hyperphosphorylated tau protein. A relatively small number of dominant mutations in the amyloid precursor and presenilin genes are known to cause early onset Alzheimer’s disease. Over the past two decades, genome wide association studies (GWAS) have identified multiple loci and single nucleotide polymorphisms (SNPs) associated with the much more common, late-onset or sporadic form of the disease (LOAD) (3-5). Apolipoprotein E (ApoE) is a major cholesterol carrier that supports lipid transport and injury repair in the brain. The ε4 allele of ApoE (ApoE4) has been found to be a primary genetic risk factor for AD, associated with increased risk for both early-onset AD and LOAD (6, 7). Although only 20-30% of humans are ApoE4 carriers, these individuals account for up to 60% of all Alzheimer’s disease cases. In addition, ApoE4 is associated with an increased risk of lower age of onset (8, 9), making this an important subset of the population at high risk of developing AD.
Development of polygenic risk scoring (PRS) algorithms that can capture all the genetic contribution towards the risk of developing AD (10) is an attractive strategy to allow better clinical trials for AD prevention. PRS approaches have demonstrated accuracies of between 75 and 84% for predicting onset of AD when including APOE, sex and age in addition to PRS (11), In particular the PRS approach as developed by Escott-Price et al (12), is built as a sum of the weighted contributed of 10,000s of SNPs where the weights are the β-coefficients of each SNP association with the disease. In contrast to other PRS algorithms, where fewer SNPs have been used (for example just 31 SNPs (13)) this approach includes SNPs that are not considered as having genome wide significance in GWAS studies. However, inclusion of this vastly increased number of variants which alone carry sub-threshold significance provides an additive contribution to the overall performance that may be substantive and also reduce risk that performance is not lost when being applied across different cohorts.
Until now the analyses performed using this particular approach have been carried out to predict those individuals diagnosed with AD or MCI (14) versus those who are cognitively normal, though PRS algorithms have been used to look at a variety of AD pathology and risk by Altmann et al (15). Here we look to see how the PRS performs in predicting those individuals most likely to decline cognitively independent of whether they have cognitive impairment on entry or not.
Currently, the most frequently used approach to enrich clinical trial recruitment with participants who have increased likelihood of progressive cognitive and functional decline has been to focus on identifying individuals who are positive for amyloid biomarkers. In addition, measurement of tau in CSF often with amyloid levels, is increasing in use. We therefore also compare the ability to predict decline using PRS against that of using CSF tau and amyloid measurements.



Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD).

Sample Description

ADNI is an on-going longitudinal study that has been established to develop methods for early detection of AD and subsequent monitoring of disease trajectory using clinical, imaging and genetic data (16). Data for this analysis was collected from 515 participants, who entered the study with a diagnosis of Mild Cognitive Impairment or considered cognitively normal. In addition, 47 individuals diagnosed with AD were used to check the algorithm was performing as expected to differentiate AD cases from cognitively normal controls. All participants in addition to having suitable genetic data available had at least 4 years’ worth of follow up cognitive testing and imaging scans. Upon entry into the study 199 individuals were cognitively normal and 316 diagnosed as MCI. The average age of the total group was 73.2 years, with the CN group being on average approximately 3 years older than those diagnosed with MCI (75.1y and 72.0 y respectively). ADAS-Cog13 scores for cognitively normal and MCI groups upon entry were 9.0 and 14.9 respectively and at the 4 year assessment the average scores obtained were 9.6 and 19.8, clearly showing that on average the MCI group continued to decline compared with little change in the average score of the CN group. CSF biomarker data were not available for all participants, so analyses performed to compare PRS with biomarker (tau and amyloid) as a predictor for subsequent cognitive decline were carried out on 290 MCI subjects. Table 1a shows the classification of the ADNI dataset at baseline and changes to cognitive performance as measured by ADAS-Cog13 after 4 years. Similarly, Table 1b describes the sub-group that also had CSF biomarker data available.

Table 1. Characteristics of participants used in the analyses


Genotyping Procedures and Quality Control

The ADNI samples were genotyped using with Whole Genome Sequencing and/or the Illumina Omni 2.5M BeadChip array. Quality control checks were performed using PLINK software ( (17). Checks included exclusion of SNPs with missingness greater than 0.02, minor allele frequency of less than 0.01 and SNPs with Hardy-Weinberg equilibrium p-value less than 1 x 10-6 were also excluded. After such checks 8,990,292 SNPs were left for analysis of which approximately 114,000 were used as part of the polygenic risk scoring algorithm.

Calculation of Polygenic Risk Scores

A specifically built, proprietary software called SNPfitR was used for all subsequent PRS calculations. The PRS calculations are based on a pre-determined logistic regression model based on the modelling of the association between the incidences of variants within a large panel of SNPs with a known links to AD to the presence of the disease in a substantial cohort of subjects (Escott-Price et al12). Subject age, gender and presence of both APOE4 and APOE2 proteins are included as covariates. The software calculates the normalised sum of the individual scores weighted by their effect sizes for each SNP, adds the values for the covariates and derives the predicted risk from the model equation.
Effect sizes were determined from the IGAP study. The score contribution from SNPs with missing values were imputed based on the population frequency of the effect allele for that SNP.

Statistical Analysis

The polygenic risk scores generated were exported for the analysis presented.
The statistical analysis software package JMP 14.1.0 was used to carry out all data manipulation and analysis. The ROC analysis and AUC calculations were performed using the add in ‘Model Classification Explorer’. Values were cross checked with the AUC calculations carried out in the software.



Association of AD PRS with onset of Alzheimer’s Disease

As a check that the algorithm was performing as expected polygenic risks scores were also generated for 47 Alzheimer Disease cases and compared with those generated for the 199 cognitively normal individuals. The accuracy of prediction of clinical AD cases (n = 47) versus cognitively normal control (n = 199) was 80% AUC. Furthermore, as shown in Figure 1, PRS score is clearly associated with tau levels and, as expected, diagnostic classification. In this analysis, data were plotted in this heat density format to illustrate a clear relationship between the pTau/Aß1-42 ratio (Hansson et al18) and the stage of disease. It can be seen that those subjects classified at AD, late-MCI (LMCI) or early-MCI (EMCI), predominantly sit in the group with a PRS of 0.6 or above, whereas cognitively normal (CN) individuals tend to be in the 0.6 or lower range of the PRS scores. Importantly, as shown in Figure 2, there is a substantial overlap between different APOE genotype sub-groups. As expected, ApoE4 carriers fall within the higher end (0.6 and above) end of the PRS range and ApoE2 carriers at the lower end. However, ApoE3/3 homozygotes, representing some 60% of the Caucasian population, span the high and low ends of the PRS spectrum, thereby demonstrating the additional genetic risk information provided by the risk algorithm over APOE alone.

Figure 1. Density plots showing relationship between full PRS, pTau/Aβ(1-42) ratio and diagnostic classification (CN, EMCI, LMCI, AD)

Figure 2. Distribution of risks scores across the MCI population (n = 316) coloured by APOE genotype


Association of AD PRS with cognitive decline from an MCI baseline

Rather than using specific clinical diagnosis to categorise cases as previously used, predicting the cognitive decline likely due to AD from either an MCI or cognitively normal baseline was performed. Table 2 shows the predictive accuracy of identifying those individuals who are most likely to decline as measured by ADAS-Cog13 testing from an MCI or CN baseline, irrespective of cognitive status at baseline. The analyses were performed defining significant progression as 5-point, 10-point or 15-point decline at their 4 year follow up assessment. Though we report the accuracy for predicting decline from a cognitively normal state, the number of individuals that decline significantly within the time period is relatively low and thus results cannot be considered statistically significant. However, of the 316 individuals who entered the study with an MCI diagnosis, significant numbers had declined by at least 5 points (107), 10 points (61) and 15 points (39) on the ADAS-Cog13 scale to allow meaningful prediction accuracies to be measured. In addition to the full PRS algorithm (APOE + PRS + Age + Sex) being used to generate risk scores, prediction accuracies based on APOE status alone and total genetic risk (APOE + PRS) were calculated. The best prediction accuracy is seen for testing cases that have declined by at least 10 points at 4 years versus those that have remained cognitively stable (< 5-point decline) with an AUC of 74.8%, compared with 67.4% for APOE alone and 73.5% for APOE + PRS. A similar performance is seen when predicting those individuals with 15-point decline. In both analyses all those individuals had polygenic risk scores in the upper half of the distribution. When looking at smaller changes in cognitive performance over 4 years, addition of the polygenic risk score term to APOE did not impart greater performance. In all cases addition of age and gender as co-variates did not add any additional predictive performance in this particular group presumably due to the particular age and sex distribution between the CN and MCI groups in this particular cohort. Given that the mean age of those that declined and those that remained relatively stable were similar the contribution provided by age to the overall risk score for both groups would, in turn, be broadly equivalent.

Table 2. Performance of polygenic risk scoring algorithm to predict cognitive decline up to 4 years after entry to study

* PRS = all risk associated with the genetics other than that contribution from APOE

To evaluate whether the full algorithm could predict cognitive decline as defined by predetermined thresholds and be compared with that predicted by CSF biomarker status (figure 3), the MCI population where both genetics, CSF and CDR-SB assessment data were available was studied (n=290). There was a significant difference in progression (as defined by CDR-SB) between patients whose risk score was greater than 0.6 (n=196) versus the group whose score was less 0.6 (n=94) as early as 6 months after baseline assessment. 0.6 was chosen as a threshold based on an optimal balance between sensitivity and specificity (data not shown here) High risk patients progressed, on average, by approximately 1 point over 24 months and 2 points over 48 months compared with low risk patients who on average decline 0.2 and 0.4 points over the same timepoints. A similar evaluation was carried out to compare the predictive performance using CSF biomarker positivity as determined by a pTau/Aβ(1-42) ratio using the cut off of 0.02818 and CSF Aβ(1-42) with a threshold of 880pg/mL18. Again, there was a significant difference in progression between biomarker positive and negative patients. pTau/Aβ(1-42) ratio positive patients progressed, on average, by 1.1 and 2.9 points over 24 and 48 months respectively, whereas there was an average decline of 0.1 and 0.2 points for the negative group. Similarly using Aβ(1-42) CSF levels only, the amyloid positive group progress by 1 and 2.6 points at 24 and 48 months respectively whilst the negative group only progressed by 0,3 points on average over 48 months. The performance of the PRS was broadly similar to that of either CSF biomarker measurement in identifying those subjects at highest and lowest risk of cognitive decline on the CDR-SB scale. Furthermore, a similar analysis was performed on APOE3 homozygote individuals (n=125) only (figure 3). Again, using a threshold of 0.6 to determine the high risk group (n =49), a difference a measured by a change of CDR-SB between the two groups was shown 12 months with a clear difference at 36 months. The high risk group declined, on average by 1.5 points at 36 months compared with the low risk group who only declined, on average, by 0.5 points.

Figure 3. Time course of clinical progression in patients with MCI over 48 months. Average with standard errors by PRS group (orange >0.6; blue <0.6 at baseline) for all APOE genotypes and for APOE homozygotes only, pTau/Aβ(1-42) group (orange > 0.028; blue <0.028) and Aβ(1-42) (orange < 880pg/mL; blue >880pg/mL)



This study was designed to demonstrate the potential utility of a specific PRS algorithm in identifying individuals at highest risk of clinically significant cognitively decline within a specific time period. Previously most studies reporting the use of PRS approaches have been used to differentiate two populations with clearly different clinical phenotypes (AD versus CN) and thus not necessarily demonstrating how this approach could be used prospectively. The results of these analyses show that using polygenic scoring algorithms which have been designed to understand the genetic risk of future onset of Alzheimer’s Disease, can be applied to enrich trial populations with individuals who are more likely to decline cognitively within a certain time period.
Though APOE genotype remains an important genetic risk factor within this cohort, it is clear there is an additional genetic component that should be considered in assessing genetic risk. This will subsequently allow further risk stratification within APOE genotypes such as identifying APOE3 homozygotes who are at relatively higher risk even compared with some APOE4 carriers. This has implications in the design of clinical trials where in many trial designs possession of at least one APOE4 allele is used as an enrichment strategy in prevention trials.
It is broadly accepted that CSF-tau/amyloid ratios are a reasonable predictor of future cognitive decline (18-20) though definitive studies have yet to be performed, and testing for amyloid alone, via PET imaging or CSF remains the standard method to enrich trials with patient most likely to decline cognitively. This study shows that PRS predictions, are able to perform to a similar level in predicting further progression, as measured by CDR-SB, in patients who have an MCI diagnosis. Importantly this genetic risk assessment can be more easily accessed (cost and patient burden) through whole blood or mouth swab testing, rather than by performing an invasive lumbar puncture procedure and subsequent CSF testing; such invasive procedures are particularly challenging in elderly subjects who may be relatively cognitively robust (early MCI or prodromal). The PRS algorithm therefore represents a promising method to facilitate broad screening of potential trial participants in order to identify those at highest risk for cognitive decline. Further confirmatory testing, via the use of more invasive and expensive CSF and/or PET imaging, could then be focussed on a significantly reduced number of individuals for final patient recruitment decisions. Furthermore, a combination of PRS and tau levels (underlying genetic risk coupled with manifestation of that risk through pathology) may provide a more optimal model for likelihood of subsequent onset of AD in early symptomatic or pre-symptomatic individuals. Whilst there may be specific reasons why amyloid or tau biomarkers may be required for clinical trials focussing on treatments specifically targeting amyloid or tau, PRS may have advantages for therapies with different treatment targets independent of potential mechanisms.
Further studies will be important to determine the added value of combing amyloid/tau and PRS markers and to fully determine the utility of PRS in predicting cognitive decline in cognitively normal individuals.
It is recognised that this work has considered genetic risk together with age and gender in developing a model for predicting further development of cognitive symptoms and so does not consider other risk factors that are known to influence onset and development of disease. Combining both genetic and lifestyle risk factors for the purposes of identifying those individuals most at risk of Alzheimer’s Disease is likely to add further to the predictive accuracy.

Study Limitations

This study is not without limitations, with sample size being the primary shortcoming. This was particularly relevant in evaluating the APOE E3 homozygote only sub-group. Furthermore, studies with larger sample sizes across all diagnostic categories, including those declining from a cognitively normal baseline, is important to understand broader utility. As with most studies of this nature, observing similar performance in alternative cohorts is important and is critical towards the understanding and confirmation of polygenic risk score assessment for use in clinical trial recruitment and in clinical practice.


*Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:

Acknowledgements: Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We also acknowledge Prof. Julie Williams, Prof. Valentina Escott-Price, Dr Rebecca Sims and Dr Eftychia Bellou from the University of Cardiff for their advice on adaptation and implementation of of the polygenic risk algorithm.

Funding: Funding for this study was provided under an Innovate UK grant (Project No 5195).

Conflict of Interest: P. Daunt, A.Gibson, O.Oshota and R. Pither are all employees of Cytox Ltd. G. Davidson received payment from Cytox Ltd. for work done both within and outside the scope of this article.

Ethical Standards: The ADNI protocols were approved by all the Institutional Review Boards of the participating institutions. Only data from volunteers who had provided written informed consent were used to complete these analyses.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.



1. Prince M, Wimo A, Guerchet M et al. World Alzheimer Report 2015: the Global Impact of Dementia – An analysis of prevalence, incidence, cost and trends. Alzheimer’s Dis Int 2015; 84:425
2. Gatz M, Reynolds CA, Fratiglioni L, et al. Role of genes and environments for explaining Alzheimer’s disease. Arch Gen Psychiatry 2006; 63: 168-174
3. Lambert JC, Ibrahim-Verbaas CA, Harold D, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 2013; 45: 1452-1458
4. Naj, AC, Jun G, Beecham, GW et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet 2011; 43: 436-441
5. Seshadri, S, Fitzpatrick AL, Arfan Ikram M et al. Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 2010; 303: 1832-1840
6. Corder EH, Saunders AM, Strittmatter WJ, et al. Gene Dose of Apolipoprotein-E Type-4 Allele and the Risk of Alzheimers-Disease in Late-Onset Families. Science1993;261:921–923.
7. Farrer LA, Cupples LA, Haines JL, et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E Genotype and Alzheimer Disease: a Meta-analysis. JAMA 1997;278:1349–1356.
8. Blacker D, Haines JL, Rodes L, et al. ApoE-4 and age at onset of Alzheimer’s disease: the NIMH genetics initiative. Neurology 1997;48;139-147
9. Bonham LW, Geier EG, Fan CC, et al. Age-dependent effects of APOE ε4 in preclinical Alzheimer’s disease. Ann Clin Transl Neurol 2016;3;668-677
10. Stocker H, Möllers T, Perna L, et al, The genetic risk of Alzheimer’s disease beyond APOE ε4: systematic review of Alzheimer’s genetic risk scores. Transl Psych 2018;8;166-174
11. Escott-Price V, Myers A, Huentelman AJ, et al, Polygenic risk score analysis of pathologically confirmed Alzheimer’s disease. Ann Neurol 2017;82;311-314
12. Escott-Price V, Sims R, Williams J et al, Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain 2015;138;3673-3684
13. Desikan RS, Fan CC, Wang Y et al, Genetic assessment of age-associated Alzheimer disease risk: development and validation of polygenic hazard score. PLoS Med 2017 e1002258
14. Escott-Price V, Leonenko G, Sims R et al, Polygenic risk and hazard scores for Alzheimer’s disease prediction. Ann Clin Transl Neurol 2019;6;456-465
15. Altmann A, Schott JM, Scelsi et al, A comprehensive analysis of methods for assessing polygenic burden on Alzheimer’s disease pathology and risk beyond APOE. Brain Commun 2020;2;fcz047
16. Peterson R, Aisen PS, Beckett LA et al, Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 2010;74;201-209
17. Chang CC, Chow CC, Tellier LCAM et al, Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 2015;4;s13742
18. Hansson O, Seibyl J, Stomrud et al, CSF biomarkers of Alzheimer’s disease concord with amyloid-β PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzhemers Dement 2018;14;1470-1481.
19. Ritchie C, Smailagic N, Noel-Storr et al, CSF tau and CSF tau/ABeta ratio for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev 2017;3;CD10803.
20. Wolfsgruber S, Polcher A, Koppara A et al. Cerebrospinal fluid biomarkers and clinical progression in patients with subjective cognitive decline and mild cognitive impairment. Alzheimers Dis 2017;58;939-950.



M. Igase1, Y. Okada1, M. Ochi1, K. Igase2, H. Ochi1, S. Okuyama3, Y. Furukawa3, Y. Ohyagi1


1. Department of Geriatric Medicine and Neurology, Ehime University Graduate School of Medicine, Ehime, Japan; 2. Department of Advanced Neurosurgery, Ehime University Graduate School of Medicine, Ehime, Japan; 3. Department of Pharmaceutical Pharmacology, College of Pharmaceutical Sciences, Matsuyama University, Ehime, Japan

Corresponding Author: Michiya Igase, MD, PhD, Department of Geriatric Medicine and Neurology, Ehime University Graduate School of Medicine, 454 Shitsukawa, Toon City, Ehime 791-0295, Japan, Phone: +81-89-960-5851, Fax: +81-89-960-5852, E-mail:

J Prev Alz Dis 2018 inpress
Published online December 19, 2017,



OBJECTIVES: Dementia, which is characterized by a progressive decline in cognitive function, is a major concern in aging societies. Although a number of treatments have been approved, an effective therapy to prevent the disorder is lacking. A supplement that improves cognitive function would benefit patients. The aim of this study was to assess whether auraptene, a citrus coumarin, has a protective effect on cognitive decline.
DESIGN: A randomized, placebo-controlled, double-blind study
SETTING: Outpatient medical check-up program for cognitive disorders
PARTICIPANTS: 84 adult volunteers (they are cognitively normal) met inclusion and exclusion criteria to participate.
INTERVENTION: 42 participants received auraptene enriched (containing 6.0 mg/day of auraptene) test juice, and another participants received placebo juice.
MEASUREMENTS: 1) Mild Cognitive Impairment (MCI) Screen using the 10-word immediate recall test. 2) The Mini-Mental State Examination (MMSE). Cognitive assessment ware carried out baseline and at 24 weeks.
RESULTS: Auraptene enriched test juice did not improve cognitive function after 24 weeks compared with baseline data. However, there was a significant difference in the percentage change in cognitive function between the test and placebo orange juice groups (6.3 ± 18.9 vs. −2.4 ± 14.8, P < 0.05). Multiple regression analysis demonstrated a significant independent relationship between the percentage change in the 10-word immediate recall test score and test juice consumption including baseline 10-word immediate recall test score in all subjects.
CONCLUSION: This is the first study to assess the effectiveness of auraptene in the prevention of cognitive decline. Our results suggest that auraptene is a safe supplement for the prevention of cognitive decline.

Key words: Auraptene, Kawachibankan, randomized trial, multiple regression analysis, 10-word immediate recall test, cognitive decline, prevention



Many advances have been made in the understanding of age-related changes in cognition, especially in dementia. Dementia is characterized by a progressive decline in cognitive function and is a major concern in aging societies (1). Research has focused on cognitive and neurobiological changes that occur during aging, and there is increasing interest in developing and understanding methods to prevent, slow, or reverse the cognitive decline that occurs in normal healthy older adults. Although a number of treatments have been approved, an effective therapy to prevent the disorder is lacking (2); therefore, a supplement that improves cognitive function would greatly benefit patients.
Recently, natural products are increasingly being used for the management and treatment of central nervous system disorders (3). The key desirable characteristics of natural products are their safety, efficacy and cultural acceptability. Coumarins, phenolic compounds that are found in various plants, bacteria and fungi, are an example of such natural products. Auraptene (AUR) is a natural bioactive monoterpene coumarin ether that was first identified in citrus fruits (4), and has anti-inflammatory and anti-carcinogenic activity (5). Citrus kawachiensis (Kawachibankan) is a citrus product specific to Ehime Prefecture, Japan. We previously showed that the peels of Kawachibankan contain abundant AUR at higher concentrations than that found in other citrus peels (6, 7). Although Kawachibankan peels containe a high amounts of naringin and a middle amounts of narirutin besides AUR, our previous study suggested that the major functional compound within the Kawachibankan peels for causing the anti-inflammation effect in the model mice was AUR. The reasons for this conclusion are as follows: <1> an anti-inflammatory effect on the brain was also observed by the administration of AUR given alone; <2> the administration of authentic naringin alone had not so much effect; <3>narirutin, being an isomer of naringin, would also likely have no anti-inflammatory property. (8). Another report has demonstrated the neuroprotective and memory-enhancing effects of AUR (9) in a rat model of cerebral ischemia.
Here, we assessed whether AUR in Kawachibankan peels has a protective effect on cognitive decline in healthy human subjects.



The subjects were participants in a medical check-up program at the Ehime University Hospital Anti-aging Centre (AAC) specifically designed to evaluate atherosclerosis and cognitive decline (10).
Enrolment was conducted over a 5-month period starting in March 2016. To rule out the impact of disease or medications, we excluded participants with a history of symptomatic cardiovascular events, including coronary heart disease and ischemic stroke, and those receiving medications for diabetes, dyslipidemia, hypertension or dementia.
A total of 84 subjects (age, mean±standard deviation [SD]: 71±9 years) who fulfilled the study criteria were enrolled. Subjects were randomized into two groups by a computerized random number generator. One group received 125 mL of test juice containing 6.0 mg of AUR per day and the other received the same volume of placebo juice containing 0.1 mg of AUR per day, for a duration of 24 weeks.
The study was conducted after approval by the institutional review board for clinical trial services at Ehime University Graduate School of Medicine (Ehime, Japan) and was performed by the study investigators. Written informed consent was obtained from all participants before examination. The study was conducted in accordance with the ethical principles described in the current version of the Declaration of Helsinki.

Laboratory tests

Hematological examinations were conducted at baseline and after 24 weeks. Blood samples were collected between 9:00 and 10:00 am from the cubital vein following an overnight fast. The following routine biochemical parameters were determined in fresh samples: alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (γ-GTP), high-density lipoprotein (HDL), serum triglycerides (TG), and serum creatinine (sCr). LDL levels were calculated using the Friedewald formula (11). Estimated glomerular filtration rate (eGFR; mL/min/1.73 m2) was calculated using the Cockcroft–Gault formula (12).

Cognitive assessment

To assess cognitive function, we conducted a «Mild Cognitive Impairment (MCI) Screen» (13) using the 10-word immediate recall test. The test was performed 3 times, and the sum of the scores was calculated for each subject. The MCI Screen was derived from the protocol of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) 10-word recall test (14), which is a 10-minute, computationally scored, staff-administered, neuropsychological test to screen for MCI. The validity and specificity of this test for differentiating normal aging from MCI have been described elsewhere (13). Cross validation has been confirmed using the Clinical Dementia Rating score as a reference. The overall accuracy in discriminating both amnestic and mixed cognitive domain types of MCI from normal aging is 97% (15). The Mini-Mental State Examination (MMSE), a global cognitive function test (16), was used to rule out apparent dementia in this study. Cognitive assessment were carried out baseline and at 24 weeks.

Statistical analysis

All continuous variables are expressed as mean ± SD, unless otherwise indicated. Comparisons between the two groups were assessed using the Student’s t test. The Wilcoxon signed-rank test was used to analyze differences in 10-word immediate recall test scores for each group at 24 weeks after the initial administration of the drinks compared with baseline. Correlations between variables were evaluated using Pearson’s correlation coefficient. We conducted simple regression and multiple regression analyses of variables that independently predict the percentage change in the 10-word immediate recall test scores in the whole study population, including those who consumed the test juice. In all comparisons, P < 0.05 was considered statistically significant. Analyses were performed using the SPSS software package for Windows version 17 (SPSS, Chicago, IL, USA). All participants were advised to follow their routine diets during the study.



Eighty-four subjects were randomly assigned to receive either test juice (42 subjects) or placebo juice (42 subjects). Of the 84 subjects, 2 participants did not complete the study because they moved to a new residence during the study period. As a result, a total of 82 subjects were included in the final analysis. Their mean age was 71 years, and 27 (33%) were men (Table 1). Although there were no patients with dementia diagnosed by MMSE, 3 participants had been diagnosed with MCI by MCI screen (Table 1). There was no significant difference in education level between the two groups.

Table 1. Characteristics of participants

Table 1. Characteristics of participants

Values are expressed as mean ± standard deviation. BMI, body mass index; MCI, mild cognitive impairment; SBP, systolic blood pressure; DBP, diastolic blood pressure; AST, aspartate aminotransferase; ALT, alanine aminotransferase; γ-GTP, gamma-glutamyltransferase; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, serum triglyceride; sCr, serum creatinine; eGFR, creatinine-based estimated glomerular filtration rate.


Change in the 10-word immediate recall test scores

Changes in the 10-word immediate recall test scores are shown in Table 2. Participants in the test juice group had higher scores at week 24 compared to week 0, but this difference was not statistically significant. In contrast, the percentage change in the 10-word immediate recall test score was significantly higher in the test juice group compared to the placebo group (P < 0.05). There was a positive correlation between the percentage change in the 10-word immediate recall test score and baseline 10-word immediate recall test score in the whole population and in the test juice group (Table 3). Multiple regression analysis demonstrated a significant independent relationship between the percentage change in the 10-word immediate recall test score and test juice consumption including baseline 10-word immediate recall test score in all subjects (Table 3).

Table 2. Change in the 10-word immediate recall test score performed 3 times

Table 2. Change in the 10-word immediate recall test score performed 3 times

Values are expressed as mean ± standard deviation; MMSE, Mini-Mental State Examination; *Intergroup comparison (P < 0.05, vs. placebo).


Table 3. Simple regression and multiple regression analyses of variables that independently predict changes in the 10-word immediate recall test scores (/30)

Table 3. Simple regression and multiple regression analyses of variables that independently predict changes in the 10-word immediate recall test scores (/30)

*Adjusted for age and sex.


Biochemical evaluations

The results of the baseline and 24-week laboratory examinations are shown in Table 4. None of the biochemical parameters showed any significant variation during the study period. These findings demonstrate the safety of the test juice in humans.


Table 4. Blood test results of participants

Table 4. Blood test results of participants

Values are expressed as mean ± standard deviation; sCr, serum creatinine; BUN, blood urea nitrogen; eGFR, creatinine-based estimated glomerular filtration rate; LDL, low-density lipoprotein; HDL, high-density lipoprotein; TG, serum triglyceride; AST, aspartate aminotransferase; ALT, alanine aminotransferase; γ-GTP, gamma-glutamyltransferase; hsCRP, high-sensitivity C-reactive protein.



In this clinical study, we provided the first evidence that AUR-containing juice has a protective effect on cognitive decline in healthy human subjects. Our previous study showed that dried peel powder of Kawachibankan peels containe a high amount of naringin, middle amounts of narirutin besides AUR. Although the major compound in the dried peels of Kawachibankan peels was naringin, the results obtained in our study suggested that the major functional compound within the dried peels of Kawachibankan peels for causing the anti-inflammation effect in the model mice was AUR. The reasons for this conclusion are as follows: (1) an anti-inflammatory effect on the brain was also observed by the administration of authentic AUR given alone; (2) the administration of authentic naringin alone had not so much effect; (3) narirutin, being an isomer of naringin, would also likely have no anti-inflammatory property. For selection of the treatment arm dosage, we converted based on results of animal model (conversion index was 100.) (8).Clinical trials on cognitive decline
The lack of a current cure for dementia has led to an increasing interest in establishing strategies for the prevention of cognitive decline, which is a surrogate marker for dementia. Previous reports have shown that supplementation with omega-3 polyunsaturated fatty acids, which have anti-inflammatory effects, might protect against cognitive decline and Alzheimer’s disease (17-19). However, these findings were from cohort studies. Results from randomized controlled trials of up to 2 years’ duration are conflicting (20).
Although the etiology of dementia is assumed to be multi-factorial, many intervention studies target single factors that influence cognitive function. The results of several trials of multi-domain interventions (e.g., exercise, mental training and diet) have been positive (21). The results of our small clinical study suggest that AUR in the peels of citrus Kawachibankan protects against cognitive decline in healthy human subjects. However, we did not take lifestyle factors into account. AUR combined with lifestyle interventions might have a greater effect in preventing cognitive decline. A large cohort study is needed to investigate this further.
What is the mechanism by which AUR affects cognitive function?
AUR, a citrus coumarin, improved spatial learning and ameliorated cognitive impairment in a rat model of vascular dementia (9). The preventive mechanism of AUR is hypothesized to be related to its anti-inflammatory effects. We previously demonstrated that AUR exerts anti-inflammatory effects in the ischemic brain (22). Treatment of mice with AUR for eight days immediately after ischemia-inducing surgery suppressed neuronal cell death in the hippocampus, presumably through its anti-inflammatory effects in the brain. We showed that AUR passes through the blood–brain barrier and directly exerts anti-inflammatory effects in the brain (23). AUR suppressed microglial activation, COX-2 expression in astrocytes, and COX-2 mRNA expression in the hippocampus, and was still detectable in the brain 60 min after intraperitoneal administration. These results indicate that AUR directly exerts anti-inflammatory actions on the brain, which may underlie its beneficial effects on cognitive function.



The peels of Kawachibankan (citrus kawachiensis), a citrus product from Ehime Prefecture, Japan, contain abundant levels of AUR. The results of our study suggest that AUR may be beneficial as a neuroprotective agent for the treatment of neurological disorders in a clinical setting.


Conflict of interest: All participating authors declare no conflict of interest.

Ethical standards: The study was conducted after approval by the institutional review board for clinical trial services at Ehime University Graduate School of Medicine (Ehime, Japan) and was performed by the study investigators. Written informed consent was obtained from all participants before examination. The study was conducted in accordance with the ethical principles described in the current version of the Declaration of Helsinki.



