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J. Cummings1, P. Aisen2, L.G. Apostolova3, A. Atri4, S. Salloway5, M. Weiner6


1. Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA; 2. Alzheimer’s Treatment Research Institute, University of Southern California, San Diego, CA, USA; 3. Departments of Neurology, Radiology, Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA; 4. Banner Sun Health Research Institute, Banner Health, Sun City, AZ; Center for Brain/Mind Medicine, Harvard Medical School, Boston, MA, USA; 5. Butler Hospital and Warren Alpert Medical School of Brown University, Providence RI, USA; 6. Departments of Radiology and Biomedical Imaging, Medicine, Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, USA

Corresponding Author: Jeffrey Cummings, MD, ScD, 1380 Opal Valley Street, Henderson, NV 89052,, T: 702-902-3939

J Prev Alz Dis 2021;
Published online July 20, 2021,



Aducanumab has been approved by the US Food and Drug Administration for treatment of Alzheimer’s disease (AD). Clinicians require guidance on the appropriate use of this new therapy. An Expert Panel was assembled to construct Appropriate Use Recommendations based on the participant populations, conduct of the pivotal trials of aducanumab, updated Prescribing Information, and expert consensus. Aducanumab is an amyloid-targeting monoclonal antibody delivered by monthly intravenous infusions. The pivotal trials included patients with early AD (mild cognitive impairment due to AD and mild AD dementia) who had confirmed brain amyloid using amyloid positron tomography. The Expert Panel recommends that use of aducanumab be restricted to this population in which efficacy and safety have been studied. Aducanumab is titrated to a dose of 10 mg/kg over a 6-month period. The Expert Panel recommends that the aducanumab be titrated to the highest dose to maximize the opportunity for efficacy. Aducanumab can substantially increase the incidence of amyloid-related imaging abnormalities (ARIA) with brain effusion or hemorrhage. Dose interruption or treatment discontinuation is recommended for symptomatic ARIA and for moderate-severe ARIA. The Expert Panel recommends MRIs prior to initiating therapy, during the titration of the drug, and at any time the patient has symptoms suggestive of ARIA. Recommendations are made for measures less cumbersome than those used in trials for the assessment of effectiveness in the practice setting. The Expert Panel emphasized the critical importance of engaging in a process of patient-centered informed decision-making that includes comprehensive discussions and clear communication with the patient and care partner regarding the requirements for therapy, the expected outcome of therapy, potential risks and side effects, and the required safety monitoring, as well as uncertainties regarding individual responses and benefits.

Key words: Alzheimer’s disease, aducanumab, Aduhelm™, appropriate use, titration, ARIA, amyloid imaging, MRI.


Aducanumab (Aduhelm™) has been approved by the US Food and Drug Administration (FDA) for the treatment of Alzheimer’s disease (AD). The Prescribing Information for aducanumab (1) provides key facts on aducanumab such as dose, titration, pharmacokinetics, and side effects. The Clinical Studies section describes the clinical trials that led to the approval of aducanumab. Many details of the clinical use of this new agent are not detailed in the Prescribing Information (1) and there is a need for specific recommendations regarding how to use aducanumab appropriately. Experts with experience in AD research, AD clinical trials and drug development, AD clinical care, and use of aducanumab were assembled to develop consensus recommendations for the appropriate use of aducanumab in clinical practice.
The Prescribing Information (1) provides the “on label” prescribing instructions. The Expert Panel recommends that the appropriate use of aducanumab in real-world clinical practice should pragmatically mimic the use of aducanumab in the EMERGE and ENGAGE clinical trials that led the FDA to approve aducanumab. After the initial Prescribing Information was published, the FDA adjusted the indication section from “indicated for the treatment of Alzheimer’s disease” to “indicated for the treatment of Alzheimer’s disease…should be initiated in patients with mild cognitive impairment or mild dementia stage of the disease, the population in which treatment was initiated in clinical trials” (1, 2). Some of the Expert Panel recommendations are more specific or more restrictive than the information provided in the Prescribing Information (1). The recommendations are within the scope of use articulated in the Prescribing Information (1) The Expert Panel describes the appropriate use of aducanumab for the practicing clinician; we do not address trial outcomes, approval strategies, cost, insurance coverage, or reimbursement issues. The Expert Panel recommendations apply to practices in the Unites States where aducanumab is currently approved. Recommendations may change as more data on the use of aducanumab and more data from the trials become available. These recommendations are meant to assist practitioners in using aducanumab safely; they do not replace clinician judgement in the delivery of care to individual patients.



Aducanumab is a monoclonal antibody directed to the N-terminus of the amyloid beta peptide (Aß). It was derived through a process of reverse translation in which blood lymphocytes from healthy elderly individuals who were cognitively normal or had unusually slow cognitive decline served as a source of antibody genes for the generation of recombinant human antibodies (3).
The Expert Panel recommends that patients treated with aducanumab closely resemble those included in the pivotal clinical trials (4, 5). Pragmatic adjustments will be required for use of aducanumab outside of the trial setting, and the translation of clinical trial protocol requirements to clinical practice is summarized in Table 1. Efficacy and safety have been assessed in the early AD population of patients with mild cognitive impairment (MCI) due to AD and mild dementia due to AD confirmed by amyloid positron emission tomography (PET) and are unknown for individuals with preclinical AD, those with more severe AD dementia, or those with cognitive impairment that is not confirmed to be AD by Aß studies.

Table 1. Clinical trial enrollment criteria and appropriate use criteria for aducanumab in clinical practice

Aß – amyloid beta protein; AD – Alzheimer’s disease; APOE – apolipoprotein E; CDR – Clinical Dementia Rating; cm – centimeter; CSF – cerebrospinal fluid; HIV – human immunodeficiency virus; MMSE – Mini Mental State Examination; MoCA – Montreal Cognitive Assessment; MRI – magnetic resonance imaging; PET – positron emission tomography; RBANS – Repeatable Battery for the Assessment of Neuropsychological Status; TIA – transient ischemic attack


Appropriate Patient


The Expert Panel recommends that patients appropriate for treatment with aducanumab have a diagnosis of early AD established by a diagnostic evaluation that includes: 1) detailed history that is sufficient to establish the nature and time course of cognitive symptoms, functional changes, and behavioral status; 2) objective corroboration of cognitive decline using standardized testing; 3) detailed neurological and physical examination; 4) review of all current medications and supplements; 5) laboratory testing sufficient to exclude other concomitant disorders that can cause cognitive decline including a complete blood count, electrolyte panel, thyroid stimulating hormone, lipids and triglycerides, liver function tests, and serum vitamin B12 level; and 6) magnetic resonance imaging (MRI) of the brain to rule out other conditions that could present with cognitive decline (e.g., normal pressure hydrocephalus, vascular dementia, slow going neoplasm, subdural hematoma) and to assess possible exclusions for use of aducanumab (discussed below) (6-8). This assessment will determine if the patient has clinical findings consistent with early AD.
Patients with early AD meet the clinical criteria of stage 3 and 4 of the FDA staging approach (9). Stage 3 consists of individuals with subtle or more apparent detectable abnormalities on sensitive neuropsychological measures and mild but detectable functional impairment. The functional impairment in this stage is not severe enough to warrant a diagnosis of overt dementia. Stage 4 includes individuals with cognitive impairment and mild but definite functional decline.
To quantify the cognitive and functional changes, early AD patients in the aducanumab trials had scores on the Clinical Dementia Rating (CDR) (10) global rating of 0.5. This instrument assesses cognitive (memory, orientation, judgment, and problem solving) and functional (community affairs, home and hobbies, and personal care) domains. In addition, trial participants had Mini Mental State Examination (MMSE) (11) scores of 24-30. The MMSE is commonly used in clinical practice and is a useful tool for identifying appropriate patients. The standard error of measurement on the MMSE is 1 point, and the minimum detectable difference is 3 points (12, 13). The test-retest reliability of MMSE is 2-4 points (14). These studies indicate that scores of 21 and higher would not be detectably different from the range of MMSE scores of patients included in the pivotal trials (MMSE range of 24-30). The Phase 1B study of aducanumab had encouraging results in patients with MMSE scores of 20-30 (15). The Expert Panel recommends that patients with MMSE scores of 21 or higher or who have a similar level of performance on an alternate reliable and valid assessment are appropriate for treatment with aducanumab. An alternative assessment that provides reliable information similar to that of the MMSE is the Montreal Cognitive Assessment (MoCA) (16). The MoCA is a more challenging test than the MMSE resulting in lower scores when compared to the MMSE. Scores of 17 and higher on the MoCA are equivalent to MMSE scores of 21-30 in early symptomatic AD (17). In settings where neuropsychological testing is available, a diagnosis of early AD can be based on more extensive cognitive, functional, and behavioral assessments (18).

Use of cognitive enhancing agents in aducanumab candidates

A newly diagnosed patient with MCI due to AD may be started on aducanumab since cholinesterase inhibitors and memantine are not approved for this stage of AD. Patients with early AD may be on a cholinesterase inhibitor or memantine when referred for possible treatment with aducanumab; these patients can remain on their standard of care while being treated with aducanumab. Patients diagnosed with mild AD dementia can have treatment with aducanumab before or following initiation of treatment with a cholinesterase inhibitor. If patients with MCI progress to mild AD dementia, treatment with a cholinesterase inhibitor (donepezil, rivastigmine, galantamine) can be considered. Memantine is not approved for mild AD dementia. If patients progress to moderate or severe AD, memantine treatment can be considered as monotherapy or in conjunction with a cholinesterase inhibitor (19).

Amyloid status

All patients included in the pivotal trials had positive amyloid positron emission tomography (PET). Demonstration of amyloid burden is critical to establishing the presence of the target for amyloid lowering therapies. The clinical diagnosis of AD is often not confirmed by amyloid studies and up to 40% of patients diagnosed with early AD do not have the amyloid pathology when studied with amyloid imaging (20). Appropriate Use Criteria of amyloid imaging suggest that the imaging is appropriate when: a) there is a cognitive complaint and cognitive impairment has been objectively confirmed impairment; b) AD is a possible diagnosis, but the diagnosis is uncertain after a comprehensive evaluation by a dementia expert; and c) knowledge of the presence or absence of amyloid-beta pathology is expected to increase diagnostic certainty and alter management (21). These criteria are fulfilled in the situation where a patient is being considered for treatment with aducanumab: they have the symptoms of early AD, additional diagnostic certainty is needed, and management will be based on the outcome.
Three amyloid PET tracers are approved by the FDA: florbetapir, florbetaben, and flutametamol (22-24). Table 2 provides the criteria for a positive scan for each tracer. Scan interpretation is best done by radiologists or nuclear medicine specialists; training programs for amyloid PET interpretation are available for each ligand. The Expert Panel recommends that programs offering aducanumab treatment and using amyloid PET to confirm the diagnosis of AD should ensure the availability of individuals properly trained in amyloid PET interpretation.


Table 2. Criteria for a positive amyloid PET for the three approved amyloid PET tracers (from drugs@FDA: FDA-Approved Drugs)


Lumbar puncture and assessment of cerebrospinal fluid (CSF) biomarkers (Aβ42, Aβ40 total tau, phosphorylated tau [p-tau]) provide an alternative to amyloid PET and are more widely available (25). Several CSF measures can be indicative of the presence of AD including low Aβ42, low Aβ42/Aβ40 ratio, abnormal Aβ42/tau ratios, and abnormal Aβ42/p-tau ratios (26-28). Practitioners should use Clinical Laboratory Improvement Amendments (CLIA)-certified facilities and follow the laboratory’s guidelines for optimal AD-related assays. If CSF results are ambiguous, amyloid imaging is recommended. Amyloid PET and CSF AD signature studies provide equally valid information (29); CSF Aβ42 levels correlate inversely with brain amyloid on PET with CSF levels declining as Aβ is deposited in the cortex (30). Changes in CSF Aβ42 levels precede changes in amyloid PET (31); individuals with abnormal CSF and normal amyloid PET imaging are usually without symptoms and they lack evidence of amyloid plaques which are the target of aducanumab. The Expert Panel recommends that these patients not be treated with aducanumab. Re-imaging with amyloid PET in 1-3 years may be warranted in this group of individuals.
Lumbar puncture can be performed by physicians, nurse practitioners, or physicians’ assistants/associates with low patient morbidity and high safety (32). Lumbar puncture may not be possible in those with pathological or surgical changes of the lumbar spine; fluoroscopic guidance may be useful in such cases. Lumbar puncture is contraindicated in those with clotting disorders or who are on anticoagulants. Prothrombin time (PT) and partial thromboplastin time (PTT) can be obtained to ensure normal clotting parameters before proceeding with lumbar puncture.
Amyloid imaging or CSF biomarker analyses in persons with the clinical features of early AD will reveal that some of these cognitively impaired individuals do not have AD, exhibit evidence of neurodegeneration, and fulfill criteria for suspected non-Alzheimer pathology (SNAP) (33). Discovery of the non-amyloid status of these individuals assists clinicians in making management decisions (34). The Expert Panel recommends that individuals with SNAP not be treated with aducanumab.
Lumbar puncture with findings consistent with AD or PET with elevated brain amyloid confirm the diagnosis of AD in patients with the clinical syndrome of early AD. Failure to confirm the diagnosis of AD with amyloid biomarkers could result in administering aducanumab to patients who do not have AD and who lack the target pathology of the agent. The Expert Panel recommends that all patients considered for treatment with aducanumab have the diagnosis of AD confirmed by clinically validated amyloid studies such as amyloid PET or CSF analysis.

Genetic testing

Genetic testing to determine the apolipoprotein E (APOE) genotype of the participants was required in the pivotal trials. ARIA of the effusion (ARIA-E) or hemorrhagic (ARIA-H) type are more common in APOE ε 4 (APOE-4) gene carriers and understanding this effect in trials is important (35). ARIA may be more common in APOE-4 homozygotes and can be severe (36). The Prescribing Information (1) instructions for use of aducanumab do not require APOE genotyping and the dosing and monitoring of individuals with and without an APOE-4 allele are identical. The Expert Panel recommends that patients and care partners be engaged in a patient-centered discussion of the risk that an APOE-4 genotype confers for the risk of ARIA. This discussion will determine if genotype information would influence their decision to be treated with aducanumab and if they wish to pursue APOE genotyping.
If patients, care partners, or referring clinicians request APOE genotyping prior to the decision to use aducanumab or if the individual has determined their genotype through a commercial service, the Expert Panel recommends that the clinician be prepared to discuss the increased risk for ARIA in the presence of an APOE-4 allele as well as the consequences, monitoring, and management of ARIA if it occurs (discussed below). Genotyping provides transgenerational information on risk of AD for first degree relatives. Parents, siblings, and children of APOE-4 heterozygotes have a 50% chance of being an APOE-4 carrier with an increased risk of AD, and first-degree relatives of APOE-4 homozygotes have a 100% chance of being APOE-4 carriers and have an increased risk of AD. Clinicians may request genetic counseling to assist patients and caregivers in understanding the implications of their genotype (37, 38).

