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FINANCIAL MANAGEMENT SKILLS IN AGING, MCI AND DEMENTIA: CROSS SECTIONAL RELATIONSHIP TO 18F-FLORBETAPIR PET CORTICAL Β-AMYLOID DEPOSITION

S. Tolbert, Y. Liu, C. Hellegers, J.R. Petrella, M.W. Weiner, T.Z. Wong, P. Murali Doraiswamy, for the ADNI Study Group

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Background: There is a need to more fully characterize financial capacity losses in the preclinical and prodromal stages of Alzheimer’s disease (AD) and their pathological substrates. Objectives: To test the association between financial skills and cortical β-amyloid deposition in aging and subjects at risk for AD. Design: Cross-sectional analyses of data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-3) study conducted across 50 plus sites in the US and Canada. Setting: Multicenter biomarker study. Participants: 243 subjects (144 cognitively normal, 79 mild cognitive impairment [MCI], 20 mild AD). Measurements: 18F-Florbetapir brain PET scans to measure global cortical β-amyloid deposition (SUVr) and the Financial Capacity Instrument Short Form (FCI-SF) to evaluate an individual’s financial skills in monetary calculation, financial concepts, checkbook/register usage, and bank statement usage. There are five sub scores and a total score (range of 0–74) with higher scores indicating better financial skill. Results: FCI-SF total score was significantly worse in MCI [Cohen’s d= 0.9 (95%CI: 0.6-1.2)] and AD subjects [Cohen’s d=3.1(CI: 2.5-3.7)] compared to normals. Domain scores and completion times also showed significant difference. Across all subjects, higher cortical β-amyloid SUVr was significantly associated with worse FCI-SF total score after co-varying for age, education, and cognitive score [Cohen’s f2=0.751(CI: 0.5-1.1)]. In cognitively normal subjects, after covarying for age, gender, and education, higher β -amyloid PET SUVr was associated with longer task completion time [Cohen’s f2=0.198(CI: 0.06-0.37)]. Conclusion: Using a multicenter study sample, we document that financial capacity is impaired in the prodromal and mild stages of AD and that such impairments are, in part, associated with the extent of cortical β-amyloid deposition. In normal aging, β-amyloid deposition is associated with slowing of financial tasks. These data confirm and extend prior research highlighting the utility of financial capacity assessments in at risk samples.

CITATION:
S. Tolbert ; Y. Liu ; C. Hellegers ; J.R. Petrella ; M.W. Weiner ; T.Z. Wong ; P. Murali Doraiswamy ; for the ADNI Study Group (2019): Financial Management Skills in Aging, MCI and Dementia: Cross Sectional Relationship to 18F-Florbetapir PET Cortical β-amyloid Deposition. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.26

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APPLICATION OF THE NIA-AA RESEARCH FRAMEWORK: TOWARDS A BIOLOGICAL DEFINITION OF ALZHEIMER’S DISEASE USING CEREBROSPINAL FLUID BIOMARKERS IN THE AIBL STUDY

S.C. Burnham, P.M. Coloma, Q.-X. Li, S. Collins, G. Savage, S. Laws, J. Doecke, P. Maruff, R.N. Martins, D. Ames, C.C. Rowe, C.L. Masters, V.L. Villemagne