1.     Tariq S, Barber PA. Dementia risk and prevention by targeting modifiable vascular risk factors. J Neurochem, 2017. doi: 10.1111/jnc.14132.
2.     Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D, Ballard C, Banerjee S, Burns A, Cohen-Mansfield J, Cooper C, Fox N, Gitlin LN, Howard R, Kales HC, Larson EB, Ritchie K, Rockwood K, Sampson EL, Samus Q, Schneider LS, Selbæk G, Teri L, Mukadam N. Dementia prevention, intervention, and care. Lancet, 2017. doi: 10.1016/S0140-6736(17)31363-6.
3.     Beaubrun G, Gray GE. A review of herbal medicines for psychiatric disorders. Psychiatr Serv. 2000;51: 1130-4.
4.     Genovese S, Epifano F. Auraptene: a natural biologically active compoundwith multiple targets, Curr. Drug Targets 2011;12: 381–386.
5.     Murakami A, Nakamura Y, Tanaka T, Kawabata K, Takahashi D, Koshimizu K, Ohigashi H. Suppression by citrus auraptene of phorbol ester-andendotoxin-induced inflammatory responses: role of attenuation of leukocyteactivation, Carcinogenesis 2000;21: 1843-50.
6.     Furukawa Y, Okuyama S, Amakura Y, Watanabe S, Fukata T, Nakajima M, Yoshimura M, Yoshida T. Isolation and characterization of activators of ERK/MAPK from Citrus plants. International Journal of Molecular Sciences 2012;13: 1832-45.
7.     Amakura Y, Yoshimura M, Ouchi K, Okuyama S, Furukawa Y, Yoshida T. Characterization of constituents in peel of Citrus kawachiensis (Kawachibankan). Bioscience, Biotechnology, and Biochemistry 2013;77: 1977-80.
8.     Okuyama S, Yamamoto K, Mori H, Toyoda N, Yoshimura M, Amakura Y, Yoshida T, Sugawara K, Sudo M, Nakajima M, Furukawa Y. Auraptene in the peels of Citruskawachiensis (Kawachi Bankan) ameliorates lipopolysaccharide-inducedinflammation in the mouse brain, Evid Based Complement Alternat Med, 2014. doi: 10.1155/2014/408503.
9.     Ghanbarabadi M, Iranshahi M, Amoueian S, Mehri S, Motamedshariaty VS, Mohajeri SA. Neuroprotective and memory enhancing effects of auraptene in a rat model of vascular dementia: Experimental study and histopathological evaluation. Neuroscience Letters 2016;623, 13–21.
10.     Okada Y, Kohara K, Ochi M, Nagai T, Tabara Y, Igase M, Miki T. Mechanical stresses, arterial stiffness, and brain small vessel diseases: Shimanami Health Promoting Program Study. Stroke 2014;45: 3287-3292.
11.     Friedewald WT, Levy RI, and Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972;18: 499-502.
12.     Gault MH, Longerich LL, and Harnett JD. Predicting glomerular function from adjusted serum creatinine. Nephron 1992;62: 249-256.
13.     Cho A, Sugimura M, Nakano S, Yamada T. The Japanese MCI screen for early detection of Alzheimer’s disease and related disorders. Am J Alzheimers Dis Other Demen 2008;23: 162–166.
14.     Morris JC, Heyman A, Mohs RC, Hughes JP, van Belle G, Fillenbaum G, Mellits ED, Clark C. The Consortium to establish a registry for Alzheimer’s disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology 1989;9: 1159–1165.
15.     Trenkle DL, Shankle WR, Azen SP. Detecting cognitive impairment in primary care: Performance assessment of three screening instruments. J Alzheimers Dis 2007;11: 323-335.
16.     Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12: 189–198.
17.     Heude B, Ducimetière P, Berr C; EVA Study. Cognitive decline and fatty acid composition of erythrocyte membranes—the EVA Study. Am J Clin Nutr 2003;77:803-8.
18.     Schaefer EJ, Bongard V, Beiser AS, Lamon-Fava S, Robins SJ, Au R, Tucker KL, Kyle DJ, Wilson PW, Wolf PA. Plasma phosphatidylcholine docosahexaenoic acid content and risk of dementia and Alzheimer disease: the Framingham Heart Study, Arch Neurol 2006;63: 1545-50.
19.     Tan ZS, Harris WS, Beiser AS, Au R, Himali JJ, Debette S, Pikula A, Decarli C, Wolf PA, Vasan RS, Robins SJ, Seshadri S. Red blood cell omega-3 fatty acid levels and markers of accelerated brain aging. Neurology 2012;78: 658-64.
20.     Andrieu S, Coley N, Lovestone S, Aisen PS, Vellas B. Prevention of sporadic Alzheimer’s disease: lessons learned from clinical trials and future directions. Lancet Neurol 2015;14: 926-44.
21.     Schneider N, Yvon C. A review of multidomain interventions to support healthy cognitive ageing. J Nutr Health Aging 2013;17: 252-7. doi: 10.1007/s12603-012-0402-8.
22.     Okuyama, S.; Minami, S.; Shimada, N.; Makihata, N.; Nakajima, M.; Furukawa, Y. Anti-inflammatory and neuroprotective effects of auraptene, a citrus coumarin, following cerebral global ischemia in mice. Eur J Pharmacol 2013;699: 118–123.
23.     Okuyama S, Morita M, Kaji M, Amakura Y, Yoshimura M, Shimamoto K, Ookido Y, Nakajima M, Furukawa Y.  Auraptene Acts as an Anti-Inflammatory Agent in the Mouse Brain. Molecules 2015;20: 20230-9.



C. Hooper1, P. De Souto Barreto1, M. Pahor2, M. Weiner3, B. Vellas1,4


1. Gérontopôle, Department of Geriatrics, CHU Toulouse, Purpan University Hospital, Toulouse, France; 2. Department of Aging and Geriatric Research, Institute on Aging, College of Medicine, University of Florida, 2004 Mowry Road, Gainesville, Florida; 3. University of California San Francisco, School of Medicine, 4150 Clement Street, San Francisco, California. USA; 4. INSERM UMR 1027, Toulouse, France.

Corresponding Author: Claudie Hooper, 1Gérontopôle, Department of Geriatrics, CHU Toulouse, Purpan University Hospital, Toulouse, France, Tel  : +33 (5) 61 77 64 25,
Fax : +33 (5) 61 77 64 75

J Prev Alz Dis 2018;5(1):78-84/ONLINE EXCLUSIVE
Published online June 13, 2017,



Significant research attention has focussed on the identification of nutraceutical agents for the prevention of cognitive decline as a natural means of cognitive preservation in the elderly. There is some evidence for a reduction of brain omega 3 polyunsaturated fatty acids (n-3 PUFAs) in normal aging and in Alzheimer’s disease. n-3 PUFAs exhibit anti-inflammatory and anti-amyloidogenic properties as well as being able to reduce tau phosphorylation. Many observational studies have demonstrated a link between n-3 PUFAs and cognitive aging, and some, but not all, randomized controlled trials have demonstrated a benefit of n-3 PUFA supplementation on cognition, particularly in those subjects with mild cognitive impairment. The identification of a biomarker that reflects n-3 PUFA intake over time and consequent tissue levels is required. In this narrative review we discuss the evidence associating red blood cell membrane n-3 PUFAs with cognitive function and structural brain changes associated with Alzheimer’s disease.

Key words: Docosahexaenoic acid, omega 3 polyunsaturated fatty acids, cognitive decline, Alzheimer’s disease, red blood cell.



Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder of aging characterised by progressive memory loss, cognitive impairment and the inability to carry out functional activities of daily living (1). AD is characterised pathologically by the presence of cerebral β-amyloid (Aβ), neurofibrillary tangles composed of hyper-phosphorylated tau and neurodegeneration (2). The presence of the apolipoprotein E (ApoE) ε4 allele is the main genetic risk factor associated with sporadic disease, which is the predominant form of AD (3). Other factors that have been reported to influence the onset of AD include diet as well as physical and mental activity (4, 5).
n-3 PUFAs

Omega 3 polyunsaturated fatty acids (n-3 PUFAs) are dietary factors that have received significant research attention in relation to their beneficial effects on cognitive decline. The main n-3 PUFAs used in the body are docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) and are mostly obtained from the consumption of oily fish or through dietary supplementation (6). DHA and EPA can also be synthesised to a limited extent from α-linolenic acid (ALA) obtained from plant oils (6). Docasapentaenoic acid (DPA) is one of the less extensively studied n-3 PUFAs, which nonetheless plays a role in influencing health outcomes that are responsive to DHA and EPA (7, 8).
DHA is the major fatty acid in neuronal membranes (30 %) and is enriched in synaptosomal membranes (9). DHA is involved in multiple inter-related brain functions including cell membrane fluidity, signal transduction and neurotransmission (10–12). DHA is thought to be the main n-3 PUFA involved in cerebral metabolism and as a consequence has been most extensively studied. The major facilitator superfamily domain-containing protein 2a (Mfsd2a) has recently been identified as the major transporter for the uptake of DHA (in the lysophosphatidylcholine form) into the brain (13). EPA also crosses the blood brain barrier (BBB), although through an as yet unidentified mechanism (14) and is present in brain tissue, albeit at considerably lower concentrations (15). EPA and DHA exhibit overlapping and unique biological roles (15, 16) and EPA serves as a precursor to DHA in the biosynthetic pathway.

n-3 PUFAs and AD

Cerebral DHA levels are known to be deficient in AD specifically in brain regions associated with disease (17,18) and DHA is decreased in the brain in normal human aging (19). There are a number of possible explanations which could account for the reduction in cerebral DHA including insufficient dietary intake, reduced BBB transit, increased neuronal death or genetic variability in delta-5 desaturase (FADS1) and delta-6 desaturase (FADS2); enzymes involved in the rate limiting steps of DHA and EPA synthesis (20). Increased levels of plasma fatty acid binding proteins, which are known to increase with age (21, 22), could also account for reduced cerebral DHA and/or increased oxidative damage to fatty acids caused by free radicals associated with age and inflammation could lead to diminished cerebral levels (23–25).
Many observational studies have demonstrated a link between n-3 PUFAs and cognitive aging related to AD (26). Some randomised controlled trials (RCTs) have also demonstrated a benefit of n-3 PUFA supplementation, particularly in terms of immediate recall, attention and processing speed in patients with mild cognitive impairment (MCI), but not in those with AD or in healthy subjects (27). MCI if present in conjunction with cerebral Aβ represents the prodromal stage of AD and offers a window of opportunity for therapeutic intervention. Identifying elderly patients at risk of cognitive decline is important to enable timely treatment before AD pathology becomes irreversible; as such n-3 PUFAs might offer a potential well tolerated, inexpensive treatment in the early stages of AD.
There is increasing evidence to suggest that n-3 PUFAs play a role in the disease mechanisms associated with AD. n-3 PUFAs have been shown to promote long term potentiation (LTP), a mechanism underpinning functional plasticity the basis of learning and memory, in cell culture and animal models (11,28). There is also evidence to suggest that DHA confers neuro-protection in part through the direct inhibition of tau phosphorylation (29,30). Moreover, DHA and EPA have been shown to alter amyloid precursor protein (APP) processing in favour of reduced Aβ production (31–35).
In terms of inflammation, n-3 PUFAs are known to displace omega 6 polyunsaturated fatty acids (n-6 PUFAs) from cell membranes resulting in the production of more benign eicosanoids that possess less potent inflammatory and thrombotic effects and are less efficacious vasoconstrictors (6,36). Thus, changing DHA/EPA concentrations in cell membranes might serve to ameliorate cerebral inflammation, which is known to fuel AD pathology (37). In addition, increased membrane n-3 PUFA content might reduce the incidence of stroke, which is associated with an increased incidence of AD (38, 39) and vascular dementia (40). n-3 PUFAs also specifically suppress the expression of pro-inflammatory cytokines and promote microglial phagocytosis of Aβ and increase neurotrophin production (41, 42). n-3 PUFAs skew macrophage/microglial polarisation towards an M2 anti-inflammatory phenotype indicative of tissue repair (41, 43) and provide the building blocks for the production of E and D series resolvins, protectins and maresins – collectively known as specialized pro-resolving mediators (SPMs) (44). These mediators are involved in the resolution and termination of the inflammatory response, which was originally thought to be a passive process. Diminished levels of SPMs are found in the hippocampus (31) and in the entorhinal cortex in AD (45), which is in accordance with the chronic pro-inflammatory micro-environment associated with AD brain (46). Interestingly, SPMs have been shown to promote microglial phagocytosis of Aβ and reduce M1 microglial cell surface marker expression in addition to possessing neuroprotective properties (45). Thus, n-3 PUFA dietary supplementation might serve to reduce cerebral inflammation both directly and indirectly through the production of SPMs thereby limiting bystander damage to neurons and subsequent neurodegeneration. Furthermore, pro-inflammatory microglial activation has been purported to provide the link between Aβ plaques and hyper-phosphorylated tau (37, 47), therefore promoting microglial M2 anti-inflammatory activity and phagocytosis of Aβ through the increased consumption of n-3 PUFAs might serve to ameliorate tau pathology, the best clinical correlate of neurodegeneration (48,4 9), hence curtailing AD-related symptoms.

Rationale for this narrative review

A number of systematic reviews and meta-analyses have addressed the effects of n-3 PUFA supplementation on measures of cognitive aging associated with AD and MCI (27, 50–52). Furthermore, clinical signs of AD and MCI have frequently been correlated with plasma DHA and/or EPA levels (9, 53, 54). However, plasma levels of fatty acids (free, cholesteryl esters or phospholipid bound) reflect recent dietary intake over a time frame of a few days and therefore do not represent a true picture of steady-state n-3 PUFA levels (55). RBC fatty acid concentration might represent a more reliable measurement of dietary habits and nutritional status; considering that fatty acids are stable in RBC membranes for up to 3 months corresponding to the lifespan of a RBC (55). RBC fatty acid concentrations have also been shown to reflect tissue concentrations (56). Thus, in this narrative review we aim to present the literature whereby RBC n-3 PUFA levels have been investigated in association with cognitive function and brain structure related to AD. A systematic review was out of the scope of this study.



In April, 2016, a search without time date span limitation was performed in Pubmed. A search strategy was implemented using key words in an ‘AND’ combination to identify studies pertaining to RBC n-3 PUFA (red blood cell, erythrocyte, omega 3 or PUFA) and cognitive function and brain structure related to AD (cogniti*, atrophy, dementia, ‘mild cognitive impairment’, Alzheimer*). Titles were subjected to screening followed by an assessment of relevant abstracts by one author. Articles had to meet the following inclusion criteria: (a) quantitative assessment of RBC n-3 PUFA levels reported in the article and (b) assessment of cognitive outcomes or brain structure. An updating literature search was performed in February 2017 to add recent key references. Only manuscripts written in English were included. Note, the definition of RBC ‘total n-3 PUFAs’ differs between studies, therefore the precise fatty acids measured in a specific study and designated as ‘total n-3 PUFAs’ are specified where relevant in parentheses in this review. Of note, the studies described here should be compared with caution considering the divergent roles that different n-3 PUFAs play in biology, particularly, DHA and EPA. The specific terms describing n-3 PUFAs should not be considered as interchangeable.


Results and discussion

Study characteristics

As a result of the initial and updating literature searches 14 articles were included in this review (Table 1) (57–70). Of the studies 12 were of observational design and 2 were RCTs. Four studies were performed in the USA, two studies were performed in France, Taiwan, Australia and Scotland and one study was undertaken in Germany and another Canada. Sample sizes varied between 46 to 2157 participants with subjects ranging from cognitively normal to demented with a diagnosis of AD.

Table 1. Summary of the main findings of the studies relating RBC n-3 PUFAs to parameters associated with cognitive function and brain structure in relation to AD

Table 1. Summary of the main findings of the studies relating RBC n-3 PUFAs to parameters associated with cognitive function and brain structure in relation to AD

References listed sequentially as mentioned in the main text.

Studies relating to RBC n-3 PUFAs and cognition

The Etude du Vieillissement Artérial (EVA) was the first study to relate fatty acid composition of RBC membranes with cognitive decline. This observational study consisted of 246 cognitively normal participants and demonstrated an inverse association between cognitive decline over a four year period and the ratio of n-3 to n-6 PUFAs in RBC membranes measured at baseline (57). It was suggested that higher levels of RBC n-6 PUFAs could reflect a deficiency in brain levels of n-3 PUFAs, which in turn could affect cognition. Subsequent studies have also associated RBC PUFA with cognition. A cross-sectional study investigating nutritional biomarkers of AD comprising 46 subjects has shown that a reduction in RBC DHA correlates with a reduction in MMSE score (58). Higher RBC EPA has been associated with better ADAS-cog scores in a small RCT comprising 46 participants with either MCI or AD (59). A longitudinal observational analysis of 350 participants born in 1936 has reported that those who take fish oil supplements have significantly greater RBC n-3 PUFA levels and that total RBC n-3 PUFA (exact fatty acids not specified) and the ratio of DHA to AA were associated with better cognitive performance in late life before and after adjustment for childhood IQ (60). Higher RBC EPA levels as well as total RBC n-3 PUFAs (defined as the sum of ALA, EPA, DHA and DPA) were positively associated with cognitive composite scores in a cross sectional study of 132 participants formerly suffering from depression at risk of cognitive decline (61). This association was independent of age and sex but, was no longer significant after adjustment for education. This may reflect a wider association between cognitive function and healthy lifestyle related to higher education. Moreover, a cross-sectional study of 79 participants has reported that patients with MCI have lower RBC EPA and higher depressive scores (62).
We have recently completed a three year RCT known as the Multidomain Alzheimer Preventive Trial (MAPT), which was designed to assess the effects of DHA (800 mg) and EPA (to a maximum of 225mg), multidomain intervention (comprising of nutritional counselling, physical exercise counselling and cognitive training) and a combination of the two on alterations in cognitive function in frail subjects with memory complaints aged over seventy (4). In the main analysis of MAPT, no significant effects of the interventions were found on cognition after adjustment for multiple testing (63). Exploratory sub-group analysis showed that participants on n-3 PUFA supplementation with a low omega-3 index (DHA + EPA ≤ 4.83 %, representing the lowest quartile of omega 3 index distribution) at baseline showed a trend towards less cognitive decline over 36 months in comparison to subjects on placebo with low baseline omega-3 index. Furthermore, exploratory within group analysis of MAPT data has shown that participants in the placebo group with a low omega-3 index at baseline underwent significant cognitive decline over 36 months, whereas those in the placebo group with a higher omega-3 index (quartiles 2-4) remained stable. Consistent with our findings, results from the KORA (KOoperativen Gesundheitsforschung in der Region Augsburg)-Age study have shown a cross-sectional association between low omega 3 index (< 5.7 %) and cognitive impairment in an elderly population of 720 subjects with cognitive status ranging from cognitively normal to suspected dementia (64).

n-3 PUFAs and the role of ApoE ε4

A longitudinal observational study comprising 120 participants has demonstrated cognitive benefits associated with higher total RBC n-3 PUFAs (exact fatty acids not specified), but only in the absence of the ApoE ε4 allele (65). In this study cognitive performance at the age of around 64 and cognitive changes between approximately 64 to 68 years of age were related to RBC n-3 PUFA on recruitment and ApoE ε4 allele status. This report was an extension of the study by Whalley et al., 2004, described above and used their original sample of patients born in 1936 (60). In accordance with these findings, some studies have shown that the protective cognitive effects of n-3 PUFAs are seen only in ApoE ε4 non-carriers (71,72) and that plasma DHA levels show little change in ApoE ε4 carriers despite supplementation (73). In contrast, a recent study has shown the opposite, demonstrating that n-3 PUFA consumption is related to slower cognitive decline in ApoE ε4 carriers (74). Thus, the effects of ApoE ε4 on n-3 PUFA status and cognition warrants further research investigation.

Studies relating to RBC n-3 PUFAs and structural brain changes

The Women’s Health Initiative Memory Study (WHIMS) Magnetic Resonance Imaging (MRI) a longitudinal observational study comprising 1111 dementia-free participants demonstrated that a higher baseline RBC omega 3 index correlated with larger total brain volumes and hippocampal volume measured 8 years on (66). Furthermore, a recent cross-sectional analysis of 1575 dementia-free participants from the Framingham Offspring cohort has shown that subjects in the lowest quartile with regard to RBC DHA levels exhibit lower total brain volumes but, greater white matter hyperintensity volumes without any significant changes in hippocampal volume (67). Participants in the lowest quartile of the Framingham Offspring cohort with regard to RBC DHA and omega 3 index also had lower scores in tests of visual memory, executive function and abstract thinking. Interestingly, the presence of increased white matter hyperintensities in the latter study is suggestive of dementia of a more vascular nature.

RBC n-3 PUFAs and cognition: the negative study findings

An observational analysis performed on 2157 cognitively-intact elderly women enrolled on an RCT designed to investigate the effects of hormone therapy on normal cognitive aging failed to show an association of baseline RBC DHA and EPA levels with cognitive change. Cognitive assessments were performed at a median of 3 years after randomization and then annually with a median follow up of 5.9 years (68). Another cross-sectional observational study of 390 cognitively normal older adults from the Older People, Omega 3 and Cognitive Health trial (EPOCH) found no evidence to support the hypothesis that higher RBC n-3 PUFA (exact fatty acids not specified) exert a beneficial effect on baseline cognitive performance (69).  In fact, the results suggest a small negative effect of fish intake in childhood and in older age on older-age cognitive function that was tentatively hypothesised to be attributable to higher concentrations of the environmental neurotoxin methyl-mercury in the fish. Furthermore, the Canadian Study of Health and Ageing (CSHA), a longitudinal observational study comprising 663 participants demonstrated there to be no association between baseline total RBC n-3 PUFAs, (defined as the sum of RBC EPA, DHA, DPA, ALA, stearidonic acid, eicosatrienoic acid and eicosatetranoic acid), RBC DHA or RBC EPA and the incidence of AD measured after a median follow up period of 4.9 years (70). However, exposure to n-3 PUFAs assessed approximately 5 years before a diagnosis of dementia may not reflect a true representation of dietary habits. A stable and sustained regular intake of n-3 PUFAs is probably required to confer mental health benefits in the elderly.



Here we present evidence associating RBC n-3 PUFAs with cognitive function and brain structure associated with AD. The measurement of RBC n-3 PUFAs as a biomarker of n-3 PUFA status is advantageous considering its stability over time. There is also some evidence from RCTs to suggest that the administration of n-3 PUFAs (DHA and EPA) can prevent cognitive decline. Further studies aimed at clarifying the relationship between RBC n-3 PUFAs and cognitive impairment are required. Future research should aim to specifically explore the effects of n-3 PUFA supplementation on cognitive decline and brain structure in n-3 PUFA deficient elderly subjects. It might be that n-3 PUFA supplementation per se is not beneficial in the prevention of disease, but rather re-establishing homoeostatic levels in deficient elderly subjects is pivotal for the preservation of cognition.  The mechanisms through which n-3 PUFAs operate, (neurodegenerative versus vascular and the involvement of inflammation) also deserves further research attention specifically as a function of ApoE ε4 status and FADS haplotype. The expression of such genetic variants could account for the differential responses to n-3 PUFA supplementation observed between studies. In fact, we are at present in the process of planning such a trial as an offspring study to MAPT in order to address some of these important as yet unanswered questions.


Funding/support: There was no external funding for this study.

Conflicts of Interest: The authors declare no conflict of interest. There were no financial relationships with any organizations that might have an interest in the submitted work or no other relationships or activities that could appear to have influenced the submitted work.



1.     von Strauss E, Viitanen M, De Ronchi D, Winblad B, Fratiglioni L. Aging and the occurrence of dementia: findings from a population-based cohort with a large sample of nonagenarians. Arch Neurol. 1999 May;56(5):587–92.
2.     Hardy J, Allsop D. Amyloid deposition as the central event in the aetiology of Alzheimer’s disease. Trends Pharmacol Sci. 1991 Oct;12(10):383–8.
3.     Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, Small GW, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science. 1993 Aug 13;261(5123):921–3.
4.     Vellas B, Carrie I, Gillette-Guyonnet S, Touchon J, Dantoine T, Dartigues JF, et al. MAPT Study: A Multidomain approach for preventing Alzheimer’s disease: Design and baseline data. J Prev Alzheimers Dis. 2014 Jun;1(1):13–22.
5.     Köbe T, Witte AV, Schnelle A, Lesemann A, Fabian S, Tesky VA, et al. Combined omega-3 fatty acids, aerobic exercise and cognitive stimulation prevents decline in gray matter volume of the frontal, parietal and cingulate cortex in patients with mild cognitive impairment. NeuroImage. 2015 Oct 1;
6.     Simopoulos AP. Omega-3 fatty acids in inflammation and autoimmune diseases. J Am Coll Nutr. 2002 Dec;21(6):495–505.
7.     Rissanen T, Voutilainen S, Nyyssönen K, Lakka TA, Salonen JT. Fish oil-derived fatty acids, docosahexaenoic acid and docosapentaenoic acid, and the risk of acute coronary events: the Kuopio ischaemic heart disease risk factor study. Circulation. 2000 Nov 28;102(22):2677–9.
8.     Oda E, Hatada K, Katoh K, Kodama M, Nakamura Y, Aizawa Y. A case-control pilot study on n-3 polyunsaturated fatty acid as a negative risk factor for myocardial infarction. Int Heart J. 2005 Jul;46(4):583–91.
9.     Kyle DJ, Schaefer E, Patton G, Beiser A. Low serum docosahexaenoic acid is a significant risk factor for Alzheimer’s dementia. Lipids. 1999;34 Suppl:S245.
10.     Guixà-González R, Javanainen M, Gómez-Soler M, Cordobilla B, Domingo JC, Sanz F, et al. Membrane omega-3 fatty acids modulate the oligomerisation kinetics of adenosine A2A and dopamine D2 receptors. Sci Rep. 2016;6:19839.
11.     McGahon BM, Martin DS, Horrobin DF, Lynch MA. Age-related changes in synaptic function: analysis of the effect of dietary supplementation with omega-3 fatty acids. Neuroscience. 1999;94(1):305–14.
12.     Lin Q, Ruuska SE, Shaw NS, Dong D, Noy N. Ligand selectivity of the peroxisome proliferator-activated receptor alpha. Biochemistry (Mosc). 1999 Jan 5;38(1):185–90.
13.     Nguyen LN, Ma D, Shui G, Wong P, Cazenave-Gassiot A, Zhang X, et al. Mfsd2a is a transporter for the essential omega-3 fatty acid docosahexaenoic acid. Nature. 2014 May 22;509(7501):503–6.
14.     Freund Levi Y, Vedin I, Cederholm T, Basun H, Faxén Irving G, Eriksdotter M, et al. Transfer of omega-3 fatty acids across the blood-brain barrier after dietary supplementation with a docosahexaenoic acid-rich omega-3 fatty acid preparation in patients with Alzheimer’s disease: the OmegAD study. J Intern Med. 2014 Apr;275(4):428–36.
15.     Dyall SC. Long-chain omega-3 fatty acids and the brain: a review of the independent and shared effects of EPA, DPA and DHA. Front Aging Neurosci. 2015;7:52.
16.     Song C, Shieh C-H, Wu Y-S, Kalueff A, Gaikwad S, Su K-P. The role of omega-3 polyunsaturated fatty acids eicosapentaenoic and docosahexaenoic acids in the treatment of major depression and Alzheimer’s disease: Acting separately or synergistically? Prog Lipid Res. 2016 Jan 4;62:41–54.
17.     Prasad MR, Lovell MA, Yatin M, Dhillon H, Markesbery WR. Regional membrane phospholipid alterations in Alzheimer’s disease. Neurochem Res. 1998 Jan;23(1):81–8.
18.     Söderberg M, Edlund C, Kristensson K, Dallner G. Fatty acid composition of brain phospholipids in aging and in Alzheimer’s disease. Lipids. 1991 Jun;26(6):421–5.
19.     McNamara RK, Liu Y, Jandacek R, Rider T, Tso P. The aging human orbitofrontal cortex: decreasing polyunsaturated fatty acid composition and associated increases in lipogenic gene expression and stearoyl-CoA desaturase activity. Prostaglandins Leukot Essent Fatty Acids. 2008 May;78(4–5):293–304.
20.     Ameur A, Enroth S, Johansson A, Zaboli G, Igl W, Johansson ACV, et al. Genetic adaptation of fatty-acid metabolism: a human-specific haplotype increasing the biosynthesis of long-chain omega-3 and omega-6 fatty acids. Am J Hum Genet. 2012 May 4;90(5):809–20.
21.     Niizeki T, Takeishi Y, Takabatake N, Shibata Y, Konta T, Kato T, et al. Circulating levels of heart-type fatty acid-binding protein in a general Japanese population: effects of age, gender, and physiologic characteristics. Circ J Off J Jpn Circ Soc. 2007 Sep;71(9):1452–7.
22.     Pelsers MM, Chapelle JP, Knapen M, Vermeer C, Muijtjens AM, Hermens WT, et al. Influence of age and sex and day-to-day and within-day biological variation on plasma concentrations of fatty acid-binding protein and myoglobin in healthy subjects. Clin Chem. 1999 Mar;45(3):441–3.
23.     Nourooz-Zadeh J, Liu EH, Yhlen B, Anggård EE, Halliwell B. F4-isoprostanes as specific marker of docosahexaenoic acid peroxidation in Alzheimer’s disease. J Neurochem. 1999 Feb;72(2):734–40.
24.     Montine TJ, Neely MD, Quinn JF, Beal MF, Markesbery WR, Roberts LJ, et al. Lipid peroxidation in aging brain and Alzheimer’s disease. Free Radic Biol Med. 2002 Sep 1;33(5):620–6.
25.     Bazan NG. Omega-3 fatty acids, pro-inflammatory signaling and neuroprotection. Curr Opin Clin Nutr Metab Care. 2007 Mar;10(2):136–41.
26.     Cederholm T, Salem N, Palmblad J. ω-3 fatty acids in the prevention of cognitive decline in humans. Adv Nutr Bethesda Md. 2013 Nov;4(6):672–6.
27.     Mazereeuw G, Lanctôt KL, Chau SA, Swardfager W, Herrmann N. Effects of ω-3 fatty acids on cognitive performance: a meta-analysis. Neurobiol Aging. 2012 Jul;33(7):1482.e17-29.
28.     Minogue AM, Lynch AM, Loane DJ, Herron CE, Lynch MA. Modulation of amyloid-beta-induced and age-associated changes in rat hippocampus by eicosapentaenoic acid. J Neurochem. 2007 Nov;103(3):914–26.
29.     Ma Q-L, Yang F, Rosario ER, Ubeda OJ, Beech W, Gant DJ, et al. Beta-amyloid oligomers induce phosphorylation of tau and inactivation of insulin receptor substrate via c-Jun N-terminal kinase signaling: suppression by omega-3 fatty acids and curcumin. J Neurosci Off J Soc Neurosci. 2009 Jul 15;29(28):9078–89.
30.     Green KN, Martinez-Coria H, Khashwji H, Hall EB, Yurko-Mauro KA, Ellis L, et al. Dietary docosahexaenoic acid and docosapentaenoic acid ameliorate amyloid-beta and tau pathology via a mechanism involving presenilin 1 levels. J Neurosci Off J Soc Neurosci. 2007 Apr 18;27(16):4385–95.
31.     Lukiw WJ, Cui J-G, Marcheselli VL, Bodker M, Botkjaer A, Gotlinger K, et al. A role for docosahexaenoic acid–derived neuroprotectin D1 in neural  cell survival and Alzheimer disease. J Clin Invest. 2005 Oct 1;115(10):2774–83.
32.     Grimm MOW, Kuchenbecker J, Grösgen S, Burg VK, Hundsdörfer B, Rothhaar TL, et al. Docosahexaenoic acid reduces amyloid beta production via multiple pleiotropic mechanisms. J Biol Chem. 2011 Apr 22;286(16):14028–39.
33.     Perez SE, Berg BM, Moore KA, He B, Counts SE, Fritz JJ, et al. DHA diet reduces AD pathology in young APPswe/PS1 Delta E9 transgenic mice: possible gender effects. J Neurosci Res. 2010 Apr;88(5):1026–40.
34.     Lim GP, Calon F, Morihara T, Yang F, Teter B, Ubeda O, et al. A diet enriched with the omega-3 fatty acid docosahexaenoic acid reduces amyloid burden in an aged Alzheimer mouse model. J Neurosci Off J Soc Neurosci. 2005 Mar 23;25(12):3032–40.
35.     Yang X, Sheng W, Sun GY, Lee JC-M. Effects of fatty acid unsaturation numbers on membrane fluidity and α-secretase-dependent amyloid precursor protein processing. Neurochem Int. 2011 Feb;58(3):321–9.
36.     Keli SO, Feskens EJ, Kromhout D. Fish consumption and risk of stroke. The Zutphen Study. Stroke J Cereb Circ. 1994 Feb;25(2):328–32.
37.     McGeer PL, McGeer EG. The amyloid cascade-inflammatory hypothesis of Alzheimer disease: implications for therapy. Acta Neuropathol (Berl). 2013 Oct;126(4):479–97.
38.     Zhou J, Yu J-T, Wang H-F, Meng X-F, Tan C-C, Wang J, et al. Association between stroke and Alzheimer’s disease: systematic review and meta-analysis. J Alzheimers Dis JAD. 2015;43(2):479–89.
39.     de la Torre JC. How do heart disease and stroke become risk factors for Alzheimer’s disease? Neurol Res. 2006 Sep;28(6):637–44.
40.     Viswanathan A, Rocca WA, Tzourio C. Vascular risk factors and dementia: how to move forward? Neurology. 2009 Jan 27;72(4):368–74.
41.     Hjorth E, Zhu M, Toro VC, Vedin I, Palmblad J, Cederholm T, et al. Omega-3 fatty acids enhance phagocytosis of Alzheimer’s disease-related amyloid-β42 by human microglia and decrease inflammatory markers. J Alzheimers Dis JAD. 2013;35(4):697–713.
42.     Moon D-O, Kim K-C, Jin C-Y, Han M-H, Park C, Lee K-J, et al. Inhibitory effects of eicosapentaenoic acid on lipopolysaccharide-induced activation in BV2 microglia. Int Immunopharmacol. 2007 Feb;7(2):222–9.
43.     Chang HY, Lee H-N, Kim W, Surh Y-J. Docosahexaenoic acid induces M2 macrophage polarization through peroxisome proliferator-activated receptor γ activation. Life Sci. 2015 Jan 1;120:39–47.
44.     Spite M, Serhan CN. Novel lipid mediators promote resolution of acute inflammation: impact of aspirin and statins. Circ Res. 2010 Nov 12;107(10):1170–84.
45.     Zhu M, Wang X, Hjorth E, Colas RA, Schroeder L, Granholm A-C, et al. Pro-Resolving Lipid Mediators Improve Neuronal Survival and Increase Aβ42 Phagocytosis. Mol Neurobiol. 2016 May;53(4):2733–49.
46.     McGeer PL, McGeer EG. Inflammation of the brain in Alzheimer’s disease: implications for therapy. J Leukoc Biol. 1999 Apr;65(4):409–15.
47.     Zotova E, Nicoll JA, Kalaria R, Holmes C, Boche D. Inflammation in Alzheimer’s disease: relevance to pathogenesis and therapy. Alzheimers Res Ther. 2010 Jan 22;2(1):1.
48.     Gómez-Isla T, Hollister R, West H, Mui S, Growdon JH, Petersen RC, et al. Neuronal loss correlates with but exceeds neurofibrillary tangles in Alzheimer’s disease. Ann Neurol. 1997 Jan;41(1):17–24.
49.     Giannakopoulos P, Herrmann FR, Bussière T, Bouras C, Kövari E, Perl DP, et al. Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer’s disease. Neurology. 2003 May 13;60(9):1495–500.
50.     Huang TL. Omega-3 fatty acids, cognitive decline, and Alzheimer’s disease: a critical review and evaluation of the literature. J Alzheimers Dis JAD. 2010;21(3):673–90.
51.     Sydenham E, Dangour AD, Lim W-S. Omega 3 fatty acid for the prevention of cognitive decline and dementia. Cochrane Database Syst Rev. 2012 Jun 13;(6):CD005379.
52.     Issa AM, Mojica WA, Morton SC, Traina S, Newberry SJ, Hilton LG, et al. The efficacy of omega-3 fatty acids on cognitive function in aging and dementia: a systematic review. Dement Geriatr Cogn Disord. 2006;21(2):88–96.
53.     Conquer JA, Tierney MC, Zecevic J, Bettger WJ, Fisher RH. Fatty acid analysis of blood plasma of patients with Alzheimer’s disease, other types of dementia, and cognitive impairment. Lipids. 2000 Dec;35(12):1305–12.
54.     Corrigan FM, Van Rhijn AG, Ijomah G, McIntyre F, Skinner ER, Horrobin DF, et al. Tin and fatty acids in dementia. Prostaglandins Leukot Essent Fatty Acids. 1991 Aug;43(4):229–38.
55.     Arab L. Biomarkers of fat and fatty acid intake. J Nutr. 2003 Mar;133 Suppl 3:925S–932S.
56.     Harris WS, Sands SA, Windsor SL, Ali HA, Stevens TL, Magalski A, et al. Omega-3 fatty acids in cardiac biopsies from heart transplantation patients: correlation with erythrocytes and response to supplementation. Circulation. 2004 Sep 21;110(12):1645–9.
57.     Heude B, Ducimetière P, Berr C, EVA Study. Cognitive decline and fatty acid composition of erythrocyte membranes–The EVA Study. Am J Clin Nutr. 2003 Apr;77(4):803–8.
58.     Wang W, Shinto L, Connor WE, Quinn JF. Nutritional biomarkers in Alzheimer’s disease: the association between carotenoids, n-3 fatty acids, and dementia severity. J Alzheimers Dis JAD. 2008 Feb;13(1):31–8.
59.     Chiu C-C, Su K-P, Cheng T-C, Liu H-C, Chang C-J, Dewey ME, et al. The effects of omega-3 fatty acids monotherapy in Alzheimer’s disease and mild cognitive impairment: a preliminary randomized double-blind placebo-controlled study. Prog Neuropsychopharmacol Biol Psychiatry. 2008 Aug 1;32(6):1538–44.
60.     Whalley LJ, Fox HC, Wahle KW, Starr JM, Deary IJ. Cognitive aging, childhood intelligence, and the use of food supplements: possible involvement of n-3 fatty acids. Am J Clin Nutr. 2004 Dec;80(6):1650–7.
61.     Chiu C-C, Frangou S, Chang C-J, Chiu W-C, Liu H-C, Sun I-W, et al. Associations between n-3 PUFA concentrations and cognitive function after recovery from late-life depression. Am J Clin Nutr. 2012 Feb;95(2):420–7.
62.     Milte CM, Sinn N, Street SJ, Buckley JD, Coates AM, Howe PRC. Erythrocyte polyunsaturated fatty acid status, memory, cognition and mood in older adults with mild cognitive impairment and healthy controls. Prostaglandins Leukot Essent Fatty Acids. 2011 Jun;84(5–6):153–61.
63.     Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol. 2017 Mar 27;
64.     Lukaschek K, von Schacky C, Kruse J, Ladwig K-H. Cognitive Impairment Is Associated with a Low Omega-3 Index in the Elderly: Results from the KORA-Age Study. Dement Geriatr Cogn Disord. 2016;42(3–4):236–45.
65.     Whalley LJ, Deary IJ, Starr JM, Wahle KW, Rance KA, Bourne VJ, et al. n-3 Fatty acid erythrocyte membrane content, APOE varepsilon4, and cognitive variation: an observational follow-up study in late adulthood. Am J Clin Nutr. 2008 Feb;87(2):449–54.
66.     Pottala JV, Yaffe K, Robinson JG, Espeland MA, Wallace R, Harris WS. Higher RBC EPA + DHA corresponds with larger total brain and hippocampal volumes: WHIMS-MRI study. Neurology. 2014 Feb 4;82(5):435–42.
67.     Tan ZS, Harris WS, Beiser AS, Au R, Himali JJ, Debette S, et al. Red blood cell ω-3 fatty acid levels and markers of accelerated brain aging. Neurology. 2012 Feb 28;78(9):658–64.
68.     Ammann EM, Pottala JV, Harris WS, Espeland MA, Wallace R, Denburg NL, et al. ω-3 fatty acids and domain-specific cognitive aging: secondary analyses of data from WHISCA. Neurology. 2013 Oct 22;81(17):1484–91.
69.     Danthiir V, Hosking D, Burns NR, Wilson C, Nettelbeck T, Calvaresi E, et al. Cognitive performance in older adults is inversely associated with fish consumption but not erythrocyte membrane n-3 fatty acids. J Nutr. 2014 Mar;144(3):311–20.
70.     Kröger E, Verreault R, Carmichael P-H, Lindsay J, Julien P, Dewailly E, et al. Omega-3 fatty acids and risk of dementia: the Canadian Study of Health and Aging. Am J Clin Nutr. 2009 Jul;90(1):184–92.
71.     Huang TL, Zandi PP, Tucker KL, Fitzpatrick AL, Kuller LH, Fried LP, et al. Benefits of fatty fish on dementia risk are stronger for those without APOE epsilon4. Neurology. 2005 Nov 8;65(9):1409–14.
72.     Barberger-Gateau P, Raffaitin C, Letenneur L, Berr C, Tzourio C, Dartigues JF, et al. Dietary patterns and risk of dementia: the Three-City cohort study. Neurology. 2007 Nov 13;69(20):1921–30.
73.     Plourde M, Vohl M-C, Vandal M, Couture P, Lemieux S, Cunnane SC. Plasma n-3 fatty acid response to an n-3 fatty acid supplement is modulated by apoE epsilon4 but not by the common PPAR-alpha L162V polymorphism in men. Br J Nutr. 2009 Oct;102(8):1121–4.
74.     van de Rest O, Wang Y, Barnes LL, Tangney C, Bennett DA, Morris MC. APOE ε4 and the associations of seafood and long-chain omega-3 fatty acids with cognitive decline. Neurology. 2016 May 4;