Neurological, medical, and psychiatric illness

The Expert Panel recommends that patients with neurological disorders that could account for or contribute to the clinical syndrome of the patients not be treated with aducanumab. This would include patients with parkinsonism, evidence of stroke or widespread white matter ischemic changes, or rapidly progressive dementia. Similarly, recent major psychiatric illness may compromise the ability to adhere to therapy and treatment should be deferred until behavioral stability is established. Poorly controlled or serious medical illnesses (e.g., cancer, heart failure) were exclusions for trial participation and if such illnesses are present in an individual being considered for treatment with aducanumab, the medical condition should be managed and stable prior to initiating treatment. Exclusionary factors are often less rigorous in routine care than in clinical trials but should not be so different as to threaten the generalizability of the trial results to the patient or increase the risk of treatment (39).
Aducanumab has not been studied for its reproductive or teratogenic effects and aducanumab should be administered to younger sexually active AD patients only if they are using contraceptive methods.

Clotting status

Aducanumab is associated with ARIA. Patients with evidence of microhemorrhage on MRI (discussed below) or with clotting abnormalities or who were on anticoagulants were excluded from the pivotal trials. It is not known if these exclusions affected the rate of microhemorrhage associated with aducanumab therapy. The risk of severe ARIA in a person receiving anticoagulants or with a clotting disorder is sufficient to exclude them from treatment with aducanumab. Platelet anti-aggregation agents are allowable as concomitant therapy. Lumbar puncture for confirmation of amyloid status should not be performed on patients being treated with anticoagulants; the occurrence of perispinal hemorrhage and spinal cord compression are low but can occur and the risk should be avoided (40).

Concomitant Medications

There are no adverse drug-drug interactions noted in the Prescribing Information (1). Drugs used in routine care of patients with AD were allowed to be used by participants in the pivotal trials. The Expert Panel agreed that aducanumab may be co-administered with other drugs used in the treatment of AD including cholinesterase inhibitors (donepezil, rivastigmine, galantamine), memantine, and psychotropic agents (antidepressants, antipsychotics, hypnotics).

MRI prior to initiating treatment

Concern for the occurrence of ARIA motivated avoiding administration of aducanumab to patients who had evidence of substantial cerebrovascular disease at baseline in the pivotal trials. The protocol excluded patients who had acute or subacute hemorrhage, macrohemorrhage, greater than 4 microhemorrhages, cortical infarction (>1.5 cm), 1 lacunar infarction (>1.5 cm), diffuse white matter disease, or any areas of superficial siderosis (41). The Expert Panel recommends that these exclusions be observed in clinical practice when choosing appropriate patients for treatment with aducanumab. An MRI including T1, T2 or fluid attenuated inversion recovery (FLAIR), T2* gradient recalled echo (GRE) sequences or susceptibility weighted imaging (SWI), and diffusion weighted imaging should be obtained within 1 year of initiating treatment with aducanumab (and more recently if there is any evidence of stroke since the last MRI). A 3-Tesla magnet MRI will reveal more microhemorrhages than a 1.5 Tesla magnet device, and SWI sequences will reveal more ARIA than GRE images (42). Changes from a baseline scan is the basis for ARIA-related decision making, and the Expert Panel recommends that practitioners use the same MRI device with the same imaging protocol for a given patient whenever possible to assist in comparing the images. Computerized tomography (CT) does not provide sufficient information to determine risk at baseline or to monitor ARIA; individuals who cannot have an MRI (e.g., have a pacemaker incompatible with MRI, metallic brain vessel aneurysm clip, or metallic object in an eye) should not be treated with aducanumab.

Knowledgeable engagement

In the clinical trials of aducanumab, informed consent from the patient and care partner were required for participation. In clinical care, formal informed consent is not required but a similar approach should be used to ensure that the patient and care partner/family member/companion understand the requirements for treatment and the expected outcome of therapy. Patients with early AD have the cognitive capacity to comprehend the possible benefit or harms of aducanumab treatment. Key aspects of informed therapy include discussion of requirements for monthly infusions and periodic MRI and the risk of adverse events including ARIA. The anticipated duration of therapy is indefinite and longer treatment with disease-modifying agents is expected to have greater effects on the disease course (43); the optimal duration of therapy is unknown and it may be possible to reduce the frequency of infusions when amyloid levels have been substantially reduced but this has not yet been determined. Those considering aducanumab therapy should understand that the expected benefit is slowing of cognitive and functional decline; improvement of the current clinical state is not anticipated. Patients should have disease state education regarding the course of AD and the availability of cognitive enhancing agents. Educational programs can improve mood, reduce anxiety, and ameliorate caregiver burden (44). The Expert Panel recommends that appropriate use of aducanumab includes providing information on the requirements for treatment and the expected outcomes, potential risks and side effects, and burdens related to administration and monitoring.
Special efforts are required to engage minority patients and to communicate the need for care and the opportunities for treatment. Minority patients report being “unheard” in medical conversations (45). Historically, use of AD therapies such as cholinesterase inhibitors has been less in African American, Latino, and Asian populations than among White AD patients (46). Addressing concerns about the deleterious effects and stigma of diagnosis and raising awareness of potential benefits of disease identification and treatment may influence the willingness of minority patients to discuss cognitive symptoms with clinicians (47). Minority patients often prefer clinicians who share their language and culture (48). The Expert Panel recommends that clinicians strive to engage diverse patients in diagnosis and treatment discussions with the goal of achieving equity among diverse groups in the use of aducanumab.

Appropriate Treatment

Aducanumab initiation and Titration

Aducanumab infusions are done monthly and require approximately one hour to complete. Infusions should be at least 21 days apart. The first and 2nd infusion dose is 1 mg/kg; the 3rd and 4th infusions are with doses of 3 mg/kg; the 5th and 6th infusions are dosed at 6 mg/kg; the 7th infusion and beyond involve monthly infusions of 10 mg/kg (Figure 1). Aducanumab is supplied in vials of 170 mg/1.7 mL or 300 mg/3 mL and is added to an infusion bag of 100 mL of 0.9% sodium chloride. The data from the pivotal trials and the Phase 1B trial of aducanumab suggest that 10 mg/kg is the target dose (15). Lower doses may not produce benefit and may cause ARIA. The Expert Panel recommends that patients be titrated to 10 mg/kg. If that is not possible, the clinician should engage in a patient-centered discussion as to whether to continue treatment with lower doses of aducanumab.

Figure 1. Aducanumab dosing and MRI monitoring schedule (Prescribing Information (1) and Expert Panel recommendation; © J Cummings; illustrator M de la Flor, PhD)


Management of missed doses has not been studied. The Expert Panel recommends that if a patient misses a dose, the next infusion should be administered as soon as possible at the dose administered in the previous infusion. If a patient misses three or more doses and requires continued treatment, titration should be re-initiated beginning at a dose level one step below that previously administered (e.g., if the patient was at 6 mg/kg previously, they would resume at a dose level of 3 mg/kg) with the dose increased every other month as described for treatment initiation.
Infusions may be done in a clinician’s office; in general infusion centers providing intravenous (IV) therapies to patients with cancer, arthritis, or other disorders; in specialized aducanumab infusion facilities; or at home. Home infusions are administered by a visiting nurse. General infusion center personnel may not be familiar with interacting with cognitively impaired patients and may require specialized training to ensure that the patient has a positive experience fostering a sense of well-being and conducive to treatment adherence. Clinicians should ask patients about any recent symptoms suggestive of ARIA before each infusion. Evidence of coagulopathy, symptoms suggestive of stroke, or poorly controlled blood pressure may be reasons to defer therapy and reevaluate the patient.

ARIA monitoring and management

The most common adverse event produced by aducanumab is ARIA. Aducanumab is associated with a substantially increased rate of ARIA compared to rates observed in natural history studies or trial placebo groups. ARIA (ARIA-E and ARIA-H) occurred in 35.2% of patients on high dose aducanumab compared to an occurrence rate of 2.7% in the placebo group (Table 3) (5). Among those receiving aducanumab, ARIA-E was most commonly observed in participants who were APOE-4 gene carriers (43%) and least often in those without the APOE-4 gene (20.3%). Both symptomatic and asymptomatic ARIA are more common in APOE-4 gene carriers. Thirty percent of ARIA-E were mild (< 5cm on FLAIR imaging with hyperintensity confined to one location); 58% were moderate (5-10 cm involving more than one location); and 13% were severe (> 10 cm) (2). Most ARIA occurs in the first 8 months of treatment during the titration period but can occur any time in the treatment course. ARIA was successfully managed in most patients participating in the pivotal trials without discontinuing treatment; ARIA led to discontinuation from the trials in 6.2% of patients on aducanumab and 0.6% of patients on placebo.

Table 3. Occurrence of ARIA in the entire population and in participants with and without the APOE-4 allele in the two pivotal trials combined (10 mg/kg dose) (5)


Most ARIA events (74%) detected by MRI have no accompanying symptoms. Among those with symptomatic ARIA, symptoms were mild in 67.7%, moderate in 28.3%, and severe in 4% (4). The most common symptoms reported were confusion or altered mental status (5%), dizziness (4%), visual disturbances (2%), and nausea (2%) (2). ARIA episodes typically resolved in 4-16 weeks.

MRIs should be obtained at least 1 year prior to the initiation of treatment and more recently (preferably within 6 months) if there is any suggestion of an intervening central nervous system event (e.g., sudden worsening, transient ischemic attacks). After treatment initiation, MRIs should be obtained before the 5th infusion (before initiating the 6 mg/kg dose); prior to the 7th infusion (before infusion of the first dose of 10 mg/kg); and before the 12th infusion (e.g., before the 6th dose of 10 mg/kg). Given the rate of ARIA-E with the 10 mg/kg dose in the phase 3 studies, especially among APOE-4 carriers, some clinicians may decide to obtain an MRI before the 10th dose, after 3 doses of 10 mg/kg have been administered to avoid failure to detect ARIA that may require active management. MRI studies for ARIA should include FLAIR, T2* GRE and quick DWI. An optional 4th sequence would be either 3D T1 or 3D T2 SPACE (depending on the type of MRI available). In addition to these scheduled MRIs, patients should have an MRI whenever they have symptoms suggestive of ARIA such as headache, vomiting and/or nausea, confusion, dizziness, visual disturbance, gait difficulties, loss of coordination, tremor, transient ischemic attack, new onset seizures, or significant and unexpected acute cognitive decline.
If patients with ARIA (ARIA-E or ARIA-H) have symptoms, treatment should be suspended, and a clinical assessment and neurological examination performed (Figure 2). MRI should be repeated in 1 month; if the ARIA-E has resolved or the ARIA-H is stabilized, treatment can be resumed. If ARIA-E has not resolved and ARIA-H is worsening, treatment is withheld, and monthly MRIs obtained until treatment can be re-initiated or a decision is made to terminate treatment. If three or more doses are missed before restarting aducanumab, the dose should be re-titrated as described above. Aducanumab should not be re-initiated in patients with severe symptomatic ARIA (e.g., seizure, stroke-like syndromes).

Figure 2. Management strategy for ARIA. Patients with severe symptomatic ARIA are not re-titrated and are not candidates for further treatment with aducanumab (Expert Panel recommendation; © J Cummings; illustrator M de la Flor, PhD)


If patients are asymptomatic and the MRI reveals severe or moderate ARIA-E or severe or moderate ARIA-H (Table 4), treatment is suspended, and management follows the procedures described for patients with symptoms (Figure 2). If asymptomatic patients have mild ARIA-E or mild ARIA-H, treatment is continued, and MRIs are obtained at monthly intervals until ARIA-E is resolved or ARIA-H is stable. There is limited information on best practices for management of moderate ARIA-E or moderate ARIA-H and recommendations may evolve.

Table 4. MRI severity levels of ARIA-E and ARIA-H as described in the aducanumab Prescribing Information (1)


Clinicians providing aducanumab need access to MRI facilities and to radiologists familiar with detection and reporting of ARIA-E and ARIA-H. Inexperienced readers may fail to detect signs of ARIA when interpreting scans (35, 49). CT is not sufficient for ARIA monitoring.

Non-ARIA side effect monitoring

Overall adverse events were experienced by 86.9% of patients on placebo and 91.6% of patients on high dose aducanumab in the pivotal trials (5). Adverse events reported more often in patients receiving aducanumab included headache (20.5% vs 15.2% in placebo), falls (15% vs 11.8% in placebo), and diarrhea (8.9% vs 6.8% in placebo). Serious adverse events occurred in 13.9% of patients on placebo and 13.6% of patients receiving aducanumab. There were 5 fatalities among patients on placebo and 8 among those on aducanumab. The Expert Panel recommends vigilance for all potential side effects in patients treated with aducanumab with special attention to headache, falls, and diarrhea.

Effectiveness monitoring

Efficacy was assessed in the pivotal trials using the Clinical Dementia Rating – Sum of Boxes (CDR-sb) (10), Alzheimer’s Disease Assessment Scale – Cognitive Subscale (ADAS-cog) (50), Alzheimer’s Disease Cooperative Study Activities of Daily Living MCI version (ADCS-ADL-MCI) scale (51), MMSE (11), and the Neuropsychiatric Inventory (NPI) (52). These tools were used to assess patients directly (ADAS-Cog; MMSE; portions of the CDR-sb) or through interviews with care partners (ADCS-ADL-MCI; NPI; parts of the CDR-sb). The time of administration of this panel is approximately 2 hours and some of the instruments take substantial training and experience to be administered reliably (e.g., CDR-sb) (53). Use of such a battery is impractical in many medical or neurological practice settings. Objective assessments requiring less time and training may provide insight in the patient’s course; and the clinician should employ tools commonly used in practice. No improvement in cognition or function is anticipated with disease modifying therapy (DMT); slowing of decline and prolongation of the optimal clinical state is the goal of treatment (43). The heterogeneity of decline in early AD makes it difficult to conclude that a slowly progressive disorder is being slowed more by aducanumab (54).
Several means of monitoring treatment effects in the open label practice environment can be considered. The mean change on the MMSE over twelve months in the placebo group in PRIME was (-2.5), in ENGAGE (-3.5), and in EMERGE (-3.3). This provides a range of scores against which the decline in the patient on aducanumab might be compared. The drug-placebo differences observed in EMERGE may guide clinician expectations for the impact of aducanumab on disease progression: this included 18%-27% differences on cognitive decline, 40% difference on functional decline, and 87% difference in behavioral changes. The decline in the late period of MCI due to AD is predictable based on observations in the early MCI period (55). The clinician and care partner may observe differences in the rate of change when aducanumab is introduced and titrated to the 10 mg/kg dose.
The MMSE (11) is commonly used in clinical settings and may be used to monitor patients treated with aducanumab. The MoCA is an alternative to the MMSE (16). The AD8 is a brief informant interview assessing orientation, judgement, memory, and function (56, 57). The AD8 has been shown to have concurrent validity with the CDR used in the pivotal trials and distinguishes patients with MCI (CDR 0.5) from normal elderly with sensitivity of 74% and specificity of 86%. The NPI-Questionnaire is a brief version of the NPI that can be completed by the informant and reviewed by the clinician (58). These three tools are related to or derived from instruments used in the aducanumab pivotal trials. The Functional Activities Questionnaire (FAQ) is a functional rating scale relevant to early AD and is sufficiently brief to be used to assess functional abilities in patients treated with aducanumab (59). The FAQ has good discriminant validity in distinguishing MCI from dementia and performed similarly to the ADCS-ADL-MCI scale in comparative studies (60). These tools are sufficiently brief to be used in practice settings and could be considered for use in evaluating patients receiving aducanumab. Clinicians familiar with CDR administration may consider annual administration of this instrument to assess patient cognitive and functional abilities. The Expert Panel recommends that objective, validated tests to be used longitudinally to assess patients treated with aducanumab.