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BACKGROUND: The National Institute on Aging and Alzheimer’s Association (NIA-AA) have proposed a new Research Framework: Towards a biological definition of Alzheimer’s disease, which uses a three-biomarker construct: Aß-amyloid, tau and neurodegeneration AT(N), to generate a biomarker based definition of Alzheimer’s disease. OBJECTIVES: To stratify AIBL participants using the new NIA-AA Research Framework using cerebrospinal fluid (CSF) biomarkers. To evaluate the clinical and cognitive profiles of the different groups resultant from the AT(N) stratification. To compare the findings to those that result from stratification using two-biomarker construct criteria (AT and/or A(N)). DESIGN: Individuals were classified as being positive or negative for each of the A, T, and (N) categories and then assigned to the appropriate AT(N) combinatorial group: A-T-(N)-; A+T-(N)-; A+T+(N)-; A+T-(N)+; A+T+(N)+; A-T+(N)-; A-T-(N)+; A-T+(N)+. In line with the NIA-AA research framework, these eight AT(N) groups were then collapsed into four main groups of interest (normal AD biomarkers, AD pathologic change, AD and non-AD pathologic change) and the respective clinical and cognitive trajectories over 4.5 years for each group were assessed. In two sensitivity analyses the methods were replicated after assigning individuals to four groups based on being positive or negative for AT biomarkers as well as A(N) biomarkers. SETTING: Two study centers in Melbourne (Victoria) and Perth (Western Australia), Australia recruited MCI individuals and individuals with AD from primary care physicians or tertiary memory disorder clinics. Cognitively healthy, elderly NCs were recruited through advertisement or via spouses of participants in the study. PARTICIPANTS: One-hundred and forty NC, 33 MCI participants, and 27 participants with AD from the AIBL study who had undergone CSF evaluation using Elecsys® assays. INTERVENTION (if any): Not applicable. MEASUREMENTS: Three CSF biomarkers, namely amyloid β1-42, phosphorylated tau181, and total tau, were measured to provide the AT(N) classifications. Clinical and cognitive trajectories were evaluated using the AIBL Preclinical Alzheimer Cognitive Composite (AIBL-PACC), a verbal episodic memory composite, an executive function composite, California Verbal Learning Test – Second Edition; Long-Delay Free Recall, Mini-Mental State Examination, and Clinical Dementia Rating Sum of Boxes scores. RESULTS: Thirty-eight percent of the elderly NCs had no evidence of abnormal AD biomarkers, whereas 33% had biomarker levels consistent with AD or AD pathologic change, and 29% had evidence of non-AD biomarker change. Among NC participants, those with biomarker evidence of AD pathology tended to perform worse on cognitive outcome assessments than other biomarker groups. Approximately three in four participants with MCI or AD had biomarker levels consistent with the research framework’s definition of AD or AD pathologic change. For MCI participants, a decrease in AIBL-PACC scores was observed with increasing abnormal biomarkers; and increased abnormal biomarkers were also associated with increased rates of decline across some cognitive measures. CONCLUSIONS: Increasing biomarker abnormality appears to be associated with worse cognitive trajectories. The implementation of biomarker classifications could help better characterize prognosis in clinical practice and identify those at-risk individuals more likely to clinically progress, for their inclusion in future therapeutic trials.

CITATION:
S.C. Burnham ; P.M. Coloma ; Q.-X. Li ; S. Collins ; G. Savage ; S. Laws ; J. Doecke ; P. Maruff ; R.N. Martins ; D. Ames ; C.C. Rowe ; C.L. Masters ; V.L. Villemagne (2019): Application of the NIA-AA Research Framework: Towards a Biological Definition of Alzheimer’s Disease using Cerebrospinal Fluid Biomarkers in the AIBL Study. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.25

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JPAD Volume 6, N°03 - 2019

 

What next in ad drug development?

EDITORIAL: FAILURE AFTER FAILURE. WHAT NEXT IN AD DRUG DEVELOPMENT?

P.S. Aisen

J Prev Alz Dis 2019;6(3):150

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CITATION:
P.S. Aisen (2019): Editorial: Failure After Failure. What Next in AD Drug Development?. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.23

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EDITORIAL: TAU BASED THERAPEUTICS: ALTERNATIVE APPROACHES IN THE WAR ON ALZHEIMER’S DISEASE

M. Grundman

J Prev Alz Dis 2019;6(3):151-152

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CITATION:
M. Grundman (2019): Editorial: Tau Based Therapeutics: Alternative Approaches in the War on Alzheimer’s Disease. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.13

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EDITORIAL: IS NOW THE TIME FOR COMBINATION THERAPIES FOR ALZHEIMER DISEASE?