H. Murayama1,2, S. Shinkai1, M. Nishi1, Y. Taniguchi1, H. Amano1, S. Seino1, Y. Yokoyama1, H. Yoshida3, Y. Fujiwara1, H. Ito4

1. Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan; 2. Institute of Gerontology, The University of Tokyo, Tokyo, Japan; 3. Faculty of Medical Science and Welfare, Tohoku Bunka Gakuen University, Sendai, Japan; 4. Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan.

Corresponding Author: Hiroshi Murayama, Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan, Tel: +81-3-3964-3241, fax: +81-3-3579-4776, Email:

J Prev Alz Dis 2017;4(2):93-99
Published online September 13, 2016,


Background: Cognitive function can substantially decline over a long period, and understanding the trajectory of cognitive function is important. However, little is known about the linkage between nutritional biomarkers and long-term cognitive change.
Objectives: We analyzed 13-year longitudinal data for older Japanese to examine the associations of serum albumin and hemoglobin levels with the trajectory of cognitive function.
Design: Longitudinal study.
Setting: Community-based. Participants: A total of 1,744 community-dwelling adults aged 65 years or older who participated in annual health examinations in Kusatsu town, Gunma Prefecture, Japan, from 2002–2014. Measurements: Cognitive function was assessed annually by the Mini-Mental State Examination (MMSE). Albumin and hemoglobin levels at baseline (the year when a respondent first participated in the health examination) were divided into quartiles. Hierarchical linear modeling was used to analyze intrapersonal and interpersonal differences in cognitive function.
Results: Participants’ MMSE scores decreased at an accelerated rate over the 13-year period. Participants with the lowest baseline albumin level (below the first quartile line) showed a greater accelerated decline in MMSE scores over time, compared with those with the highest level (above the third quartile line). Moreover, MMSE scores in participants with a lower hemoglobin level and lower MMSE score at baseline tended to decline faster over time at an accelerated rate.
Conclusions: These findings yield new insights about the complex and diverse roles of these nutritional biomarkers on the trajectory of cognitive function in old age.

Key words: Albumin, hemoglobin, cognitive decline, trajectory, older Japanese.



In 2015, the total estimated worldwide cost of dementia was 818 billion USD, and this is estimated to rise to 2 trillion USD by 2030 (1). Currently, strategies for dementia are a global public health issue. Long-term pathological changes in the brain cause gradual deterioration in cognitive function in people with dementia, including Alzheimer’s disease. Cognitive decline is the prodromal stage of Alzheimer’s disease, and prevention of cognitive decline may contribute to controlling future onset of dementia. .
Many studies have examined risk factors for cognitive decline, including physical, medical, social, economic, behavioral, and genetic factors (2, 3). Nutritional factors also affect cognitive decline, and several studies have investigated the relationship between cognitive decline and specific micronutrients, including folic acid (4) and homocysteine (4, 5). Nutritional biomarkers are less subject to error compared with dietary data, and are used to measure nutritional status. In particular, serum albumin and hemoglobin levels are known to reflect nutrition status (6-9), and are recognized as good predictors of cognitive impairment and cognitive decline (10-15). Ng et al. demonstrated that lower albumin levels were associated with cognitive decline at 1–2 years from baseline in community-dwelling Chinese adults (10). Shah et al. analyzed longitudinal data for US adults with an average follow-up period of 3.3 years, and found that lower and higher hemoglobin levels were associated with an increased risk for developing Alzheimer’s disease and more rapid cognitive decline (15).
Most previous studies used cross-sectional data to assess the associations between albumin and hemoglobin levels and cognitive impairment/decline, or at most, longitudinal data between two time points over a relatively short interval. However, cognitive function can substantially decline over a long period. Therefore, understanding the trajectory of cognitive function or the level of cognition and its rate of change over time, and the link between nutritional biomarkers and long-term cognitive change may contribute to developing and improving interventions to prevent cognitive decline. In addition, many previous studies reporting prospective relationships between albumin/hemoglobin levels and cognitive decline were conducted in Western countries. Given differences between Western and non-Western societies including race/ethnicity and dietary habits, it is important to examine these relationships in non-Western countries, particularly Asian nations with aging populations.
To address these gaps in knowledge, this study aimed to examine the associations between serum albumin and hemoglobin levels and the trajectory of cognitive function, using 13-year longitudinal data for community-dwelling older Japanese aged 65 years or older at baseline.



Data for this study were collected as part of comprehensive health examinations conducted in the town of Kusatsu, Gunma Prefecture, Japan. In addition to an annual preventive health check-up for residents aged 40 years or older, residents aged 65 years or older received a geriatric assessment from 2002–2014. Annual assessments were performed at the same local public health center in the same manner each year. Participation in the health check-up was optional. Details of the study design have been previously reported (12). All participants provided written informed consent under conditions approved by the Ethics Committee at Tokyo Metropolitan Institute of Gerontology.
Data for the present study were drawn from 1,744 adults aged 65 years or older who lived in Kusatsu at their first participation in the annual assessments for 2002–2014. The total number of observations was 7,154 (average 4.1 observations per participant). The year when a respondent first participated in the health examination was regarded as the baseline year for that individual.

Cognitive Function

Participants’ cognitive function was assessed each survey year, using the Mini-Mental State Examination (MMSE) comprising 11 questions. The MMSE score ranges from 0 to 30, with lower scores indicating poorer global cognitive ability (16). In data collection, the MMSE was administered by well-trained staff.

Blood Biomarkers

Serum albumin and hemoglobin levels at baseline were used in the analyses. Non-fasting blood samples were collected using standard procedures. Samples were analyzed at the Sanaikai Clinic. This laboratory is regularly monitored by several domestic authorities. In the analyses, albumin and hemoglobin levels were divided into quartiles: ≤3.9 (Q1), 4.0–4.1 (Q2), 4.2–4.3 (Q3), and ≥4.4 (Q4) for albumin (g/dL); ≤12.9 (Q1), 13.0–13.7 (Q2), 13.8–14.6 (Q3), and ≥14.7 (Q4) for hemoglobin (g/dL). The highest level (Q4) was set as the reference category.


Covariates comprised sociodemographic factors and health conditions at baseline. Sociodemographic factors included age, sex, number of household members, years of education, and long-term occupation. Health conditions included comorbidities, self-rated health, depressive mood, body mass index, functional capacity, usual gait speed, resting systolic blood pressure, blood sugar level, and lipid level.
Information on comorbidities was collected in a medical interview conducted by a doctor or registered nurse, and included five diseases: hypertension, cardiovascular disease, cerebrovascular diseases, hyperlipidemia, and diabetes mellitus. Body mass index was calculated from actual measured height and weight (kg/m2). Depressive mood was assessed using the Geriatric Depression Scale short-form, with total scores ranging from 0–15 (17). A cutoff point of 5/6 was adopted with a score of ≥6 indicating depressive mood. To determine functional capacity, we assessed basic activities of daily living and higher-order competence of independence. Basic activities of daily living entailed the sum of difficulties experienced in five activities (i.e., dressing, walking, bathing, eating, and using the toilet). To assess higher-order competence of independence, we used the Tokyo Metropolitan Institute of Gerontology Index of Competence (TMIG-IC), consisting of 13 items (total score range 0–13) (18). Higher basic activities of daily living and TMIG-IC scores indicated greater independence. Usual gait speed was measured by trained staff using a straight 11-m walkway on a flat floor marked with tape at 3 m and 8 m. Participants were requested to walk at their usual pace. The time required to walk 5 m was measured once, and gait speed was calculated (m/s). Blood sugar and lipid levels were measured by blood glucose and triglyceride, respectively. These data were collected in the same manner as for serum albumin and hemoglobin levels. In the analyses, usual gait speed, systolic blood pressure, and blood glucose and triglyceride levels were divided into quartiles.
In addition to these covariates, we used information obtained in the baseline year for each participant (i.e., the first year the respondent entered the survey) to adjust for differences in starting point of participation in the survey.

Statistical Analyses

Hierarchical linear models were used to estimate the trajectory of cognitive function from 2002–2014 as assessed by the MMSE. Intrapersonal differences in the average MMSE were modeled in the Level 1 equation:
YiT  =  π0i  +  π1iTime  +  π2iTime2  +  εiT,
where YiT is MMSE by individual i at time T, π0i is the intercept (i.e., level), π1i is the linear slope (i.e., rate of linear change), and π2i is the quadratic slope (i.e., rate of acceleration) for individual i over time. Time is the distance (in years) of assessment from baseline. εiT is the random error in MMSE for individual i at time T. Time was centered on its grand mean.
Interpersonal variations in the MMSE trajectories were specified in the Level 2 equation:
πpi  =  βp0  +  ΣβpqXqi  +  rpi,
where Xqi is the qth covariate associated with individual i, and βpq represents the effect of variable Xq on the pth growth parameter (πp) (i.e., intercept and linear and quadratic slopes). rpi is a random effect with a mean of 0. All covariates (Level 2) were centered on their grand mean. All models were fitted by using HLM 7.
To adjust for selection bias, we included mortality and moving from the town during the observation period in the Level 2 equation. Mortality and moving were viewed as confounding variables rather than predictors of MMSE score. This was because they can potentially bias the results toward healthier, longer-living subjects who may differ in their level and rate of change in MMSE score from those who die or move away during the study period, particularly in older populations (19). Therefore, binary indicators were created for mortality (alive at the end of the study [0], died during the study [1]) and moving (stayed in town during the study [0], moved away from town during the study [1]). This information was collected from the town administrative records.
We focused on the associations of nutritional biomarkers with the level of MMSE score and its rate of change and acceleration, using a modeling strategy involving several steps. In Model 1, we adjusted for sociodemographic factors and the baseline year. In Model 2, we added health conditions to Model 1. We further adjusted for mortality and moving from town in Model 3, and baseline MMSE score in Model 4. Finally, we tested differences in effects of nutritional markers on MMSE trajectory by age, sex, and baseline cognitive level. To achieve this, we added the interactions between both baseline albumin and hemoglobin levels and each of these variables (age, sex, and baseline MMSE score) to the intercept and linear and quadratic slopes to Model 4.



Table 1 shows participants’ descriptive characteristics at baseline. Participants’ mean age was 71.4 years (standard deviation=6.0), 57.1% were women, and 22.3% lived alone. During the follow-up period, 16.8% of participants died and 8.8% moved away from town. The mean MMSE score at baseline was 26.9 (standard deviation=3.1). Participants’ characteristics by baseline albumin and hemoglobin levels (quartiles) are shown in Supplementary Tables 1 and 2. Those with the lowest albumin and hemoglobin levels (Q1) had lower MMSE scores compared with other levels.


Table 1. Participants’ Baseline Characteristics

Values represent mean (standard deviation) or %; MMSE: Mini-Mental State Examination; TMIG-IC: Tokyo Metropolitan Institute of Gerontology Index of Competence.


Using linear, quadratic, and cubic functions, we mapped the trajectory of MMSE scores between 2002 and 2014. The unconditional model showed that MMSE scores decreased following a quadratic (accelerating) trajectory, with an intercept (at mid-time point) of 27.215 (P<0.001), a linear slope of 0.011 (P=0.421), and a quadratic slope of −0.020 (P<0.001). This indicated that the MMSE score was 26.9 at baseline and declined to 25.7 over 13 years, at an accelerating rate (data not shown in tables). The cubic slope coefficient was not significant in the unconditional model. Therefore, cubic function was not included in subsequent analyses.

Table 2 shows the association between baseline albumin level and the MMSE score trajectory. In Model 1, participants with the lowest albumin level (Q1) had a greater accelerated decline in MMSE score compared with those with the highest albumin level (Q4) (b=−0.034, P=0.037). This association remained after adjusting for baseline health conditions in Model 2, mortality and moving away in Model 3, and baseline MMSE score in Model 4 (e.g., b=−0.046, P=0.025 in Model 4). There was no association between baseline albumin level and the intercept and linear slope. Figure 1 presented the MMSE trajectories by baseline albumin level, based on Model 3. Relative to Q4, the MMSE score in Q1 declined faster over time at an accelerated rate.

Table 2. Association Between Baseline Albumin Level and MMSE Score Trajectory

MMSE: Mini-Mental State Examination; SE: standard error; Model 1: Adjusted for age, sex, number of household members, years of education, long-term occupation at baseline, and baseline year; Model 2: Additionally adjusted by adding comorbidities, self-rated health, depressive mood, body mass index, functional capacity (basic activities of daily living and higher order competence of independence), usual gait speed, systolic blood pressure, blood glucose, and triglyceride at baseline to Model 1; Model 3: Additionally adjusted by adding mortality and moving away from town during the observation period to Model 2. Model 4: Additionally adjusted by adding baseline MMSE score to Model 3; Albumin level quartiles: ≤3.9 (Q1), 4.0–4.1 (Q2), 4.2–4.3 (Q3), and ≥4.4 (Q4) (g/dL); All covariates were added for the intercept (π0i), linear slope (π1i), and quadratic slope (π2i).


Table 3 shows the association between baseline hemoglobin level and MMSE score trajectory. There was a significant association between baseline hemoglobin level and quadratic slope in Models 1 through 3 (e.g., b=−0.024, P=0.043 for Q1; b=−0.020, P=0.089 for Q2; and b=−0.027, P=0.007 for Q3 in Model 3). This indicated that, compared with the group with the highest hemoglobin level at baseline (Q4), MMSE scores for the three lower groups declined faster over time at an accelerated rate. Figure 2 illustrates this association based on Model 3. However, this trend was non-significant after adjusting for baseline MMSE score in Model 4.


Table 3. Association Between Baseline Hemoglobin Levels and MMSE Score Trajectory

MMSE: Mini-Mental State Examination; SE: standard error; Model 1: Adjusted for age, sex, number of household members, years of education, long-term occupation at baseline, and baseline year; Model 2: Additionally adjusted by adding comorbidities, self-rated health, depressive mood, body mass index, functional capacity (basic activities of daily living and higher order competence of independence), usual gait speed, systolic blood pressure, blood glucose, and triglyceride at baseline to Model 1; Model 3: Additionally adjusted by adding mortality and moving away from town during the observation period to Model 2.; Model 4: Additionally adjusted by adding baseline MMSE score to Model 3; Hemoglobin level quartiles: ≤12.9 (Q1), 13.0–13.7 (Q2), 13.8–14.6 (Q3), and ≥14.7 (Q4) (g/dL); All covariates were added for the intercept (π0i), linear slope (π1i) and quadratic slope (π2i).



Figure 1. Mini-Mental State Examination (MMSE) Score Trajectories by Baseline Albumin Level, Based on Model 3



Figure 2. Mini-Mental State Examination (MMSE) Score Trajectories by Baseline Hemoglobin Level, Based on Model 3.


Finally, we examined whether the effects of nutritional markers on the MMSE trajectory varied by age, sex, and baseline MMSE score, by adding the interactions between baseline albumin/hemoglobin levels and these variables to Model 4 on the intercept, linear slope, and quadratic slope. We observed no significant interaction between albumin/hemoglobin levels and either age or sex. Moreover, there was no significant interaction between albumin level and baseline MMSE score. However, we found a significant interaction between hemoglobin level and baseline MMSE score on the quadratic slope (b=−0.016, P=0.015 for Q1×MMSE; b=−0.014, P=0.016 for Q2×MMSE; b=−0.013, P=0.089 for Q3×MMSE; data not shown in tables). This suggested that the effect of baseline hemoglobin level on the quadratic slope was stronger in participants with lower baseline MMSE scores (i.e., MMSE scores for participants with a lower hemoglobin level and a lower MMSE score at baseline tended to decline faster over time at an accelerated rate).



Based on 13-year longitudinal data for community-dwelling older Japanese, this is the first study to examine the associations between albumin and hemoglobin levels and MMSE trajectory. We found that MMSE scores in adults aged 65 years or older declined at an accelerated rate over time, and that a lower albumin level was associated with an accelerated decline in MMSE after adjusting for potential covariates. Furthermore, a lower hemoglobin level was associated with an accelerated decline in MMSE score, particularly in those with a lower baseline MMSE score. The MMSE score generally decreased over time during the observation period, although the score increased slightly from baseline for some years. This might be partly caused by regression to the mean, but should be investigated in future studies.
The association between albumin level and cognitive decline may be explained by pathophysiological mechanisms. Albumin is a negative acute-phase protein with low levels in chronic systemic inflammatory activity. Recently, evidence has increased for the involvement of inflammatory mechanisms in the pathogenesis of dementia, including Alzheimer’s disease (20, 21). Therefore, chronic, systemic, low-grade inflammation might be a mediating factor in this process. In the present study, MMSE scores in participants with the lowest albumin level (Q1) declined below 25 over the 13-year follow-up period (Figure 1). Some previous studies reported that a cutoff point of 26/27 in the MMSE can appropriately detect future dementia (22, 23). Moreover, even after we controlled for baseline MMSE score in Model 4, baseline albumin level was associated with an accelerated decline in MMSE score. This implies that intervention for malnutrition may be effective in preventing cognitive decline and onset of dementia, regardless of cognitive level.
In our study, a lower hemoglobin level was associated with the quadratic slope in Models 1–3. Chronic brain hypo-oxygenation due to a low hemoglobin level might be a biological mechanism underlying this association. A critical reduction in brain oxygenation causes reversible cognitive impairment (24), and there is increasing evidence to support this mechanism (11, 25, 26). However, after adjusting for the baseline MMSE score in Model 4, this association became non-significant. This indicated that cognitive level at baseline accounted for the association. Therefore, we additionally examined whether the effect of hemoglobin on the MMSE trajectory differed by baseline MMSE score as well as by age and sex. We found that the effect of baseline hemoglobin level on the quadratic slope was stronger in participants with lower baseline MMSE scores. A previous cross-sectional study reported there was no association between hemoglobin level and MMSE score in those without cognitive impairment (MMSE ≥24), but the association was significant in the whole sample (i.e., those with and without cognitive impairment) (13). Our finding that the effect of hemoglobin level on accelerated decline in MMSE score was greater among those with a lower MMSE score is partially supported by this previous study; and at the same time, provides more dynamic evidence on the relationship between hemoglobin level and long-term cognitive decline.
Other mechanisms may explain the associations between nutritional biomarkers and long-term cognitive decline observed in this study. First, a lack of micronutrients may mediate these associations. Low albumin and low hemoglobin levels (e.g., anemia) accompany early reduction in food intake and loss of weight, which characterize the preclinical stages of dementia (27). Low albumin and hemoglobin levels are also associated with deficiency in several micronutrients, such as folate and vitamin B12, which have deleterious effects on cognitive decline (14, 28). Second, frailty may play an important role in explaining our observed associations. Malnutrition has been identified as a frailty phenotype (29, 30), and low albumin and hemoglobin levels are potential markers of frailty (31). Moreover, frailty is related to cognitive impairment (32). Therefore, frailty could mediate the associations among albumin, hemoglobin, and cognitive decline.
This study has some limitations. First, we did not include all risk factors for cognitive decline that were reported in previous studies, such as tobacco use, physical and leisure activities, food intake, and micronutrients (2, 3). Moreover, some markers of inflammation (e.g., C-reactive protein, interleukin-6, and α1-antichymotrypsin (21, 33)) and genetic markers (e.g., apolipoprotein E-ε4 (10)) are related to both cognitive decline and albumin/hemoglobin. Further research including these covariates is necessary. Second, we regarded serum albumin and hemoglobin as nutritional indicators. These markers are known to reflect nutritional level, even though they may be affected by factors other than nutrition, such as inflammation (6-9). However, it is unclear if these findings can be applied in our older Japanese sample, and thus the principal that albumin and hemoglobin can be used as nutritional indicators should be confirmed. Third, our sample consisted of residents of one town who participated in an annual health examination. Therefore, they might be healthier than the general older adult population (i.e., healthy volunteer effect). This may have attenuated the associations between nutritional biomarkers and the trajectory of cognitive function in our sample. Fourth, data for this study were obtained from a single area. Therefore, the generalizability of these findings should be examined by conducting further studies in different settings. Kusatsu has an altitude of approximately 1,200 m, and hemoglobin levels are elevated in residents at high altitude (34). In fact, because of the high altitude of the study setting, our participants’ hemoglobin levels were higher than those of general Japanese older adults (35). Although we used quartiles of the nutritional biomarkers in the analysis and not absolute criteria, further examinations in other settings are necessary. Fifth, we assessed global cognitive function with the MMSE, and the association between nutritional status and cognitive decline might differ by domain (26). Therefore, in future studies, we should examine the associations between nutritional biomarkers and long-term cognitive decline in specific domains.
Moreover, albumin and hemoglobin levels are not necessarily unchanged over time. Therefore, because serum albumin and hemoglobin were assessed in each survey year as well as at baseline, we additionally performed multilevel linear regression models after accounting for the time-variation in each biomarker (adding albumin and hemoglobin levels at each survey year in the Level 1 equation; data not shown). The associations between baseline albumin and hemoglobin levels and MMSE trajectory were similar to those generated from the model without time-varying variables (Model 4): a lower albumin level was associated with an accelerated decline in MMSE, but hemoglobin level was not. This indicates our results are robust, even after adjusting for the time-variation of each biomarker.
A major strength of our study is the inclusion of longitudinal data for community-dwelling older Japanese over a 13-year period. Most previous studies used an a priori categorization of cognitive impairment or cognitive decline, and examined its relationship with albumin and hemoglobin levels. However, cognitive function substantially declines over a long period. Therefore, we assessed the trajectory of participants’ cognitive function (i.e., long-term cognitive change) using multiple observations, and examined its association with nutritional biomarkers. Our study provided a dynamic dimension to further understanding of the associations between albumin and hemoglobin and long-term cognitive decline in older Japanese. Moreover, we found deleterious effects of low albumin and hemoglobin levels on long-term cognitive change in our study population, a trend consistent with that observed in Western populations. This implies that the associations of albumin and hemoglobin levels with cognitive decline are universal, regardless of cultural and racial/ethnic differences. Therefore, nutritional interventions, particularly for malnutrition, to prevent cognitive decline and future onset of dementia should be considered worldwide.

Funding: This study was supported by the Tokyo Metropolitan Institute of Gerontology, the Research Institute of Science and Technology for Society, the Japan Science and Technology Agency (RISTEX/JST), and JSPS KAKENHI Grant Numbers 20390190, 20659105, 21390212, 24390173, and 24890302.

Conflict of Interest: The authors have no financial or any other type of personal conflicts of interest to disclose.

Ethical Standards: The Ethics Committee at Tokyo Metropolitan Institute of Gerontology approved this study, which complies with the current laws of Japan.