Stopping therapy

The appropriate timing and strategies for stopping aducanumab therapy have not been studied. Stopping treatment might be informed by patient preferences, care partner decisions, or clinician recommendations based on a perceived lack of effect, ARIA-related concerns, or inability of the patient to adhere to the treatment regimen. Aducanumab should be stopped in all patients manifesting severe symptoms (e.g., seizures, stroke-like manifestations) in the presence of ARIA. Stopping treatment in other ARIA-related circumstances depends on whether ARIA-E resolves after suspending therapy, whether ARIA-H stabilizes when treatment is withheld, the patient’s clinical status, and clinician-patient alignment on the benefit/harm ratio of resuming treatment.
Aducanumab has not been tested in patients with moderate or severe AD and progression into the more advanced phases of AD will prompt reassessment of treatment continuation. Progression into moderate dementia is signaled by progression to CDR global score of 2.0, decline of MMSE scores below 20, and loss of autonomy on key ADLs. The Expert Panel recommends that clinicians carefully review the evidence of benefit and the potential risk in patients who progress to moderate dementia after appropriate use of aducanumab in early AD.

Primary Care Clinicians collaboration

The availability of aducanumab may create a demand for detection, diagnosis and treatment of early AD that can overwhelm an unprepared healthcare system (61). Providing treatment with aducanumab requires high proficiency and sufficient resources including close collaborations with comprehensive multi-disciplinary teams. With too few specialists currently available to respond to the possible number of patients who are candidates for treatment, there are opportunities to forge new models of hub-and-spoke dementia specialist-primary care collaborations and peer-to-peer counseling to partially fill these needs and respond to workforce gaps. The Expert Panel recommends including community organizations, Alzheimer Association chapters, primary care clinicians, memory-care enabled nurses and nurse practitioners, and other creative collaborations and solutions to meet the needs of patients seeking care and encountering the difficulty of being assessed because of shortages of memory care specialists in the current health care system (62-64).

Appropriate Patient Discussions

Aducanumab is an unprecedented therapy; it is the first drug approved for treatment of AD based on plaque lowering and addressing the underlying pathophysiology of AD. Clinicians, patients, care partners, and stakeholders of the healthcare system must learn and adjust to the new therapeutic circumstances. Discussions with patients and care partners are particularly important. They require information regarding the possible benefits of aducanumab, the side effects including ARIA, and the likely need for long term adherence to treatment. Dementia medication discontinuation rates have been shown to be higher in African American and Hispanic patients than White patients; racially and ethnically appropriate strategies may be required to optimize adherence (65). Referral to the Alzheimer’s Association ( and other trusted community sources can assist the clinician in providing reliable information.

Aducanumab Treatment in Non-AD Amyloid-Bearing Conditions and Atypical AD

Autosomal dominant AD is produced by mutations of presenilin 1, presenilin 2, or the amyloid precursor protein gene. Patients typically develop amyloid plaques as evidenced by amyloid PET in their mid to late 30’s and progress to MCI due to AD and mild AD dementia at age 45 to 55 (66). The individuals have the canonical features of AD at autopsy (67). Few if any of these patients were included in the aducanumab clinical trials. The Expert Panel Recommends that if patients with autosomal dominant AD meet all other criteria for aducanumab treatment described in Table 1, they could be considered candidates for aducanumab and the option can be discussed with families. They should be informed of the scarcity of data in patients with the inherited form of AD.
Individuals with Down syndrome essentially uniformly develop brain amyloid plaques and often have symptoms of dementia in midlife (68, 69). The presence of amyloid plaques in Down syndrome suggests that treatment with aducanumab may be beneficial. There are many differences between Down syndrome and late onset AD, and the Expert Panel recommends against treating Down syndrome patients with aducanumab until more data are available.
Patients with AD may present with atypical syndromes such as logopenic aphasia, posterior cortical atrophy, or frontal AD (70). These patients have metabolic scans that reflect the regional dysfunction corresponding to their clinical presentation; other biomarkers are characteristic of AD (71). Few patients with atypical features were included in the aducanumab trials. The Expert Panel recommends that if patients with atypical AD meet all the criteria for the appropriate use of aducanumab, they can be considered as candidates for aducanumab treatment while cautioning patients and families that little information regarding use of aducanumab is available on patients with these clinical profiles.
Patients with dementia with Lewy bodies (DLB) have MCI that progresses to dementia. They have characteristic clinical features including parkinsonism, visual hallucinations, fluctuating cognition, and rapid eye movement sleep behavior disorder (72). Patients with DLB may have pure Lewy body pathology or may have concomitant Lewy body changes and Aβ plaques. Those with Aβ plaques will have positive amyloid PET imaging (73). The Expert Panel Recommends that patients with DLB not be treated with aducanumab; the effect of treatment in patients with mixed amyloid and Lewy body pathology is unknown.
The ability to image cognitively normal individuals or conduct lumbar puncture and CSF analyses allows the detection of persons in the preclinical phases of the AD continuum. These individuals have amyloid plaques in the brain but are cognitively normal. All participants in the aducanumab clinical trials were symptomatic and met criteria for MCI due to AD or mild AD dementia. There are no data on the utility of treating individuals in the preclinical disease state with aducanumab. The Expert Panel recommends against treating patients in the preclinical phase of AD with aducanumab until additional data are available.
Care partners seek means of improving quality of life for their loved one regardless of the degree of the patient’s dementia-related disability. Patients with moderate to severe AD and their caregivers will seek information about aducanumab and may wish to be treated. There are no data available on the use of aducanumab in moderate and severe AD. The Expert Panel recommends against beginning aducanumab therapy in patients with moderate to severe AD (e.g, those with cognitive deficits beyond mild severity and requiring substantial assistance with activities of daily living). These patients require comprehensive compassionate care, and their support must continue regardless of DMT therapy status. Multidisciplinary interventions at this stage can significantly improve quality of life (64, 74).
The amyloid, tau, neurodegeneration (AT(N)) framework is influential in the biomarker classification of AD (75). Using this approach, A+T-N-, A+T+N-, and A+T+N+ patients would be considered candidates for treatment with aducanumab if they have early AD and meet all treatment criteria (Table 1). A+T-N+ patients may have some disorder such as vascular pathology in addition to amyloidosis that may impact aducanumab therapy. Further evaluation of these patients is required before proceeding with therapy.
Patients with cerebral amyloid angiopathy may have positive amyloid PET (76). Use of aducanumab in these patients may promote ARIA (77). The Expert Panel recommends that aducanumab not be used in patients with cerebral amyloid angiopathy.

Potential Future Changes in Appropriate Use of Aducanumab

AD science is evolving rapidly in both diagnostic and therapeutic technologies. Blood tests that assist in the diagnosis of AD could have a transformative influence on the care of AD patients and the appropriate use of aducanumab. Blood assays that determine the Aß42/40 ratio have good correspondence with amyloid PET status (receiver operator curse area under the curve [AUC] 0.88) and this improves when combined with patient age and APOE-4 genotype (AUC 0.94) (78). Plasma hyperphosphorylated tau (p-tau) 181 and p-tau 217 are abnormal in early AD and correlates significantly with amyloid burden on PET (79-81). One of these plasma-based markers or a panel of markers possibly including APOE genotype could eventually provide a diagnosis of brain amyloidosis in patients with symptoms of early AD or could function as a case-finding tool to identify patients likely to have an abnormal amyloid PET.
Blood tests may not be the only means of identifying amyloidosis in patients with the clinical syndrome of early AD. Amyloid is deposited in the retina in AD, and retinal imaging might be another means of detecting central nervous system amyloidosis (82, 83). Digital biomarkers could play a role in case finding or diagnostic confirmation. Voice and language analyses, for example, are promising means of identifying early AD (84, 85).
Currently, aducanumab treatment is administered with the plan of continuing at least until the patient reaches the moderate stage of AD dementia. However, once significant amyloid lowering has been achieved it may be possible to reduce the frequency of infusions. The durability of amyloid lowering was explored in a trial with another amyloid-targeting monoclonal antibody (86) with encouraging results.
Prevention of AD is an important goal of AD research. Trials of aducanumab during the preclinical phases of AD when the brain has high levels of amyloid but cognition remains largely normal may expand the range of individuals appropriate for treatment (87)
Patients with Down syndrome that meet all the other criteria for treatment with aducanumab may become treatment-eligible when additional studies have been conducted and additional data are available (88).



Aducanumab is a new treatment for AD. It provides opportunities and challenges for its introduction into the management of AD patients. Aducanumab requires substantial infrastructure for appropriate administration: expert clinicians skilled in recognition of early AD; amyloid PET or lumbar puncture capability; experts in amyloid PET interpretation or CSF analysis: infusion center availability; and access to MRI and experts in recognition and management of ARIA (Table 5). Genetic counseling may be required in some circumstances, and all patients and care partners require education and support. Building this infrastructure for the appropriate use of aducanumab will require time, resources, and creative planning. Appropriate use of aducanumab requires a commitment to patient-centered care and best-practices for the safe delivery of this new treatment.

Table 5. Resources needed for the appropriate use of aducanumab (Expert Panel Recommendations)


Disclosure and Conflicts of Interest: JC has provided consultation to Acadia, Alkahest, AriBio, Avanir, Axsome, Behren Therapeutics, Biogen, Cassava, Cerecin, Cerevel, Cortexyme, EIP Pharma, Eisai, GemVax, Genentech, Green Valley, Grifols, Janssen, Jazz, Karuna, LSP, Merck, Novo Nordisk, Otsuka, ReMYND, Resverlogix, Roche, Signant Health, Sunovion, Suven, United Neuroscience, and Unlearn AI pharmaceutical and assessment companies. Dr. Cummings owns the copyright of the Neuropsychiatric Inventory. Dr Cummings has the following research support: NIGMS P20GM109025; NINDS U01NS093334; NIA R01AG053798; NIA P20AG068053; NIA R35AG71476. LGA has provided consultation to Eli Lilly, Biogen, and Two Labs. LGA receives the following research support: NIA U01 AG057195, NIA R01 AG057739, NIA P30 AG010133, Alzheimer Association LEADS GENETICS 19-639372, Roche Diagnostics RD005665, AVID Pharmaceuticals, Life Molecular Imaging. LGA has received honoraria for participating in independent data safety monitoring boards and providing educational CME lectures and programs. LGA has stock in Cassava Sciences. AA has received honoraria for consulting; participating in independent data safety monitoring boards; providing educational lectures, programs, and materials; or serving on advisory boards for AbbVie, Acadia, Allergan, the Alzheimer’s Association, Axovant, AZ Therapies, Biogen, Grifols, Harvard Medical School Graduate Continuing Education, JOMDD, Lundbeck, Merck, Roche/Genentech, Novo Nordisk, Sunovion, and Suven. PA has received research funding from NIA, FNIH, the Alzheimer’s Association, Janssen, Lilly and Eisai, and personal fees from Biogen, Merck, Roche, Abbvie, ImmunoBrain Checkpoint, Rainbow Medical and Shionogi. SS was a site PI and co-chair of the investigator steering committee for the ENGAGE trial and he receives research support and consultancy fees from Lilly, Biogen, Avid, Eisai, Genentech, and Roche. MW has served on Advisory Boards for Eli Lilly, Cerecin/Accera, Roche, Alzheon, Inc., Merck Sharp & Dohme Corp., Nestle/Nestec, PCORI/PPRN, Dolby Family Ventures, National Institute on Aging (NIA), Brain Health Registry and ADNI. He serves on Editorial Boards for Alzheimer’s & Dementia, TMRI and MRI. He has provided consulting and/or acted as a speaker to Cerecin/Accera, Inc., BioClinica, Nestle/Nestec, Roche, Genentech, NIH, The Buck Institute for Research on Aging, FUJIFILM-Toyama Chemical (Japan), Garfield Weston, Baird Equity Capital, University of Southern California (USC), Cytox, and Japanese Organization for Medical Device Development, Inc. (JOMDD) and T3D Therapeutics. He holds stock options with Alzheon, Inc., Alzeca, and Anven.