J.C. Morris

J Prev Alz Dis 2019;6(3):153-154

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CITATION:
J.C. Morris (2019): Editorial: Is Now the Time for Combination Therapies for Alzheimer Disease?. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.15

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EDITORIAL: BLOOD TESTS FOR ALZHEIMER’S DISEASE AND RELATED DISORDERS

E.M. Reiman

J Prev Alz Dis 2019;6(3):155-156

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CITATION:
E.M. Reiman (2019): Editorial: Blood Tests for Alzheimer’s Disease and Related Disorders. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.20

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ANTI-TAU TRIALS FOR ALZHEIMER’S DISEASE: A REPORT FROM THE EU/US/CTAD TASK FORCE

J. Cummings, K. Blennow, K. Johnson, M. Keeley, R.J. Bateman, J.L. Molinuevo, J. Touchon, P. Aisen, B. Vellas, and the EU/US/CTAD Task Force

J Prev Alz Dis 2019;6(3):157-163

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Efforts to develop effective disease-modifying treatments for Alzheimer’s disease (AD) have mostly targeted the amyloid β (Aβ) protein; however, there has recently been increased interest in other targets including phosphorylated tau and other forms of tau. Aggregated tau appears to spread in a characteristic pattern throughout the brain and is thought to drive neurodegeneration. Both neuropathological and imaging studies indicate that tau first appears in the entorhinal cortex and then spreads to the neocortex. Anti-tau therapies currently in Phase 1 or 2 trials include passive and active immunotherapies designed to prevent aggregation, seeding, and spreading, as well as small molecules that modulate tau metabolism and function. EU/US/CTAD Task Force members support advancing the development of anti-tau therapies, which will require novel imaging agents and biomarkers, a deeper understanding of tau biology and the dynamic interaction of tau and Aβ protein, and development of multiple targets and candidate agents addressing the tauopathy of AD. Incorporating tau biomarkers in AD clinical trials will provide additional knowledge about the potential to treat AD by targeting tau.

CITATION:
J. Cummings ; K. Blennow ; K. Johnson ; M. Keeley ; R.J. Bateman ; J.L. Molinuevo ; J. Touchon ; P. Aisen ; B. Vellas ; and the EU/US/CTAD Task Force (2019): Anti-Tau Trials for Alzheimer’s Disease: A Report from the EU/US/CTAD Task Force. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.14

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COMBINATION THERAPY FOR ALZHEIMER’S DISEASE: PERSPECTIVES OF THE EU/US CTAD TASK FORCE

S. Gauthier, J. Alam, H. Fillit, T. Iwatsubo, H. Liu-Seifert, M. Sabbagh, S. Salloway, C. Sampaio, J.R. Sims, B. Sperling, R. Sperling, K.A. Welsh-Bohmer, J. Touchon, B. Vellas, P. Aisen, and the EU/US/CTAD Task Force

J Prev Alz Dis 2019;6(3):164-168

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Combination therapy is expected to play an important role for the treatment of Alzheimer’s disease (AD). In October 2018, the European Union-North American Clinical Trials in Alzheimer’s Disease Task Force (EU/US CTAD Task Force) met to discuss scientific, regulatory, and logistical challenges to the development of combination therapy for AD and current efforts to address these challenges. Task Force members unanimously agreed that successful treatment of AD will likely require combination therapy approaches that target multiple mechanisms and pathways. They further agreed on the need for global collaboration and sharing of data and resources to accelerate development of such approaches.

CITATION:
S. Gauthier ; J. Alam ; H. Fillit ; T. Iwatsubo ; H. Liu-Seifert ; M. Sabbagh ; S. Salloway ; C. Sampaio ; J.R. Sims ; B. Sperling ; R. Sperling ; K.A. Welsh-Bohmer ; J. Touchon ; B. Vellas ; P. Aisen ; and the EU/US/CTAD Task Force (2019): Combination Therapy for Alzheimer’s Disease: Perspectives of the EU/US CTAD Task Force. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.12

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PLASMA BIOMARKERS OF AD EMERGING AS ESSENTIAL TOOLS FOR DRUG DEVELOPMENT: AN EU/US CTAD TASK FORCE REPORT

R.J. Bateman, K. Blennow, R. Doody, S. Hendrix, S. Lovestone, S. Salloway, R. Schindler, M. Weiner, H. Zetterberg, P. Aisen, B. Vellas, and the EU/US CTAD Task Force