1.    Alzheimer’s Disease International. World Alzheimer report 2015: The global impact of dementia. 2015. Alzheimer’s Disease International, London.
2.    Xu W, Tan L, Wang HF, et al. Meta-analysis of modifiable risk factors for Alzheimer’s disease. J Neurol Neurosurg Psychiatry 2015;86:1299–1306.
3.    Plassman BL, Williams JW Jr, Burke JR, Holsinger T, Benjamin S. Systematic review: Factors associated with risk for and possible prevention of cognitive decline in later life. Ann Intern Med 2010;153:182–193.
4.    Mooijaart SP, Gussekloo J, Frölich M, Jolles J, Westendorp RG, de Craen AJ. Homocysteine, vitamin B-12, and folic acid and the risk of cognitive decline in old age: The Leiden 85-Plus study. Am J Clin Nutr 2005;82:866–871.
5.    Teunissen CE, Blom AH, Van Boxtel MP, et al. Homocysteine: a marker for cognitive performance? A longitudinal follow-up study. J Nutr Health Aging 2003;7:153–159.
6.    Cabrerizo S, Cuadras D, Gomez-Busto F, Artaza-Artabe I, Ciancas FM, Malafarina V. Serum albumin and health in older people: Review and meta analysis. Maturitas 2015;81:17–27.
7.    Omran ML, Morley JE. Assessment of protein energy malnutrition in older persons, part II: Laboratory evaluation. Nutrition 2000;16:131–140.
8.    Mitrache C, Passweg JR, Libura J, et al. Anemia: An indicator for malnutrition in the elderly. Ann Hematol 2001;80:295–298.
9.    Thomson CA, Stanaway JD, Neuhouser ML, et al. Nutrient intake and anemia risk in the Women’s Health Initiative Observational Study. J Am Diet Assoc 2011;111:532–541.
10.    Ng TP, Niti M, Feng L, Kua EH, Yap KB. Albumin, apolipoprotein E-ε4 and cognitive decline in community-dwelling Chinese older adults. J Am Geriatr Soc 2009;57:101–106.
11.    Onem Y, Terekeci H, Kucukardali Y, et al. Albumin, hemoglobin, body mass index, cognitive and functional performance in elderly persons living in nursing homes. Arch Gerontol Geriatr 2010;50:56–59.
12.    Taniguchi Y, Shinkai S, Nishi M, et al. Nutritional biomarkers and subsequent cognitive decline among community-dwelling older Japanese: A prospective study. J Gerontol A Biol Sci Med Sci 2014;69:1276–1283.
13.    Ng TP, Feng L, Niti M, Yap KB. Albumin, haemoglobin, BMI and cognitive performance in older adults. Age Ageing 2008;37:423–429.
14.    La Rue A, Koehler KM, Wayne SJ, Chiulli SJ, Haaland KY, Garry PJ. Nutritional status and cognitive functioning in a normally aging sample: A 6-y reassessment. Am J Clin Nutr 1997;65:20–29.
15.    Shah RC, Buchman AS, Wilson RS, Leurgans SE, Bennett DA. Hemoglobin level in older persons and incident Alzheimer disease: Prospective cohort analysis. Neurology 2011;77:219–226.
16.    Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189–198.
17.    Burke WJ, Roccaforte WH, Wengel SP. The short form of the Geriatric Depression Scale: A comparison with the 30-item form. J Geriatr Psychiatry Neurol 1991;4:173–178.
18.    Koyano W, Shibata H, Nakazato K, Haga H, Suyama Y. Measurement of competence: Reliability and validity of the TMIG Index of Competence. Arch Gerontol Geriatr 1991;13:103–116.
19.    Harel O, Hofer SM, Hoffman L, Pedersen NL, Johansson B. Population inference with mortality and attrition in longitudinal studies on aging: A two-stage multiple imputation method. Exp Aging Res 2007;33:187–203.
20.    Holmes C, Cunningham C, Zotova E, et al. Systemic inflammation and disease progression in Alzheimer disease. Neurology 2009;73:768–774.
21.    Schram MT, Euser SM, de Craen AJ, et al. Systemic markers of inflammation and cognitive decline in old age. J Am Geriatr Soc 2007;55:708–716.
22.    Kukull WA, Larson EB, Teri L, Bowen J, McCormick W, Pfanschmidt ML. The Mini-Mental State Examination score and the clinical diagnosis of dementia. J Clin Epidemiol 1994;47:1061–1067.
23.    O’Bryant SE, Humphreys JD, Smith GE, et al. Detecting dementia with the Mini-Mental State Examination in highly educated individuals. Arch Neurol 2008;65:963–967.
24.    Croughwell ND, Newman MF, Blumenthal JA, et al. Jugular bulb saturation and cognitive dysfunction after cardiopulmonary bypass. Ann Thorac Surg 1994;58:1702–1708.
25.    Atti AR, Palmer K, Volpato S, Zuliani G, Winblad B, Fratiglioni L. Anaemia increases the risk of dementia in cognitively intact elderly. Neurobiol Aging 2006;27:278–284.
26.    Deal JA, Carlson MC, Xue QL, Fried LP, Chaves PHM. Anemia and 9-year domain-specific cognitive decline in community-dwelling older women: The Women’s Health and Aging Study II. J Am Geriatr Soc 2009;57:1604–1611.
27.    Stewart R, Masaki K, Xue QL, et al. A 32-year prospective study of change in body weight and incident dementia: The Honolulu-Asia Aging Study. Arch Neurol 2005;62:55–60.
28.    Riggs KM, Spiro A 3rd, Tucker K, Rush D. Relations of vitamin B-12, vitamin B-6, folate, and homocysteine to cognitive performance in the Normative Aging Study. Am J Clin Nutr 1996;63:306–314.
29.    Xue QL, Bandeen-Roche K, Varadhan R, Zhou J, Fried LP. Initial manifestations of frailty criteria and the development of frailty phenotype in the Women’s Health and Aging Study II. J Gerontol A Biol Sci Med Sci 2008;63:984–990.
30.    Boulos C, Salameh P, Barberger-Gateau P. Malnutrition and frailty in community dwelling older adults living in a rural setting. Clin Nutr 2016;35:138–143.
31.    Ferrucci L, Cavazzini C, Corsi A, et al. Biomarkers of frailty in older persons. J Endocrinol Invest 2002;25(10 Suppl):10–15.
32.    Samper-Ternent R, Al Snih S, Raji MA, Markides KS, Ottenbacher KJ. Relationship between frailty and cognitive decline in older Mexican Americans. J Am Geriatr Soc 2008;56:1845–1852.
33.    Dik MG, Jonker C, Hack CE, Smit JH, Comijs HC, Eikelenboom P. Serum inflammatory proteins and cognitive decline in older persons. Neurology 2005;64:1371–1377.
34.    Dirren H, Logman MH, Barclay DV, Freire WB. Altitude correction for hemoglobin. Eur J Clin Nutr 1994;48:625–632.
35.    Ministry of Health, Labour and Welfare. The National Health and Nutrition Survey in Japan, 2013. 2013. Ministry of Health, Labour and Welfare, Tokyo.


C. Vassalle1, L. Sabatino2, A. Pingitore2, K. Chatzianagnostou1, F. Mastorci2, R. Ceravolo3

1. Fondazione CNR-Regione Toscana G Monasterio, Pisa, Italy; 2. Institute of Clinical Physiology-CNR, Pisa, Italy; 3. Neurology Institute-Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.

Corresponding Author: Dr. Cristina Vassalle, Fondazione G.Monasterio CNR-Regione Toscana, Pisa, Italy Phone: +39-050-3152199; Fax:+39-050-3153525. e-mail:

J Prev Alz Dis 2017;4(1):58-64
Published online August 16, 2016,


 This review aims to focus on main antioxidants- abundantly contained in the diet- as well as of the whole Mediterranean diet and life-style and their relationship with cognitive function, especially critical in two phases of life, in children until adolescence and oldness. The role of emerging biochemical and molecular biomarkers as opportunity to estimate more accurately nutritional assumption and requirement, in terms of cognitive preservation and disease risk, will be also discussed.  The cluster of factors within the Mediterranean pattern -which include not only nutritional, but also physical, social, and stimulating aspects- is still largely understudied as a whole, but it is proposed as attractive research area and tool for public health planning of prevention and intervention.

Key words: Nutrition, cognitive decline, aging, Mediterranean diet, Mediterranean life-style, antioxidants, biomarkers.


The progressive aging of western population is closely related to increased costs imposed by social-sanitary needs in over 65 subjects. Cognitive decline consists in a deterioration of cognitive function, characterized by increasing difficulties with cognitive processing speed, process information, language, think abstractly and problem solving, including aspects related to memory, executive functioning and reasoning (1). This condition may progress to dementia when the symptoms are severe enough to impact significantly with daily life (2). In this context, cognitive decline often related to progressive loss of personal independence and disability, will represent one of the main problem to face in the future society. Conversely, the knowledge of underlying mechanisms of brain morphology and function will constitute a critical step in developing preventive and therapeutic strategies that meet the demands of an aging society.
Normally, cognitive abilities increase up until adolescence or early adulthood and then they progressively decline. However, the age at which the decline begins and the speed of ability diminishes are highly variable among subjects (Figure 1) (3). Life-style habit, including nutrition and diet, can contribute to individual differences, and early life-phase living conditions may affect late-life health outcomes.

Figure 1. Functional capacity trend along life span and interval of intervention to maintain the best possible levels of brain functionality and prevent cognitive impairment and disability

Therefore, this review will focus on available evidences of single antioxidants or Mediterranean diet (MeD) and their effects on cognitive function in two critical phases of life: 1) from childhood to adolescence and 2) oldness. Moreover, since a low endogenous antioxidant status and inflammation may represent key factors for cognitive decline, the possible use of biomarkers related to these processes as tool to assess the relationship between diet and cognitive decline will be also evaluated. Finally, the cluster of factors within the Mediterranean style-life, still largely understudied as a whole including not only dietary, but also physical, social, and stimulating  factors, is proposed as attractive research area and tool for public health planning of prevention and intervention.

Nutrition in early life and later cognitive function

The effects of nutrition on cognitive function is crucial since pre-birth life and factors like the diet,  intake of folates or maternal vitamin B12 status during pregnancy or lactating have significant impact on successive measures of language, memory, and perception. However, there is a lack of pregnancy and birth cohorts to study ageing from the life-course perspective and to monitor how subjects age according to different biological as well as lifestyle variables (Table 1). In fact, different endpoints have been evaluated mostly in childhood, due to the  many difficulties arising to plan prospective studies with 10 years or more of follow-up (4-7). Moreover, often studies do not include subjects with frankly reduced nutrient status, and in most cases, the retrospective nature of data collection for food insufficiency in childhood may raise concern of recall bias.  In any case, some interesting observations may be extrapolated by available findings, suggesting that malnutrition during the first years of life drives risk for significant functional morbidity in adulthood. In fact, poor living conditions in early life may cause higher risk for chronic conditions such as depression, hypertension, Type 2 diabetes (T2D), and obesity, which in turn may drive to worse neuronal outcomes later during the adulthood. In this context, recent findings from a 40-year longitudinal study suggest that moderate to  severe malnutrition during infancy is associated with impaired IQ and academic skills in adulthood (8). Accordingly, very recent data revealed that food insufficiency in childhood would independently increase the risk of developing dementia in old age by 81%, after adjusting for sociodemographic factors (9). Conversely, results from the  1932 Scottish Mental Survey evidenced that lower B12 at age 79 is associated with cognitive decline between age 11 and 79, while serum folates at age 79 correlates with those at age 11 (10). On 2435 participants in the community-based Coronary Artery Risk Development in Young Adults (CARDIA) study of black and white men and women (18-30 years at the time of enrollment, 1985-86), the diet score was associated with cognitive function of the following 5 years and even 25 years, evidencing the importance of early adoption and maintenance of quality diet to preserve intellectual capacity along all life span (11).

Table 1. Nutrition and age-related cognitive impairment: actual limits and needs

Whether the results of these trials are not simple to perform and interpret in terms of underlying molecular mechanisms, some interesting insights may derive by experimental studies. Interestingly, very recent data suggest that improved cognition in adult rats -subjected to low calorie diet feeding during youth- may result from the increase of brain-derived neurotrophic factor (BDNF) involved in hippocampal neurogenesis (12). In particular, neuronal nuclear antigen-neuron marker expressing cells, which are involved in memory and are located in hippocampus dentate gyrus, resulted increased (12). Moreover, hippocampus and prefrontal cortex BDNF levels were increased, while serum glucose concentration and values of malondialdehyde (marker of lipid peroxidation) appear reduced in serum and hippocampus (12). Thus, BDNF could represent a critical underlying factor in the relationship between nutrition and cognition.

Antioxidants, diet and cognitive decline in elderly

Data from WHO predicted that the occurrence of cognitive impairment and dementia will interest 29 million people worldwide in 2020 (13). This alarming information is anyway alleviated by the prediction that even a small delay of the onset of Alzheimer disease (AD) may markedly reduce the disease prevalence and, consequently, even modest interventions postponing disease onset could translate in a major public health impact (14). In light of this consideration, many studies on the effects of single key nutrients -folate, vitamin B12, and vitamin E- in the elderly have been recently reviewed (15). Authors concluded that supplementation may protect against cognitive decline but only in elderly subjects with low status of these vitamins (for folate is <12 nmol/L or vitamin E intake <6.1 mg/day) (15). No clear definitive data emerge for vitamin B12 (15). Some other studies revealed an interaction between plasma concentrations of folate and vitamin B12 in relation to cognitive performance. In a large population of 2203 Norwegian elderly aged 72-74 years, where cognitive performance was assessed by six cognitive tests, vitamin B12 in the lowest quartile (< 274 pmol/L), combined with plasma folate in the highest quartile (>18.5 nmol/L) were associated with a reduced risk of cognitive impairment (16). Other results from the Framinghan cohort suggested that low vitamin B12 levels (<258 pmol/L) may predict cognitive decline, being a higher cognitive decline rate observed in subjects with low vitamin B12 and high plasma folate (>21.75 nmol/L) or supplemental folates (17).  In other studies, high folate or folic acid supplements resulted detrimental to cognition in older people with low vitamin B12 levels (18). Conversely, a recent study on the effects of 2-year folic acid and vitamin B12 supplementation on cognitive performance in elderly people with elevated homocysteine levels (2,919 elderly participants, ≥65 years) showed no change in cognitive performance (19). Nonetheless, in the Chicago Health and Aging Project (516 participants) higher levels of vitamin B12 were associated with slower rates of cognitive decline, although homocysteine concentration had no relationship to cognitive decline (20).
Recently, there has been increasing interest in the potential of flavanols, contained in fruit and vegetables, to improve cognitive functions in elderly. A high-flavanol intake was found to enhance dentate gyrus activity (hippocampal region considered involved in age-related memory decline), as measured by Functional Magnetic Resonance Imaging and cognitive tests (21). Moreover, these molecules are able to improve regional cerebral perfusion in elderly, which can be one of the possible acute mechanism by which flavanols exert their benefits on cognitive performance (22). Moreover, other data suggested that also the habitual consumption of green tea may be effective in reduce the risk of cognitive decline in elderly population (23, 24). Interestingly, thiamine (or vitamin B1 from cereals, meat, vegetables) deficiency is common in frail elderly (especially hospitalized and institutionalized subjects) and has been related to AD and other neurodegenerative conditions. (25-28). However, the role of thiamine on cognitive function in elderly subjects was recently reviewed, and authors concluded that at now there are no definitive data to clearly recognize the role of thiamine in neurological impairment and disease (28).
Although even the results on a single nutrient may be complex to understand, because often contradictory or associated to variables effect according to nutrient concentration, as in the case of vitamins and homocysteine, studies on the effects of a single food may be further limited because do not focus on the interactive effects with other components in a whole diet. In fact, intake is clearly based on a complex interaction of both macro- (proteins, fats, carbohydrates) and micro-nutrients (vitamins, minerals), and evaluation of the effects of a single nutrient or food could be not satisfactory enough. Actually, an interesting approach to examine the link between nutrition and cognitive function is the evaluation of whole dietary patterns, thus considering potential synergies among nutrients, as found in a balanced diet. In this context, both the Dietary Approaches to Stop Hypertension (DASH) and MeD dietary patterns were associated with consistently higher levels of cognitive function in elderly men and women over an 11-year period in a prospective design (29). These results were confirmed in the 826 Memory and Aging Project (826 participants, aged 81.5 ± 7.1 years) where both the DASH and MeD patterns resulted associated with slower rates of cognitive decline (30). The molecular mechanisms by which DASH and MeD may be related to beneficial effects on cognitive performance have not yet been fully cleared. However, the commonly diffused use of whole grains, nuts and legumes may be responsible for the similar protective effects of both DASH and MeD dietary patterns. This observation may be an important starting point for public-health nutrition recommendations worldwide, according to the wide diffusion of these food types in the diet of majority world populations.

MeD and cognitive decline in elderly

MeD is the food pattern typical of population living in Italy and other Mediterranean countries, characterized by a high fish, vegetable and  fruit consumption,  use of extravirgin  olive oil, low intake of dairy derivatives, sugar infrequent consume, moderate red wine use, red meat consumption (only 1-2 times/week). Noteworthy, a high adherence to MeD is associated with longevity and provides significant protection against morbidity and mortality related to chronic diseases including cancer, metabolic syndrome, depression, cardiovascular and neurodegenerative diseases (31).
There are also findings that a better adherence to MeD may reduce the risk of cognitive decline and some forms of dementia (32). In particular, a meta-analysis assessed the association between the MeD and Mild Cognitive Impairment (MCI) or AD from five prospective cohort studies with at least one year of follow up. Higher MeD adherence confers a reduced risk (33% lower) of developing both MCI and AD, and a reduced risk of progressing from MCI to AD (33). Another meta-analysis evaluated the association between MeD and a number of brain-related conditions, stroke, depression, and cognitive impairment (8 studies covered cognitive impairment), suggesting that high MeD adherence was strongly associated with reduced risk for cognitive impairment (MCI, dementia and AD) (34). These data were confirmed by other results from recent systematic revision and meta-analysis, evidencing that MeD adherence reduced the risk of developing MCI and AD, and the progression from MCI to AD (35-37). The interaction mechanism between MeD adherence and cognition could be almost in part linked to the oleocanthal, an extra-virgin olive-oil bioactive component, recently supposed to be involved in the modulation of tau protein, one of the main causes of Alzheimer neurodegeneration (38). Accordingly, greater adherence to MeD was associated with better scores in several cognitive function tests in elderly subjects (>60 years) living in a Polish rural community (39), thus suggesting some benefits even in non-Mediterranean populations when the main MeD principles were adopted. Conversely, adverse effects of ‘Western’ dietary patterns against the consumption of high vegetable and plant-based diet was evidenced in elderly (>60 years) included in the Australian Diabetes, Obesity and Lifestyle Study (40).

Oxidative stress and inflammatory biochemical markers in the relationship between MeD and cognitive decline

Biochemical markers represent indices of a biological state or condition, measurable in biological samples, especially into the blood. As antioxidants are major determinants of protective MeD effects, the measurements of inflammatory and oxidative stress biomarkers may contribute to fill the gap between nutrition and MeD on one side and age-related cognitive impairment and AD on the other. In particular, MeD adoption increases levels of carotenoids, vitamin A and vitamin E, and reduced oxidative stress and inflammation biomarker levels (e.g. uric acid, SH groups, SOD and GPx activities, FRAP and TRAP, TNF-α, and IL-10 cytokines, and malondialdehyde in the erythrocytes as marker of lipid peroxidation) (41). Recent data also suggested that MeD adherence favorably modifies levels of oxidative stress biomarkers (CoQ and β-carotene, isoprostanes and oxidized low-density lipoproteins) in elderly subjects (42). Some of these oxidative stress and inflammatory biomarkers have been proposed to evidence early risk AD profiles, even in the pre-symptomatic stage, and as additive tools for AD diagnosis, and prognosis (43-46). However, at now, there is scarcity of data on the possibility to modulate levels of inflammatory and oxidative stress biomarkers by nutrients and dietary patterns in patients with MCI or AD. In particular, there are not significant evidences for a role of C reactive protein, a common index of inflammation, in the association between MeD and lower risk of AD (118 incident AD cases during a 4-year follow-up in 1219 non-demented subjects aged  over 65 yrs) (47).
Beneficial effects of MeD have supposed to be related to prevention of shortening of telomeres, nucleoprotein structures that protect the ends of chromosomes, whose integrity is closely related to antioxidant availability (48-50). In this context, results from the PREDIMED-NAVARRA (PREvención con DIeta MEDiterránea-NAVARRA) study evidenced that diet significantly modulates telomere length, as an inverse relationship was observed between obesity parameters (body weight, body mass index, waist circumference and waist to height ratio) and telomere length (48). Telomeres may also retain potential value as risk biomarkers for MCI and AD, as shorter telomere length has been associated with several age-related chronic degenerative diseases (51, 52). Nonetheless, to the best of our knowledge there are no studies which evaluate telomere modulation by MeD in patients with cognitive decline.
The “case” of resveratrol is interesting, because this polyphenol (contained in grapes, some nuts and dried fruits, and red wine) exerts beneficial effects in in vitro models of neudegenerative diseases, including AD, Parkinson and Huntington’s disease, epilepsy, amyotrophic lateral sclerosis, although results on animal and patients are still lacking (53). In particular, its neuroprotective actions appear almost in part related to the  activation of  the sirtuins’ family member SIRT1 (35, 54-56). Increased SIRT1 expression has been related to antioxidant upregulation, and downregulation of pro-apoptotic factors through the involvement of Forkhead box O transcription factors (Fox01-06) and oxidative stress reduction (35). Moreover, SIRT1 negatively regulates p66Shc, that increases intracellular ROS levels through an oxidoreductase activity, and NF-κB, whose DNA-binding capacity is decreased by deacetylation of its RelA/p subunit by SIRT1 (35). Thus, SIRT1, with these multiple effects, has been related to different critical pathways, regarding metabolic control, DNA repair, apoptosis, cell survival, development, inflammation, and mitochondrial function (35). Other components of the MeD can also induce SIRT1, such as the polyphenol quercetin contained in red wine and red onions (57), and polyphenols in extra virgin olive oil (58). Thus, all these nutrients and the MeD as a whole deserve to be evaluated in future studies as safe, no-pharmacological SIRT1 activating tools, able to elicit endogenous neural protection, and preventing MCI and AD.
Interestingly, the emerging interaction between genes and dietary intake may account for the complex interplay between individual shape and external environment, and supports the concept of variable individual response to nutrition and heterogeneity in cognitive ageing. Accordingly, the benefits on lipid profile of fish oil fatty acids eicosapentaenoic and docosahexaenoic acids consumption appeared dependent on Apolipoprotein E genotypes in the 2340 subjects enrolled in the Multi-Ethnic Study of Atherosclerosis (mean age 61±10 yrs) (59).  Moreover, cognitive decline in T2D appears associated with an altered metabolomic profile involving sphingolipids, bile acids, and uric acid metabolism (60). Very interesting experimental data showed how maternal folate depletion and high-fat feeding from weaning may affect DNA methylation and DNA repair in brain of adult mouse, causing a high sensibility to oxidative damage (61). Nutritional interventions in the adulthood could positively counteract epigenetic changes, including DNA methylation and microRNA, associated with ageing (62). However, recent data, obtained on 9-week-old C57BL/6J mice exposed to a high-fat diet for 15 weeks, showed the development of diet-induced obesity and insulin resistance with the onset of irreversible epigenetic modifications in the brain, which persisted also if normal metabolic homeostasis is restored (63). In this context, results from the NU-AGE multidisciplinary consortium of 30 partners from 17 European Union countries are expected. This study aims to evaluate whether a one-year Mediterranean whole diet can affect  physical and cognitive status in the elderly (65-79 years of age), utilizing biomarkers obtained by a series of analyses, including omics (transcriptomics, epigenetics, metabolomics and metagenomics) (64).

Mediterranean life-style and cognitive decline in elderly

The MeD was recognized by UNESCO in 2010 as a cultural heritage of Humanity (65). Although nutritional elements represent the core of the Mediterranean lifestyle, several other aspects must be considered, such as para-dietetic (gastronomy, preparation, setting) and meta-dietetic (cultural) features of MeD, which impact on health and well-being (66). In particular, the Mediterranean Diet Foundation has evidenced additive aspects other than food, such as conviviality, socialization, biodiversity and seasonality, and moderate physical activity as essential complement to the MeD dietary pattern (66). Specifically, frugality and moderation in food consumption characterized this life-style. There is biodiversity and use of seasonal, traditional food, short production and distribution line. In the traditional MeD life-style, family has lunch together at home, food was consumed slowly, with a glass of red wine, conviviality and conversation ensured, together with periods of calm and mid-day rest. Outdoor living included walking, gardening and raising their own vegetables. Moreover, they cook their own food for their own pleasure and others’ satisfaction. The conviviality aspect of eating is important, as it contributes to increase communication and socialization, whereas dedicate time to food preparation and intensify multisensory stimulation through tactile, gustative, and visual stimuli are also involved.  This cluster of factors related in the MeD life-style has not yet been evaluated in association to cognitive function and various diseases. However, it is known that these parameters are individually important to cognitive function and may interact synergistically. Socialization and physical activity resulted associated to a better cognitive performance (67).Very recent studies address mealtimes, seen as an opportunity for social interaction, which may improve health and behavior in elderly people (68-70). Food and physical activity are two parameters closely interrelated, which represent complementary aspects of the energy balance that have regulated brain development and function during human evolution. The combination of an healthy dietary approach together with appropriate physical activities during life-time can contribute to maintain or decrease cognitive impairment (71). Accordingly, dietary factors, such as a diet poor in saturated fat, and exercise have been evidenced as important determinants of cognitive performance in experimental and human studies, modulating neuronal and behavioral plasticity through the increase of BDNF levels (72, 73).


Although there are still main pitfalls (Table 1), available data suggested that amount, content of food, meal frequency and context might represent a non-pharmacological, low-cost, and low-tech interventional option, effective as environmental inducer of brain plasticity for the prevention and improvement of cognitive function across all life span.
The possible use of oxidative and inflammatory biomarkers to assess the relationship between diet and cognitive impairment and neurological disease is also promising, although a substantial amount of work remains in terms of replicating the few findings already available. The rapid development of metabolomics and other “omics” techniques may increase the chance of finding relevant parameters for a more reliable and accurate assessment of association of food intake with functional status and disease risk profile markers. In the future, the identification and use of nutritional biomarkers could allow to estimate more accurately nutritional assumption and requirement, taking into account bioavailability and individual differences, and opening innovative approaches for personalized nutritional plan of cognitive decline prevention.
More important, scientific and clinical medical advances had created a profound evolution in the concept of health, that according to WHO is defined as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”. In this context, the concept of synergy between factors within the Mediterranean pattern -which include not only nutritional, but also physical, social, and stimulating aspects- offers the opportunity to evaluate the impact of a comprehensive lifestyle cluster. Such holistic approach may help to develop more efficacious interventional strategies  promoting wellbeing across the life span and preventing disabilities and cognitive impairment  beyond the biological, psychological and social barriers that aging implies.

Conflict of interest: None



1.    Hardman RJ, Kennedy G, Macpherson H, Scholey AB, Pipingas A. A randomized controlled trial investigating the effects of Mediterranean diet and aerobic exercise on cognition in cognitively healthy older people living independently within aged care facilities: the Lifestyle Intervention in Independent Living Aged Care (LIILAC) study protocol (ACTRN12614001133628). Nutr J 2015;14:53.
2.    Vega JN, Newhouse PA. Mild cognitive impairment: diagnosis, longitudinal course, and emerging treatments. Curr Psychiatry Rep. 2014;16:490.
3.    Hartshorne JK, Germine LT. When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psychol Sci 2015;26:433-43.
4.    Burkhalter TM, Hillman CH. A narrative review of physical activity, nutrition, and obesity to cognition and scholastic performance across the human lifespan. Adv Nutr 2011;2:201-6S.
5.    Bhate V, Deshpande S, Bhat D, Joshi N, Ladkat R, Watve S, et al. Vitamin B12 status of pregnant Indian women and cognitive function in their 9-year-old children. Food Nutr Bull 2008;29:249-54.
6.    Bonilla C, Lawlor DA, Taylor AE, Gunnell DJ, Ben-Shlomo Y, Ness AR, et al. Vitamin B-12 status during pregnancy and child’s IQ at age 8: a Mendelian randomization study in the Avon longitudinal study of parents and children. PLoS One 2012;7:e51084.
7.    Villamor E, Rifas-Shiman SL, Gillman MW, Oken E. Maternal intake of methyl-donor nutrients and child cognition at 3 years of age. Paediatr Perinat Epidemiol 2012;26:328-35.
8.    Waber DP, Bryce CP, Girard JM, Zichlin M, Fitzmaurice GM, Galler JR. Impaired  IQ and academic skills in adults who experienced moderate to severe infantile malnutrition: a 40-year study. Nutr Neurosci. 2014;17:58-64.
9.    Momtaz YA, Haron SA, Hamid TA, Ibrahim R, Masud J. Does food insufficiency in  childhood contribute to dementia in later life? Clin Interv Aging 2014;10:49-53.
10.    Starr JM, Pattie A, Whiteman MC, Deary IJ, Whalley LJ. Vitamin B-12, serum folate, and cognitive change between 11 and 79 years. J Neurol Neurosurg Psychiatry 2005;76:291-2
11.    Zhu N, Jacobs DR, Meyer KA, He K, Launer L, Reis JP, et al. Cognitive function in a middle aged cohort is related to  higher quality dietary pattern 5 and 25 years earlier: the CARDIA study. J Nutr Health Aging 2015;19:33-38.
12.    Kaptan Z, Akgün-Dar K, Kapucu A, Dedeakayoğulları H, Batu Ş, Üzüm G. Long term consequences on spatial learning-memory of low-calorie diet during adolescence in female rats; hippocampal and prefrontal cortex BDNF level, expression of NeuN and cell proliferation in dentate gyrus. Brain Res 2015 Jun 10. (doi: 10.1016/j.brainres.2015.05.041. PubMed PMID: 26072462)
13.    Haan MN, Wallace R. Can dementia be prevented? Brain aging in a population-based context. Annu Rev Public Health 2004;25:1–24.
14.    Brookmeyer R, Gray S, Kawas C. Projections of Alzheimer’s disease in the United States and the public health impact of delaying disease onset. Am J Public Health 1998;88:1337-42.
15.    Barnes JL, Tian M, Edens NK, Morris MC. Consideration of nutrient levels in studies of cognitive decline. Nutr Rev 2014;72:707-19.
16.    Doets EL, Ueland PM, Tell GS, Vollset SE, Nygård OK, Van’t Veer P, et al. Interactions between plasma concentrations of folate and markers of vitamin B(12) status with cognitive performance in elderly people not exposed to folic acid fortification: the Hordaland Health Study. Br J Nutr 2014;111:1085-95.
17.    Morris MS, Selhub J, Jacques PF. Vitamin B-12 and folate status in relation to decline in scores on the mini-mental state examination in the framingham heart study. J Am Geriatr Soc 2012;60:1457-64.
18.    Moore EM, Ames D, Mander AG, Carne RP, Brodaty H, Woodward MC, et al. Among vitamin B12 deficient older people, high folate levels are associated with  worse cognitive function: combined data from three cohorts. J Alzheimers Dis 2014;39:661-8.
19.    van der Zwaluw NL, Dhonukshe-Rutten RA, van Wijngaarden JP, Brouwer-Brolsma EM, van de Rest O, In’t Veld PH, et al. Results of 2-year vitamin B treatment on cognitive performance: secondary data from an RCT. Neurology 2014;83:2158-66.
20.    Tangney CC, Tang Y, Evans DA, Morris MC. Biochemical indicators of vitamin B12 and folate insufficiency and cognitive decline. Neurology 2009;72:361-7.
21.    Brickman AM, Khan UA, Provenzano FA, Yeung LK, Suzuki W, Schroeter H, et al. Enhancing dentate gyrus function with dietary flavanols improves cognition in older adults. Nat Neurosci. 2014;17:1798-803.
22.    Lamport DJ, Pal D, Moutsiana C, Field DT, Williams CM, Spencer JP, et al.  The effect of flavanol-rich cocoa on cerebral perfusion in healthy older adults during conscious resting state: a placebo controlled, crossover, acute trial. Psychopharmacology (Berl). 2015 Jun 7. (Epub ahead of print) PubMed PMID: 26047963.
23.    Ide K, Yamada H, Takuma N, Park M, Wakamiya N, Nakase J, et al. Green tea consumption affects cognitive dysfunction in the elderly: a pilot study. Nutrients 2014;6:4032-42.
24.    Noguchi-Shinohara M, Yuki S, Dohmoto C, Ikeda Y, Samuraki M, Iwasa K, et al. Consumption of green tea, but not black tea or coffee, is associated with reduced risk of cognitive decline. PLoS One 2014;9:e96013.
25.    Suter PM, Haller J, Hany A, Vetter W. Diuretic use: a risk for subclinical thiamine deficiency in elderly patients. J Nutr Health Aging. 2000;4:69-71.
26.    Nazmi A, Weatherall M, Wilkins B, Robinson GM. Thiamin concentration in geriatric hospitalized patients using frusemide. J Nutr Gerontol Geriatr. 2014;33:47-54.
27.    Lengyel CO, Whiting SJ, Zello GA. Nutrient inadequacies among elderly residents of long-term care facilities. Can J Diet Pract Res. 2008;69:82-8.
28.    Koh F, Charlton K, Walton K, McMahon AT. Role of dietary protein and thiamine  intakes on cognitive function in healthy older people: a systematic review. Nutrients. 2015;7:2415-39.
29.    Wengreen H, Munger RG, Cutler A, Quach A, Bowles A, Corcoran C, et al. Prospective study of Dietary Approaches to Stop Hypertension- and Mediterranean-style dietary patterns and age-related cognitive  change: the Cache County Study on Memory, Health and Aging. Am J Clin Nutr 2013;98:1263-71.
30.    Tangney CC, Li H, Wang Y, Barnes L, Schneider JA, Bennett DA, et al. Relation of DASH- and Mediterranean-like dietary patterns to cognitive decline in older persons. Neurology 2014;83:1410-6.
31.    Pérez-López FR, Chedraui P, Haya J, Cuadros JL. Effects of the Mediterranean diet on longevity and age-related morbid conditions. Maturitas 2009;64:67-79.
32.    van de Rest O, Berendsen AA, Haveman-Nies A, de Groot LC. Dietary patterns, cognitive decline, and dementia: a systematic review. Adv Nutr. 2015;6:154-68.
33.    Singh B, Parsaik AK, Mielke MM, Erwin PJ, Knopman DS, Petersen RC, et al. Association of mediterranean diet with mild cognitive impairment and Alzheimer’s  disease: a systematic review and meta-analysis. J Alzheimers Dis 2014;39:271-82.
34.    Psaltopoulou T, Sergentanis TN, Panagiotakos DB, Sergentanis IN, Kosti R,  Scarmeas N. Mediterranean diet, stroke, cognitive impairment, and depression: A meta-analysis. Ann Neurol 2013;74:580-91.
35.    Chatzianagnostou K, Del Turco S, Pingitore A, Sabatino L, Vassalle C. The Mediterranean Lifestyle as a Non-Pharmacological and Natural Antioxidant for Healthy Aging. Antioxidants 2015;4: 719-36.
36.    Cooper C, Sommerlad A, Lyketsos CG, Livingston G. Modifiable predictors of dementia in mild cognitive impairment: a systematic review and meta-analysis. Am  J Psychiatry. 2015;172:323-34.
37.    Solfrizzi V, Panza F. Mediterranean diet and cognitive decline. A lesson from the whole-diet approach: what challenges lie ahead? J Alzheimers Dis. 2014;39:283-6.
38.    Monti MC, Margarucci L, Tosco A, Riccio R, Casapullo A. New insights on the interaction mechanism between tau protein and oleocanthal, an extra-virgin olive-oil bioactive component. Food Funct. 2011; 2:423-8.
39.    Bajerska J, Woźniewicz M, Suwalska A, Jeszka J. Eating patterns are associated with cognitive function in the elderly at risk of metabolic syndrome from rural areas. Eur Rev Med Pharmacol Sci 2014;18:3234-45.
40.    Ashby-Mitchell K, Peeters A, Anstey KJ. Role of dietary pattern analysis in determining cognitive status in elderly Australian adults. Nutrients 2015;7:1052-67.
41.    Azzini E, Polito A, Fumagalli A, Intorre F, Venneria E, Durazzo A, et al. 2011. Mediterranean  Diet Effect: an Italian picture. Nutr J 10:125. doi: 10.1186/1475-2891-10-125
42.    González-Guardia L, Yubero-Serrano EM, Delgado-Lista J, Perez-Martinez P, Garcia-Rios A, Marin C, et al. Effects of the Mediterranean diet supplemented with coenzyme q10 on metabolomic profiles in elderly men and women. J Gerontol A Biol  Sci Med Sci. 2015;70:78-84.
43.    Hernanz A, De la Fuente M, Navarro M, Frank A. Plasma aminothiol compounds, but not serum tumor necrosis factor receptor II and soluble receptor for advanced glycation end products, are related to the cognitive impairment in Alzheimer’s disease and mild cognitive impairment patients. Neuroimmunomodulation. 2007;14:163-7.
44.    Borroni B, Di Luca M, Padovani A. Predicting Alzheimer dementia in mild cognitive impairment patients. Are biomarkers useful? Eur J Pharmacol. 2006;545:73-80.
45.    Bermejo P, Martín-Aragón S, Benedí J, Susín C, Felici E, Gil P, et al. Peripheral levels of glutathione and protein oxidation as markers in the development of Alzheimer’s disease from Mild Cognitive Impairment. Free Radic Res. 2008;42:162-70.
46.    Mangialasche F, Polidori MC, Monastero R, Ercolani S, Camarda C, Cecchetti R, et al. Biomarkers of oxidative and nitrosative damage in Alzheimer’s disease  and mild cognitive impairment. Ageing Res Rev. 2009 ;8:285-305.
47.    Gu Y, Luchsinger JA, Stern Y, Scarmeas N. Mediterranean diet, inflammatory and metabolic biomarkers, and risk of Alzheimer’s disease. J Alzheimers Dis. 2010;22:483-9.
48.    García-Calzón S, Gea A, Razquin C, Corella D, Lamuela-Raventós RM, Martínez JA, et al. Longitudinal association of telomere length and obesity indices in an intervention study with a Mediterranean diet: the PREDIMED-NAVARRA trial. Int J Obes (Lond) 2014;38: 177–82.
49.    Boccardi V, Esposito A, Rizzo MR, Marfella R, Barbieri M, Paolisso G. Mediterranean diet, telomere maintenance and health status among elderly. PLoSOne. 2013;8:e62781.
50.    Sabatino L, Botto N, Borghini A, Turchi S, Andreassi MG. Development of a new  multiplex quantitative real-time PCR assay for the detection of the mtDNA(4977)deletion in coronary artery disease patients: a link with telomere shortening. Environ Mol Mutagen. 2013;54:299-307.
51.    Roberts RO, Boardman LA, Cha RH, Pankratz VS, Johnson RA, Druliner BR, et al. Short and long telomeres increase risk  of amnestic mild cognitive impairment. Mech Ageing Dev. 2014;141-142:64-9.
52.    Zhan Y, Song C, Karlsson R, Tillander A, Reynolds CA, Pedersen NL, et al. Telomere Length Shortening and Alzheimer Disease-A Mendelian Randomization Study. JAMA Neurol. 2015;72:1202-3.
53.    Rocha-González HI, Ambriz-Tututi M, Granados-Soto V. Resveratrol: a natural compound with pharmacological potential in neurodegenerative diseases. CNS Neurosci Ther. 2008;14:234-47.  
54.    Bastianetto S, Ménard C, Quirion R. Neuroprotective action of resveratrol. Biochim Biophys Acta. 2015;1852:1195-201.
55.    Braidy N, Jayasena T, Poljak A, Sachdev PS. Sirtuins in cognitive ageing and Alzheimer’s disease. Curr Opin Psychiatry. 2012;25:226-30.
56.    Shindler KS, Ventura E, Rex TS, Elliott P, Rostami A. SIRT1 activation confers neuroprotection in experimental optic neuritis. Invest Ophthalmol Vis Sci. 2007;48:3602-9.
57.    Hung CH, Chan SH, Chu PM, Tsai KL. Quercetin is a potent anti-atherosclerotic  compound by activation of SIRT1 signaling under oxLDL stimulation. Mol Nutr Food  Res Jul 23. doi: 10.1002/mnfr.201500144
58.    Bayram B, Ozcelik B, Grimm S, Roeder T, Schrader C, Ernst IM, et al. A diet rich in olive oil phenolics reduces oxidative stress in the heart of SAMP8 mice by induction of Nrf2-dependent gene expression. Rejuvenation Res 2012 15:71-81.
59.    Liang S, Steffen LM, Steffen BT, Guan W, Weir NL, Rich SS, et al. APOE genotype modifies the association between plasma omega-3 fatty acids and plasma lipids in the Multi-Ethnic Study of Atherosclerosis (MESA). Atherosclerosis 2013;228:181-7.
60.    Zhang L, Li M, Zhan L, Lu X, Liang L, Su B, et al. Plasma metabolomic profiling of patients with diabetes-associated cognitive decline. PLoS One. 2015;10:e0126952.
61.    Langie SA, Achterfeldt S, Gorniak JP, Halley-Hogg KJ, Oxley D, van Schooten FJ, et al. Maternal folate depletion and high-fat feeding from weaning affects DNA methylation and DNA repair in brain of adult offspring. FASEB J. 2013;27:3323-34.
62.    Bacalini MG, Friso S, Olivieri F, Pirazzini C, Giuliani C, Capri M, et al. Present and future of anti-ageing epigenetic diets. Mech Ageing Dev. 2014;136-137:101-15.
63.    Wang J, Freire D, Knable L, Zhao W, Gong B, Mazzola P, et al. Childhood and adolescent obesity and long-term cognitive consequences during aging. J Comp Neurol 2015;523:757-68.
64.    Santoro A, Pini E, Scurti M, Palmas G, Berendsen A, Brzozowska A, et al; NU-AGE Consortium. Combating inflammaging through a Mediterranean whole diet approach: the NU-AGE project’s conceptual framework and design. Mech Ageing Dev. 2014;136-7:3-13.