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Z. Jiayuan1, J. Xiang-Zi2,*, M. Li-Na1, Y. Jin-Wei1, Y. Xue3


1. Psychology Nursing, Harbin Medical University, Daqing, Heilongjiang) China; 2. Business management department, Suzhou Industrial Park Institute of Service Outsourcing, Suzhou, Jiangsu, China; 3. Neurology/Daqing Longnan Hospital, Daqing, Heilongjiang, China. * co-first author

Corresponding Author: Meng Li-Na, No.39 Xinyang Street, Harbin Medical University, Daqing, Heilongjiang Province, China, Tel: 86-18604586122, Email:

J Prev Alz Dis 2021;
Published online July 5, 2021,



Background: The Objective: To assess the effectiveness of a mindfulness-based Tai Chi Chuan on physical performance and cognitive function among cognitive frailty older adults.
Design: A single-blind,three-arm randomized controlled trial.
Setting: Three communities in Daqing, China.
Participants: The study sample comprised 93 men and women aged 65 years or older who were able to walk more than 10 m without helping tools, scored 0.5 on Clinical Dementia Rating (CDR) and absence of concurrent dementia, identified pre-frailty (scored 1-2 on Fried Frailty Criteria) and frailty older adults (scored 3-5 on Fried Frailty Criteria).
Intervention: Subjects were randomly allocated to three groups: Group1, which received mindfulness intervention (formal and informal mindfulness practices); Group 2, which received Tai-Chi Chuan intervention; Group 3, which received MTCC intervention.
Measurements: The primary outcomes was cognitive frailty rate(measured by Fried Frailty Criteria and Clinical Dementia Rating-CDR) , the secondary outcome were cognitive function (measured by Min-Mental State Examination-MMES) and physical level (measured by Short physical performance battery- SPPB, Timed up and Go test-TUG and the 30-second Chair test). They were all assessed at Time 1-baseline, Time 2-after the end of 6-month intervention and the follow up (Time 3-half year after the end of 6-month intervention).
Results: The baseline characteristics did not differ among the groups.Improvements in the cognitive function (MMES), physical performance (SPPB, TUG, 30-second Chair test) were significantly difference between time-group interaction (p<.05). The rate of CF was significantly different among groups at 6-month follow-up period (χ2=6.37, p<.05). A lower prevalence of frailty and better cognitive function and physical performance were found in the Group 3 compared with other two groups at the follow-up period (p<.05).
Conclusions: MTCC seems to be effectively reverse CF, improving the cognitive and physical function among older adults, suggesting that MTCC is a preferably intervention option in community older adults with cognitive frailty.

Key words: Cognitive frailty, mindfulness, Tai Chi, physical, cognitive.




Physical and cognitive impairment frequently overlap in older adults. Recent studies have showed that an interrelationship between physical frailty and cognitive impairment existed (1). Therefore a new conceptual construct—cognitive frailty (CF)—characterized by the simultaneous existence of both cognitive impairment and frailty, was proposed in 2013 by an international consensus group (2). A recent meta-analysis which focused on CF older adults in community showed that the CF was found to be a significant predictor of the short-term and the long-term mortality and dementia, disability, and other adverse health outcomes (3). Older adults with CF are more likely to develop dementia than the two individual components, and could provide a new direction for healthy aging (4). Early diagnosis, detection and intervention of CF are of great significance for delaying the occurrence and development of dementia (5). A series of evidence suggested that physical exercise was associated with improvements in health related outcomes in older adults with frailty (6-9). However, currently no optimal interventions can be recommended for cognitive function and physical health promotion in older adults with CF as the evidence base is small and of limited quality.
In the last decade, Tai Chi Chuan (TCC) has been widely adopted in physical exercise aiming to improve physical performance in community older adults in China, which being proposed as a high efficiency exercise by the Centers for Disease Control and Prevention (CDC) (10). The pulling movement in TCC is beneficial to improve the flexibility and coordination of the body and reduce the probability of injury. Several studies proved that TCC could effectively promote physical fitness and muscle protein anabolism in frail older adults and was more effective than conventional exercise approaches for reducing the incidence of falls (11-14). The training spirit of TCC is to complete harmony of body and mind which is similar to the mindfulness skills. Mindfulness is broadly defined as present-focused, non-judgmental awareness (15). Evidence shows that mindfulness-based interventions has yielded positive effects on enhancing the ability of cognitive reserve and slowing down the aging-associated cognitive decline, promoting active aging among mild cognitive impairment (MCI) older adults in the community (9). Regarding to the consistency of core mechanism between mindfulness and TCC, some researchers have mixed systematic mindfulness training with TCC to investigate the health benefits both in physical and psychology (16-17). Mindfulness-based Tai Chi Chuan (MTCC) may be more suitable for CF older adults, however, whether MTCC can reduce cognitive frailty is currently unknown.
With the continuing growth of the CF population and the community health care policy in China, implementing a more feasible and effective intervention would widely promote healthy aging. Consequently, in this study we aim to use a rigorous randomized control trial design to investigate the effects of an MTCC intervention on cognitive function and physical performance in the CF older adults. We hypothesized that compared with mindfulness intervention and TCC exercise, MTCC would be greatly more effective in both cognitive and physical aspects among CF populations.



Study Design

This study was a single-blind, three-arm randomized controlled trial conducted in three communities in Daqing, China. All participants who met the inclusion criteria and agree to participant finished the written informed consent, and then they were allocated to group 1 (Mindfulness group) or group 2 (TCC group) or group 3 (MTCC group) by lottery method. The study was approved by the the committee on ethics in research of Harbin Medical University. Our reporting in the manuscript adheres to the CONSORT 2010 guidelines. The study was registered under Chinese Clinical Trials Registry (ChiCTR2100042851).


Participants were recruited by putting up a poster through collaboration with three communities. The inclusion criteria included: 1- aged 65 years or older; 2- had no serious mental or physical disease; 3- could walk without helping tools; 4- a score of 0.5 in CDR ; 5- pre-frail and frail older adults. These with dementia or undergoing similar or other physical and cognitive intervention were excluded. Finally 93 participants were recruited (Figure 1).

Figure 1. Study flow diagram


Randomization and Masking

Participants were randomly assigned to either a mindfulness intervention or a TCC group or a MTCC group using sequentially numbered, opaque, sealed envelopes with NCR (no carbon required) paper, conducted by a trained research assistant independent of the study design.The implement of interventions and data collection were conducted by independent teams that blinded to to group allocation.


The programs in this study were established and carried out by professional team which consisted of two psychologists (qualified in mindfulness intervention for eleven years), two physical education and sports specialists (qualified in TCC program for more than ten years) and one rehabilitation medicine specialist (major in geriatric rehabilitation). All the participants received a six-month intervention which consisted two stage: the first stage was a 1-hour group intervention (varied in size) twice weekly for three months in community facilities, such as senior or community centers and the second stage was a 1-hour individual practice twice weekly for three months. To avoid the group contamination, the interventions were separated in time and sites. The details of each group intervention were as follows,
Group 1 (mindfulness intervention): All participants received a booklet about mindfulness skills. It consisted of four basic forms of meditation practices (body scan, walking meditation, gentle yoga, sitting meditation). During group intervention, each session began with a 10-minute short-review aiming to solving the existing problems, and then a 45-minute exercises and a 5-minute summary. After participants had a good grasp of basic mindfulness practice, they were taught to integrate mindfulness into daily life such as eating, hearing, smelling, observing.During individual session, participants were required to continue mindfulness training under the supervision and guidance for 3 months.
Group 2 (TCC intervention): All participants received a picture booklet about induction of TCC. 24-Simplified TCC was conducted. During group intervention, each session began with a 10-minute warm-up (including muscle stretching and joint movement) aiming to avoid injure, and then a 45-minute exercises and a 5-minute cool-down activity (deep breathing and relaxing). Participants were taught how to carry out different TTC forms such as “Starting Posture”, “Hold the Lute”, “Cloud Hands” , “Turn and Kick with Left Heel”, etc. During individual session, participants were required to continue TCC training under the supervision and guidance for 3 months.
Group 3 (MTCC intervention): All participants received a booklet about MTCC program.The MTCC involved practice of a core of modified exercise forms mixed together mindfulness and TCC postures aimed at stimulating and integrating body, sensory, and cognitive systems, the practice focused on present, non-judgment, peaceful state involving TCC movements.
During group intervention, participants were taught how to be mindfulness and find connections between mindfulness and TCC practice. Besides, the modified MTCC forms were introduced to participants. After they grasped the basic practice skills, they began to carry out different MTTC forms including the seated formats, standing formats and stepping formats. Each session began with a 10-minute warm-up (including muscle stretching and joint movement) aiming to avoid injure, and then a 45-minute exercises and a 5-minute cool-down activity (deep breathing and relaxing). During individual session, participants were required to continue MTCC training under the supervision and guidance for 3 months.
The theme of each group were shown in Table 1. If all participants have any problems during intervention period, they could contact their tutor at any time.

Table 1. The theme of each group during group intervention


Variables and Outcomes

Demographic data was collected by self-reported questionnaires: age, sex and BMI. Assessment were conducted at baseline (Time 1-one week before commencing the program), after the intervention (Time 2-six month after commencing the program) and follow-up (Time 3-one year after commencing the program).
The primary outcome was the rate of CF at follow up period among different groups. We defined no cognitive frailty as non-frail (scored 0 on Fried Frailty Criteria) and without MCI (CDR=0). 1- Fried Frailty Criteria, which is based on the five Cardiovascular Health Study criteria defined as: slowness/unintentional weight loss/weakness/exhaustion/low physical activity (18); 2- Clinical Dementia Rating (CDR) was used to assess the cognitive function from six aspects :memory/orientation/judgment and problem solving/community affairs/home and hobbies/and personal care.The levels ranged from 0-3 (none to severe). The higher scores, the poorer cognitive performance (19).
The secondary outcomes were change of cognitive function and physical performance of participants. The assessment included: 1- Cognitive function-The Mini-Mental State Examination (MMSE) consists of 11 items was used to assess the cognitive function from five aspects :orientation/memory/attention/language ability /comprehensive/judgment. The score ranged from 0-30 that higher score indicate better cognitive function (20); 2- Physical function-Short physical performance battery (SPPB) was used to assess gait speed, chair stand, and balance tests. Total scores ranged 0-12 that higher points indicate better physical function (21); The Timed Up and Go test (TUG) was used to assess mobility and requires both static and dynamic balance, the time it took form the beginning movement until returned to the seated position was recorded in seconds (22); The 30-second Chair test was administered to assess the core strength which calculated by times (23).

Data analysis

The sample size was calculated by using the G-power 3.1 program with a power (1- β) of 0.95, a significance level of 0.05, From related data (6), we set an effect size of 0.21, finial a sample size was 75 participants, and assuming a 20% estimate loss of follow-up. Finally the sample size was 93. Statistical analyses were performed using SPSS 22.0 (IBM Corp., Armonk, NY, USA). All analyses followed the intent-to-treat principle. Categorical variables were expressed as percentages and continuous variables with mean and SD. Demographic characteristics and basic CF level were compared using a t-test and Chi-square tests. Between-group differences for the effects of intervention were assessed using the Chi-square test for CF rate.Three-way analysis of variance (ANOVA) compared outcome variables between the three groups at three assessment points (three time points × three groups). The post hoc Bonferroni test was used to assess changes within groups. Set p <0.05 as statistically significant.



There were two participants lose to follow up, one in group 1 (because of refuse to continue the study) and one in group 3 (because of be hospitalized) respectively and finally a total of 91 participants completed all the assessment.

Demographic and baseline characteristic

Baseline characteristics were not significantly different among the three groups. The mean age was 71.4±4.6 years (range = 65-81 y), and 56% (n=51) were female. The details of physical performance and cognitive function variables across groups at the baseline were illustrated in (Table 2.Characteristics of Participants). Fried’s Frailty Criteria, MMSE, SPPB, TUG, and 30-second Chair test showed no significantly different and has the comparability among groups (P > .05).

Table 2. Characteristics of Participants (N = 91)

Note. BMI- Body Mass Index;MMSE- Mini Mental State Examination ;SPPB-Short Physical Performance Battery; TUG,Time Up and Go;SD-Standard Deviation


Effects of interventions on CF rate after one year

The Group 1 (mindfulness group) showed there were 2 participants reversed to no CF state and the reversed rate was 6.7%; Group 2 (TCC group) showed there were 4 participants reversed to no CF state and the reversed rate was 12.9%; Group 3 (MTCC group) showed there were 9 participants reversed to no CF state and the reversed rate was 30%.The distribution of CF significantly differed among groups in the follow-up period (χ2=6.37, P=0.041).

Effects of interventions on physical performance and cognitive function

Table 3 revealed the changes in frailty, physical performance and cognitive function from baseline to follow-up (Time 1-Time 3) in the three groups. Group 3 showed the changes better outcomes than the Group 1 and Group 2 at the post hoc significance level in the scores for frailty(p=.039), SPPB (p=.004),TUG ( p<.000), MMSE (p=.018) in the follow up period. However, difference in score for 30-second chair test (p=.112) did not reach statistical significance difference (p>.05). The variables of physical performance (SPPB, TUG, 30-second chair test) and cognitive function (MMSE) showed significant Group×Time interaction (p<.05). The details were shown in Table 3 and Figure 2.

Table 3. Changes in all variables across time among the groups (N = 91)

Note. SPPB-Short Physical Performance Battery; TUG,Time Up and Go;MMSE- Mini Mental State Examination ;SD = standard deviation


Figure 2. Changes in all variables across time among the groups

A-C: Physical Performance. D:cognitive function. SPPB- Short Physical Performance Battery; TUG, Time Up and Go; MMSE- Mini Mental State Examination