J Prev Alz Dis 2019;6(3):169-173

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There is an urgent need to develop reliable and sensitive blood-based biomarkers of Alzheimer’s disease (AD) that can be used for screening and to increase the efficiency of clinical trials. The European Union-North American Clinical Trials in Alzheimer’s Disease Task Force (EU/US CTAD Task Force) discussed the current status of blood-based AD biomarker development at its 2018 annual meeting in Barcelona, Spain. Recent improvements in technologies to assess plasma levels of amyloid beta indicate that a single sample of blood could provide an accurate estimate of brain amyloid positivity. Plasma neurofilament light protein appears to provide a good marker of neurodegeneration, although not specific for AD. Plasma tau shows some promising results but weak or no correlation with CSF tau levels, which may reflect rapid clearance of tau in the bloodstream. Blood samples analyzed using -omics and other approaches are also in development and may provide important insight into disease mechanisms as well as biomarker profiles for disease prediction. To advance these technologies, international multidisciplinary, multi-stakeholder collaboration is essential.

CITATION:
R.J. Bateman ; K. Blennow ; R. Doody ; S. Hendrix ; S. Lovestone ; S. Salloway ; R. Schindler ; M. Weiner ; H. Zetterberg ; P. Aisen ; B. Vellas ; and the EU/US CTAD Task Force (2019): Plasma Biomarkers of AD Emerging as Essential Tools for Drug Development: An EU/US CTAD Task Force Report. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.21

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COMMENTARY: TREATING ALZHEIMER’S DISEASE : COMBINE OR FAIL ?

S. Bakchine

J Prev Alz Dis 2019;6(3):174-176

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CITATION:
S. Bakchine (2019): Commentary: Treating Alzheimer’s Disease : Combine or Fail ? . The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.16

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COMMENTARY: OPPORTUNITIES FOR COMBINATION TRIALS

C. Ballard, A. Corbett

J Prev Alz Dis 2019;6(3):177-178

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CITATION:
C. Ballard ; A. Corbett (2019): Commentary: Opportunities for Combination Trials. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.17

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COMMENTARY: COMBINATION THERAPY FOR ALZHEIMER’S DISEASE – THE NEXT STEP

F. Jessen

J Prev Alz Dis 2019;6(3):179

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CITATION:
F. Jessen (2019): Commentary: Combination Therapy for Alzheimer’s Disease – The Next Step. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.18

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COMMENTARY: COMBINATION THERAPY FOR ALZHEIMER’S DISEASE: PERSPECTIVES OF THE EU/US CTAD TASK FORCE

E. Siemers

J Prev Alz Dis 2019;6(3):180-181

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CITATION:
E. Siemers (2019): Commentary: Combination Therapy for Alzheimer’s Disease: Perspectives of the EU/US CTAD Task Force. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.19

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COMMENTARY: DEVELOPMENT OF THE BLOOD-BASED ALZHEIMER’S DISEASE LIQUID BIOPSY

H. Hampel, S. Lista, A. Vergallo, for the Alzheimer Precision Medicine Initiative (APMI)

J Prev Alz Dis 2019;6(3):182-184

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CITATION:
H. Hampel ; S. Lista ; A. Vergallo ; for the Alzheimer Precision Medicine Initiative (APMI) (2019): Commentary: Development of the Blood-Based Alzheimer’s Disease Liquid Biopsy. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.22