65.    UNESCO. Representative list of the intangible cultural heritage of humanity.
66.    Bach-Faig A, Berry EM, Lairon D, Reguant J, Trichopoulou A, Dernini S, et al. Mediterranean Diet Foundation Expert Group. Mediterranean diet pyramid today. Science and cultural updates. Public Health Nutr 2011;14:2274-84.
67.    Gallucci M, Antuono P, Ongaro F, Forloni PL, Albani D, Amici GP, et al. Physical activity, socialization and reading in the elderly over the age of seventy: what is the relation with cognitive decline? Evidence from «The Treviso  Longeva (TRELONG) study». Arch Gerontol Geriatr 2009;48:284-6.
68.    Holm L, Lund TB, Niva M. Eating practices and diet quality: a population study of four Nordic countries. Eur J Clin Nutr 2015;69:791-8.
69.    Keller H, Carrier N, Duizer L, Lengyel C, Slaughter S, Steele C. Making the most of mealtimes (M3): grounding mealtime interventions with a conceptual model. J Am Med Dir Assoc 2014;15:158-61.
70.    Whear R, Abbott R, Thompson-Coon J, Bethel A, Rogers M, Hemsley A, et al. Effectiveness of mealtime interventions on behavior symptoms of people with dementia living in care homes: a systematic review. J Am  Med Dir Assoc 2014;15:185-93.
71.    Yannakoulia M, Kontogianni M, Scarmeas N. Cognitive health and Mediterranean diet: just diet or lifestyle pattern? Ageing Res Rev 2015;20:74-8.
72.    Murphy T, Dias GP, Thuret S. Effects of diet on brain plasticity in animal and human studies: mind the gap. Neural Plast 2014;2014:563160.
73.    Gomez-Pinilla F, Gomez AG. The influence of dietary factors in central nervous system plasticity and injury recovery. PM R 2011;3:S111-6.


K.J. Anstey1, R. Eramudugolla1, D.E. Hosking1, N.T. Lautenschlager2,3, R.A. Dixon4

1. Centre for Research on Ageing, Health and Wellbeing, Research School of Population Health, Australian National University; 2. Academic Unit for Psychiatry of Old Age, St. Vincent’s Health, Department of Psychiatry, University of Melbourne; 3. School of Psychiatry and Clinical Neurosciences & WA Centre for Health and Ageing, University of Western Australia; 4. Department of Psychology, University of Alberta, Canada

Corresponding Author: Kaarin J. Anstey, Centre for Research on Ageing, Health and Wellbeing, Florey Building 54, Mills Road, Australian National University, Canberra, ACT 0200, Australia  Tel: (612) 61258410; Fax: (612) 61251558; Email:

J Prev Alz Dis 2015;2(3):189-198
Published online June 16, 2015,


Dementia risk reduction is a global health and fiscal priority given the current lack of effective treatments and the projected increased number of dementia cases due to population ageing. There are often gaps among academic research, clinical practice, and public policy. We present information on the evidence for dementia risk reduction and evaluate the progress required to formulate this evidence into clinical practice guidelines. This narrative review provides capsule summaries of current evidence for 25 risk and protective factors associated with AD and dementia according to domains including biomarkers, demographic, lifestyle, medical, and environment. We identify the factors for which evidence is strong and thereby especially useful for risk assessment with the goal of personalising recommendations for risk reduction. We also note gaps in knowledge, and discuss how the field may progress towards clinical practice guidelines for dementia risk reduction.

Key words: Risk factor, Alzheimer disease, cognitive decline, prevention, risk assessment.  


Until effective therapeutics for Alzheimer’s disease (AD) are available, secondary prevention is an important focus for health professionals in relation to dementia. Secondary prevention encompasses risk reduction in those with multiple established risk factors for dementia, as well as interventions to slow progression of cognitive decline in adults with cognitive impairment or minor neurocognitive disorders (1). The development and integration of risk assessment and clinical risk management for AD and dementia is emerging rapidly in the research field, with the development of online risk assessment tools, education programs and lifestyle interventions (2, 3). However, for this research to have substantial public policy improvements, it must be translated, tested, and evaluated in clinical and community settings.  At present, risk management, including risk reduction and protection elevation, is the only available approach with potential for a large impact on the projected rates of dementia given population aging. The emerging focus on the development and implementation of efficacious dementia risk reduction protocols is consistent with best practice as applied to other chronic conditions or diseases and it has the advantages of being relatively inexpensive and translatable.   

The development of clinical practice guidelines and policies relating to dementia prevention requires a sound evidence base on risk and protective factors, as well as a framework for applying advice to relevant population-subgroups. This review provides an overview of 25 risk and protective factors for late-life AD and dementia and then describes how this knowledge may inform the development of risk assessment procedures and risk management targets in the context of clinical practice guidelines.

Multidomain assessment of risk and protective factors for AD

Many studies report research on risk and protective factors in relation to AD, while some only report results in relation to a general outcome of dementia. In this article we consult published meta-analyses and large-scale cohort studies to note which risk and protective factors have been linked to AD specifically or to AD and dementia more generally. There are fewer reports relating risk and protective factors specifically to vascular cognitive impairment because the prevalence of distinct vascular dementia (VaD) is lower than AD: hence, this review does not address that outcome.  There is often both vascular and Alzheimer pathology contributing to neurocognitive disorders. Notably, a lack of precision in diagnosis is a feature of the observational research on which much of the risk and protective factors have been based.

Established risk and protective factors for AD and dementia come from several domains, exacerbating the complexity of conducting a thorough risk assessment. That some factors may operate interactively or synergistically increases the need for careful interpretation of risk profiles. Assessing multiple domains of risk simultaneously permits an evaluation of overall risk profile, including the development of panels of risk factors, risk factor composite scores, and interactions among two or more synergistic risk factors.  Systematic review and meta-analysis of the current state of knowledge of risk factors for dementia is beyond the scope of a single article. We present a summary of the current knowledge in this area, drawing on key review articles, meta-analyses (1, 4-8) and individual papers, with more detail provided on recent findings on diet and dementia risk, which is an emerging area of interest.

Our framework for linking risk and protective factors to individual patient outcomes is depicted in Figure 1 (adapted from (9)). This figure shows the relationships between risk and protective factors for dementia, and clinical assessment goals. As depicted in the first column, there are five clusters of risk and protective factors, including biomarkers (not reviewed in this article), demographic factors, lifestyle, medical and environmental risk.  The second column illustrates that these factors flow together in individual cases and likely interact (synergistically, interactively, complementary) in unique ways to lead to individualized outcomes.  The third column identifies three general clusters of cognitive/clinical status outcomes, including relatively healthy brain and cognitive aging, typical non-demented cognitive trajectories, and cognitive impairment and dementia.  Subsequently, the fourth column presents an associated direction of personalized consultation regarding risk-related recommendations.  For healthy brain aging, advice will focus on sustained protective support and risk reduction.  For typical or normative aging, advice should focus on risk reduction and increase in protective behaviors.  For preclinical dementia or MCI, advice may focus on immediate risk control and perhaps reduction, especially targeting modifiable factors in the medical and lifestyle domains. Table 1 summarises the information on risk and protective factors as organized into the same demographic, lifestyle, medical and environmental domains.

Table 1. Summary of findings on risk and protective factors for AD and dementia


The role of biomarkers: Indicators of mechanisms

Risk and protective factors exert detectable and potentially manageable influence on the course of neurodegenerative disease through relatively diffuse or as yet undetermined biological mechanisms.  In some cases, biological markers (biomarkers) can be linked to somewhat more specific biological pathways associated with cognitive impairment, AD, and dementia.  It is likely that biomarkers will play an increasingly important role in risk assessment in the future (eg (10)).  Given the heterogeneity of aetiologies, mechanisms and phenotypes of dementia, biomarkers and their pathways may be ultimately used in compiling risk profiles for groups or individuals. Notably, modifiable risk factors that interact with specific biomarkers offer specific opportunities for early alterations in the course of the disease. Although a review of biomarkers for AD is beyond the scope of this article, the information provided by some well-known and typically accessible biomarkers can provide crucial supplemental information for the overall dementia risk profile (11). However, it is known that at autopsy, a large minority of individuals who died without dementia have AD neuropathology (12, 13); hence, detection of disease processes does not necessarily mean that an individual will develop AD.  This uncertainty does not change the need to target preventive strategies among at-risk individuals but indicates that individuals with biomarkers for AD may not necessarily express the clinical symptoms. (Autosomal mutations are an exception.)  As with all diseases that have multiple risk factors, where prediction (prior to diagnosis) is never entirely accurate, and which develop after decades of gradual accumulation of pathology, a comprehensive risk appraisal is required.

Figure 1. Model of Multidomain influences on major cognitive phenotypes in ageing including AD and dementia.Risk assessment and consultation goals are indicated for each phenotype

Adapted from Dolcos et al (2012)

Eventually, biomarkers of common pathogenic processes leading to brain ageing and concomitant Alzheimer pathology may provide a framework by which to organise risk and protective factors. For example, markers of inflammation are evident in a number of conditions that increase risk of AD including abdominal obesity, Type II diabetes and exposure to air pollution. However, most useful for present purposes would be information pertaining to genetic variants with known and elevated risk for AD. A recent large meta-analysis of genome-wide association studies in those of European ancestry found 11 new susceptibility loci for AD (14). The current state of knowledge indicates important supplemental information for constructing risk profiles could include (at least) APOE ε4 status.  For this article, we turn attention to risk and protective functions from the four domains of factors that operate through indirect (and often modifiable) pathways.

The demographic domain

Demographic risk factors include both modifiable and determinable non-modifiable characteristics, and enable profiling of population sub-groups at increased risk of dementia using population-level characteristics. Risk of AD and dementia strongly increases with chronological age (15), and in most countries is higher for women than men (15, 16). Low levels of formal school education increases the risk of AD and dementia (17). At present it is unknown whether increasing levels of education later in life confers the same protection as equivalent years of education obtained earlier in life. Higher levels of education appear to be associated with high level of cognitive function into late life, but not with reduced rate of decline (18).  In a related area, results have been inconsistent regarding bilingualism as a possible protective factor against late onset dementia. Although some evidence has suggested bilinguals have a delayed onset of dementia due to increased cognitive reserve (19) others have studied samples including monolinguals and bilinguals and found no difference in rate of cognitive decline or onset of dementia (20). A demographic characteristic that is rarely discussed in detail is race. It appears that specific racial and ethnic groups have higher rates of AD risk factors.  Some groups may have a higher or lower risk in relation to specific biological risk factors such as APOE (21), with evidence the APOE ε4 allele does not influence dementia progression in sub-Saharan Africans. Among developing countries, prevalence estimates of dementia for adults aged 65 and older are higher in certain Asian and Latin American countries, but are low (1-3%) in India and sub-Saharan Africa (22).  A recent study has shown that adults of Hispanic origin have earlier onset of dementia than non-Hispanics, adjusting for APOE genotype (23).  However, not enough data are available to produce quantitative pooled estimates of these effects.  Far more research is required to evaluate how risk profiles vary by race and ethnicity, which may potentially explain significant variation in the strength of specific genetic, medical or lifestyle factors as risk or protective in relation to AD. However, there is now sufficient evidence to incorporate age and sex into risk scores for incident dementia.

Lifestyle domain

Lifestyle-related risk factors for AD and dementia have been the focus of much recent research due to their modifiability. The prime lifestyle factors for which there is a body of evidence in relation to dementia risk include physical activity, diet, smoking, cognitive engagement and social engagement. 

Physical activity

There is consistent evidence that physical activity is associated with reduced AD and dementia risk, with higher levels of activity associated with the lowest risk (24). The benefits of physical activity for cognitive health appear to accumulate over the life course. For example, higher fitness levels in young adulthood has been linked with better cognitive outcomes in mid-adulthood (25), and better midlife fitness has been linked to reduced risk of late-life dementia (26). However, there is also evidence that taking up physical activity in old age can still impact positively on cognitive and functional performance (27). The effect of physical activity on brain ageing and neurodegeneration is also corroborated by neuroimaging studies and intervention studies (28, 29) and intervention durations of 6 months and longer are reported as being  more effective than shorter durations (30). To date, the majority of positive findings of trials are from samples of cognitively healthy older adults. A much smaller number of trials to date focused on trials with at-risk populations, especially those with subjective memory complaints or mild cognitive impairment. Some studies have reported significant benefits in the cognitive domains of attention, executive functions and memory (31, 32); however, other reports did not demonstrate such benefits (33). The inconsistency in results highlights the need for more high-quality later randomized controlled trials and a number of those are currently under way.

Dietary components and dietary patterns

The dietary component with the strongest link to AD and dementia risk reduction is oily fish, with three or more servings a week being associated with lower risk (34, 35).  Studies have consistently shown a relationship between low levels of alcohol intake (rather than abstinence) and reduced risk of AD, dementia and cognitive decline (36, 37). However, it is possible that this association partly reflects selection bias. Specifically, abstainers may include former heavy drinkers, with resultant poor cognitive and general health, and heavy drinkers are less likely to persist in longitudinal studies (38) . There is some evidence that n-3 fatty acids (39) and Vitamin B  may be beneficial for those in the early stages of decline although a recent meta-analysis of 11 trials found no cognitive benefits associated with Vitamin B supplementation (40).

The Mediterranean diet (MeDi) (Figure 2) was shown in a meta-analysis of five studies conducted with over 2-8 years follow-up to be associated with 33% reduced risk of cognitive impairment (MCI or AD) (41) and adherence to the MeDi has also been associated with reduced cognitive decline (42, 43). The cognitive benefits of the MeDi were confirmed by a 5-year randomised controlled trial (RCT). Those who consumed a MeDi supplemented with extra-virgin olive oil or mixed nuts had higher mean Mini-Mental State Examination (MMSE) scores and Clock Drawing Test scores than those who consumed a low fat control diet (44). The DASH diet (Dietary Approaches to Stop Hypertension) (45) includes whole grains, poultry, fish, and nuts and is reduced in saturated fats, red meats, sweets, and sugar-containing beverages. The two studies that have investigated associations between DASH dietary patterns and cognitive decline both found the DASH diet to be protective against cognitive decline (43, 46).

Figure 2. The Traditional Mediterranean Diet Pyramid

Adapted from Willett 1995 et al. (Copyright 1994 Oldways Preservation & Exchange Trust)


Smoking in late-life has been shown to increase the risk of AD, VaD and dementia (47, 48) and it is inferred from this that smoking earlier in adulthood is also associated with increased risk, although specific data on this are presently lacking. Smoking cessation is associated with less late-life (over 70) cognitive decline and brain atrophy than continued smoking (49) providing strong support for advising patients to cease smoking even at older ages.

Cognitive engagement 

Engaging in cognitively stimulating activities in late life (e.g., reading, playing puzzles and attending museums and concerts) is associated with a lower risk of AD and dementia (50, 51).  However effective dosage and type of cognitive activity are not yet known, and to date, there is no reliable evidence for an effect of cognitive training programs on delaying dementia. Research on the benefits of cognitive engagement is often confounded, as individuals with higher initial cognitive ability also engage in more cognitively stimulating lifestyles.

Social engagement

There is consistent evidence that higher levels of social engagement are associated with reduced risk of AD and dementia (52, 53), even in adults with the APOE e4 genotype (54), and there is RCT evidence that it increases brain volume (55). Social engagement measures include different types of relationships, living arrangements, size and quantity of social networks and amount of social activities.

Medical domain

Cardiometabolic risk factors in midlife have been linked to late-life cognitive decline, AD and late-life dementia (56, 57) but late-life cardiometabolic risk factors have less clear associations with dementia. The link between abnormally high or low blood pressure in late life and dementia risk is inconsistent (58), with some evidence that low blood pressure may increase cognitive decline through reduced perfusion (59).  While high blood pressure increases risk of stroke, and stroke is a strong risk factor for dementia (60), high blood pressure in late life has not been consistently linked with cognitive decline or AD. Findings relating blood pressure to risk of AD and dementia are complex, and influenced by methodological issues such as the length of follow up, whether or not treatment is evaluated, and whether trajectories are modelled that examine both increasing and decreasing hypertension at different stages of the adult life-course (58). Support has not been found in systematic reviews for a link between hypertension and AD (61). It is possible that the inconsistency associated with blood pressure and AD is due to the measure of blood pressure used in cohort studies. Peripheral hypertension is usually measured, and yet in old age, peripheral hypertension has a low correlation with central hypertension, which is the true risk factor for cerebrovascular changes and AD (62). In general, it appears that high blood pressure in midlife may represent a risk for dementia in later life (63). If untreated, hypertension in middle age that increases into old age may increase dementia risk, although a decline in blood pressure is seen in the period prior to the development of AD.

A recent systematic review of atrial fibrillation (AF) as a risk factor for cognitive impairment (defined as MMSE<24) or any type of dementia (DSM-IV criteria) identified an increased risk associated with AF both with and without history of stroke, despite study heterogeneity (64).

Stroke has also been considered as a risk factor for AD even after controlling for the presence of other cardiometabolic risk factors such as hypertension, diabetes and heart disease (65, 66). Individuals with a history of stroke had an earlier onset as well as a higher incidence of AD relative to those without a stroke history, although the risk was highest in those with recognized vascular risk factors. It is possible that a stroke may accelerate or bring above threshold the level of neuropathology and cognitive impairment required for progression to AD in those with mild or sub-clinical pathology (65).  In terms of other forms of dementia and vascular dementia in particular, a meta-analysis of 30 studies (66) reported that even after adjusting for other vascular risk factors, recurrent stroke increases the prevalence of dementia, with a rise in incidence after each additional stroke, suggesting stroke contributes significantly to the pathology leading to dementia over and above existing cardiovascular risk.

There is consistent evidence that elevated blood glucose (including Type II diabetes) increases the risk for cognitive decline, AD and dementia (67), and that this risk is independent of other cardiometabolic risk factors associated with diabetes (68, 69). There is also emerging evidence that pre-diabetic and sub-clinical levels of high blood glucose also predict cognitive decline and dementia (69, 70) although there have been no meta-analyses of this association.

Obesity and being overweight during midlife has been associated with increased late-life AD and dementia risk (71) with midlife obesity conferring double the risk of late-life AD.  The relationship between late-life obesity and dementia risk is unclear (71, 72) with the balance of evidence presently suggesting that it is not associated with increased risk. One study has shown that weight loss predicted AD similarly in overweight and normal weight adults, indicating that trajectory of weight loss rather than BMI was predictive of dementia in older adults (73).

High serum cholesterol during midlife is associated with elevated AD and dementia risk (74); however, this relationship is not consistently evident for high cholesterol in late life (75).

Systematic reviews have shown that clinically diagnosed depression and depressive symptoms, in both midlife and late-life, are each consistently associated with elevated risk of AD, cognitive decline and dementia (76-78). In late-life, evidence suggests that late-onset depression may also represent a prodrome of AD (79). Altogether these findings suggest that screening for depression is an essential component of risk profiling for AD.

Head injury during adulthood is associated with increased AD and dementia risk, specifically where the injuries were moderate or severe and occurred frequently (80-82). Repetitive concussion and head injuries as experienced by boxers is also associated with risk of developing a distinct neurodegenerative syndrome (83-85). Although not modifiable, information on history of head injury may contribute to an overall picture of a patient’s accumulated lifetime exposure to risks for AD.

Elevated plasma homocysteine increases the risk of vascular disease and stroke (86) and has been associated, albeit inconsistently, with poorer cognitive performance and dementia risk (87). Homocysteine levels increase with age and are dependent on Vitamin B metabolism (86). A number of trials in older adults have tested whether lowering homocysteine by vitamin B supplementation slows cognitive decline (40). Those with MCI and higher initial baseline homocysteine levels demonstrated better cognitive and clinical outcomes (88) and reduced brain atrophy (89). Recent preliminary evidence also suggests that the impact of homocysteine on older-age cognition may be dependent on its interactive effects with cholesterol (90).

There is some evidence from observational studies that certain classes of drugs such as anti-hypertensives, statins (91, 92), non-steroidal anti-inflammatories (NSAIDS) (93)  (but see (94)) and hormone replacement therapy (HRT) (95), are associated with reduced AD and dementia risk. However, RCTs are either lacking or, apart from some isolated findings, do not generally support dementia risk reduction through statin therapy (96) , the use of anti-hypertensives (97), or anti-inflammatories (98). A recent follow-up of RCTs indicate that HRTs have a complex pattern of risks and benefits for women’s health (99) and are thus not recommended for dementia prevention. One large study showed increased risk of dementia associated with NSAIDS (94). In one review of hypertensives, a significant effect (-18% incidence) was found for diuretic or dihydropyridine calcium channel blockers as part of active treatment for hypertension, although the overall pooled effect of hypertensives was not significant (100). Therefore, at this point in time, while medication is recommended to treat medical conditions associated with risk of AD, we lack high quality evidence of their role in prevention. On the other hand, the reduction in rates of dementia observed recently in several countries has been speculatively attributed in part to better management of cardiovascular risk factors, better health care and increased levels of education (101) .

Drugs with anticholinergic properties are used for treating common medical conditions such as asthma, urinary incontinence, seasonal allergies, insomnia, depression, and other psychiatric conditions (102, 103). Age-related decrease in cholinergic receptors and in the increase in blood-brain barrier permeability (102) increase the risk of anticholinergic medication causing cognitive impairment. The Adult Changes in Thought study found that a 10-year cumulative dose-response relationship was demonstrated between anticholinergic drug use and increased risk for all-cause dementia and AD. Associations remained robust across sub-group analyses and subclasses of anticholinergic medication use (103). These findings add weight to earlier clinical recommendations that elderly patients using anticholinergic medication should be monitored for cognitive dysfunction and if adverse effects are suspected, medications could be withdrawn (102).

Environmental domain

There is evidence for increased risk of dementia (Parkinson’s Disease with Dementia, and Alzheimer’s Disease and other dementias) in individuals exposed to very high levels of pesticides (104, 105). While there is insufficient data to date on the link between air pollution and dementia risk, there is some evidence high levels of air pollution is associated with greater cognitive impairment in older adults (106-108) and air pollution has been associated with AD neuropathology (108).

Conclusion: From risk assessment to risk reduction

This review has evaluated evidence pertaining to 25 factors that have been associated in epidemiological literature with increased risk of AD and dementia in some studies. Some of these factors are now considered uncontroversial risk or protective factors for AD (109) despite the lack of RCT evidence. The way in which knowledge about risk factors for AD and dementia is obtained does not map well onto the hierarchical model of the widely used GRADE system for ranking the quality of evidence. This is because most of the information on dementia risk is epidemiological and not experimental. However, clinical practice guidelines typically use systems such as GRADE in their development using consensus among experts. We argue that in the field of dementia prevention, it will be necessary to carefully consider the optimal methods of grading evidence so that routinely prioritising RCTs may not be the best approach. It is more appropriate that bodies of evidence are considered holistically or integratively (animal models, short-term RCTs, long term epidemiological studies, neuropathological evidence) in relation to putative risk factors and their mechanisms. Adopting rigorous yet realistic criteria is likely to be the most pragmatic approach to developing guidelines while evidence is still being collated and evaluated in this field.

Our review demonstrates that the multiple domains of risk and protection are populated by a variety of specific factors that independently or interactively may contribute to incident dementia in older adults. The body of evidence continues to grow and understanding of risk factors is becoming increasingly nuanced with (a) synergistic and modifier effects being increasingly addressed in observational research and (b) more specific (rather than global) questions being addressed in clinical trials. Broader questions that can now start to be addressed include consideration of the requirements to develop specific clinical practice guidelines for dementia prevention, and methods for evaluating risk assessment for AD and dementia.

To move this field towards evidence-based clinical practice guidelines, research needs to provide more specific information on the quantity or dose of factors that are required for protection. For example, specific prescriptions of physical activity, or specific dietary advice, should be further evaluated in trials. Recent findings from multidomain trials will be able to guide research and personal advice (110, 111). In some cases the qualitative types or range of activities that are protective is not known. For example, the value of brain training compared with a lifestyle of active reading has never been evaluated, and water-based physical activity has rarely been evaluated in relation to level and trajectory of cognitive function in aging. This lack of specific information reduces the clarity with which practical advice may be developed and distributed to individuals.

For risk factors such as blood glucose, more specific guidelines on levels of risk are required, as recent research shows that even within the normal range, variation in blood glucose is associated with future risk of dementia (70). Clinical practice guidelines also need to take account of the patient’s age and life course, considering the cumulative or synergistic effects of risk factors on other risk factors and outcomes that may ultimately increase late-life dementia risk or protection. For example, obesity in young adulthood may increase the risk of Type II Diabetes in mid-adulthood which in turn increases the risk of dementia in late adulthood.

Risk assessment for AD is now possible using well researched questionnaires, medical tests, checklists and online tools. There are now tools available to address risk in a range of ages and circumstances, from the population level in middle age, through to risk among older adults with brain atrophy and impaired IADLs. Risk assessment tools provide clinicians with validated means of assessing risk or identifying areas where protection may be increased, when they are administered to the appropriate group.

There is not yet a standard practice in this field but the evidence is now strong enough to support personalized recommendations for risk reduction by increasing levels of education in young adulthood, increasing physical, cognitive and social activity throughout adulthood, reducing cardiovascular risk factors including diabetes in middle-age, through lifestyle and medication, treating depression, adopting a healthy diet and physical activity, avoiding pesticides and heavy air pollution and teaching avoidance of all potential dangers to brain health while enhancing potential protective factors.

Now that risk assessment for dementia is possible, researchers and policy makers can also start to identify markers of success in dementia prevention interventions. We have previously argued that risk reduction, as opposed to dementia prevention, is a more realistic and useful immediate goal when focussing on the majority of the population who are middle-aged and have no cognitive symptoms (112). At the individual level, risk reduction is the most meaningful outcome, as the time frames for dementia prevention are so long and it is not possible to estimate the true contribution of genetic risk factors at the individual level. At the population level, estimates of incidence and prevalence over decades remain the most objective measures of disease burden; however, there are other factors that may indicate success in prevention strategies. These include delaying the age of onset of dementia, slowing the progression of disease and reducing the overall health burden associated with dementia.