To our best knowledge, this is the first three-arm RCT study to compare the effects of three intervention programs on cognitive function and physical performance in community older adults with CF. The main finding in this study revealed that the 6-month MTCC intervention was optimal in reversing cognitive frailty, significantly improving the cognitive function and physical performance. According to the definition of CF by the International Academy on Nutrition and Aging (IANA) (24), in our study participants were recruited based on the cognitive state (a point of 0.5 in CDR, without concurrent dementia) with pre-frailty (scored 1-2 on Fried Frailty Criteria) or frailty level (scored 3-5 on Fried Frailty Criteria). The participants in MTCC group showed a highest reverse rate of CF (30%) at 6-month follow-up. Moreover, the MTCC group also showed significant improvement from baseline in SPPB, TUG and MMSE at the end of intervention and follow-up period.
Regarding the improvement of physical performance, participants in the TCC group and MTCC group gained more benefits than mindfulness group. Both of the TCC and MTCC group received the Chinese traditional exercise program- Tai Chi Chuan. In our study, we applied the 24-simplified TCC to participants which was more suitable for older adults. After 6-month intervention, the physical function of gait speed, static and dynamic balance, range of motion, reflex control and core strength were increased. Previous studies have reported that TCC training leads to positive changes in neuromuscular functions in older adults (13, 25). The findings from our study are aligned with other RCT trails on the effect of TCC exercise on promoting physical benefits in older adults (26-27). As one of the highest-tier evidence-based health-promoting and disease prevention exercise, TCC has been widely adopted by communities in China. In our study, TCC exercise training program was adjusted to be easily implementable according to the condition and demand of older adults. Compared with TCC group, MTCC received modified Tai Chi Chuan program that was a mindfulness-based TCC consisted of training of mind-body coordination, the beneficial effects on physical function was more significant. The aim of practice was to achieve the state of «motion in quietness» and focus on the moment. In MTCC program, participants were taught to carry out gentle TCC postures under the continuous attention of consciousness to avoid brute force so as to improve the effect of exercise.
In terms of cognitive function, significant change in MMSE was found between group and time interaction in the present study. What’s more, the MMSE score in MTCC group yielded the highest among the three groups. The participants in MTCC group received the mindfulness skills which combined with TCC exercise, both of them were beneficial to cognitive recovery. The systemic review reported that mindfulness training has a positive effect on cognitive functions in a wide group of populations, including the aging adults with early cognitive degeneration (28). A recent study found that the mindfulness practice could enhance functional brain connectivity in the default mode network in older adults with cognitive impairment and reduce hippocampal volume atrophy in MCI with positive impacts on brain regions most related to dementia (29). Regarding to the receptivity of the older adults, we integrated the systemic mindfulness skills into daily life and exercise which was easily for older adults to grasp and practice everyday. After the 6-month intervention , most of CF older adults in MTCC group could practice by themselves and continue to practice in future. Training with mindfulness involved TCC, participants cultivated self-regulation of attention, allowing attention to be maintained on the immediate exercise experience allowing for increased recognition in the present that helped them to achieve a “harmony of body and mind” state to promote health.
One major strength of our study was that we adopted a multidomain program – MTCC-involving not only cognitive training (mindfulness skills) but also combined with physical activity (TCC) to facilitate training-driven brain plasticity and better performance in physical and cognitive functions. To our best knowledge, this was the first RCT design to investigate the effectiveness of MTCC program on the cognitive function and physical performance in CF older adults. The MTCC training yielded a notable result in reversing the CF. After 6-month follow up, thirty percentage of participants in MTCC group had no frailty and MCI which was very impressive. For Chinese community older adults, the MTCC program were easy to grasp and apply to their daily life. The lose of follow-up rate was very low (2.75%) which represented participants adhere with the study. Moreover, most of participants reflected the program was helpful and useful and they would like to practice after the end of intervention. Previous studies have proved that cognitive intervention or physical exercise had beneficial effects on older adults (30-32). It was recommended that the comprehensive program which led to both promotion in physical performance and cognitive function should be targeted to the older adults (33-34), however fewer feasible interventions could systematically and effectively improve the conditions of CF older adults. In this study we designed a MTCC program tailored for older adults which was easily implementable and efficient. In accordance with hypothesis, a six-month MTCC training program resulted in a long-term effect on a significant reduction in the CF rate and promotion in the cognitive and physical functions among older adults with CF. During the MTCC training, participants were required to focus on the present moment, involving self awareness and understanding of every movement process which involved posture shift and center of gravity shift. The weight of the body changed all the time to strengthen the ability of control the balance of the body (35-36). The outstanding benefits of MTCC may due to capture the similarities of mindfulness and TCC and combine them to create the overlapping effect through practice.
Although we use a rigorous and well-controlled randomized trials, the study has several limitations. Firstly, due to the limitations of funding, the validated biochemical measures for CF older adults were not used in this study, further study investigations using subjective and objective indicators are recommended to investigate the mechanism of the interventions.
Secondly, although this trial was conducted in multicenter, these communities were in one province. To generalize the findings could be enhanced by involving more states.



In conclusion, our findings proved that mindfulness practice and TCC exercise were beneficial in the domains of cognitive function and physical performance among CF older adults. Moreover, the current randomized controlled trail showed evidence that the tailored MTCC program combined mindfulness practice and TCC exercise was most effective in reversing CF state. MTCC was a efficacious and innovative program that could serve as a model to improve cognitive function and physical performance in community older adults with CF in China.


Funding: This study was supported by grants from the Ministry of education, humanities and social sciences research projects [Fund No.17YJCZH129].

Acknowledgments: All authors were grateful for all the participants and community staffs in this study for their corporation.

Conflict of interest: All authors have declared no conflicts of interest for this article.

Ethical standard: The study design was approved by the Institutional Review Board of Harbin Medical University. All participants signed written informed consent.



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33. Srisuwan P, Nakawiro D, Chansirikarnjana S, Kuha O, Chaikongthong P.Suwannagoot T.Effects of a Group-Based 8-Week Multicomponent Cognitive Training on Cognition, Mood and Activities of Daily Living among Healthy Older Adults: A One-Year Follow-Up of a Randomized Controlled Trial.The Journal of Prevention of Alzheimer’s Disease 2020;2:112-120. DOI:10.14283/jpad.2019.42
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A.P. Porsteinsson1, R.S. Isaacson2, S. Knox3, M.N. Sabbagh4, I. Rubino5
1. University of Rochester School of Medicine and Dentistry, Rochester, NY, USA; 2. Weill Cornell Medical Center and New York-Presbyterian, New York, NY, USA;
3. Biogen International GmbH, Baar, Switzerland; 4. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; 5. Biogen Inc, Cambridge, MA, USA

Corresponding Author: Sean Knox, MBChB. Biogen International GmBH, Neuhofstrasse 30, 6340 Baar, Switzerland. Phone: +41413921976; Email:

J Prev Alz Dis 2021;3(8):371-386
Published online June 9, 2021,


Alzheimer’s disease is a progressive, irreversible neurodegenerative disease impacting cognition, function, and behavior. Alzheimer’s disease progresses along a continuum from preclinical disease, to mild cognitive and/or behavioral impairment and then Alzheimer’s disease dementia. Recently, clinicians have been encouraged to diagnose Alzheimer’s earlier, before patients have progressed to Alzheimer’s disease dementia. The early and accurate detection of Alzheimer’s disease-associated symptoms and underlying disease pathology by clinicians is fundamental for the screening, diagnosis, and subsequent management of Alzheimer’s disease patients. It also enables patients and their caregivers to plan for the future and make appropriate lifestyle changes that could help maintain their quality of life for longer. Unfortunately, detecting early-stage Alzheimer’s disease in clinical practice can be challenging and is hindered by several barriers including constraints on clinicians’ time, difficulty accurately diagnosing Alzheimer’s pathology, and that patients and healthcare providers often dismiss symptoms as part of the normal aging process. As the prevalence of this disease continues to grow, the current model for Alzheimer’s disease diagnosis and patient management will need to evolve to integrate care across clinical disciplines and the disease continuum, beginning with primary care. This review summarizes the importance of establishing an early diagnosis of Alzheimer’s disease, related practical ‘how-to’ guidance and considerations, and tools that can be used by healthcare providers throughout the diagnostic journey.

Key words: Alzheimer’s disease, early diagnosis, diagnostic work-up.


Dementia is among the greatest global health crises of the 21st century. Currently, more than 50 million people are living with dementia worldwide (1), with this number estimated to triple to 152 million by 2050 as the world’s population grows older (2). Alzheimer’s disease (AD) is the most common cause of dementia and is thought to account for 60–80% of dementia cases (3). Currently, the total annual cost for AD and other dementias in the USA is $305 billion and is predicted to increase to more than $1.1 trillion by 2050 (3). This substantial economic burden includes not only healthcare and hospice support for patients with AD (3) but also lost productivity from patients and caregivers (4).
AD is a progressive, neurodegenerative disease associated with cognitive, functional, and behavioral impairments, and characterized by two underlying pathological hallmarks: the progressive accumulation of extracellular amyloid beta (Aβ) plaques and intracellular neurofibrillary tangles (NFTs) (3). In AD, aggregated Aβ plaques are deposited within the brain as a result of either reduced Aβ clearance or excessive production (5); plaque deposition typically occurs ~20 years before the onset of cognitive impairment (6, 7). NFTs are formed by the abnormal accumulation of hyperphosphorylated-tau protein (5); these can be detected 10–15 years before the onset of symptoms (6, 7).
AD follows a progressive disease continuum that extends from an asymptomatic phase with biomarker evidence of AD (preclinical AD), through minor cognitive (mild cognitive impairment [MCI]) and/or neurobehavioral (mild behavioral impairment [MBI]) changes to, ultimately, AD dementia. A number of staging systems have been developed to categorize AD across this continuum (7–9). While these systems vary in terms of how each stage is defined, all encompass the presence/absence of pathologic Aβ and NFTs, as well as deficits in cognition, function, and behavior (7–9). As a result, subtle but important differences exist in the nomenclature for each stage of AD depending on the selected clinical and research classifications (Figure 1).

Figure 1. Stages within the Alzheimer’s disease continuum

The AD continuum can be classified into different stages from preclinical AD to severe AD dementia; the nomenclature associated with each stage varies between the different clinical and research classifications. This figure provides a summary of the different naming conventions that are used within the AD community and the symptoms associated with each stage of the continuum; *Mild behavioral impairment is a construct that describes the emergence of sustained and impactful neuropsychiatric symptoms that may occur in patients ≥50 years old prior to cognitive decline and dementia (112); Abbreviations: Aβ, amyloid beta. AD, Alzheimer’s disease. FDA, Food and Drug Administration. IWG, International Working Group. MCI, mild cognitive impairment. NIA-AA, National Institute on Aging—Alzheimer’s Association

Preclinical AD, as the earliest stage in the AD continuum, comprises a long asymptomatic phase, in which individuals have evidence of AD pathology but no evidence of cognitive or functional decline, and their daily life is unaffected (8) (Figure 1). The duration of preclinical AD can vary between individuals, but typically lasts 6–10 years depending on the age of onset (10, 11). The risk of progression from preclinical AD to MCI due to AD (with/without MBI) depends on a number of factors, including age, sex, and apolipoprotein E (ApoE) status (11, 12); however, not all individuals who have underlying AD pathology will go on to develop MCI or AD dementia (13, 14). A recent meta-analysis of six longitudinal cohorts followed up for an average of 3.8 years found that 20% of patients with preclinical AD progressed to MCI due to AD (11). A further study by Cho et al., with an average follow-up rate of 4 years, found that 29.1% of patients with preclinical AD progressed to MCI due to AD (12).
For patients who do progress to MCI due to AD (with/without MBI), initial clinical symptoms typically include short-term memory impairment, followed by subsequent decline in additional cognitive domains (15) (Figure 1). On a day-to-day basis, an individual with MCI due to AD may struggle to find the right word (language), forget recent conversations (episodic memory), struggle with completing familiar tasks (executive function), or get lost in familiar surroundings (visuospatial function) (15, 16). As individuals have varying coping mechanisms and levels of cognitive reserve, patients’ experiences and symptomology vary widely; however, patients tend to remain relatively independent at this stage, despite potential marginal deficits in function. The prognosis for patients with MCI due to AD can be uncertain; one study that followed up patients with MCI due to AD for an average of 4 years found that 43.4% progressed to AD dementia (12). Other studies reported 32.7% and 70.0% of individuals with MCI due to AD progress to AD dementia within 3.2 and 3.6 years of follow-up, respectively (17, 18). Patients who do progress to AD dementia will develop severe cognitive deficits that interfere with social functioning and will require assistance with activities of daily living (7) (Figure 1). As the disease progresses further, increasingly severe behavioral symptoms will develop that significantly burden patients and their caregivers, and the disease ultimately results in severe loss of independence and the need for round-the-clock care (3).
An early diagnosis of AD can provide patients the opportunity to collaborate in the development of advanced care plans with their family, caregivers, clinicians, and other members of the wider support team. Importantly, it also enables patients to seek early intervention with symptomatic treatment, lifestyle changes to maintain quality of life, and risk-reduction strategies that can provide clinically meaningful reductions in cognitive, functional, and behavioral decline (19–22). It can also help reduce healthcare system costs and constraints: a study by the Alzheimer’s Association found that diagnosing AD in the early stages could save approximately $7 trillion. These savings were due to lower medical and long-term care costs for patients with managed MCI than for those with unmanaged MCI and dementia (3). Furthermore, an early diagnosis will be vital for patients when a therapy addressing the underlying pathology of AD becomes available; currently 19 biologic compounds are under Phase 2 or 3 investigation (23). Physicians will need to be prepared for the approval of these treatments, to optimize the potential benefit and prolong preservation of patients’ cognitive function and independence beyond that associated with current standard of care (19).
As the prevalence of AD continues to grow, the advancement of AD patient diagnosis will require an orchestrated effort, starting in the primary care setting and subsequently involving multiple healthcare provider (HCP) specialties (e.g., nurse practitioner [NP] or physician assistant [PA]) throughout the disease continuum. Galvin et al. recently highlighted the need for HCPs to work as an integrated, patient-centered care team to accommodate the growing and diverse population of patients with AD, beginning with diagnosis (24). For patients to receive a timely diagnosis, it is vital to implement an approach that minimizes the burden placed on the patient, clinician, and healthcare system (25). Here, we summarize the importance of establishing an early diagnosis of AD, related practical ‘how-to’ guidance and considerations, and tools that can be used by healthcare providers throughout the diagnostic journey.

The importance of an early diagnosis

Historically, a diagnosis of AD has been one of exclusion, and one only made in the latter stages of disease (26); however, the disease process can take years to play out, exacting a significant toll on the patient, caregiver, and healthcare system along the way (27).
To mitigate this burden, the early and accurate detection of AD-associated symptoms in clinical practice represents a critically needed but challenging advancement in AD care (19, 28–30). Usually, a patient with early signs/symptoms of AD will initially present in a primary care setting (30). For some patients, minor changes in cognition and/or behavior may be detected during a routine wellness visit or an appointment to discuss other comorbidities (24). As the PCP is often the first to observe a patient’s initial symptomatology, it is vital they recognize the early signs and symptoms, and understand how to use the most appropriate assessment tools designed to detect these early clinical effects of the disease.
Because the neuropathologic hallmarks of AD (Aβ plaques and NFTs) can be detected decades prior to the onset of symptoms (6, 7), biomarkers reflecting this underlying pathology represent an important opportunity for early identification of patients at greatest risk of developing MCI due to AD. Biomarkers support the diagnosis of AD (especially important early on when symptoms can be subtle), and the U.S. Food and Drug Administration (FDA) has recently published guidelines that endorse their use in this population (9). The National Institute on Aging—Alzheimer’s Association (NIA-AA) has recently created a research framework that acknowledges the use of biomarkers for diagnosing AD in vivo and monitoring disease progression (7).
Important biomarker information can be gathered from imaging modalities such as magnetic resonance imaging (MRI) and positive emission tomography (PET) that visualize early structural and molecular changes in the brain, respectively (25, 30). Fluid biomarker testing, such as cerebrospinal fluid (CSF) can also be used; CSF biomarkers can directly reflect the presence of Aβ and aggregated tau within the brain (7, 31). As will be discussed in more depth later in this article, a large number of clinical studies have shown that Aβ and tau biomarkers can contribute diagnostically important information in the early stages of disease (32). There is ongoing research to expand the current range of tests that can be used by clinicians as part of the multistage diagnostic process (25). For instance, once approved, blood-based biomarkers could be used to identify patients at risk of developing AD and for monitoring disease progression (33, 34), which would also reduce the current capacity constraints associated with PET imaging (25).