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Original Research

MACHINE LEARNING ALGORITHM HELPS IDENTIFY NONDIAGNOSED PRODROMAL ALZHEIMER’S DISEASE PATIENTS IN THE GENERAL POPULATION

O. Uspenskaya-Cadoz, C. Alamuri, L. Wang, M. Yang, S. Khinda, Y. Nigmatullina, T. Cao, N. Kayal, M. O’Keefe, C. Rubel

J Prev Alz Dis 2019;6(3):185-191

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Background: Recruiting patients for clinical trials of potential therapies for Alzheimer’s disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R&D pipeline includes around 112 agents. In the United States alone, 150 clinical trials are seeking 70,000 participants. Most people with early cognitive impairment consult primary care providers, who may lack time, diagnostic skills and awareness of local clinical trials. Machine learning and predictive analytics offer promise to boost enrollment by predicting which patients have prodromal AD, and which will go on to develop AD. Objectives: The authors set out to develop a machine learning predictive model that identifies prodromal AD patients in the general population, to aid early AD detection by primary care physicians and timely referral to expert sites for biomarker confirmation of diagnosis and clinical trial enrollment. Design: The authors use a classification machine learning algorithm to extract patterns within healthcare claims and prescription data three years prior to AD diagnosis/AD drug initiation. Setting: The study focused on subjects included within proprietary IQVIA US data assets (claims and prescription databases). Patient information was extracted from January 2010 to July 2018, for cohorts aged between 50 and 85 years. Participants: A total of 88,298,289 subjects aged between 50 and 85 years were identified. For the positive cohort, 667,288 subjects were identified who had 24 months of medical history and at least one record with AD or AD treatment. For the negative cohort, 3,670,254 patients were selected who had a similar length of medical history and who were matched to positive cohort subjects based on the prevalence rate. The scoring cohort was selected based on availability of recent medical data of 2-5 years and included 72,670,283 subjects between the ages of 50 and 85 years. Intervention (if any): None. Measurements: A list of clinically-relevant and interpretable predictors was generated and extracted from the data sets for each subject, including pharmacological treatments (NDC/product), office/specialist visits (specialty), tests and procedures (HCPCS and CPT), and diagnosis (ICD). The positive cohort was defined as patients who have AD diagnosis/AD treatment with a 3 years offset as an estimate for prodromal AD diagnosis. Supervised ML techniques were used to develop algorithms to predict the occurrence of prodromal AD cases. The sample dataset was divided randomly into a training dataset and a test dataset. The classification models were trained and executed in the PySpark framework. Training and evaluation of LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, and GBTClassifier were executed using PySpark’s mllib module. The area under the precision-recall curve (AUCPR) was used to compare the results of the various models. Results: The AUCPRs are 0.426, 0.157, 0.436, and 0.440 for LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, and GBTClassifier, respectively, meaning that GBTClassifier (Gradient Boosted Tree) outperforms the other three classifiers. The GBT model identified 222,721 subjects in the prodromal AD stage with 80% precision. Some 76% of identified prodromal AD patients were in the primary care setting. Conclusions: Applying the developed predictive model to 72,670,283 U.S. residents, 222,721 prodromal AD patients were identified, the majority of whom were in the primary care setting. This could drive major advances in AD research by enabling more accurate and earlier prodromal AD diagnosis at the primary care physician level , which would facilitate timely referral to expert sites for in-depth assessment and potential enrolment in clinical trials.

CITATION:
O. Uspenskaya-Cadoz ; C. Alamuri ; L. Wang ; M. Yang ; S. Khinda ; Y. Nigmatullina ; T. Cao ; N. Kayal ; M. O’Keefe ; C. Rubel (2019): Machine Learning Algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer’s Disease Patients in the General Population . The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.10

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TREATMENT EFFECTS OF VORTIOXETINE ON COGNITIVE FUNCTIONS IN MILD ALZHEIMER’S DISEASE PATIENTS WITH DEPRESSIVE SYMPTOMS: A 12 MONTH, OPEN-LABEL, OBSERVATIONAL STUDY

E. Cumbo, S. Cumbo, S. Torregrossa, D. Migliore

J Prev Alz Dis 2019;6(3):192-197

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BACKGROUND/OBJECTIVES:depressive symptoms are common in Alzheimer’s disease(AD). Aim of the study was to investigate the efficacy of vortioxetine compared with other conventional antidepressants on cognitive functions in AD patients with depressive symptoms. DESIGN: Prospective, randomized, 12 month, parallel-group study. SETTING: All participants were evaluated on-site at Neurodegenerative Disorders Unit, ASP2 Caltanissetta(Italy). PARTICIPANTS: 108(71 female, 37 male) AD patients with depression(mean age 76.7± 4.3). INTERVENTION: Randomized subjects received vortioxetine, 15 mg/day(n=36) or other common antidepressants(n=72). MEASURES:primary outcome was change from baseline in the MMSE; secondary outcomes were change in Attentive Matrices, Raven Coloured Progressive Matrices, Digit Span, HAM-D and Cornell scale. RESULTS: Statistically significant improvement vs. controls was observed for vortioxetine on most of the cognitive tests and showed significantly baseline-to-endpoint reduction in both HAM-D and Cornell total scores.The most commonly reported adverse events were nausea and headache for votioxetine; nausea in the control group. CONCLUSIONS: Vortioxetine had a beneficial effect on cognition and mood in elderly AD patients and was safe and well tolerated.