Acknowledgements: KJA is funded by NHMRC Research Fellowship # 1002560. RAD is supported in part by a Canada Research Chair (Tier 1) and a grant from the National Institutes of Health (NIA; R01 AG008235). The research is supported by the Dementia Collaborative Research Centres. DH is funded by Australian Research Council Centre of Excellence in Population Ageing Research (#CE110001029).

Conflict of interests: Authors have no conflicts of interest to declare.


1. Fratiglioni L, Qiu C. Prevention of cognitive decline in ageing: dementia as the target, delayed onset as the goal. Lancet Neurol. 2011;10:778-9.

2. Barnes DE, Covinsky KE, Whitmer RA, Kuller LH, Lopez OL, Yaffe K. Commentary on «Developing a national strategy to prevent dementia: Leon Thal Symposium 2009.» Dementia risk indices: A framework for identifying individuals with a high dementia risk. Alzheimer’s & Dementia 2010;6:138-41.

3. Solomon A, Mangialasche F, Richard E, Andrieu S, Bennett DA, Breteler M, et al. Advances in the prevention of Alzheimer’s disease and dementia. J Intern Med. 2014;275:229-50.

4. Qiu C, Kivipelto M, von Strauss E. Epidemiology of Alzheimer’s disease: occurrence, determinants, and strategies toward intervention. Dialogues Clin Neurosci. 2009;11:111-28.

5. Ritchie K, Carriere I, Ritchie CW, Berr C, Artero S, Ancelin ML. Designing prevention programmes to reduce incidence of dementia: prospective cohort study of modifiable risk factors. BMJ. 2010;341:c3885.

6. Barnes DE, Covinsky KE, Whitmer RA, Kuller LH, Lopez OL, Yaffe K. Predicting risk of dementia in older adults: The late-life dementia risk index. Neurol. 2009;73:173-9.

7. Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol. 2011;10:819-28.

8. Williams JW, Plassman BL, Burke J, Benjamin S. Preventing Alzheimer’s disease and cognitive decline. Evid Rep Technol Assess (Full Rep). 2010:1-727.

9. Dolcos S, MacDonald SW, Braslavsky A, Camicioli R, Dixon RA. Mild cognitive impairment is associated with selected functional markers: integrating concurrent, longitudinal, and stability effects. Neuropsychology. 2012;26:209-23.

10. Waragai M, Hata S, Suzuki T, Ishii R, Fujii C, Tokuda T, et al. Utility of SPM8 plus DARTEL (VSRAD) ombined with magnetic resonance spectroscopy as djunct techniques for screening and predicting dementia due to Alzheimer’s disease in clinical practice. J Alzheimers Dis. 2014.

11. Hall JR, Wiechmann AR, Johnson LA, Edwards M, Barber RC, Cunningham R, et al. The impact of APOE status on relationship of biomarkers of vascular risk and systemic inflammation to neuropsychiatric symptoms in Alzheimer’s disease. J Alzheimers Dis. 2014.

12. Katzman R, Terry R, DeTeresa R, Brown T, Davies P, Fuld P, et al. Clinical, pathological, and neurochemical changes in dementia: a subgroup with preserved mental status and numerous neocortical plaques. Ann Neurol. 1988;23:138-44.

13. Stern Y. Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord. 2006;20:S69-74.

14. Lambert J-C, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45:1452-8.

15. Jorm AF, Dear KB, Burgess NM. Projections of future numbers of dementia cases in Australia with and without prevention. Aust N Z J Psychiatry. 2005;39:959-63.

16. Alzheimer Disease International. World Alzheimer’s Report. In: Prince M, Jackson J, editors. London: King’s College London; 2009.

17. Caamaño-Isorna F, Corral M, Montes-Martínez A, Takkouche B. Education and dementia: a meta-analytic study. Neuroepidemiology. 2006;26:226-32.

18. Zahodne LB, Glymour MM, Sparks C, Bontempo D, Dixon RA, MacDonald SW, et al. Education does not slow cognitive decline with aging: 12-year evidence from the Victoria longitudinal study. J Int Neuropsychol Soc. 2011;17:1039-46.

19. Bialystok E, Craik FI, Luk G. Bilingualism: consequences for mind and brain. Trends Cogn Sci. 2012;16:240-50.

20. Zahodne LB, Schofield PW, Farrell MT, Stern Y, Manly JJ. Bilingualism does not alter cognitive decline or dementia risk among Spanish-speaking immigrants. Neuropsychology. 2014;28:238-46.

21. Murrell JR, Price B, Lane KA, Baiyewu O, Gureje O, Ogunniyi A, et al. Association of apolipoprotein E genotype and Alzheimer disease in African Americans. Arch Neurol. 2006;63:431-4.

22. Kalaria RN, Maestre GE, Arizaga R, Friedland RP, Galasko D, Hall K, et al. Alzheimer’s disease and vascular dementia in developing countries: prevalence, management, and risk factors. Lancet Neurol. 2008;7:812-26.

23. Fitten LJ, Ortiz F, Fairbanks L, Bartzokis G, Lu P, Klein E, et al. Younger age of dementia diagnosis in a Hispanic population in southern California. Int J Geriatr Psychiatry. 2014.

24. Hamer M, Chida Y. Physical activity and risk of neurodegenerative disease: a systematic review of prospective evidence. Psychol Med. 2009;39:3-11.

25. Zhu N, Jacobs DR, Jr., Schreiner PJ, Yaffe K, Bryan N, Launer LJ, et al. Cardiorespiratory fitness and cognitive function in middle age: The CARDIA Study. Neurol. 2014;82:1339-46.

26. Defina LF, Willis BL, Radford NB, Gao A, Leonard D, Haskell WL, et al. The association between midlife cardiorespiratory fitness levels and later-life dementia: a cohort study. Ann Intern Med. 2013;158:162-8.

27. Almeida OP, Khan KM, Hankey GJ, Yeap BB, Golledge J, Flicker L. 150 minutes of vigorous physical activity per week predicts survival and successful ageing: a population-based 11-year longitudinal study of 12 201 older Australian men. Br J Sports Med. 2014;48:220-5.

28. Erickson KI, Raji CA, Lopez O, Becker JT, Rosano C, Newman AB, et al. Physical activity predicts gray matter volume in late adulthood. Neurology. 2010;75:1415-22.

29. Erickson KI, Voss MW, Prakash RS, Basak C, Szabo A, Chaddock L, et al. Exercise training increases size of hippocampus and improves memory. Proc Natl Acad Sci USA. 2011;108:3017-22.

30. Lautenschlager NT. Physical activity and cognition in older adults with mild cognitive impairment and dementia. Neurodegen Dis Management. 2013;3:211-8.

31. Lautenschlager NT, Cox KL, Flicker L, Foster JK, van Bockxmeer FM, Xiao J, et al. Effect of physical activity on cognitive function in older adults at risk for Alzheimer disease: a randomized trial. JAMA. 2008;300:1027-37.

32. Baker LD, Frank LL, Foster-Schubert K, Green PS, Wilkinson CW, McTiernan A, et al. Effects of aerobic exercise on mild cognitive impairment: a controlled trial. Arch Neurol. 2010;67:71-9.

33. Scherder EJ, Van Paasschen J, Deijen JB, Van Der Knokke S, Orlebeke JF, Burgers I, et al. Physical activity and executive functions in the elderly with mild cognitive impairment. Aging Ment Health. 2005;9:272-80.

34. Barberger-Gateau P, Raffaitin C, Letenneur L, Berr C, Tzourio C, Dartigues JF, et al. Dietary patterns and risk of dementia: the Three-City cohort study. Neurol. 2007;69:1921-30.

35. Huang TL, Zandi PP, Tucker KL, Fitzpatrick AL, Kuller LH, Fried LP, et al. Benefits of fatty fish on dementia risk are stronger for those without APOE ε4. Neurology. 2005;65:1409-14.

36. Anstey KJ, Mack HA, Cherbuin N. Alcohol consumption as a risk factor for dementia and cognitive decline: meta-analysis of prospective studies. Am J Geriatr Psychiatry. 2009;17:542-55.

37. Peters R, Peters J, Warner J, Beckett N, Bulpitt C. Alcohol, dementia and cognitive decline in the elderly: a systematic review. Age Ageing. 2008;37:505-12.

38. Roizen R, Fillmore K, Chikritzhs T, Stockwell T. Light-to-moderate drinking and dementia risk: the former drinkers problem re-visited. Addict Research Theory. 2013;21:181-93.

39. Freund-Levi Y, Eriksdotter-Jonhagen M, Cederholm T, Basun H, Faxen-Irving G, Garlind A, et al. Omega-3 fatty acid treatment in 174 patients with mild to moderate Alzheimer disease: OmegAD study: A randomized double-blind trial. Arch Neurol. 2006;63:1402-8.

40. Clarke R, Bennett D, Parish S, Lewington S, Skeaff M, Eussen SJ, et al. Effects of homocysteine lowering with B vitamins on cognitive aging: meta-analysis of 11 trials with cognitive data on 22,000 individuals. Am J Clin Nutr. 2014;100:657-66.

41. Singh B, Parsaik AK, Mielke MM, Erwin PJ, Knopman DS, Petersen RC, et al. Association of Mediterranean diet with mild cognitive impairment and Alzheimer’s disease: a systematic review and meta-analysis. J Alzheimers Dis. 2014;39:271-82.

42. Lourida I, Soni M, Thompson-Coon J, Purandare N, Lang IA, Ukoumunne OC, et al. Mediterranean diet, cognitive function, and dementia: a systematic review. Epidemiology. 2013;24:479-89.

43. Tangney CC, Hong L, Wang Y, Barnes L, Schneider JA, Bennett DA, et al. Relation of DASH- and Mediterranean-like dietary patterns to cognitive decline in older persons. Neurology. 2014.

44. Martínez-Lapiscina EH, Clavero P, Toledo E, Estruch R, Salas-Salvadó J, San Julián B, et al. Mediterranean diet improves cognition: the PREDIMED-NAVARRA randomised trial. J Neurol, Neurosurg & Psychiatry. 2013.

45. Vogt TM, Appel LJ, Obarzanek EVA, Moore TJ, Vollmer WM, Svetkey LP, et al. Dietary Approaches to Stop Hypertension: rationale, design, and methods. J Am Diet Assoc. 1999;99:S12-S8.

46. Wengreen H, Munger RG, Cutler A, Quach A, Bowles A, Corcoran  C, et al. Prospective study of Dietary Approaches to Stop Hypertension- and Mediterranean-style dietary patterns and age-related cognitive change: the Cache County study on memory, health and aging. Am J Clin Nutr. 2013;98:1263-71.

47. Anstey KJ, von Sanden C, Salim A, O’Kearney R. Smoking as a risk factor for dementia and cognitive decline: a meta-analysis of prospective studies. Am J Epidemiol. 2007;166:367-78.

48. Peters R, Poulter R, Warner J, Beckett N, Burch L, Bulpitt C. Smoking, dementia and cognitive decline in the elderly: a systematic review. BMC Geriatr. 2008;8:36.

49. Almeida OP, Garrido GJ, Alfonso H, Hulse G, Lautenschlager NT, Hankey GJ, et al. 24-month effect of smoking cessation on cognitive function and brain structure in later life. Neuroimage. 2011;55:1480-9.

50. Wilson RS, Scherr PA, Schneider JA, Tang Y, Bennett DA. Relation of cognitive activity to risk of developing Alzheimer disease. Neurology. 2007;69:1911-20.

51. Valenzuela M, Brayne C, Sachdev P, Wilcock G, Matthews F. Cognitive lifestyle and long-term risk of dementia and survival after diagnosis in a multicenter population-based cohort. Am J Epidemiol. 2011;173:1004-12.

52. Bassuk SS, Glass TA, Berkman LF. Social disengagement and incident cognitive decline in community-dwelling elderly persons. Ann Intern Med. 1999;131:165-73.

53. Fratiglioni L, Wang HX, Ericsson K, Maytan M, Winblad B. Influence of social network on occurrence of dementia: a community-based longitudinal study. Lancet. 2000;355:1315-9.

54. Ferrari C, Xu WL, Wang HX, Winblad B, Sorbi S, Qiu C, et al. How can elderly apolipoprotein E ε4 carriers remain free from dementia? Neurobiol Aging. 2013;34:13-21.

55. Mortimer JA, Ding D, Borenstein AR, DeCarli C, Guo Q, Wu Y, et al. Changes in brain volume and cognition in a randomized trial of exercise and social interaction in a community-based sample of non-demented Chinese elders. J Alzheimers Dis. 2012;30:757-66.

56. Kivipelto M, Helkala EL, Laakso MP, Hanninen T, Hallikainen M, Alhainen K, et al. Midlife vascular risk factors and Alzheimer’s disease in later life: longitudinal, population based study. BMJ. 2001;322:1447-51.

57. Fratiglioni L, Qiu C. Prevention of common neurodegenerative disorders in the elderly. Exp Gerontol. 2009;44:46-50.

58. Joas E, Backman K, Gustafson D, Ostling S, Waern M, Guo X, et al. Blood pressure trajectories from midlife to late life in relation to dementia in women followed for 37 years. Hypertension. 2012;59:796-801.

59. Glodzik L, Rusinek H, Pirraglia E, McHugh P, Tsui W, Williams S, et al. Blood pressure decrease correlates with tau pathology and memory decline in hypertensive elderly. Neurobiol Aging. 2014;35:64-71.

60. Dregan A, Wolfe CD, Gulliford MC. Does the influence of stroke on dementia vary by different levels of prestroke cognitive functioning?: A cohort study. Stroke. 2013;44:3445-51.

61. Power MC, Weuve J, Gagne JJ, McQueen MB, Viswanathan A, Blacker D. The association between blood pressure and incident Alzheimer disease: A systematic review and meta-analysis. Epidemiology. 2011;22:646-59.

62. Palatini P, Casiglia E, Gasowski J, Gluszek J, Jankowski P, Narkiewicz K, et al. Arterial stiffness, central hemodynamics, and cardiovascular risk in hypertension. Vascular health and risk management. 2011;7:725-39.

63. Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol. 2011.

64. Kwok CS, Loke YK, Hale R, Potter JF, Myint PK. Atrial fibrillation and incidence of dementia: a systematic review and meta-analysis. Neurology. 2011;76:914-22.

65. Honig LS, Ming-Xin T, Albert S, Costa R, Luchsinger JA, Manly JJ, et al. Stroke and the risk of Alzheimer disease. Arch Neurol. 2003;60:1707-12.

66. Pendlebury ST, Rothwell PM. Prevalence, incidence, and factors associated with pre-stroke and post-stroke dementia: a systematic review and meta-analysis. Lancet Neurol. 2009;8:1006-18.

67. Cheng G, Huang CT, Deng H, Wang H. Diabetes as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies. Intern Med J. 2012;42:484-91.

68. Biessels GJ, Staekenborg S, Brunner E, Brayne C, Scheltens P. Risk of dementia in diabetes mellitus: a systematic review. Lancet Neurol. 2006;5:64-74.

69. Lu FP, Lin KP, Kuo HK. Diabetes and the risk of multi-system aging phenotypes: a systematic review and meta-analysis. PLOS-One. 2009;4.

70. Crane PK, Walker R, Hubbard RA, Li G, Nathan DM, Zheng H, et al. Glucose levels and risk of dementia. N Engl J Med. 2013;369:540-8.

71. Anstey KJ, Cherbuin N, Budge M, Young J. Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies. Obes Rev. 2011.

72. Sabia S, Kivimaki M, Shipley M, Marmot M, Singh-Manoux A. Body mass index over the adult life course and cognition in late midlife: the Whitehall II cohort study. Am J Clin Nutr. 2009;89:601-7.

73. Power BD, Alfonso H, Flicker L, Hankey GJ, Yeap BB, Almeida OP. Changes in body mass in later life and incident dementia. Int Psychogeriatr. 2013;25:467-78.

74. Anstey KJ, Lipnicki DM, Low LF. Cholesterol as a risk factor for dementia and cognitive decline: a systematic review of prospective studies with meta-analysis. Am J Geriatr Psychiatry. 2008;16:343-54.

75. Di Paolo G, Kim T-W. Linking lipids to Alzheimer’s disease: cholesterol and beyond. Nat Rev Neurosci. 2011;12:285-96.

76. Ownby RL, Crocco E, Acevedo A, John V, Loewenstein D. Depression and risk for Alzheimer disease: systematic review, meta-analysis, and metaregression analysis. Arch Gen Psychiatry. 2006;63:530-8.

77. Barnes DE, Yaffe K, Byers AL, McCormick M, Schaefer C, Whitmer RA. Midlife vs late-life depressive symptoms and risk of dementia: differential effects for Alzheimer disease and vascular dementia. Arch Gen Psychiatry. 2012;69:493-8.

78. Diniz BS, Butters MA, Albert SM, Dew MA, Reynolds CF. Late-life depression and risk of vascular dementia and Alzheimer’s disease: systematic review and meta-analysis of community-based cohort studies. Br J Psychiatry. 2013;202:329-35.

79. Lee GJ, Lu PH, Hua X, Lee S, Wu S, Nguyen K, et al. Depressive symptoms in mild cognitive impairment predict greater atrophy in Alzheimer’s disease-telated regions. Biol Psychiatry. 2012;71:814-21.

80. Fleminger S, Oliver DL, Lovestone S, Rabe-Hesketh S, Giora A. Head injury as a risk factor for Alzheimer’s disease: the evidence 10 years on; a partial replication. J Neurol Neurosurg Psychiatry. 2003;74:857-62.

81. Mayeux R, Ottman R, Maestre GE, Ngai BS, Tang MX, Ginsberg H, et al. Synergistic effects of traumatic head injury and apolipoprotein-e4 in patients with Alzheimer’s disease. Neurology. 1995;45:555-7.

82. Plassman BL, Havlik RJ, Steffens DC, Helms MJ, Newman TN, Drosdick D, et al. Documented head injury in early adulthood and risk of Alzheimer’s disease and other dementias. Neurology. 2000;56:1158-66.

83. Jordan BD. The clinical spectrum of sport-related traumatic brain injury. Nat Rev Neurol. 2013;9:222-30.

84. Smith DH, Johnson VE, Steward W. Chronic neuropathologies of single and repetitive TBI: substrates of dementia? Nat Rev Neurol. 2013:211-21.

85. Stern RA, Danshvar DH, Baugh CM, Seichepine DR, Montenigro PH, Riley DO, et al. Clinical presentation of chronic traumatic encephalopathy. Neurology. 2013;81:1122-9.

86. The Homocysteine Studies Collaboration. Homocysteine and risk of ischemic heart disease and stroke. JAMA. 2002;288:2015-22.

87. Ho RCM, Cheung MWL, Fu E, Win H, Zaw MH, Ng A, et al. Is high homocysteine level a risk factor for cognitive decline in elderly? A systematic review, meta-analysis, and meta-regression. Am J Geriatr Psychiatry. 2011;19:607-17.

88. de Jager CA, Oulhaj A, Jacoby R, Refsum H, Smith AD. Cognitive and clinical outcomes of homocysteine-lowering B-vitamin treatment in mild cognitive impairment: a randomized controlled trial. Int J Geriatr Psychiatry. 2012;27:592-600.

89. Smith AD, Smith SM, de Jager CA, Whitbread P, Johnston C, Agacinski G, et al. Homocysteine-lowering by B vitamins slows the rate of accelerated brain atrophy in mild cognitive impairment: A randomized controlled trial. PLOS-One. 2010.

90. Cheng Y, Jin Y, Unverzagt FW, Su L, Yang L, Ma F, et al. The relationship between cholesterol and cognitive function is homocysteine-dependent. Clin Interv Aging. 2014;9:1823-9.

91. Song Y, Nie H, Xu Y, Zhang L, Wu Y. Association of statin use with risk of dementia: a meta-analysis of prospective cohort studies. Geriatr Gerontol Int. 2013;13:817-24.

92. Wong WB, Lin VW, Boudreau D, Devine EB. Statins in the prevention of dementia and Alzheimer’s disease: A meta-analysis of observational studies and an assessment of confounds. Pharmacoepidemiol Drug Saf. 2013;22:345-58.

93. Etminan M, Gill S, Samii A. Effect of non-steroidal anti-inflammatory drugs on risk of Alzheimer’s disease: systematic review and meta-analysis of observational studies. BMJ. 2003;327:128.

94. Breitner JC, Haneuse SJ, Walker R, Dublin S, Crane PK, Gray SL, et al. Risk of dementia and AD with prior exposure to NSAIDs in an elderly community-based cohort. Neurology. 2009;72:1899-905.

95. Zandi PP, Carlson MC, Plassman BL. Hormone replacement therapy and incidence of Alzheimer disease in older woment: the Cache County study. JAMA. 2002;288:2123-9.

96. Scott HD, Laake K. Statins for the prevention of Alzheimer’s disease and dementia. Cochrane database of systematic reviews. 2009;CD003160:1-11.

97. McGuiness B, O’Hare J, Craig D, Bullock R, Maloug R, Passmore P. Cochrane review on ‘Statins for the treatment of dementia’. Int J Geriatr Psychiatry. 2013;28:119-26.

98. Breitner JC, Baker LD, Montine TJ, Meinert CL, Lyketsos C, Ashe KH, et al. Extended results of the Alzheimer’s disease anti-inflammatory prevention trial. Alzheimer’s & Dementia. 2011;7:402-11.

99. Manson JE, Chlebowski RT, Stefanick ML, et al. Menopausal hormone therapy and health outcomes during the intervention and extended post-stopping phases of the women’s health initiative randomized trials. JAMA. 2013;310:1353-68.

100. Staessen JA, Thijs L, Richart T, Odili AN, Birkenhager WH. Placebo-controlled trials of blood pressure-lowering therapies for primary prevention of dementia. Hypertension. 2011;57:e6-7.

101. Matthews FE, Arthur A, Barnes LE, Bond J, Jagger C, Robinson L, et al. A two-decade comparison of prevalence of dementia in individuals aged 65 years and older from three geographical areas of England: results of the Cognitive Function and Ageing Study I and II. Lancet. 2013.

102. Campbell N, Boustani M, Limbil T, Ott C, Fox C, Maidment I, et al. The cognitive impact of anticholinergics: A clinical review. Clin Interv Aging. 2009;4:225-33.

103. Gray SL, Anderson ML, Dublin S, et al. Cumulative use of strong anticholinergics and incident dementia: A prospective cohort study. JAMA Int Med. 2015;175:401-7.

104. Hayden KM, Norton MC, Darcey D, Ostbye T, Zandi PP, Breitner JC, et al. Occupational exposure to pesticides increases the risk of incident AD: the Cache County study. Neurology. 2010;74:1524-30.

105. Priyadarshi A, Khuder SA, Schaub EA, Shrivastava S. A meta-analysis of Parkinson’s disease and exposure to pesticides. Neurotoxicology. 2000;21:435-40.

106. Chang K-H, Chang M-Y, Muo C-H, Wu T-N, Chen C-Y, Kao C-H. Increased risks of dementia in patients exposed to nitrogen dioxide and carbon monoxide: A population-based retrospective cohort study. PLOS-One. 2014.

107. Brown TP, Rumsby PC, Capleton AC, Rushton L, Levy LS. Pesticides and Parkinson’s Disease: is there a link? Environ Health Perspect. 2006;114:156-64.

108. Calderon-Garciduenas L, Kavanaugh M, Block M, D’Angiulli A, Delgado-Chavez R, Torres-Jardon R, et al. Neuroinflammation, hyperphosphorylated tau, diffuse amyloid plaques, and down-regulation of the cellular prion protein in air pollution exposed children and young adults. J Alzheimers Dis. 2012;28:93-107.

109. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol. 2014;13:788-94.

110. Ngandu T, Lehtisalo J, Solomon A, Levalahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet. 2015.

111. Vellas B, Carrie I, Gillette-Guyonnet S, Touchon J, Dantoine T, Dartigues JF, et al. MAPT study: a multidomain approach for preventing Alzheimer’s disease: design and baseline data. Journal for the Prevention of Alzheimer’s Disease. 2014;1:13-22.

112. Anstey KJ, Bahar-Fuchs A, Herath P, Rebok GW, Cherbuin N. A 12-week multidomain intervention versus active control to reduce risk of Alzheimer’s disease: study protocol for a randomized controlled trial. Trials. 2013;14:60-.

113. Kalantarian S, Stern TA, Mansour M, Ruskin JN. Cognitive impairment associated with atrial fibrillation a meta-analysis. Ann Intern Med. 2013;158:338-46.

114. Gudala K, Bansal D, Schifano F, Bhansali A. Diabetes mellitus and risk of dementia: A meta-analysis of prospective observational studies. Journal of Diabetes Investigation. 2013;4:640-50.

115. Loef M, Walach H. Midlife obesity and dementia: meta-analysis and adjusted forecast of dementia prevalence in the United States and China. Obesity. 2013;21:E51-E5.

116. Gao Y1 HC, Zhao K, Ma L, Qiu X, Zhang L, Xiu Y, Chen L, Lu W, Huang C, Tang Y, Xiao Q. Depression as a risk factor for dementia and mild cogniive impairment: a meta-analysis of longitudinal studies. Int J Geriatr Psychiatry. 2013;28:441-9.

117. Ho RCM, Cheung MWL, Fu E, Win H, Zaw MH, A. N, et al. Is high homocysteine level a risk factor for cognitive decline in elderly? A systematic review, meta-analysis, and meta-regression. Am J Geriatr Psychiatry. 2011;19:607-17.

118. Zhou B, Teramukai S, Fukushima M. Prevention and treatment of dementia or Alzheimer’s disease by statins: a meta-analysis. Dement Geriatr Cogn Disord. 2007;23:194-201.

119. Swiger KJ, Manalac RJ, Blumenthal RS, Blaha MJ, Martin SS. Statins and cognition: A systematic review and meta-analysis of short- and long-term cognitive effects. Mayo Clinic Proceedings. 2013;88:1213-21.

120. Levi Marpillat N, Macquin-Mavier I, Tropeano AI, Bachoud-Levi AC, Maison P. Antihypertensive classes, cognitive decline and incidence of dementia: a network meta-analysis. J Hypertens. 2013;31:1073-82.

121. Chang-Quan H, Hui W, Chao-Min W, Zheng-Rong W, Jun-Wen G, Yong-Hong L, et al. The association of antihypertensive medication use with risk of cognitive decline and dementia: a meta-analysis of longitudinal studies. Int J Clin Pract. 2013;65:1295-305.

122. Wang J, Tan L, Wang HF, Tan CC, Meng XF, Wang C, et al. Anti-inflammatory drugs and risk of Alzheimer’s disease: an updated systematic review and meta-analysis. J Alzheimers Dis. 2014.

123. LeBlanc ES, Janowsky J, Chan BKS, Nelson HD. Hormone replacement therapy and cognition: systematic review and meta-analysis. JAMA. 2001;285:1489-99.




T.J. Littlejohns1,2, K. Kos2, W.E. Henley2, E. Kuźma2, D.J. Llewellyn2


1. Clinical Trials Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; 2. University of Exeter Medical School,  University of Exeter, Exeter, UK

 Corresponding Author: Dr. David J. Llewellyn, University of Exeter Medical School, College House, Heavitree Road, Exeter, UK, EX1 2LU,

J Prev Alz Dis 2016;3(1):43-52
Published online June 1, 2015,


Emerging evidence suggests that low vitamin D concentrations are potentially involved in the pathogenesis of dementia. This is of particular interest when considering the high prevalence of vitamin D deficiency in elderly adults and the urgent need to identify modifiable risk factors for dementia. Studies have found that vitamin D is implicated in procognitive and neuroprotective functions, including the reduction of Alzheimer’s disease hallmarks such as amyloid beta and phosphorylated tau. Cross-sectional studies have consistently found that vitamin D concentrations are significantly lower in individuals with Alzheimer’s disease and cognitive impairment compared to healthy controls. Longitudinal studies support an association between low vitamin D concentrations and an increased risk of dementia and cognitive decline. Neuroimaging studies are beginning to uncover the potential neurodegenerative and cerebrovascular mechanisms that underlie these associations such as white matter hyperintensities and enlarged ventricular volume, although there is currently a lack of longitudinal studies. In contrast to observational studies, findings from interventional studies have produced mixed results on the benefits of vitamin D supplementation on dementia and cognitive outcomes. Interpretation of the findings from these studies is hampered by several major methodological limitations, such as small sample sizes, inadequate doses and inclusion of participants unlikely to benefit from vitamin D supplementation. There is a need for large double-blind randomised-control trials investigating whether vitamin D supplementation can halt or delay the risk of dementia-related outcomes in individuals with low vitamin D concentrations.

Key words: Vitamin D, dementia, cognitive decline, neuroimaging.


Dementia is currently recognised as a major public health priority on a worldwide scale. As a result of the ageing population, the estimated global prevalence of dementia cases is projected to treble from 35.6 million in 2010 to 115.4 million in 2050 (1). However, there is currently a lack of well-established modifiable risk factors to halt or delay the progression of the disease. Recently, vitamin D has emerged as a promising target for therapeutic intervention. The aim of this review is to provide an overview and discussion of the current state of evidence regarding vitamin D and dementia-related outcomes.      

Vitamin D is a fat soluble steroid like hormone which has a well-established role in maintaining bone health by regulating calcium metabolism (2). It exists in two isoforms, vitamin D2 and D3. Around 90% of vitamin D is synthesised in the skin by the action of ultraviolet radiation from sunlight exposure on the cholesterol precursor, 7-dehydrocholestorol (2,3). Other sources include diet, including fortified foods, and supplementation (4). Vitamin D is initially biologically inert and requires two separate hydroxylation steps in order to be converted into its active form. First to 25-hydroxyvitamin D (25(OH)D) in the liver and then to the active hormonal form, 1,25-dihydroxyvitamin D in the kidney (5).

The major circulating form of vitamin D is 25(OH)D which can be measured in the serum and is a useful indicator of an individual’s typical vitamin D status due to its half-life of around 15 days (6). Vitamin D status is defined using clinically relevant cut-points although there is some debate concerning which concentrations are optimal for general health (7,8). Typically, vitamin D deficiency is defined as a 25(OH)D level of less than 50 nmol/L, with severe deficiency defined as less than 25 nmol/L and insufficiency between 50 and 75 nmol/L (4). Based on these criteria approximately 1 billion individuals worldwide are either vitamin D deficient or insufficient (4). Elderly adults are particularly at risk of vitamin D deficiency for a variety of reasons, for example ageing reduces skin thickness resulting in a decrease in cutaneous concentrations of vitamin D3 precursor 7-dehydrocholesterol (9). Other reasons include a lack of adequate exposure to sunlight, decreased intake of vitamin D from diet, impaired intestinal absorption and impaired hydroxylation in the liver and kidney (10).

Emerging evidence suggests that vitamin D is involved in a wide variety of functions unrelated to bone health, such as immune function and vascular processes (11). Longitudinal studies have found that low vitamin D concentrations are also associated with an increased risk of colorectal cancer (12), type 2 diabetes (13) and cardiovascular disease (14). Furthermore, meta-analyses of randomised-controlled trials suggest that vitamin D supplementation reduces the risk of all-cause mortality (15). There is also increasing interest in the potential role of low vitamin D concentrations in the pathogenesis of cognitive decline, dementia and Alzheimer’s disease (AD) (16–18). The next section provides an overview of the multiple functions that vitamin D has been implicated in throughout the brain from in vitro and in vivo studies.

Vitamin D and the brain: animal studies and potential mechanisms

Vitamin D in the central nervous system

The central nervous system has been identified as a major target for vitamin D activity (5). The main vitamin D metabolites are present in the human cerebrospinal fluid (CSF), which circulates and surrounds the brain, and CSF concentrations are highly correlated with concentrations in blood plasma (19). Both the 1,25-dihydroxyvitamin D3 receptor and 1α-hydroxylase, the enzyme responsible for synthesizing the bioactive form of vitamin D, are widely distributed throughout the human brain. Additionally, the vitamin D receptor and enzyme are densely located in areas associated with memory and higher order cognition, such as the CA1 and CA2 regions in the hippocampus as well as the dentate gyrus, cingulate gyrus and prefrontal cortex (20).