Practical guide for an early diagnosis of Alzheimer’s disease in clinical practice

As already raised, recent recommendations for evolving AD care to a more patient-centric, transdisciplinary model include guidance on realizing an efficient diagnostic process—one in which HCPs, payers, and specialists are encouraged to combine their efforts to ensure the early warning signs of AD are not overlooked (24). The recommendations include dividing the diagnosis of AD into the following steps: detect, assess/differentiate, diagnose, and treat (Figure 2). We present here a practical guide for the early diagnosis of AD, based on this outlined approach, including a case study to highlight each of these key steps.

Figure 2. A stepwise infographic to highlight key stages within the diagnostic process, along with the recommended tests to support each step

The diagnostic process for AD can be divided into the following steps: detect, assess/differentiate, diagnose, and treat. It is important for clinicians to utilize appropriate tests when investigating a patient suspected of having AD in the early stages. Here, we highlight the most valuable tests for each step and which ones should be used in a primary care or specialist setting; *FDG-PET is usually considered after a diagnostic work-up; Abbreviations: A-IADL-Q, Amsterdam Instrumental Activities of Daily Living Questionnaire. Aβ, amyloid beta. Ach, acetylcholine. BG, blood glucose. CSF, cerebrospinal fluid. FAQ, Functional Activities Questionnaire. FAST, Functional Analysis Screening Tool. FDG-PET, fluorodeoxyglucose-PET. GDS, Geriatric Depression Scale. IQCODE, Informant Questionnaire on Cognitive Decline in the Elderly. Mini-Cog, Mini Cognitive Assessment Instrument. MMSE, Mini-Mental State Examination. MoCA, Montreal Cognitive Assessment. MRI, magnetic resonance imaging. NMDA, N-Methyl-D-aspartic acid. NPI-Q, Neuropsychiatric Inventory Questionnaire. PCP, primary care physician. PET, positive emission tomography. p-tau, phosphorylated tau. QDRS, Quick Dementia Rating System. TSH, thyroid-stimulating hormone. t-tau, total tau

Table 1. Patient case study

Abbreviations: Aβ, amyloid beta. ApoE, apolipoprotein E. HgbA1c, hemoglobin A1c. MoCA, Montreal Cognitive Assessment. MRI, magnetic resonance imaging. PCP, primary care physician. p-tau, phosphorylated tau. t-tau, total tau

Step 1: Detect

The role of primary care in the early detection of AD

The insidious and variable emergence of symptoms associated with AD and other dementias can make recognition extremely challenging, particularly in a primary care setting (30, 35). Clinicians often have limited time with patients, so it is vital that they are able to quickly and accurately recognize the early signs and symptoms associated with AD (Table 2) (3, 30, 36), and training for nurses, NPs, and PAs who may have more time to observe patients should provide substantial benefits. Although extremely variable, initial symptoms may include short-term memory loss or psychological concerns, including depressive symptoms and a loss of purpose (36).

able 2. Symptoms associated with suspected early stage Alzheimer’s disease

Patients, family members, and even HCPs themselves may present barriers to the diagnosis of early-stage AD. Patients may hide their symptoms or even avoid making an appointment until their symptoms significantly affect their day-to-day life due to fear of the stigma associated with a diagnosis of AD (19). Additionally, patients, family members, and PCPs/HCPs may dismiss or misinterpret symptoms as simply part of the normal aging process (30). Retrieving information from a trusted family member or informant/caregiver is essential when trying to assess a patient for suspected AD, as this perspective can provide a more objective understanding of the daily routine, mood, and behavior of the patient, and how this may have changed over time (30). For patients presenting with even subtle symptoms associated with AD, it is important that the PCP/HCP conducts an initial assessment to confirm the presence of symptoms using a validated assessment for early-stage AD detection (Figure 2; Step 2: Assess/Differentiate).

Case study: Presentation

A 63-year-old Caucasian male (J.K.) presented to his PCP with short-term memory loss over the last 2 years (Table 1A). Accompanied by his wife, he acknowledged his job had been affected by issues with his short-term memory; however, he considered his memory similar to that of his peers. His wife reported that people at work had started to notice him struggling to keep up, and also that family had to remind him of his upcoming appointments. He admitted to having intermittent depressive symptoms and anxiety, as well as irritability. Based on the patient’s symptoms, the PCP felt his presentation warranted further clinical assessment.

Step 2: Assess and differentiate

Primary care: Initial assessment when a patient presents

When a patient initially presents with symptoms consistent with early stages of AD, a clinician must first conduct a comprehensive clinical assessment to rule out other potential non-AD causes of cognitive impairment (Figure 2). PCPs are well placed to conduct these initial assessments, as they may not require specialist input or hospital tests. During the initial assessment, the primary objective of the clinician should be to exclude possible reversible causes of cognitive impairment, such as depression, or vitamin, hormone, and electrolyte deficiencies (37). The initial assessment should include a thorough history to identify potential risk factors associated with AD, including a family history of AD or related dementias in first-degree relatives (31, 38). Other known risk factors for AD that should be identified include age, female sex, ApoE ε4 status, physical inactivity, low education, diabetes, and obesity (3). It is also important to review for pre-existing medical conditions or prescribed medications that could be a cause of the patient’s cognitive impairment (36). Additionally, when conducting a thorough history, open-ended, probing questions should be directed to both the patient and the informant to ascertain how the patient’s cognition has changed over time and how the cognitive deficits affect their everyday activities; example questions for the initial assessment are detailed in Table 3 (30). Engaging with informants/caregivers is key to capturing additional information to help support all assessments. A routine differential diagnosis of AD begins with a detailed history, physical and neurologic examinations, and bloodwork analyses, followed by cognitive assessments and functional evaluation (Figure 2).

Table 3. Example questions for a clinician conducting an initial assessment with a patient and caregiver (30)

Primary care: Physical examination and blood analyses

A physical examination and blood tests can identify comorbid contributory medical conditions and reversible causes of cognitive impairment. A physical examination, including a mental status and neurological assessment, should be conducted to detect conditions such as depression and, for example, to look for signs such as issues with speaking or hearing as well as signs that could indicate a stroke (37). As part of the physical exam, a physician may ask the patient about diet and nutrition, review all medications (to see if these are the cause of any cognitive impairment, e.g. anti-cholinergics, analgesics, or sleep aids and anxiolytics), check blood pressure, temperature and pulse, and listen to the heart and lungs (36, 39).
Blood tests can rule out potentially treatable illnesses as a cause of cognitive impairment, such as vitamin B12 deficiency or thyroid disease (37). Suggested blood analyses include: 1) complete blood cell count; 2) blood glucose; 3) thyroid-stimulating hormone; 4) serum B12 and folate; 5) serum electrolytes; 6) liver function; and 7) renal function tests (30). Although not routinely used in clinical practice, clinicians may request ApoE genotyping, as this can help assess the genetic risk of developing AD. ApoE is the dominant cholesterol carrier within the brain that supports lipid transport and injury repair (40, 41), and the APOE gene exists as three polymorphic alleles: APOE ε2, ε3, and ε4. The ε4 allele of ApoE is associated with increased AD risk, whereas the ε2 allele is protective (40, 42). The number of ApoE ε4 alleles a person carries increases their risk of developing AD and the age of disease onset (43). Homozygous ε4 carriers (those with two copies of the ε4 allele) have the greatest risk of developing AD and the lowest average age of onset (43). In some practice settings, ApoE genotyping can only be conducted by a genetic counselor; a referral for more comprehensive genetic testing may be considered by the HCP if there is a family history of early-onset AD or dementia. Consumer tests are also becoming more readily available for patients wanting to determine their risk of developing diseases such as AD based on genetic risk factors (44).

Primary care: Cognitive, functional, and behavioral assessments

Cognitive assessments

If a patient is suspected of having AD following an initial assessment in primary care, and they are <65 years old, or if the case is complex, a referral to a dementia specialist such as a neurologist, geriatrician, or geriatric psychiatrist may be required for further evaluation. The specialist would then use an appropriate battery of cognitive, functional, and behavioral tests to assess the different aspects of disease, and ultimately to confirm diagnosis. However, not all patients with suspected cognitive deficits are immediately referred to a dementia specialist at this stage, which is only partly due to limited numbers of specialists (25) (Figure 2). In clinical practice, a two-stage process is often employed. This involves an initial ‘triage’ step conducted by non-specialists to clinically assess and select those patients who require further evaluation by a dementia specialist (45). During this ‘triage’ step, there are several clinical assessments available to non-specialists for assessing the presence of cognitive and functional impairments and behavioral symptoms (Table 4) (28, 35, 46–55).

Table 4. Cognitive, functional, and behavioral assessments to support the diagnosis of Alzheimer’s disease in a primary care and specialist setting

*Personal communication; Abbreviations: AD, Alzheimer’s disease. A-IADL-Q, Amsterdam Instrumental Activities of Daily Living Questionnaire. FAQ, Functional Activities Questionnaire. FAST, Functional Assessment Screening Tool. GDS, Geriatric Depression Scale. IQCODE, Informant Questionnaire on Cognitive Decline in the Elderly. MCI, mild cognitive impairment. Mini-Cog, Mini Cognitive Assessment Instrument. MMSE, Mini-Mental State Examination. MoCA, Montreal Cognitive Assessment. NPI-Q, Neuropsychiatric Inventory Questionnaire. QDRS, Quick Dementia Rating System

Previous research has shown that clinicians have a tendency to choose one assessment over another due to their familiarity with the assessment, time constraints, or specific resources available to them within their clinic (30), but clinicians need to be aware of, and prepared to use, the most patient-appropriate assessments: the cultural, educational, and linguistic needs of the patient are important considerations (30, 36, 56–58). Some assessments have been translated into different languages or shortened, or have education-adjusted scoring classifications, where required (56–58).
Cognitive assessments that can be conducted quickly (<10 minutes), such as the Mini-Mental State Examination (MMSE) or Montreal Cognitive Assessment (MoCA), can be used by non-specialists to identify the presence and severity of cognitive impairment in patients before referring to a dementia specialist (Table 4) (36). Both the MMSE and MoCA are used globally in clinical practice, particularly in primary care, but vary in terms of their sensitivity to identify AD in the early stages (28, 59). The MMSE is sensitive and reliable for identifying memory and language deficits in general but has limitations in identifying impairments in executive functioning (59). MoCA was originally developed to improve the detection of MCI (28) and is more sensitive than the MMSE in its assessment of memory, visuospatial, executive, and language function, and orientation to time and place (59). Both tests are relatively easy to administer and take around 10 minutes to complete. Neither assessment requires extensive training by the clinician, although MoCA users do need to undergo a 1-hour certification as mandated by the MoCA Clinic and Institute (28, 60).
For time-constrained clinicians, the Mini Cognitive Assessment Instrument (Mini-Cog) may be an appropriate tool to assess cognitive deficits that focus on memory, and components of visuospatial and executive function (Table 4). The assessment includes the individual learning three items from a list, drawing a clock, and then recalling the three-item list. The Mini-Cog can be useful for clinicians in primary care, as it requires no training and the results are easy to interpret. As an alternative to these tests, PCPs might also consider using an informant-based structured questionnaire such as the AD8 or Informant Questionnaire on Cognitive Decline in the Elderly to help guide discussions with the patient and caregiver (Table 4) (28).

Functional assessments

Functional assessments are valuable in identifying changes in a patient’s day-to-day functioning through the evaluation of their instrumental activities of daily living (IADLs). IADLs are complex activities that are necessary for the individual to function independently (e.g., cooking, shopping, and managing finances) and can be impaired during the early stages of cognitive impairment. While it is possible that functional decline may occur as a part of normal aging, a decline in a person’s IADL performance is strongly associated with neurodegenerative diseases such as AD (61). In the early stages of AD, patients may be functionally independent, and any impairment in IADLs may be subtle, such as difficulties paying bills or driving to new places. A patient’s functional independence is essential for their well-being and mental health (62), particularly in the early stages of the disease when the individual may still be working and socializing relatively independently (3). Consequently, functional independence is one of the most important clinical features for patients with AD. As the disease progresses, and patients have increasing functional impairment, this significantly impacts on their independence, and subsequently their and their family/caregiver’s quality of life.
Functional assessment is, therefore, an integral part of the diagnostic process for AD. The Functional Activities Questionnaire (FAQ) is an informant questionnaire that assesses the patient’s performance over a 4-week period and may take only a few minutes to complete (Table 4). The questionnaire is scored from ‘normal’ to ‘dependent’, using numerical values assigned to categories, with higher scores indicative of increasing impairment (47). Previous research has shown that the FAQ has high sensitivity and reliability for detecting mild functional impairment in patients with MCI (47).
Determining an individual’s functional independence can be challenging and the clinician may require additional input from an informant to determine a patient’s functional decline and their ongoing ability to conduct activities of daily living (37). The clinician can gain greater insight through the informant into the patient’s day-to-day life and any issues the patient is having at home. This type of information is vital to the clinician, and when combined with other assessment tools, can help to narrow the differential diagnosis.

Behavioral assessments

Patients with suspected AD may experience several behavioral symptoms such as anxiety, disinhibition, apathy, and depression (Table 2). In the early stages of disease, such symptoms are generally associated with poor long-term outcomes and caregiver burden, and are particularly distressing to both patients and their families (63). It is important for clinicians to use appropriate assessments to identify behavioral and psychiatric symptoms that are caused by neurodegenerative diseases, such as AD, rather than by alternative causes, such as a mood disorder.
The Geriatric Depression Scale (GDS) and Neuropsychiatric Inventory Questionnaire (NPI-Q) can be used by clinicians to assess neuropsychiatric symptoms in patients for whom early-stage AD is suspected (Table 4). The GDS is a 15-item (or longer 30-item) questionnaire that assesses mood, has good reliability in older populations for detecting depression, and can be completed by the patient within 5–10 minutes (63). The NPI-Q can be used in conjunction with or as an alternative to the GDS. The NPI-Q is completed by a knowledgeable informant or caregiver who can report on the patient’s neuropsychiatric symptoms. The NPI-Q can be conducted in around 5 minutes to determine both the presence and severity of symptoms across several neuropsychiatric domains including depression, apathy, irritability, and disinhibition (49). Consequently, as it assesses depression, it can be used as an alternative to GDS if time constraints do not allow for both to be completed.
Behavioral symptoms can be non-specific, so it is important for clinicians to consider and rule out other potentially treatable causes of impairment when assessing this domain. For example, depression is associated with concentration and memory issues (64); apathy can occur in non-depressed elderly individuals and can impact cognitive function (65). Signs/symptoms such as social withdrawal, feelings of helplessness, or loss of purpose should be investigated closely, as these could be indicative of depression alone. It is important for clinicians to recognize that if changes over time in cognitive symptoms and mood symptoms match, then depression is most likely to be the root cause of subtle cognitive decline, rather than AD (28).