CITATION:
E. Cumbo ; S. Cumbo ; S. Torregrossa ; D. Migliore (2019): Treatment Effects of Vortioxetine on Cognitive Functions in Mild Alzheimer’s Disease Patients with Depressive Symptoms: A 12 Month, Open-Label, Observational Study. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.24

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Review Articles

ADVANCING ALZHEIMER’S DISEASE TREATMENT: LESSONS FROM CTAD 2018

B. Vellas, L.J. Bain, J. Touchon, P.S. Aisen

J Prev Alz Dis 2019;6(3):198-203

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The 2018 Clinical Trials on Alzheimer’s Disease (CTAD) conference showcased recent successes and failures in trials of Alzheimer’s disease treatments. More importantly, the conference provided opportunities for investigators to share what they have learned from those studies with the goal of designing future trials with a greater likelihood of success. Data from studies of novel and non-amyloid treatment approaches were also shared, including neuroprotective and regenerative strategies and those that target neuroinflammation and synaptic function. New tools to improve the efficiency and productivity of clinical trials were described, including biomarkers and machine learning algorithms for predictive modeling.

CITATION:
B. Vellas ; L.J. Bain ; J. Touchon ; P.S. Aisen (2019): Advancing Alzheimer’s Disease Treatment: Lessons from CTAD 2018. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.11

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DIETARY FAT INTAKE AND COGNITIVE FUNCTION AMONG OLDER POPULATIONS: A SYSTEMATIC REVIEW AND META-ANALYSIS

G.-Y. Cao, M. Li, L. Han, F. Tayie, S.-S. Yao, Z. Huang, P. Ai, Y.-Z. Liu, Y.-H. Hu, B. Xu

J Prev Alz Dis 2019;6(3):204-211

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Objective: The associations between dietary fat intake and cognitive function are inconsistent and inconclusive. This study aimed to provide a quantitative synthesis of prospective cohort studies on the relationship between dietary fat intake and cognitive function among older adults. Methods: PubMed, EMBASE, PsycINFO and Web of Science databases were searched for prospective cohort studies published in English before March 2018 reporting cognitive outcomes in relation to dietary fat intake. Four binary incident outcomes included were mild cognitive impairment (MCI), dementia, Alzheimer disease (AD) and cognitive impairment. The categories of dietary fat intake were based on fat consumption or the percentage of energy from fat consumption, including dichotomies, tertiles, quartiles and quintiles. The relative risk (RR) with the corresponding 95% confidence intervals (CIs) was pooled using a random effects model. Results: Nine studies covering a total of 23,402 participants were included. Compared with the lowest category of consumption, the highest category of saturated fat intake was associated with an increased risk of cognitive impairment (RR = 1.40; 95% CI: 1.02-1.91) and AD (RR: 1.87, 95% CI: 1.09-3.20). The total and unsaturated fat intake was not statistically associated with cognitive outcomes with significant between-study heterogeneity. Conclusion: This study reported a detrimental association between saturated fat intake and cognitive impairment and mixed results between unsaturated fat intake and selected cognitive outcomes. Given the substantial heterogeneity in the sample size and methodology used across studies, the evidence presented here should be interpreted with caution.

CITATION:
G.-Y. Cao ; M. Li ; L. Han ; F. Tayie ; S.-S. Yao ; Z. Huang ; P. Ai ; Y.-Z. Liu ; Y.-H. Hu ; B. Xu (2019): Dietary Fat Intake and Cognitive Function among Older Populations: A Systematic Review and Meta-Analysis. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.9

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