Vitamin D and neurological functions

Vitamin D has been implicated in neuroprotective functions and appears to play an important role in brain development. In vitro studies suggest that vitamin D is involved in neurotrophic support by inducing nerve growth factors and neurite outgrowth in embryonic rat hippocampal neurons (21). In contrast, rats born to vitamin D3 deficient mothers demonstrated a pronounced reduction in nerve growth factor in addition to decreased glial-derived neurotrophic factor compared to control rats (22). Furthermore, the vitamin D3 deplete rats experienced significant negative changes in brain development as well as more cell proliferation, which is indicative of an imbalance between cell mitosis and apoptosis (22). Similarly, in another study, rats born to vitamin D3 deficient mothers had larger lateral ventricles, reduced nerve growth factor content and reduced expression of genes involved in neuronal structure compared to controls at ten weeks of age (23). This provides evidence that the deleterious effects observed at birth on the rat brain persist into adulthood. Vitamin D has also been implicated in regulating neurotransmitter levels. Rats treated with neurotoxic doses of methamphetamine, had an attenuated depletion of serotonin and dopamine when given vitamin D compared to controls (24). In rats, vitamin D treatment resulted in increased activity in choline acetyltransferase, an enzyme involved in the synthesis of the acetylcholine neurotransmitter in specific brain nuclei (25). Furthermore, vitamin D had a drastic inhibitory effect on the expression of the inducible form of nitric oxide synthase in activated microglia and astrocytes in rats, an enzyme involved in the production of nitric oxide which is implicated in the brain’s immune responses (26).

Changes in vitamin D regulated neuronal calcium (Ca2+) status have been implicated in pathological processes associated with dementia and AD (27), with evidence of dysregulated  L-Type voltage-sensitive calcium channel (L-VSCC) function (28). In rodents, administration of 1,25-dihydroxyvitamin D promotes neuroprotection and reduces the Ca2+ mediated hippocampal biomarkers of aging through the downregulation of L-VSCC activity (29,30). Serum Ca2+ concentrations are also regulated by parathyroid hormone (PTH), which is in turn involved in the conversion of 25(OH)D to 1,25-dihydroxyvitamin D (31). Furthermore, vitamin D deficiency may lead to hyperparathyroidism which has been linked with non-skeletal health outcomes, such as the loss of muscle strength and mass (32) and sudden cardiac death (33). It is plausible that high PTH concentrations could mediate the association between low vitamin D concentrations and dementia-related outcomes. The evidence, mainly from poor quality cross-sectional studies, shows an inconsistent association between high PTH concentrations and poorer cognitive outcomes. (34–40).

Vitamin D and dementia related processes 

Animal and cellular studies have also implicated vitamin D in a variety of potential protective mechanisms that are associated with dementia and AD. In vitro, vitamin D increased the phagocytic clearance of amyloid plaques by stimulating macrophages obtained from AD patients (41,42), and reduced amyloid induced cytotoxicity and apoptosis in primary cortical neurons in rat embryo neocortices (43). In aged rats, decline in learning and memory was ameliorated by vitamin D3 supplementation via a subcutaneous injection, whereas no improvement was observed in controls (44). Furthermore, in the same study, supplementation resulted in a decreased amyloid β burden and an increased clearance of amyloid as well as a reduction in the pro-inflammatory cytokine, IL-1β, and an increase in the anti-inflammatory cytokine, IL-10 (44). Similarly, vitamin D supplementation by subcutaneous injection in rats reduced cognitive decline and age-related tau phosphorylation (45). After surgery induced tissue damage to the liver, mice administered with vitamin D3 through an intraperitoneal injection, experienced a reduced risk of postoperative cognitive dysfunction which appeared to be mediated through the inhibition of inflammatory cytokines and molecules (46). In cortical cell cultures axon degeneration induced by amyloid β peptide and glutamate was reduced by vitamin D given in combination with the N-methyl-D-aspartate receptor antagonist memantine (47).

Vitamin D and cognitive decline: human observational studies

Cross-sectional studies

Epidemiological studies have expanded on the findings from animal and cellular research by investigating whether vitamin D deficiency is associated with the development of cognitive disorders in elderly adults. Several systematic reviews and meta-analyses of cross-sectional and case-control studies have found that low serum vitamin D concentrations are consistently associated with prevalent cognitive impairment, AD, and dementia (48–51). The most recent meta-analysis found that in seven case-control studies serum 25(OH)D concentrations were 1.4 standard deviations lower in a total of 357 AD patients compared to 648 controls (49). Similarly, a meta-analysis of eight studies consisting of a total of 2,749 participants, found that those who were serum 25(OH)D deficient (<50 nmol/L) scored on average 1.2 points lower on the Mini-Mental State Examination, a test of global cognitive function, than those who were serum 25(OH)D sufficient (≥50 nmol/L) (48). However, findings from cross-sectional studies should be interpreted with caution as reverse causation remains a possibility. For example, lower vitamin D concentration may be the result of behavioural changes, such as dietary changes and reduced sunlight exposure due to the onset of dementia and cognitive impairment (52).

Longitudinal studies – cognitive decline or impairment

To address this issue, several prospective studies have investigated the association between low vitamin D concentrations in elderly adults and the risk of cognitive decline and impaired cognitive functioning. Six prospective studies have found that low vitamin D concentrations are significantly associated with an increased risk of either cognitive decline (53–57) or reduced cognitive functioning (58). In contrast, in 1,604 men aged 65 years and older, there was a non-significant monotonic increase in the odds of global cognitive decline on the Modified Mini-Mental State Examination over a mean follow-up period of 4.6 years across vitamin D quartiles measured at baseline (59). Similarly, in 299 participants aged 85 years and older, there was no association between vitamin D tertiles and incident global cognitive impairment on the Standardized Mini-Mental State Examination over a 3 year period (60). However, a systematic review and meta-analysis on vitamin D concentrations and specific cognitive domains suggests a strong association between low vitamin D concentrations and a range of executive dysfunctions, such as impaired processing speed, mental shifting and information updating (50). Only a modest association was observed with episodic memory, although the few studies that explored this association were all cross-sectional. 

Longitudinal studies – dementia

Five studies have investigated the association between vitamin D and dementia-related outcomes in elderly adults, with four measuring serum 25(OH)D concentrations (61–65) and one measuring vitamin D dietary intake (66). In the latter study, in 498 women with a mean age of 79.8 years, increased vitamin D dietary intake was associated with a reduced risk of AD but not non-AD dementias over a 7 year follow-up period (66).

Three of the four studies that measured 25(OH)D concentrations found that higher 25(OH)D concentrations were associated with a reduced risk of dementia-related outcomes (61–63). In 40 high-functioning elderly women aged 75 years or older, severe vitamin D deficiency (<25 nmol/L) was associated with a higher risk of non-AD dementias but not AD compared to non-severe vitamin D deficiency (≥25 nmol/L) over 7 years (61). In contrast, in 10,186 individuals, severe vitamin D deficiency (<25 nmol/L) was associated with AD but not vascular dementia compared to those with vitamin D sufficiency over a median follow-up of 21 years (62). The first study consisted of a very small sample size which likely resulted in a lack of statistical power (61) whereas the second study relied on dementia diagnoses from unstandardized medical records which may have resulted in considerable misclassification (62). To address these limitations we performed our own analyses and investigated the association between serum 25(OH)D concentrations and comprehensive adjudicated all-cause dementia and AD diagnoses in 1,658 individuals over a follow-up of 5.6 years (63). We found that participants who were 25(OH)D deficient (≥25-50 nmol/L) at baseline had a 53% increased risk of developing all-cause dementia and a 69% increased risk of developing AD compared to those who were 25(OH)D sufficient (≥50 nmol/L). Furthermore, participants who were severely deficient (<25 nmol/L), had more than double the risk of developing all-cause dementia and AD compared to those with sufficient concentrations. We also found strong evidence of a threshold effect, where the risk of developing all-cause dementia and AD increased markedly below concentrations of around 50 nmol/L. Furthermore, the difference in dementia and AD risk by clinically relevant categories of serum 25(OH)D concentrations became apparent after 2-3 years.

However, in 133 hospitalized participants, with a mean age of 85.2 years, who were either cognitively normal or had mild cognitive impairment (MCI) at baseline, 25(OH)D concentrations were not significantly associated with dementia conversion rates over a 2 year follow-up period (65). It should be noted that the study consisted of a small sample size and short follow-up period, whilst the inclusion of unhealthy participants limits the generalizability of the study findings.

Vitamin D and cognitive decline: interventional studies

Current interventional studies

Four studies have investigated the effects of vitamin D supplementation on cognitive outcomes in elderly individuals (see Table 1), three were interventional studies (67–69) and one utilised a post-hoc design (70). Overall, three studies found that vitamin D supplementation did not improve either cognitive outcomes (67,68,70) or reduce the risk of dementia/MCI compared to controls (70). In contrast, one study found that those who received vitamin D3 supplementation (800 IU per day or 100,000 IU per month) experienced improved global cognition and executive functioning abilities over a 16 month follow-up period compared to controls (69). However, methodological weaknesses such as small sample sizes (67–69), short follow-up periods (67,68), lack of participant randomisation (67, 69) as well as heterogeneous doses of vitamin D supplementation and baseline vitamin D levels make it difficult to interpret the results of the interventional studies. The post-hoc study also has several major limitations (70). Through utilising a post-hoc design the study was not optimised to investigate the benefit of vitamin D supplementation on dementia risk. Additionally, the mean baseline 25(OH)D levels of 49 nmol/L were very close to 50 nmol/L, a concentration that we observed in our study as a potential threshold for all-cause dementia and AD risk (63). Due to the relatively high serum 25(OH)D concentrations it is unlikely that many of these individuals were at an increased risk of developing dementia and therefore it is unsurprising that no significant difference was found in comparison with the placebo group. Finally, participants in the intervention group received only 400 IU/day of vitamin D, which may be too low a dose to have an effect on dementia risk. Based on the lack of high quality clinical trials in the area it is clear that well-designed double blind randomised trials are necessary to investigate the potential of vitamin D supplementation to prevent or treat cognitive and dementia-related disorders. 

Table 1: Overview of studies investigating the effects of vitamin D supplementation on cognitive outcomes in elderly adults

Abbreviations: AD = Alzheimer’s disease, ADAS-cog = Alzheimer’s Disease Assessment Scale-Cognitive subscale; IQR = Interquartile Range, MMSE = Mini-Mental State Examination, RCT = Randomised Controlled Trial, SD = Standard Deviation, WMS-RLM = Wechsler Memory Scale-Revised Logical Memory, WHI = Women’s Health Initiative, WHISCA = Women’s Health Initiative Study of Cognitive Aging


Ongoing interventional studies

There are currently several ongoing trials in the area. The VITamin D and OmegA-3 Trial (VITAL) is a randomised, double-blind, placebo controlled trial in multi-ethnic healthy US participants which is investigating the effects of vitamin D and omega-3 supplementation for the primary prevention of cancer over a period of 5 years (71). In the VITAL-COG ancillary study, cognitive decline is being measured as a secondary outcome in 3,226 men and women aged 60 or more. The study is utilising a 2×2 factorial design, with participants randomised to a combination of 2000 IU/day of vitamin D3, 1 g/day omega-3 and a placebo. The primary endpoint of VITAL-Cog is a global composite score of cognitive decline, with two secondary endpoints measuring change over time in episodic memory and executive function score. The estimated completion date for the study is October 2017.

The DO-HEALTH study is another ongoing randomised, double-blind placebo controlled trial that aims to investigate whether a combination of 2000 IU/day vitamin D, omega-3 and physical exercise can prevent disease with five primary endpoints, one of which is cognitive decline as assessed by the Montreal Cognitive Assessment. A total of 2,152 community-dwelling participants aged 70 years and older will be assigned to a group using a 2x2x2 factorial design and will be followed for a 3 year period. Serum 25(OH)D concentrations will be measured at baseline as well as at 12, 24 and 36 months to assess adherence to treatment. The estimated completion date for the study is June 2017.

Vitamin D and cognitive decline: neuroimaging studies 

Based on the available evidence from epidemiological studies it is plausible that vitamin D deficiency could be linked with pathological changes in the brain associated with neurodegenerative and cerebrovascular disorders (72). Furthermore, investigating the association between vitamin D concentrations and neuroimaging abnormalities could provide an insight into the potential mechanisms underlying the association with dementia-related disorders.

As reviewed above, two animal studies have examined the effect of vitamin D deficiency on the structural development of the brain. Compared to control rats born to vitamin D3 sufficient mothers, rats born to vitamin D3 deficient mothers had a 30% increase in hemisphere length, which suggests defected cortex development during embryogenesis (22). Moreover, vitamin D3 deficient pups had a 200% increase in lateral ventricle volume, which is indicative of atrophy in the surrounding regions. In a similar study, rats with transient vitamin D3 deficiency during early development demonstrated enlarged lateral ventricular volume in adulthood compared to control rats (23).

There have been several cross-sectional studies investigating the association between vitamin D concentrations and a variety of structural neurological and cerebrovascular outcomes in elderly individuals (see Table 2). Overall, the available evidence from eleven cross-sectional studies appears to indicate that higher vitamin D concentrations are associated with a lower prevalence of neuroimaging abnormalities, particularly white matter hyperintensities (73–75), greater white matter volume (76) and decreased ventricular volume (77). It should be noted however that reverse causality remains a concern. As a result, prospective studies are necessary in order to determine the temporal relationship.

Table 2: Overview of cross-sectional neuroimaging studies investigating the association between vitamin D and neuroimaging outcomes in elderly adults

Abbreviations: aCMI = amnestic Mild Cognitive Impairment, AD = Alzheimer’s Disease, DLB = Dementia with Lewy Bodies, MCI = Mild; Cognitive Impairment, SCI = Subjective Cognitive Impairment, SD = Standard Deviation; *   Population characteristics apply to analytic samples unless otherwise stated; †  Characteristic details only available for original study sample size of 318; ‡  Characteristic details only available for original study sample size of 104; §  Characteristics details only available for original study sample size of 75  

To date only one prospective study has investigated the association between baseline vitamin D concentrations and the risk of developing future neuroimaging abnormalities in elderly adults (78). In 888 participants with a mean age of 62.3 years, lower vitamin D concentrations were not associated with change in white matter hyperintensity volume, incident white matter hyperintensities or incident infarcts over a follow-up period of approximately 10 years (78). The study also stratified the main analyses into black and white participants and found a similar lack of association. Whilst 888 participants at follow up is a large sample size, there was a high rate of participant drop out between the first MRI scan (n=1,622) and the second MRI scan (n=888) which might have biased the findings through non-random attrition. Statistical methods to account for the missing data and non-random attrition might have helped to address this source of potential bias. In order to confirm the relationship of neurodegenerative and/or cerebrovascular changes/impairments that underlie the association between vitamin D and dementia, further prospective studies are necessary.

Limitations of current evidence and future aims

There are several limitations of the current evidence base regarding vitamin D and its potential association with cognitive decline, dementia and AD. There is a lack of uniformity in the operationalization of vitamin D concentrations in observational studies, with a variety of different cut-points used to investigate the association with cognitive and neuroimaging outcomes. This is perhaps unsurprising considering there is an ongoing debate surrounding what the optimal vitamin D concentrations are for general health. In the US the Institute of Medicine (7) recommends serum 25(OH)D concentrations of ≥50 nmol/L as sufficient whereas the Endocrine Society (8) recommends concentrations ≥75 nmol/L. There is a need to standardise clinically relevant 25(OH)D cut-points in order to aid the interpretation of the research literature.

Serum 25(OH)D concentrations have been measured using different techniques, mainly radioimmunoassay (RIA) or liquid chromatography-tandem mass spectrometry (LC-MS). These assays vary in precision and accuracy with evidence suggesting a low inter-assay comparability (79). A recent meta-analysis observed significant heterogeneity in the differences in vitamin D concentrations between AD patients and controls on the basis of the assay used (48). Whilst LC-MS is often considered as the ‘gold standard’ for measuring 25(OH)D concentrations (80), substantial variation has been observed when comparing within-assay results from the approach (81). Consequently, efforts are currently underway to ensure standardisation of serum 25(OH)D measurements across laboratories worldwide (82). There is also emerging evidence that free and bioavailable 25(OH)D might be a more reliable indicator of vitamin D status compared to total 25(OH)D, although these findings have been in relation to bone mineral density (83,84). It would be informative to investigate whether similar associations are observed in relation to dementia-related outcomes.

A variety of different cognitive and dementia-related outcomes have been utilised. Cognitive function has been measured using a variety of different neuropsychological tests, which makes it difficult to synthesize the evidence. Long-term prospective studies have focused on either global cognitive function or executive functioning. There is a need for prospective studies to investigate the association between vitamin D and other cognitive domains, particularly memory which appears to be more weakly associated in cross-sectional studies. The majority of current prospective studies that have investigated the association with dementia-related outcomes have been limited by either small sample sizes (61, 65) or a reliance on medical records to inform dementia diagnosis (62). Large prospective studies which utilise comprehensive adjudicated dementia diagnoses as opposed to retrieving the diagnosis from medical records are necessary to explore the relationship with dementia-related outcomes, such as vascular dementia. Furthermore, prospective studies of longer duration exploring neuroimaging outcomes will provide useful insights into potential mechanisms as most current neuroimaging studies have been cross-sectional resulting in the possibility of reverse causation. Additionally, the findings from current cross-sectional neuroimaging studies are difficult to interpret due to the wide range of methodologies and outcomes utilised. As new studies are published with comparable methods, a meta-analysis that synthesises common outcomes would be useful to clarify the relationship between vitamin D and neuroimaging abnormalities.

Prospective studies have found that low vitamin D concentrations are associated with a wide variety of non-skeletal diseases. However, intervention studies have so far failed to show a consistent benefit of vitamin D supplementation for cognitive and dementia-related outcomes. A recent systematic review concluded that the discrepancy in findings between observational and intervention studies is likely explained by low vitamin D concentrations being a marker of ill health as opposed to a causative factor (85). It should be noted that the associations observed in prospective observational studies regarding dementia and cognitive-related disorders do not support the possibility of reverse causation, given that there has been no interaction with baseline cognition observed (53), and the link with incident dementia was not driven by ‘early converters’ (those who developed dementia within a year of follow-up) (63). Ongoing trials will provide further insight into the issue of causation in relation to dementia-related outcomes, with results expected during 2017.


Evidence from animal and cellular studies suggests that vitamin D has multiple functions throughout the central nervous system and could be implicated in the prevention and treatment of disorders such as dementia and AD. Cross-sectional and case-control studies confirm that vitamin D concentrations are lower in individuals with cognitive impairment and dementia although reverse causality remains a possibility. To address this, longitudinal studies have found that low vitamin D concentrations are associated with an increased risk of cognitive decline, all-cause dementia and AD. Future neuroimaging studies may uncover a link with specific abnormalities that could explain the observed associations between vitamin D concentrations and dementia-related disorders. Clinical trials investigating the effect of vitamin D supplementation on cognitive outcomes have produced mixed findings; however a variety of methodological weaknesses limit the interpretability of these findings. Large double-blind, randomised, placebo controlled trials are currently ongoing and should provide results within the next few years. Taken together this body of evidence suggests that vitamin D is a promising therapeutic target in the prevention and treatment of dementia and AD.

Conflict of interests: The authors have no conflicts of interests to disclose

Funding: It was provided by NIRG-11-200737 from the Alzheimer’s Association, the Mary Kinross Charitable Trust, the Halpin Trust, the Sir Halley Stewart Trust, the Age Related Diseases and Health Trust, the Rosetrees Trust (to D.J.L.) and the James Tudor Foundation (to D.J.L. and E.K.). This research was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula at the Royal Devon and Exeter NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. None of the funding sources had any role in the preparation of the manuscript.


1. Alzheimer’s Disease International. World Alzheimer Report 2009. London; 2009. 

2. DeLuca HF. Overview of general physiologic features and functions of vitamin D. Am J Clin Nutr. 2004;80:1689S – 1696S. 

3. Holick MF. Vitamin D: a millenium perspective. J Cell Biochem. 2003;88:296–307. 

4. Holick MF. Vitamin D deficiency. N Engl J Med. 2007;357:266–81. 

5. Deluca GC, Kimball SM, Kolasinski J, Ramagopalan S V, Ebers GC. The role of vitamin D in nervous system health and disease. Neuropathol Appl Neurobiol. 2013;458–84. 

6. Jones KS, Assar S, Vanderschueren D, Bouillon R, Prentice A, Schoenmakers I. Predictors of 25(OH)D half-life and plasma 25(OH)D concentration in The Gambia and the UK. Osteoporos Int. 2014; 

7. (IOM), Institute of Medicine. Dietary Reference Intakes for Calcium and Vitamin D. Dietary Reference Intakes for Calcium and Vitamin D. Washington DC: National Academies Press; 2011. 

8. Holick MF, Binkley NC, Bischoff-Ferrari HA, Gordon CM, Hanley DA, Heaney RP, Murad MH, Weaver CM. Evaluation, treatment, and prevention of vitamin D deficiency: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2011;96:1911–30. 

9. MacLaughlin J, Holick MF. Aging decreases the capacity of human skin to produce vitamin D3. J Clin Invest. 1985;76:1536–8. 

10. Janssen HCJP, Samson MM, Verhaar HJJ. Vitamin D deficiency, muscle function, and falls in elderly people. Am J Clin Nutr. 2002;75:611–5. 

11. Christakos S, Hewison M, Gardner DG, Wagner CL, Sergeev IN, Rutten E, Pittas AG, Boland R, Ferrucci L, Bikle DD. Vitamin D: beyond bone. Ann N Y Acad Sci. 2013;1287:45–58. 

12. Gandini S, Boniol M, Haukka J, Byrnes G, Cox B, Sneyd MJ, Mullie P, Autier P. Meta-analysis of observational studies of serum 25-hydroxyvitamin D levels and colorectal, breast and prostate cancer and colorectal adenoma. Int J Cancer. 2011;128:1414–24. 

13. Song Y, Wang L, Pittas AG, Del Gobbo LC, Zhang C, Manson JE, Hu FB. Blood 25-hydroxy vitamin D levels and incident type 2 diabetes: a meta-analysis of prospective studies. Diabetes Care. 2013;36:1422–8. 

14. Wang L, Song Y, Manson JE, Pilz S, März W, Michaëlsson K, Lundqvist A, Jassal SK, Barrett-Connor E, et al. Circulating 25-hydroxy-vitamin D and risk of cardiovascular disease: a meta-analysis of prospective studies. Circ Cardiovasc Qual Outcomes. 2012;5:819–29. 

15. Autier P, Gandini S. Vitamin D supplementation and total mortality: a meta-analysis of randomized controlled trials. Arch Intern Med. 2007;167:1730–7. 

16. Soni M, Kos K, Lang IA, Jones K, Melzer D, Llewellyn DJ. Vitamin D and cognitive function. Scand J Clin Lab Invest Suppl. 2012;243:79–82. 

17. Annweiler C, Dursun E, Féron F, Gezen-Ak D, Kalueff A V, Littlejohns T, Llewellyn DJ, Millet P, Scott T, et al. “Vitamin D and cognition in older adults”: updated international recommendations. J Intern Med. 2014; 

18. Schlögl M, Holick MF. Vitamin D and neurocognitive function. Clin Interv Aging. 2014;559–68. 

19. Balabanova S, Richter HP, Antoniadis G, Homoki J, Kremmer N, Hanle J, Teller WM. 25-Hydroxyvitamin D, 24, 25-dihydroxyvitamin D and 1,25-dihydroxyvitamin D in human cerebrospinal fluid. Klin Wochenschr. 1984;62:1086–90. 

20. Eyles DW, Smith S, Kinobe R, Hewison M, McGrath JJ. Distribution of the vitamin D receptor and 1 alpha-hydroxylase in human brain. J Chem Neuroanat. 2005;29:21–30. 

21. Brown J, Bianco JI, McGrath JJ, Eyles DW. 1,25-dihydroxyvitamin D3 induces nerve growth factor, promotes neurite outgrowth and inhibits mitosis in embryonic rat hippocampal neurons. Neurosci Lett. 2003;343:139–43. 

22. Eyles D, Brown J, Mackay-Sim A, McGrath J, Feron F. Vitamin d3 and brain development. Neuroscience. 2003;118:641–53. 

23. Féron F, Burne THJ, Brown J, Smith E, McGrath JJ, Mackay-Sim A, Eyles DW. Developmental vitamin D3 deficiency alters the adult rat brain. Brain Res Bull. 2005;65:141–8. 

24. Cass WA, Smith MP, Peters LE. Calcitriol protects against the dopamine- and serotonin-depleting effects of neurotoxic doses of methamphetamine. Ann N Y Acad Sci. 2006;1074:261–71. 

25. Sonnenberg J, Luine VN, Krey LC, Christakos S. 1,25-Dihydroxyvitamin D3 treatment results in increased choline acetyltransferase activity in specific brain nuclei. Endocrinology. 1986;118:1433–9. 

26. Garcion E, Nataf S, Berod A, Darcy F, Brachet P. 1,25-Dihydroxyvitamin D3 inhibits the expression of inducible nitric oxide synthase in rat central nervous system during experimental allergic encephalomyelitis. Brain Res Mol Brain Res. 1997;45:255–67. 

27. Mattson MP, Barger SW, Cheng B, Lieberburg I, Smith-Swintosky VL, Rydel RE. beta-Amyloid precursor protein metabolites and loss of neuronal Ca2+ homeostasis in Alzheimer’s disease. Trends Neurosci. 1993;16:409–14. 

28. Veng LM, Mesches MH, Browning MD. Age-related working memory impairment is correlated with increases in the L-type calcium channel protein alpha1D (Cav1.3) in area CA1 of the hippocampus and both are ameliorated by chronic nimodipine treatment. Mol Brain Res. 2003;110:193–202. 

29. Brewer LD, Thibault V, Chen KC, Langub MC, Landfield PW, Porter NM. Vitamin D hormone confers neuroprotection in parallel with downregulation of L-type calcium channel expression in hippocampal neurons. J Neurosci. 2001;21:98–108. 

30. Brewer LD, Porter NM, Kerr DS, Landfield PW, Thibault O. Chronic 1alpha,25-(OH)2 vitamin D3 treatment reduces Ca2+ -mediated hippocampal biomarkers of aging. Cell Calcium. 2006;40:277–86. 

31. Shoback D. Hypoparathyroidism. N Engl J Med. 2008;359:391–403. 

32. Visser M, Deeg DJH, Lips P. Low vitamin D and high parathyroid hormone levels as determinants of loss of muscle strength and muscle mass (sarcopenia): the Longitudinal Aging Study Amsterdam. J Clin Endocrinol Metab. 2003;88:5766–72. 

33. Deo R, Katz R, Shlipak MG, Sotoodehnia N, Psaty BM, Sarnak MJ, Fried LF, Chonchol M, De Boer IH, et al. Vitamin D, parathyroid hormone, and sudden cardiac death: results from the Cardiovascular Health Study. Hypertension. 2011;58:1021–8. 

34. Hagström E, Kilander L, Nylander R, Larsson E-M, Michaëlsson K, Melhus H, Ahlström H, Johansson L, Lind L, Ärnlöv J. Plasma parathyroid hormone is associated with vascular dementia and cerebral hyperintensities in two community-based cohorts. J Clin Endocrinol Metab. 2014;99:4181–9. 

35. Björkman MP, Sorva AJ, Tilvis RS. Does elevated parathyroid hormone concentration predict cognitive decline in older people? Aging Clin Exp Res. 2010;22:164–9. 

36. Johansson P, Almqvist EG, Johansson J-O, Mattsson N, Andreasson U, Hansson O, Wallin A, Blennow K, Zetterberg H, Svensson J. Cerebrospinal fluid (CSF) 25-hydroxyvitamin D concentration and CSF acetylcholinesterase activity are reduced in patients with Alzheimer’s disease. PLoS One. 2013;8:e81989. 

37. Kipen E, Helme RD, Wark JD, Flicker L. Bone density, vitamin D nutrition, and parathyroid hormone levels in women with dementia. J Am Geriatr Soc. 1995;43:1088–91. 

38. Ogihara T, Miya K, Morimoto S. Possible participation of calcium-regulating factors in senile dementia in elderly female subjects. Gerontology. 1990;36 Suppl 1:25–30. 

39. Shore D, Wills MR, Savory J, Wyatt RJ. Serum parathyroid hormone concentrations in senile dementia (Alzheimer’s disease). J Gerontol. 1980;35:656–62. 

40. Kalaitzidis RG, Karasavvidou D, Tatsioni A, Balafa O, Pappas K, Spanos G, Pelidou SH, Siamopoulos KC. Risk factors for cognitive dysfunction in CKD and hypertensive subjects. Int Urol Nephrol. 2013;45:1637–46. 

41. Masoumi A, Goldenson B, Ghirmai S, Avagyan H, Zaghi J, Abel K, Zheng X, Espinosa-Jeffrey A, Mahanian M, et al. 1alpha,25-dihydroxyvitamin D3 interacts with curcuminoids to stimulate amyloid-beta clearance by macrophages of Alzheimer’s disease patients. J Alzheimer’s Dis. 2009;17:703–17. 

42. Mizwicki MT, Menegaz D, Zhang J, Barrientos-Durán A, Tse S, Cashman JR, Griffin PR, Fiala M. Genomic and nongenomic signaling induced by 1α,25(OH)2-vitamin D3 promotes the recovery of amyloid-β phagocytosis by Alzheimer’s disease macrophages. J Alzheimer’s Dis. 2012;29:51–62. 

43. Dursun E, Gezen-Ak D, Yilmazer S. A novel perspective for Alzheimer’s disease: vitamin D receptor suppression by amyloid-β and preventing the amyloid-β induced alterations by vitamin D in cortical neurons. J Alzheimer’s Dis. 2011;23:207–19. 

44. Briones TL, Darwish H. Vitamin D mitigates age-related cognitive decline through the modulation of pro-inflammatory state and decrease in amyloid burden. J Neuroinflammation. 2012;9. 

45. Briones TL, Darwish H. Decrease in age-related tau hyperphosphorylation and cognitive improvement following vitamin D supplementation are associated with modulation of brain energy metabolism and redox state. Neuroscience. 2014;262:143–55. 

46. Tian A, Ma H, Cao X, Zhang R, Wang X, Wu B. Vitamin D Improves Cognitive Function and Modulates Th17/T reg Cell Balance After Hepatectomy in Mice. Inflammation. 2014; 

47. Annweiler C, Brugg B, Peyrin J-M, Bartha R, Beauchet O. Combination of memantine and vitamin D prevents axon degeneration induced by amyloid-beta and glutamate. Neurobiol Aging. 2014;35:331–5. 

48. Balion C, Griffith LE, Strifler L, Henderson M, Patterson C, Heckman G, Llewellyn DJ, Raina P. Vitamin D, cognition, and dementia: a systematic review and meta-analysis. Neurology. 2012;79:1397–405. 

49. Annweiler C, Llewellyn DJ, Beauchet O. Low serum vitamin D concentrations in Alzheimer’s disease: a systematic review and meta-analysis. J Alzheimer’s Dis. 2013;33:659–74. 

50. Annweiler C, Montero-Odasso M, Llewellyn DJ, Richard-Devantoy S, Duque G, Beauchet O. Meta-analysis of memory and executive dysfunctions in relation to vitamin D. J Alzheimer’s Dis. 2013;37:147–71. 

51. Etgen T, Sander D, Bickel H, Sander K, Förstl H. Vitamin D deficiency, cognitive impairment and dementia: a systematic review and meta-analysis. Dement Geriatr Cogn Disord. 2012;33:297–305. 

52. Dickens AP, Lang IA, Langa KM, Kos K, Llewellyn DJ. Vitamin D, cognitive dysfunction and dementia in older adults. CNS Drugs. 2011;25:629–39. 

53. Llewellyn DJ, Lang IA, Langa KM, Muniz-Terrera G, Phillips CL, Cherubini A, Ferrucci L, Melzer D. Vitamin D and risk of cognitive decline in elderly persons. Arch Intern Med. 2010;170:1135–41. 

54. Slinin Y, Paudel M, Taylor BC, Ishani A, Rossom R, Yaffe K, Blackwell T, Lui L-Y, Hochberg M, Ensrud KE. Association between serum 25(OH) vitamin D and the risk of cognitive decline in older women. J Gerontol A Biol Sci Med Sci. 2012;67:1092–8. 

55. Wilson VK, Houston DK, Kilpatrick L, Lovato J, Yaffe K, Cauley J a, Harris TB, Simonsick EM, Ayonayon HN, et al. Relationship between 25-hydroxyvitamin D and cognitive function in older adults: the Health, Aging and Body Composition Study. J Am Geriatr Soc. 2014;62:636–41. 

56. Toffanello ED, Coin A, Perissinotto E, Zambon S, Sarti S, Veronese N, De Rui M, Bolzetta F, Corti M-C, et al. Vitamin D deficiency predicts cognitive decline in older men and women: The Pro.V.A. Study. Neurology. 2014; 

57. Perna L, Mons U, Kliegel M, Brenner H. Serum 25-hydroxyvitamin D and cognitive decline: a longitudinal study among non-demented older adults. Dement Geriatr Cogn Disord. 2014;38:254–63. 

58. Breitling LP, Perna L, Müller H, Raum E, Kliegel M, Brenner H. Vitamin D and cognitive functioning in the elderly population in Germany. Exp Gerontol. 2012;47:122–7. 