Primary care clinician checklist

If AD is still suspected following clinical assessment, referral to a specialist for further diagnostic testing, including imaging and fluid biomarkers, may be required. It is important the clinician confirms the following checks/assessments before the patient undergoes further evaluation:

Primary care clinician checklist

• Confirm medical and family history
• Review the patient’s medications for any that could cause cognitive impairment
• Perform blood tests to eliminate potential reversible causes of cognitive impairment
• Conduct a quick clinical assessment to confirm the presence of cognitive impairment

Specialist role in assessment

Following the initial assessment in primary care, further cognitive, behavioral, functional, and imaging assessments can be carried out in a specialist setting. With their additional AD experience, access to other specialties, and possibly fewer time constraints than the PCP, the specialist is able to conduct a more comprehensive testing battery, using additional clinical assessments and biomarkers to determine causes of impairment and confirm diagnosis (Figure 2).

Cognitive assessments

Because the cognitive impacts of early-stage AD may vary from patient to patient, it is important to consider which cognitive domains are affected in these early stages when considering which assessments to use. Specialists are able to conduct a full neuropsychological test battery that covers the major cognitive domains (executive function, social cognition/emotions, language, attention/concentration, visuospatial and motor function, learning and memory); preferably, a battery should contain more than one test per domain to ensure adequate sensitivity in capturing cognitive impairment (66). This step can help with obtaining an in-depth understanding of the subtle changes in cognition seen in the early stages of AD and enables the clinician to monitor subsequent changes over time.
Typically, episodic memory, executive function, visuospatial function, and language are the most affected cognitive domains in the early stages of AD (29, 67, 68). Currently, most cognitive assessment tools focus on a subset of the overall dimensions of cognition; it is therefore vital the clinician chooses the correct test to assess impairment in these specific cognitive domains that could be indicative of AD in the early stages. As cognitive impairment in the early stages of AD can be subtle and vary significantly between individuals (29), clinicians must choose appropriate, sensitive tests that can detect these changes and account for a patient’s level of activity and cognitive reserve (29). If there is large disparity in results across cognitive assessments, it is important for the clinician to shape their assessments based on the patient’s history. If the patient’s history is positive for neurodegenerative disease, but one assessment does not reflect this, it is important to conduct further tests to ascertain the cause of the cognitive impairment.
The Quick Dementia Rating System (QDRS) can be used by specialists to assess cognitive impairment (Table 4). This short questionnaire (<5 minutes) is completed by a caregiver/informant and requires no training. The QDRS assesses several cognitive domains known to be affected by AD, including memory, language and communication abilities, and attention. The questionnaire can reliably discriminate between individuals with and without cognitive impairment and provides accurate staging for disease severity (28).

Functional assessments

The Amsterdam IADL Questionnaire (A-IADL-Q) and Functional Assessment Screening Tool (FAST) can both be used to assess a patient’s functional ability (Table 4) (53). The A-IADL-Q is a reliable computerized questionnaire that monitors a patient’s cognition, memory, and executive functioning over time. This questionnaire is completed by an informant of the patient and takes 10 minutes to complete (53). For patients with suspected early stage AD, the A-IADL-Q is a useful tool to monitor subtle changes in IADL independence over time and is less influenced by education, gender, and age than other functional assessments (53). The FAST is a useful assessment for clinicians to identify the occurrence of functional and behavioral problems in patients with suspected AD. The questionnaire is completed by informants who interact with the patient regularly; informants are required to answer Yes/No to a number of questions focusing on social and non-social scenarios (55).

Structural imaging

Structural imaging, such as MRI, provides clinically useful information when investigating causes of cognitive impairment (69) (Figure 2). MRI is routinely conducted to exclude alternative causes of cognitive impairment, rather than support a diagnosis of AD (37, 70). It is well known that medial temporal lobe atrophy is the best MRI marker for identifying patients in the earliest stages of AD (70, 71); however, specific patterns of atrophy may also be indicative of other neurodegenerative diseases. Atrophy alone is rarely sufficient to make a diagnosis. MRI findings can help to narrow the differential diagnosis, and the results should be considered in the context of the patient’s age and clinical examination (69–71).
Clinicians are advised to take a stepwise approach when reviewing structural imaging reports of a patient with suspected AD. These steps include: 1) excluding brain pathology that may be amenable to surgical intervention (e.g., the scan will show regions of hyper- or hypointensity rather than a uniform signal); 2) assessing for brain microbleeds (e.g., looking at signal changes within different areas of the brain can identify vascular comorbidities); and 3) assessing atrophy (e.g., medial temporal lobe atrophy is characteristic of AD) (69). Radiologists can conduct a quick and easy visual rating of any medial temporal lobe atrophy; these results can then be utilized by the specialist, in conjunction with a clinical assessment, to determine the likely cause of cognitive impairment. If the clinician is unable to determine a differential diagnosis, additional confirmatory tests can be requested.
Fluorodeoxyglucose-PET (FDG-PET) is a useful structural imaging biomarker that can support an early and differential diagnosis (72); however, specialists usually prefer to use this after their initial diagnostic work-up. As the brain relies almost exclusively on glucose as its source of energy, FDG (a glucose analog) can be combined with PET to identify regional patterns of reduced brain metabolism and neurodegeneration (70,72). FDG-PET is not recommended for diagnosing patients with preclinical AD, as there is no way to ascertain whether the hypometabolism is directly related to AD pathology (73); however, clinicians may refer patients with more established symptomatology for an FDG-PET scan to identify regions of glucose hypometabolism and neurodegeneration that could be indicative of AD (70).

Case study: Assess/differentiate

The initial assessment by the primary care clinician revealed that J.K.’s medical history was significant for hypertension, dyslipidemia, mild obesity, and glucose intolerance (Table 1B). There was no history of cerebrovascular events, significant head injuries, or focal findings on the neurologic exam. Besides the vascular risk factors, no medical conditions or current medications were found to be likely contributors to the cognitive deficit. The patient had a positive family history of dementia, where the onset typically occurred in the late 60s. Genotyping showed the patient to be a homozygous carrier of two ApoE ε4 alleles. Blood tests revealed elevated serum glucose and C-reactive protein but were otherwise normal. The patient had an unremarkable mental status examination, and his MoCA score was 21/30, with points lost on orientation, recall, and naming (Table 1C).
The patient was referred to a memory clinic for further assessment. The dementia specialist referred the patient for an MRI that predominantly showed mild small vessel disease and mild generalized atrophy with a significant reduction in hippocampal volume and ratio. Based on his medical and family history, cognitive assessments, and structural imaging results, the specialist deemed the severity of cognitive impairment to be in the mild range; consequently, the specialist referred the patient for biomarker assessment to determine the underlying cause.

Step 3: Diagnose

Historically, AD was only diagnosed postmortem until we developed the ability to ascertain the underlying pathology associated with the disease in new ways, namely imaging and fluid biomarkers. However, despite supportive results from single- and multicenter trials, the use and reimbursement of imaging and fluid biomarkers to support the diagnosis of AD still vary considerably between countries (70).

Imaging biomarkers

Recent advances have allowed physicians to visualize the proteins associated with AD, namely Aβ and tau, via PET scanning. Amyloid PET is currently the only imaging approach recommended by the Alzheimer’s Association and the Amyloid Imaging Task Force to support the diagnosis of AD (70). Amyloid PET utilizes tracers (florbetapir, flutemetamol, and florbetaben) that specifically bind to Aβ within amyloid plaques; a positive amyloid PET scan will show increased cortical retention of the tracer in regions of Aβ deposition within the brain (74), thus confirming the presence of Aβ plaques in the brain (74, 75) and directly quantify brain amyloid pathology (76), thus making it a useful tool to supplement a clinical battery to diagnose AD (3, 74). However, a positive amyloid PET scan alone does not definitively diagnose clinical AD, and these results must be combined with other clinical assessments, such as cognitive assessment, for an accurate diagnosis (74). It is also important to note that amyloid PET is expensive and not readily reimbursed by health insurance providers (70); if it is not possible to access amyloid PET, biomarker confirmation can be assessed using CSF.

Fluid biomarkers

An additional or alternative tool to amyloid PET is the collection and analysis of CSF for the presence of biomarkers associated with AD pathology. Patients who have symptoms suggestive of AD can be referred for a lumbar puncture to analyze their CSF for specific AD-associated biomarkers (3). CSF biomarkers are measures of the concentrations of proteins in CSF from the lumbar sac that reflect the rates of both protein production and clearance at a given timepoint (7). Lumbar punctures can be conducted safely and routinely in an outpatient setting or memory clinic (77). However, many patients still worry about the pain and possible side effects associated with the procedure and may require additional information and support from the clinician to undertake the procedure (77). Appropriate use criteria are available for HCPs to help identify suitable patients for lumbar puncture and CSF testing (78). For example, individuals presenting with persistent, progressing, and unexplained MCI, or those with symptoms suggestive of possible AD, should be referred for lumbar puncture and CSF testing (78). However, lumbar puncture and CSF testing are not recommended for determining disease severity in patients who have already received a diagnosis of AD or in lieu of genotyping for suspected autosomal dominant mutation carriers (78).
Because there is strong concordance between CSF biomarkers and amyloid PET, either can be used to confirm Aβ burden (79). As such, CSF biomarkers are widely accepted within the AD community to support a diagnosis (80). AD biomarkers from the brain can be detected in CSF well before the onset of overt clinical symptoms in early-stage AD (6, 7). Core AD CSF biomarkers, such as Aβ42 (one of two main isoforms of Aβ and a major constituent of Aβ plaques) and phosphorylated tau (p-tau) and total tau (t-tau), can be measured to determine the presence of disease (80).
When interpreting CSF analyses for a patient with suspected AD, it is important to remember that AD is associated with decreased CSF Aβ42 and increased tau isoforms (32). Decreased CSF Aβ42 levels are a reflection of increased Aβ aggregation and deposition within the brain (32), and the concentration of CSF Aβ42 directly relates to the patient’s amyloid status (e.g., the presence or absence of significant amyloid pathology) and the total amount of Aβ peptides (e.g., Aβ42 and Aβ40) (32). Specialists’ use of ratios of these CSF biomarkers (e.g., Aβ42/40) rather than single CSF biomarkers alone has been shown to adjust for potential differences in Aβ production and provide a better index of the patient’s underlying amyloid-related pathology (81). The increase in CSF p-tau and t-tau associated with AD may directly reflect the aggregation of tau within the brain and neurodegeneration, respectively (32). P-tau in CSF provides a direct measure of the amount of hyperphosphorylated tau in the brain, which is strongly suggestive of the presence of NFTs, whereas CSF t-tau can predict the level of neurodegeneration in a patient with suspected AD; however, t-tau is also increased in other neurologic conditions (32).
Ultimately, the clinical decision to use amyloid PET or CSF to confirm amyloid and tau pathology can be affected by several practical factors (Table 5) (70, 77, 80, 82–85).

able 5. Comparison of key CSF and amyloid PET considerations for amyloid confirmation

Abbreviations: CSF, cerebrospinal fluid. PET, positron emission tomography

Emerging diagnostic tools

Access constraints for amyloid PET have driven the need for alternative sensitive and specific CSF and blood-based biomarkers that can detect AD-associated pathology in the early stages (86). Significant efforts have been undertaken over the last decade to identify blood-based biomarkers to: 1) detect AD pathology; 2) identify those at risk of developing AD in the future; and 3) monitor disease progression (33, 34, 87). At present, only a limited number of approved blood-based assays are available to clinicians to detect AD pathology (88); however, several novel assays are currently under investigation, including those measuring various phosphorylated forms of tau, including p-tau181 and p-tau217 (89). Investigational use of plasma p-tau181 (an isoform of tau) has been shown to differentiate AD from other neurodegenerative diseases and predict cognitive decline in patients with AD (33). CSF p-tau217 (a different isoform of tau) is a promising biomarker under investigation for detecting preclinical and advanced AD (86, 90). Given that blood testing is already a well-established part of clinical routines globally and can easily be performed in a variety of clinical settings, blood-based biomarkers could in future serve as the potential first step of a multistage diagnostic process. This would be a benefit to clinicians, particularly those in primary care, by helping to identify individuals requiring a referral to a specialist for diagnostic testing (87).

Case study: Diagnose

J.K. underwent a lumbar puncture for CSF analysis, which showed decreased Aβ42 and increased p-tau and t-tau protein (Table 1D). Based on the results from the genotyping, cognitive assessments, MRI, and CSF biomarkers, the clinician confirmed that the likely cause of the patient’s cognitive deficits was early-stage AD, especially in view of a positive family history of dementia with similar age of onset.

Step 4: Treat

The role of the clinician following a diagnosis of early-stage AD is to discuss the available management and treatment options while providing emotional and practical support to the patient, caregiver, and family where appropriate (37). Clinicians can also refer the patient and their caregiver(s) to social services for further support, as well as help connect them with reliable sources of information and even local research opportunities and clinical trials.
One important role for a clinician treating a patient diagnosed with early-stage AD is to closely monitor the patient’s disease progression through regular follow-up appointments (e.g., every 6–12 months); clinicians should encourage patients (and the caregiver) to make additional follow-up appointments, especially should symptoms worsen. Routine cognitive and functional assessments (Table 4) should be used to monitor disease progression; these tools can be used to identify unexpected trends, such as rapid decline, which could prompt the need for additional medical evaluation such as blood tests, imaging, or biomarker analyses. Results from such tests could help guide management and/or treatment decisions over the course of the patient’s disease.
Non-pharmacologic therapies (e.g., diet and exercise) may be employed for patients with early AD, with the goal to maintain or even improve cognitive function and retain their ability to perform activities of daily living. For patients in the early stages of disease, dietary changes (e.g., following a healthy diet high in green, leafy vegetables, fish, nuts, and berries), physical exercise, and cognitive training have demonstrated small but significant improvements in cognition (36, 91). Non-pharmacologic therapies can have a positive impact on quality of life and are generally safe and inexpensive (36); however, compliance with these non-pharmacologic therapies should be monitored by the clinician. Research suggests that multimodal therapies, such as cognitive stimulation therapy, may also be more effective when used in combination with pharmacologic treatments (91).
Several pharmacologic treatments have received regulatory approval to treat the symptoms of mild to severe AD dementia. Acetylcholinesterase inhibitors (donepezil, rivastigmine, and galantamine) and N-methyl-D-aspartate receptor antagonists (memantine) can be prescribed to patients to temporarily ameliorate the symptoms of AD dementia such as cognitive and functional decline (92–96). Meta-analyses of donepezil, rivastigmine, and galantamine have shown that patients with mild-to-moderate AD dementia experience some benefits in cognitive function, activities of daily living, and clinician-rated global clinical state (93, 94, 97). Furthermore, treatment with acetylcholinesterase inhibitors and/or memantine has also been shown to modestly improve measures of global function and temporarily stabilize measures of activities of daily living (96). However, it is important to note that these drugs provide only temporary, symptomatic benefit and that not all patients respond to treatment (36, 98). Critically, none of the current drugs available address the underlying pathophysiology or alter the ultimate disease course.
Following AD diagnosis, a comprehensive approach toward clinical care can be individualized based on the patient’s specific AD risk factors (20, 21). Clinicians should consider managing uncontrolled vascular risk factors (e.g., hypertension, hyperlipidemia, diabetes) with antithrombotics, antihypertensives, lipid-lowering, and/or antidiabetic agents, respectively, to reduce the risk of cerebrovascular ischemia and stroke, and subsequent cognitive decline (36, 99). They should also consider the management of the patient’s behavioral symptoms. For most patients in the early stages of disease, behavioral symptoms will be relatively mild, and no pharmacologic management is required; however, pharmacologic treatment, such as a low-dose selective serotonin reuptake inhibitor, can be prescribed for patients with AD-associated depression and anxiety (100, 101).