59. Slinin Y, Paudel ML, Taylor BC, Fink HA, Ishani A, Canales MT, Yaffe K, Barrett-Connor E, Orwoll ES, et al. 25-Hydroxyvitamin D levels and cognitive performance and decline in elderly men. Neurology. 2010;74:33–41. 

60. Granic A, Hill TR, Kirkwood TBL, Davies K, Collerton J, Martin-Ruiz C, von Zglinicki T, Saxby BK, Wesnes K a, et al. Serum 25-hydroxyvitamin D and cognitive decline in the very old: the Newcastle 85+ Study. Eur J Neurol. 2014;25:1–11. 

61. Annweiler C, Rolland Y, Schott AM, Blain H, Vellas B, Beauchet O. Serum vitamin D deficiency as a predictor of incident non-Alzheimer dementias: a 7-year longitudinal study. Dement Geriatr Cogn Disord. 2011;32:273–8. 

62. Afzal S, Bojesen SE, Nordestgaard BG. Reduced 25-hydroxyvitamin D and risk of Alzheimer’s disease and vascular dementia. Alzheimer’s Dement. 2014;10:296–302. 

63. Littlejohns TJ, Henley WE, Lang IA, Annweiler C, Beauchet O, Chaves PHM, Fried L, Kestenbaum BR, Kuller LH, et al. Vitamin D and the risk of dementia and Alzheimer disease. Neurology. 2014;83:920–8. 

64. Knekt P, Sääksjärvi K, Järvinen R, Marniemi J, Männistö S, Kanerva N, Heliövaara M. Serum 25-hydroxyvitamin D concentration and risk of dementia. Epidemiology. 2014;25:799–804. 

65. Graf CE, Rossi C, Giannelli S V, Nobari BH, Gold G, Herrmann FR, Zekry D. Vitamin D is not associated with cognitive status in a cohort of very old hospitalized patients. J Alzheimer’s Dis. 2014;42 Suppl 3:S53–61. 

66. Annweiler C, Rolland Y, Schott AM, Blain H, Vellas B, Herrmann FR, Beauchet O. Higher vitamin D dietary intake is associated with lower risk of Alzheimer’s Disease: a 7-year follow-up. J Gerontol A Biol Sci Med Sci. 2012;1–7. 

67. Przybelski R, Agrawal S, Krueger D, Engelke JA, Walbrun F, Binkley N. Rapid correction of low vitamin D status in nursing home residents. Osteoporos Int. 2008;19:1621–8. 

68. Stein MS, Scherer SC, Ladd KS, Harrison LC. A randomized controlled trial of high-dose vitamin D2 followed by intranasal insulin in Alzheimer’s disease. J Alzheimer’s Dis. 2011;26:477–84. 

69. Annweiler C, Fantino B, Gautier J, Beaudenon M, Thiery S, Beauchet O. Cognitive effects of vitamin D supplementation in older outpatients visiting a memory clinic: a pre-post study. J Am Geriatr Soc. 2012;60:793–5. 

70. Rossom RC, Espeland M a, Manson JE, Dysken MW, Johnson KC, Lane DS, LeBlanc ES, Lederle FA, Masaki KH, Margolis KL. Calcium and vitamin D supplementation and cognitive impairment in the women’s health initiative. J Am Geriatr Soc. 2012;60:2197–205. 

71. Manson JE, Bassuk SS, Lee I-M, Cook NR, Albert MA, Gordon D, Zaharris E, Macfadyen JG, Danielson E, et al. The VITamin D and OmegA-3 TriaL (VITAL): rationale and design of a large randomized controlled trial of vitamin D and marine omega-3 fatty acid supplements for the primary prevention of cancer and cardiovascular disease. Contemp Clin Trials. 2012;33:159–71. 

72. Annweiler C, Montero-Odasso M, Muir SW, Beauchet O. Vitamin D and brain imaging in the elderly: should we expect some lesions specifically related to hypovitaminosis D? Open Neuroimag J. 2012;6:16–8. 

73. Buell JS, Weiner DE, Tucker L, Usda JM. 25-Hydroxyvitamin D, dementia, and cerebrovascular pathology in elders receiving home services. Neurology. 2010;74:18–26. 

74. Annweiler C, Annweiler T, Bartha R, Herrmann FR, Camicioli R, Beauchet O. Vitamin D and white matter abnormalities in older adults: a cross-sectional neuroimaging study. Eur J Neurol. 2014; 

75. Sakurai T, Ogama N, Toba K. Lower vitamin D is associated with white matter hyperintensity in elderly women with Alzheimer’s disease and amnestic mild cognitive impairment. J Am Geriatr Soc. 2014;62:1993–4. 

76. Hooshmand B, Lökk J, Solomon A, Mangialasche F, Miralbell J, Spulber G, Annerbo S, Andreasen N, Winblad B, et al. Vitamin D in relation to cognitive impairment, cerebrospinal fluid biomarkers, and brain volumes. J Gerontol A Biol Sci Med Sci. 2014;1–7. 

77. Annweiler C, Montero-Odasso M, Hachinski V, Seshadri S, Bartha R, Beauchet O. Vitamin D concentration and lateral cerebral ventricle volume in older adults. Mol Nutr Food Res. 2013;57:267–76. 

78. Michos ED, Carson KA, Schneider ALC, Lutsey PL, Xing L, Sharrett AR, Alonso A, Coker LH, Gross M, et al. Vitamin D and subclinical cerebrovascular disease: the atherosclerosis risk in communities brain magnetic resonance imaging study. JAMA Neurol. 2014;71:863–71. 

79. Snellman G, Melhus H, Gedeborg R, Byberg L, Berglund L, Wernroth L, Michaëlsson K. Determining vitamin D status: a comparison between commercially available assays. Gagnier JJ, editor. PLoS One. 2010;5. 

80. Zerwekh JE. Blood biomarkers of vitamin D status. Am J Clin Nutr. 2008;87:1087S – 91S. 

81. Lai JKC, Lucas RM, Banks E, Ponsonby A-L. Variability in vitamin D assays impairs clinical assessment of vitamin D status. Intern Med J. 2012;42:43–50. 

82. Binkley N, Sempos CT. Standardizing vitamin d assays: the way forward. J Bone Miner Res. 2014;29:1709–14. 

83. Powe CE, Ricciardi C, Berg AH, Erdenesanaa D, Collerone G, Ankers E, Wenger J, Karumanchi SA, Thadhani R, Bhan I. Vitamin D-binding protein modifies the vitamin D-bone mineral density relationship. J Bone Miner Res. 2011;26:1609–16. 

84. Johnsen MS, Grimnes G, Figenschau Y, Torjesen P a, Almås B, Jorde R. Serum free and bio-available 25-hydroxyvitamin D correlate better with bone density than serum total 25-hydroxyvitamin D. Scand J Clin Lab Invest. 2014;74:177–83. 

85. Autier P, Boniol M, Pizot C, Mullie P. Vitamin D status and ill health: a systematic review. Lancet Diabetes Endocrinol. 2014;2:76–89. 

86. Farid K, Volpe-Gillot L, Petras S, Plou C, Caillat-Vigneron N, Blacher J. Correlation between serum 25-hydroxyvitamin D concentrations and regional cerebral blood flow in degenerative dementia. Nucl Med Commun. 2012;33:1048–52. 

87. Bowman GL, Silbert LC, Howieson D, Dodge HH, Traber MG, Frei B, Kaye JA, Shannon J, Quinn JF. Nutrient biomarker patterns, cognitive function, and MRI measures of brain aging. Neurology. 2012;78:241–9. 

88. Annweiler C, Beauchet O, Bartha R, Graffe A, Milea D, Montero-Odasso M. Association between serum 25-hydroxyvitamin D concentration and optic chiasm volume. J Am Geriatr Soc. 2013;61:1026–8. 

89. Annweiler C, Beauchet O, Bartha R, Hachinski V, Montero-Odasso M. Vitamin D and caudal primary motor cortex: a magnetic resonance spectroscopy study. PLoS One. 2014;9:e87314. 

90. Walhovd KB, Storsve AB, Westlye LT, Drevon CA, Fjell AM. Blood markers of fatty acids and vitamin D, cardiovascular measures, body mass index, and physical activity relate to longitudinal cortical thinning in normal aging. Neurobiol Aging. 2014;35:1055–64. 



L. Feng, M.-S. Chong, W.-S. Lim, T.-S. Lee, E.-H. Kua, T.-P. Ng


Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore; the Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore; Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore; the Neurobehavioral Disorders Program, Duke-NUS Graduate Medical School, Singapore.

Corresponding Author: Lei Feng, Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore (NUS) and National University Health System (NUHS), Tel: +65 67723491 Fax: +65 67772191,

J Prev Alz Dis 2015;2(2):136-141
Published online April 8, 2015,


The availability of empirical data from human studies in recent years have lend credence to the old axiomatic wisdom that health benefits of tea drinking extend to the area of cognition. Specifically, there is increasing interest as to whether tea drinking can delay or even prevent the onset of Alzheimer’s disease (AD). Data from several cross-sectional studies have consistently shown that tea drinking is associated with better performance on cognitive tests. This association is supported by longitudinal data from the Singapore Longitudinal Aging Study, the Chinese Longitudinal Healthy Longevity Survey and the Cardiovascular Health Study. The only two published longitudinal analyses on clinical outcome reported conflicting results: one study reported that mid-life tea drinking was not associated with risk reduction of Alzheimer’s disease in late life while the other one found that green tea consumption reduced the incidence of dementia or mild cognitive impairment. Two small trials from Korea and Japan reported encouraging but preliminary results. While the existing evidence precludes a definite conclusion as to whether tea drinking can be an effective and simple lifestyle preventive measure for AD, further research involving longer-term longitudinal studies and randomized controlled trials is clearly warranted to shed light on this topic of immense public health interest. Biological markers of tea consumption and Alzheimer diseases should be employed in future research to better delineate the underlying mechanisms of tea drinking’s protective effect on cognition.

Key words: Tea, aging, Alzheimer’s disease, dementia, cognitive decline, prevention.



It was written by Lu Yu (han yu pinyin: Lù Yǔ; 733-804) in his famous book “The Classic of Tea” that “tea as a beverage was started by Shen Nong”.  This dated the history of tea drinking to about 5000 years ago, when She Nong ruled China as a legendary emperor. Today, tea is one of the most widely consumed beverages in many countries with many well recognized health benefits.

The effects of tea drinking on cognitive function in the elderly and the potential of tea as a simple yet effective lifestyle preventive measure for Alzheimer’s disease (AD) is now attracting immense research interest. Because tea is easily available, cheap and has no side effects at usual levels of consumption, the potential of reducing disease burden at the population level is considerable. There are currently 35 millions people with AD worldwide; since the principal risk factor of AD is age with its incidence doubling every 5 years after 65 years of age, the number of Alzheimer patients is expected to rise sharply with the rapidly aging demographic trend worldwide. Since AD is akin to a  chronic condition with relatively prolonged survival after diagnosis at the mild stage, even a moderate reduction of incidence rate at the population level would have a huge impact on reducing burden of health to the healthcare system and society at large.

The urgency of establishing prevention strategy for AD lies in the fact that none of the currently approved drug therapies (namely cholinesterase inhibitors and N-methyl-D-aspartate receptor antagonist) can reverse disease progression; the condition of patients who are taking these drugs remains stable for a year or more and then may decline, though at a rate that is slower than that among untreated patients (1). The search for a disease modifying agent has proved equally elusive. Results from recent large scale phase 3 trials involving disease modifying agents such as the anti-amyloid-β monoclonal antibody drugs solanezumab (2) and bapineuzumab (3) were essentially negative. The disappointing results from these studies have provided the impetus for the increasing shift in attention to non-pharmacological preventive lifestyle measures in the battle against AD.

The disease processes of AD in the brain starts at least a decade before clinical symptoms become apparent. It is well accepted that AD is a chronic and complex disease and many researchers in the field now believe that prevention may be a more promising target than treatment of established disease. With better understanding of contributing factors through previous observational studies, there has been a surge in recent years in primary prevention trials, such as the Multi-Domain Alzheimer’s Prevention Trial (MAPT), the Prevention of Dementia by Intensive Vascular Care study (PreDIVA) and  the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER). In Singapore, an early Dementia Prevention Program (DPP) has been initiated by a group of clinicians, researchers and volunteers at the Training and Research Academy at Jurong Point (TaRA@JP) (4, 5).  Since multiple factors across the life span contribute to disease outcomes in late life, it is premature to postulate that targeting any single factor in isolation would significantly reduce disease incidence, hence the rationale for the multiple-domain approach in most of prevention studies. Nevertheless, for scientific clarity, each of the candidate preventive measures should be examined in isolation with well designed observational and interventional studies. Tea is one such promising candidates.

There is strong biological basis that supports the assertion that tea drinking may be an effective preventive measure for AD. Many molecular lesions have been detected in AD, but the overarching theme that emerge from the data is that an accumulation of misfolded proteins in the aging brain results in oxidative and inflammatory damage, which in turn leads to energy failure and synaptic dysfunction (6).  Tea contains various bioactive compounds such as the catechins, theaflavins, thearubigins and L-theanine. Those  compounds may protect the brain from AD through multiple mechanisms including the actions on upstream events such as amyloid formation (7, 8) and downstream events such as oxidative stress and inflammation (9).  In this paper, we provide a brief summary of key studies that supports the role of tea in Alzheimer prevention, along with our recommendations for future research directions.

Tea for AD: evidence from observational studies

Information and main findings from observational studies are organized according to data sources. In situations where there are multiple papers from the same cohort, these are  summarized under a single section. Key findings from the longitudinal studies are summarized in Table 1.

Table 1. Longitudinal studies on the association between tea and cognitive outcome

The Tsurugaya Project 1: cross-sectional analysis (10)

 The Tsurugaya Project 1 is a community-based study among elderly Japanese living in a defined geographical location namely the Tsurugaya district, a suburban area of Sendai City in northern Japan. In this study, information on the frequency of tea consumption was collected through items in a comprehensive geriatric assessment questionnaire. Subjects were grouped into 3 categories according to their green tea consumption frequency: ≤3 cups/week, 4–6 cups/week or 1 cup/day, and ≥2 cups/day; the volume of a typical cup of green tea in Japan is 100 milliliters. Cognitive function was assessed using the Japanese language version of the Mini-Mental State Examination (MMSE). Cognitive impairment was defined as MMSE total score <26. Based on data from 1,003  subjects aged 70 years or above,  the authors reported that higher consumption of green tea was associated with a lower prevalence of cognitive impairment.  Using < or =3 cups/week as the reference group, the odds ratio (OR) was 0.62 (95% CI: 0.33, 1.19) for 4-6 cups/week or 1 cup/day, and 0.46 (95% CI: 0.30, 0.72) for > or =2 cups/day.  

Table 2. Interventional studies on tea and cognitive outcome


The Hordaland Health Study: cross-sectional analysis (11)

The Hordaland Health Study was conducted from 1997 to 1999 as a collaboration between the University of Bergen, University of Oslo, local health services and the Norwegian Institute of Public Health.  The Cognitive Sub-study of the project recruited and assessed 2,197 participants who were born in 1925-1927 using a cognitive test battery that includes the Kendrick Object Learning Test, Trail Making Test, part A (TMT-A), modified versions of the Digit Symbol Test, Block Design, MMSE, and Controlled Oral Word Association Test. Information on habitual tea intake was collected using a Food Frequency Questionnaire (FFQ). A standard cup of tea was defined as 200 milliliters and the frequency of consumption was given per day, week, or month. Data from a total of 2,031 study participants aged between 70-74 years were used in the analysis on the association between tea consumption and cognitive function.  It was found that study participants who consumed tea had significantly better mean test scores and lower prevalence of poor cognitive performance (defined as a score in the highest decile for the TMT-A and in the lowest decile for all other tests) than those who did not. The association was dose dependent and approximately linear. The sharpest dose-response effect of tea on cognitive performance was up to ~200 milliliter/day. 

The Singapore Longitudinal Aging Study (SLAS): cross-sectional and longitudinal analysis

The Singapore Longitudinal Ageing Study (SLAS) is a community-based study on health and health-related conditions in aging. The project recruited 2,808 older adults aged 55 and above in the southeast region of Singapore from 2003 to 2005 and conducted regular follow-up assessment at a time interval of approximately 2.5 years.  In the SLAS cohort, global cognitive function was assessed using a modified version of the MMSE (12).  Neuropsychological assessment was conducted for approximately one third of the study participants at baseline; details of the battery have been described elsewhere (13, 14). Information on tea consumption was collected about the habitual intake of common types of teas among local elderly using indigenous references and terms.

Analysis based on baseline data from 2,501 SLAS participants showed an inverse relationship between tea consumption and the odds of having cognitive impairment defined as a MMSE total score of less than 24 (15). Compared with the reference group of rare or no tea intake, the ORs for low, medium, and high levels of tea intake were 0.56, 0.45, and 0.37 respectively (P for linear trend < 0.001). The protective effects were most evident for black (fermented) and oolong (semi-fermented) teas, the predominant types consumed by this population. Analysis based on data from 716 SLAS subjects who had neuropsychological test data showed that tea consumption was independently associated with better performances on global cognition (B=0.055, P=0.03), memory (B=0.031, P=0.01), executive function (B=0.032, P=0.009), and information processing speed (B=0.04, P=0.001) (16). Both black/oolong tea and green tea consumption were associated with better cognitive performance. Analysis based on 1,438 SLAS subjects who had complete MMSE data at baseline and repeated MMSE at follow-up revealed reverse association between tea drinking and the odds of having cognitive decline defined as a MMSE total score drop of 1 point or greater during the follow-up period (15). Compared with the OR for rare or no tea intake, the ORs for low, medium, and high levels of tea intake were 0.74, 0.78, and 0.57 respectively (P for linear trend = 0.042).

The Chinese Longitudinal Healthy Longevity Survey (CLHLS): longitudinal analysis and gene-environment (G×E) interaction

The CLHLS is a population based cohort study of oldest-old and comparative sub-sample of younger elders in China. In the CLHLS cohort, cognitive function was measured by the verbal fluency test at baseline (n=7,139), year 2000 (n=4,081), year 2002 (n=2,288) and year 2005 (n=913) for oldest-old participants (80-115 years old) (17). Self-reported information on tea consumption habits at the age of 60 and year 1999 was collected through face-to-face interviews. The frequency was recorded as “almost every day” or “occasionally” or “rarely or never”.  In the linear mixed effects model that adjusted for age, gender, years of schooling, physical exercise and activities score, the regression coefficient for daily drinking (at age 60) and occasional drinking was 0.72 (P<0.0001) and 0.41(P=0.01) respectively. Tea drinkers had higher verbal fluency scores throughout the follow-up period . Similar results were found for current tea drinking status at the study baseline year (1998) as a predictor variable.

An interesting GxE interactions between FOXO genotypes and tea drinking was revealed in cross-sectional analysis that involved 822 CLHLS participants who were aged 92 and above (18). Associations between tea drinking and reduced cognitive disability defined using MMSE cutoffs were much stronger among carriers of the genotypes of FOXO1A-266 or FOXO3-310 or FOXO3-292 compared with noncarriers, and it was reconfirmed by analysis of three-way interactions across FOXO genotypes, tea drinking at around age 60, and at present time. 

The Cardiovascular Health Study (CHS): longitudinal analysis

The CHS is a prospective longitudinal study of cardiovascular disease in participants aged 65 years and older. In this study, 5,201 men and women were enrolled in 1989 and 1990 from a random sample of Medicare-eligible residents in four U.S. communities (19). In the CHS, cognitive performance was assessed using the Modified Mini-Mental State (3MS) examination which was administered annually up to 9 times. The relationship of tea consumption on changes in cognitive function was analyzed for 4,809 CHS participants using Linear Mixed Models. The authors reported modestly reduced rates of cognitive decline for levels of tea consumption among women, with no such effect for men. The adjusted mean rate of decline for the reference group (women who drank less than 5 cups of tea per year) was 1.30 standard 3MS points per year. Compared with the reference group, female participants drinking tea 5-10 times/year, 1-3 times/month, 1-4 times/week, and 5+ times/ week had average annual rates of decline (95% CI) of 3MSE scores that were 0.23 (-0.14-0.60), 0.44 (0.13-0.75), 0.53 (0.24-0.82), and 0.29 (0.01-0.57) points lower, respectively.

The Cardiovascular risk factors, Aging and Dementia (CAIDE) study: longitudinal analysis

The CAIDE study is the first study that examined the association between midlife tea consumption and dementia status in late life. The investigators selected a random sample from survivors of population-based random samples firstly studied within the North Karelia Project and the FINMONICA study in 1972, 1977, 1982 or 1987 and completed follow-up assessments in 1998 for 1,409 individuals who were living in the study area in Eastern Finland (in Joensuu or Kuopio) (20). A total of 61 person were identified as demented, out of which 48 had AD.  Midlife tea consumption status of CAIDE study subjects was dichotomized into those not drinking tea (0 cup/day) versus those drinking tea (at least 1 cup/day).  In the logistic regression model, the OR of dementia and AD for tea drinkers was 1.04 (95% CI 0.59–1.84) and 0.91 (95% CI 0.48–1.71) respectively.  There was no association between midlife tea consumption and the risk of AD in late life.

The Nakajima Project: longitudinal analysis (21)

The Nakajima Project is the first longitudinal study that examined the association between green tea and the incidence of dementia and MCI. It is a population-based cohort study that examined the correlations between lifestyle factors and dementia in elderly Japanese. The study was conducted in Nakajima, in the Nanao district of Ishikawa Prefecture, Japan. Among 490 Nakajima Project participants who were assessed as cognitively normal in 2007-2008 (baseline); 26 incident dementia cases and 64 mild cognitive impairment (MCI) cases were ascertained at the follow-up survey in 2011-2013.  The OR for combined incidence of dementia and MCI was 0.32 for daily green tea consumers and 0.47 for participants who consumed green tea 1-6 days per week compared with participants who did not consume green tea at baseline.  There was no association between black tea and the incidence of dementia and MCI but the number of black tea consumers was very small: only 86 (17.6%) of the participants consumed black tea at least 1 day per week, and the number of daily tea consumers was only 6 (in contrast, the number of daily green tea consumer was 157).

Tea for AD: evidence from interventional studies

 Two interventional studies exist in the literature, one from Korea and the other from Japan. The studies targeted individuals who had self-reported cognitive deficits or were previously diagnosed with dementia, and hence strictly speaking, should not be considered as primary prevention trials. Nevertheless, the results from those two trials provide important insights from the preliminary evidence and are hence included in this discussion.

The Daejeon University Oriental Hospital study: Randomized Controlled Trial (22) 

This is a randomized, double-blind, placebo-controlled study that tests the potential of LGNC-07, a nutraceutical ingredient containing green tea extract (GTE) and L-theanine, as an intervention for cognitive improvement among individual with subjective cognitive impairment. The investigators recruited ninety-one participants (25 men and 66 women, MMSE total scores ranging from 21 to 26) from Daejeon University Oriental Hospital and randomly allocated them into LGNC-07 treatment or placebo arms. Participants took two 430-mg capsules of treatment or placebo twice a day 30 minutes after meals for 16 weeks. Treatment capsules contained 360 mg of GTE, 60 mg of L-theanine, 5.7 mg of silicone dioxide, and 4.3 mg of magnesium stearate. Neuropsychological tests and electroencephalography were conducted to evaluate the effect of LGNC-07 on memory and attention. Electroencephalograms were recorded in 24 randomly selected subjects hourly for 3 hours in eye-open, eye-closed, and reading states after a single dose (LGNC-07, n = 12; placebo, n = 12).

The investigators reported that LGNC-07 led to improvement in memory by marginally increasing delayed recognition in the Rey-Kim memory test (P = 0.057). Stratified analyses showed that LGNC-07 improved memory and selective attention by significantly increasing the Rey-Kim memory quotient and word reading in the subjects with MMSE (Korean version) scores of 21-23 (LGNC-07, n = 11; placebo, n = 9). Brain theta waves, an indicator of cognitive alertness, were increased significantly in the temporal, frontal, parietal, and occipital areas after 3 hours in the eye-open and reading states.

The White Cross Nursing Home study: single arm trial

This is a small study that was conducted from July to September 2012 at the White Cross Nursing Home in Higashi-Murayama, Japan. Study participants were asked to consume green tea powder (2 grams /day, containing 227 milligrams catechins and 42 milligrams  theanine) during meals for a period of 3 months. The consumption of other supplements that could have antioxidant effects was prohibited during the intervention period and for a seven-day washout period prior to the start of the intervention. Participants were advised to maintain their customary intake of home-brewed green tea or tea beverages during the study period.

Analysis based on twelve participants (2 men, 10 women; 3 AD, 8 Vascular Dementia, 1 Dementia with Lewy bodies; mean age 88 years) who had complete data for the trial showed significant improvement in MMSE (Japanese version): the mean score improved from 15.3 ± 7.7 before intervention to 17.0 ± 8.2 (P = 0.03).  The study demonstrates that green tea improves cognitive function even at relatively low catechin and theanine concentrations which can be obtained from ordinary levels of daily green tea intake. It should be noted that the concentration of bioactive compounds in the daily dose of green tea powder employed in this study approximately equates to two to four cups of bottled or home-brewed green tea.

Conclusions and recommendations

Take together, the body of evidence as summarized above appears to support the role of tea as an effective yet simple lifestyle preventive measure for AD. Future research involving more robust study designs are warranted, especially longitudinal studies that examine clinical  outcomes and interventional studies that target individuals at high risk of developing AD. Given that tea drinking is relatively safe and have many concomitant health benefits, practitioners can generally recommend tea drinking as a simple lifestyle measure for overall cognitive health and possible benefit in AD prevention, especially in populations with an established tea-drinking culture. As for types of tea, current observational data do not support the superiority of any type of tea (such as green tea) over others (such as black tea).

It should be noted that although the neuroprotective role of tea consumption is biologically plausible, there is a lack of good data on underlying neural mechanisms from human studies. Recently, Schmidt and colleagues demonstrated that green tea extract enhanced parieto-frontal connectivity during working memory, and that the magnitude of increased connectivity was positively correlated with improvement in task performance (23). It is unknown whether long-term  tea consumption would induce functional and structural changes in the brain.  We therefore recommend that future studies should include biomarkers of Alzheimer pathophysiology such as structural and functional imaging modalities involving Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), as well as cerebrospinal fluid based biomarkers involving tau and amyloid. Biomarkers of tea intake such as catechin and theanine (24, 25) should also be monitored.

Lastly, we recommend that a large prevention trial that is adequately powered akin to the Ginkgo Evaluation of Memory (GEM) study (26, 27) be conducted to examine the preventative effect of tea drinking among subjects with normal cognition at baseline. The authors look forward to data from such a trial to confirm or reject the notion of tea in the prevention of Alzheimer’s disease.

Dr Feng received conference travel support from Amore Pacific, Republic of Korea. He is currently planning grant application on the role of tea as a preventive for dementia.  

Conflict of interests: None of the co-authors reported potential conflict of interests.


1. Mayeux R. Clinical practice. Early Alzheimer’s disease. N Engl J Med. Jun 10 2010;362(23):2194-2201.

2. Doody RS, Thomas RG, Farlow M, et al. Phase 3 trials of solanezumab for mild-to-moderate Alzheimer’s disease. N Engl J Med. Jan 23 2014;370(4):311-321.

3. Salloway S, Sperling R, Fox NC, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med. Jan 23 2014;370(4):322-333.

4. Feng L, Chiu H, Chong M-Y, Yu X, Kua E-H. Dementia in Chinese populations: Current data and future research. Asia-Pacific Psychiatry. 2011;3(3):109-114.

5. Wu D-X, Feng L, Yao S-Q, Tian X-F, Mahendran R, Kua E-H. The early dementia prevention programme in Singapore. The Lancet Psychiatry. 2014;1(1):9-11.

6. Querfurth HW, LaFerla FM. Alzheimer’s disease. N Engl J Med. Jan 28 2010;362(4):329-344.

7. Ehrnhoefer DE, Bieschke J, Boeddrich A, et al. EGCG redirects amyloidogenic polypeptides into unstructured, off-pathway oligomers. Nat Struct Mol Biol. 2008;15(6):558-566.

8. Bieschke J, Russ J, Friedrich RP, et al. EGCG remodels mature alpha-synuclein and amyloid-beta fibrils and reduces cellular toxicity. Proc Natl Acad Sci U S A. Apr 27 2010;107(17):7710-7715.

9. Song J, Xu H, Liu F, Feng L. Tea and Cognitive Health in Late Life: Current Evidence and Future Directions. Journal of Nutrition Health & Aging. Jan 2012;16(1):31-34.

10. Kuriyama S, Hozawa A, Ohmori K, et al. Green tea consumption and cognitive function: a cross-sectional study from the Tsurugaya Project 1. Am J Clin Nutr. Feb 2006;83(2):355-361.

11. Nurk E, Refsum H, Drevon CA, et al. Intake of Flavonoid-Rich Wine, Tea, and Chocolate by Elderly Men and Women Is Associated with Better Cognitive Test Performance. J. Nutr. January 1, 2009 2009;139(1):120-127.

12. Feng L, Chong MS, Lim WS, Ng TP. The Modified Mini-Mental State Examination test: normative data for Singapore Chinese older adults and its performance in detecting early cognitive impairment. Singapore Med J. Jul 2012;53(7):458-462.

13. Feng L, Ng TP, Chuah L, Niti M, Kua EH. Homocysteine, folate, and vitamin B-12 and cognitive performance in older Chinese adults: findings from the Singapore Longitudinal Ageing Study. Am J Clin Nutr. Dec 2006;84(6):1506-1512.

14. Feng L, Li J, Yap KB, Kua EH, Ng TP. Vitamin B-12, apolipoprotein E genotype, and cognitive performance in community-living older adults: evidence of a gene-micronutrient interaction. Am J Clin Nutr. Apr 2009;89(4):1263-1268.

15. Ng TP, Feng L, Niti M, Kua EH, Yap KB. Tea consumption and cognitive impairment and decline in older Chinese adults. Am J Clin Nutr. Jul 2008;88(1):224-231.

16. Feng L, Gwee X, Kua EH, Ng TP. Cognitive function and tea consumption in community dwelling older Chinese in Singapore. J Nutr Health Aging. Jun 2010;14(6):433-438.

17. Feng L, Li J, Ng TP, Lee TS, Kua EH, Zeng Y. Tea drinking and cognitive function in oldest-old Chinese. The Journal of Nutrition, Health & Aging. 2012/09/01 2012;16(9):754-758.

18. Zeng Y, Chen H, Ni T, et al. GxE Interactions Between FOXO Genotypes and Tea Drinking Significantly Affect Cognitive Disability at Advanced Ages in China. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. June 3, 2014 2014.

19. Arab L, Biggs ML, O’Meara ES, Longstreth WT, Crane PK, Fitzpatrick AL. Gender Differences in Tea, Coffee, and Cognitive Decline in the Elderly: The Cardiovascular Health Study. Journal of Alzheimers Disease. 2011;27(3):553-566.

20. Eskelinen MH, Ngandu T, Tuomilehto J, Soininen H, Kivipelto M. Midlife Coffee and Tea Drinking and the Risk of Late-Life Dementia: A Population-Based CAIDE Study. Journal of Alzheimer’s Disease. 2009;16(1):85-91.

21. Noguchi-Shinohara M, Yuki S, Dohmoto C, et al. Consumption of green tea, but not black tea or coffee, is associated with reduced risk of cognitive decline. PLoS ONE. 2014;9(5):e96013.

22. Park SK, Jung IC, Lee WK, et al. A combination of green tea extract and l-theanine improves memory and attention in subjects with mild cognitive impairment: a double-blind placebo-controlled study. J Med Food. Apr 2011;14(4):334-343.

23. Schmidt A, Hammann F, Wolnerhanssen B, et al. Green tea extract enhances parieto-frontal connectivity during working memory processing. Psychopharmacology (Berl). Mar 19 2014.

24. Soleas GJ, Yan J, Goldberg DM. Ultrasensitive assay for three polyphenols (catechin, quercetin and resveratrol) and their conjugates in biological fluids utilizing gas chromatography with mass selective detection. J Chromatogr B. Jun 5 2001;757(1):161-172.

25. Scheid L, Ellinger S, Alteheld B, et al. Kinetics of l-Theanine Uptake and Metabolism in Healthy Participants Are Comparable after Ingestion of l-Theanine via Capsules and Green Tea. The Journal of Nutrition. December 1, 2012 2012;142(12):2091-2096.

26. Snitz BE, O’Meara ES, Carlson MC, et al. Ginkgo biloba for preventing cognitive decline in older adults: a randomized trial. JAMA. Dec 23 2009;302(24):2663-2670.

27. Kaye J. Ginkgo biloba Prevention Trials: More Than an Ounce of Prevention Learned. Arch Neurol. May 1, 2009 2009;66(5):652-654.