Specialist clinician checklist

The specialist’s role is critical to further evaluating the initial checks/assessments, providing the diagnosis, and developing the individualized patient management plan:
• Identify deficits to specific cognitive domains using appropriate tests
• Confirm functional performance, using patient and caregiver assessments
• Perform structural imaging to complete assessment of the patient
• Confirm diagnosis with imaging or fluid biomarkers
• Develop a personalized management and follow-up plan
• Direct the patient to additional support resources such as the Alzheimer’s Association

Case study: Treat

Following diagnosis, J.K. was advised on the available management options and research opportunities (Table 1E). The specialist emphasized the need to control his vascular risk factors and suggested lifestyle modifications to optimize the management of his other medical problems. The patient’s neuropsychiatric symptoms were considered mild and did not require pharmacologic intervention. The patient was also provided with details for a local social worker and directed toward further disease-specific information from the Alzheimer’s Association related to his disease. The patient was encouraged to return for additional follow-up visits so that his disease and associated symptoms could be appropriately monitored and managed.

Future perspectives

An early diagnosis of AD will become increasingly important as treatments that alter the underlying disease pathology become available—particularly given the expectation that such treatments will be more effective in preserving cognitive function, and thus prolonging independence, when given early in the course of the disease (19). The approval of such treatments will likely lead to an increased awareness of cognitive impairment and other AD-associated symptoms among both the public and non-specialists, such as those in primary care settings. This may encourage more patients/family members to seek help at an earlier stage of disease than is currently seen in community practice. Increased use of sensitive screening measures to proactively assess for the presence of AD symptoms will help identify patients suspected of having early AD. Assessment of cognitive impairment during a Medicare Annual Wellness Visit is inconsistent; the U.S. Preventative Services Task Force, whilst recognizing the importance of MCI, has maintained its decision that there is insufficient evidence to support the mandate of cognitive screening. However, sensitive screening procedures, along with the availability of disease-modifying treatments, are likely to change their recommendations. There is also a need for a mandated, standardized screening approach internationally. Together, this will result in an increase in patients requiring diagnosis, increasing the demand for specialists to evaluate and diagnose, the need for amyloid confirmation, and wait times for patients, which will collectively put further pressure on an already-stretched healthcare infrastructure (25).
Nevertheless, efforts continue within the AD field to streamline the diagnostic process. Planning for and implementing change will not only improve patient management now but also help prepare healthcare systems for an approved disease-modifying treatment for AD. A flexible, multidisciplinary team approach is recommended to integrate the care needed to detect, assess, differentiate, diagnose, treat, and monitor a diverse AD population (24). The development of tests that could be carried out routinely in a primary care setting, such as blood-based AD biomarkers, would help PCPs and non-specialists identify which patients may need further evaluation or referral to a specialist (25). Interest also remains high in advancing imaging techniques, such as amyloid and tau PET, to support a diagnosis of AD. Although amyloid and tau PET are not currently readily available, they may be useful for specialists in the future to determine disease staging or track progression, or as a surrogate marker of cognitive status (74). The introduction of new screening and diagnostic tools could ultimately help lower the burden on specialists and ensure patients are diagnosed in a timely manner.


Consensus within the AD community has recently shifted to encourage the diagnosis of AD as early as possible. This shift will enable patients to plan their future and consider symptomatic therapies and lifestyle changes that could reduce cognitive deficits and ultimately help preserve their quality of life. Promisingly, new, potentially disease-modifying therapeutic candidates are on the horizon that could be effective in early AD by targeting and ameliorating the underlying biological mechanisms (92, 102). This paper has outlined a menu of practical tools for clinicians to use in the real world to support an early diagnosis of AD and how they may best be incorporated into current clinical practice. Ultimately, a coordinated, multidisciplinary approach that encompasses primary care and specialist expertise is required to ensure timely detection, assessment and differentiation, diagnosis, and management of patients with AD.

Authors’ contributions: All authors participated in the review of the literature and in the drafting and reviewing of the manuscript. All authors read and approved the final version of the manuscript for submission.

Funding: The authors developed this manuscript concept during an assessment of Alzheimer’s disease educational needs. The development of this manuscript was funded by Biogen. Editorial support was provided by Jodie Penney, MSc, PhD, Helios Medical Communications, Cheshire, UK, which was funded by Biogen.

Acknowledgements: The authors would like to acknowledge and thank Dr. Giovanni Frisoni, Geneva University Neurocenter, for his contribution towards the development of this manuscript.

Conflict of Interest: AP reports personal fees from Acadia Pharmaceuticals, Alzheon, Avanir, Biogen, Cadent Therapeutics, Eisai, Functional Neuromodulation, MapLight Therapeutics, Premier Healthcare Solutions, Sunovion, and Syneos; grants from Alector, Athira, Avanir, Biogen, Biohaven, Eisai, Eli Lilly, Genentech/Roche, and Vaccinex. RI has nothing to disclose. MS reports personal fees from Alzheon, Athira, Biogen, Cortexyme, Danone, Neurotrope, Regeneron, Roche-Genentech, and Stage 2 Innovations; stock options from Brain Health Inc, NeuroReserve, NeuroTau, Neurotrope, Optimal Cognitive Health Company, uMethod Health, and Versanum Inc. Additionally, he has intellectual property rights with Harper Collins. SK and IR report employment with Biogen.

Electronic supplementary material: Practical Guidance extender video.

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.


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T.P. Ng1,2, T.S. Lee3, W.S. Lim4, M.S. Chong2, P. Yap5, C.Y. Cheong5, K.B. Yap6, I. Rawtaer7, T.M. Liew8, Q. Gao1, X. Gwee1, M.P.E. Ng2, S.O. Nicholas2, S.L. Wee2,9


1. Gerontological Research Programme, Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 2. Geriatric Education and Research Institute, Singapore; 3. Duke-NUS Medical School, Singapore; 4. Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore; 5. Department of Geriatric Medicine, Khoo Teck Puat Hospital, Singapore; 6. Department of Geriatric Medicine, Ng Teng Fong General Hospital, Singapore; 7. Department of Psychiatry, Sengkang General Hospital, Singapore; 8. Department of Psychiatry, Singapore General Hospital, Singapore; 9. Health and Social Sciences Cluster, Singapore Institute of Technology, Singapore

Corresponding Author: A/P Tze Pin Ng, Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Level 9, NUHS Tower Block, 1E Kent Ridge Road, Singapore 119228, Fax: 65-67772191, Email:

J Prev Alz Dis 2021;3(8):335-344
Published online April 26, 2021,



Background: Mild cognitive impairment (MCI) is a critical pre-dementia target for preventive interventions. There are few brief screening tools based on self-reported personal lifestyle and health-related information for predicting MCI that have been validated for their generalizability and utility in primary care and community settings.
Objective: To develop and validate a MCI risk prediction index, and evaluate its field application in a pilot community intervention trial project.
Design: Two independent population-based cohorts in the Singapore Longitudinal Ageing Study (SLAS). We used SLAS1 as a development cohort to construct the risk assessment instrument, and SLA2 as a validation cohort to verify its generalizability.
Setting: community-based screening and lifestyle intervention
Participants: (1) SLAS1 cognitively normal (CN) aged ≥55 years with average 3 years (N=1601); (2) SLAS2 cohort (N=3051) with average 4 years of follow up. (3) 437 participants in a pilot community intervention project.
Measurements: The risk index indicators included age, female sex, years of schooling, hearing loss, depression, life satisfaction, number of cardio-metabolic risk factors (wide waist circumference, pre-diabetes or diabetes, hypertension, dyslipidemia). Weighted summed scores predicted probabilities of MCI or dementia. A self-administered questionnaire field version of the risk index was deployed in the pilot community project and evaluated using pre-intervention baseline cognitive function of participants.
Results: Risk scores were associated with increasing probabilities of progression to MCI-or-dementia in the development cohort (AUC=0.73) and with increased prevalence and incidence of MCI-or-dementia in the validation cohort (AUC=0.74). The field questionnaire risk index identified high risk individuals with strong correlation with RBANS cognitive scores in the community program (p<0.001).
Conclusions: The SLAS risk index is accurate and replicable in predicting MCI, and is applicable in community interventions for dementia prevention.

Key words: Mild cognitive impairment, dementia, metabolic syndrome, diabetes, cardiovascular risk factors.



The number of people worldwide who are living with dementia is currently estimated at about 50 million, and doubles every 20 years (1). Currently, there is a lack of effective disease-modifying treatments for Alzheimer’s disease (AD) and dementia. Based on research evidence over the past decade of risk and protective factors for dementia, a population prevention approach currently offers a promising prospect for reducing the worldwide burden of dementia (2). A viable population-based strategy is the identification of individuals whose psychosocial, lifestyle and health characteristics put them at significant risks of developing dementia in its precursor and reversible stage of mild cognitive impairment (MCI). Such individuals represent population target for multi-domain lifestyle behavioral and health interventions. Accurate screening for risks of MCI and early dementia should also be validated for use at low cost in community and primary care settings. This is especially important in low- and middle-income countries where the majority of persons with dementia live.
There are a number of earlier reports of validated risk indexes that predict the onset of Alzheimer’s disease and dementia. However, few risk indexes have been evaluated for assessing the risk of the MCI precursor of dementia, using easily available variables in the clinical setting (3-9). The Mayo Clinic Study of Aging (MSCA) basic risk score comprises 12 general demographic and clinical variables (such as age, education, marital status, diabetes, hypertension, and body mass index [BMI 7 The Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), originally developed and validated for predicting AD and dementia, comprises up to 15 similar variables (including age, sex, education, social engagement, physical activity, cognitively stimulating activities, smoking, alcohol consumption, depression, traumatic brain injury BMI, diabetes, cholesterol, fish intake and pesticide exposure) (8). Although scores in both risk indexes were strongly associated with the progression from CN to MCI, their predictive ability was limited (with C-statistic of 0.60 for both). Not all the predictive variables in the ANU-ADRI are easily available even in the study cohort of cognitive normal persons. Both risk indexes have not been reported to be externally validated in an independent population sample. It is possible that the identified risk scores may perform differently outside of the original study population. Both risk indexes were developed and evaluated in Caucasian populations. It cannot be presumed that the risk indexes are generalizable to ethnically different populations. Furthermore, to date, there are no translational research reports of the deployment and evaluation of field versions of MCI risk prediction tools in pragmatic intervention trials in the community setting.
In this study, we used risk prediction model data from the Singapore Longitudinal Ageing Study (SLAS-1) population-based cohort to derive a risk index that predicted incident MCI or dementia from 3-5 (mean 4.5) years of follow up, using risk scores for age, sex, education, depression, life satisfaction, hearing loss, and the number of cardio-metabolic risk factors (waist circumference, pre-diabetes or diabetes, hypertension, triglycerides, and high-density lipoprotein levels). We evaluated the external validity of the SLAS risk index by assessing its predictive accuracy among participants in an independent validation cohort (SLAS-2). Finally, we designed a brief self-administered questionnaire field version of the risk index and evaluated its application outside of the SLAS study cohorts in a pilot community-based interventional trial. This was a National Innovation Challenge (NIC) community project which aimed to screen and enroll high-risk individuals for targeted multi-domain lifestyle interventions for dementia prevention.



Study design and participants

The Singapore Longitudinal Ageing Study (SLAS) is an ongoing population-based observational prospective cohort study of ageing and health transition among older adults, aged 55 and above in Singapore. The study was approved by the National University of Singapore Institutional Review Board. Written informed consent was obtained from all participants.
Two distinct population cohorts were recruited in separate waves of recruitment of community-dwelling older adults in geographically different locations. SLAS-1 cohort participants who were aged 55 years and above and residents in the South East region of Singapore (N=2804) were recruited in 2003-2004 and were followed up at two re-assessment visits approximately 3 years apart in 2005-2007 and 2007-2009 respectively. The participants in the SLAS-2 cohort (N=3246) were aged 55 and above and residents in the South West region who were recruited in 2009-2013, with follow up assessments 3 to 5 (mean 4.5) years apart conducted in 2013-2018. Trained nurses visited the participants at home to perform face-to face questionnaire interviews, and clinical and neurocognitive assessments and blood draws were performed in a local study site center. The background and methodology of the SLAS research are described in detail elsewhere (10, 11).

SLAS-1 development cohort study

We used data from the SLAS-1 development cohort to derive a prediction model and risk scores for incident MCI or dementia. The development cohort comprised cognitive normal participants (N=2611), after excluding small numbers of Malay and Indian participants, those with stroke, Parkinson’s disease, other brain disorders and injury (N=101), prevalent MCI (N=478) and dementia (N=42), and undetermined neurocognitive status (N=4) at baseline. During the follow-up period, 227 participants died and 296 were lost to follow up. The MCI risk prediction model was based on longitudinal data analysis of 1,610 individuals, who were followed up to incident MCI (N=149) or dementia (N=14).

SLAS-2 validation cohort study

We evaluated the external validity of the SLAS-1 risk model by applying the prediction algorithm on individual participants in the SLAS-2 validation cohort. We used the calculated risk scores to assess its accuracy in predicting prevalent and incident cases of MCI or dementia. Both prevalent and incident cognitive outcomes were evaluated because the practical application of the risk prediction tool should include the identification of prevalent cases of MCI and interventions to prevent MCI progression to dementia. At baseline, after excluding 219 participants with missing data on neurocognitive status, there were a total of 3051 participants with known neurocognitive status: cognitive normal (CN)=2700, MCI =265, dementia=86) and had complete data on risk factors. A total of 1323 cognitive normal participants followed up for an average of 4.5 years were re-assessed on their neurocognitive status (CN=1157, MCI=69, dementia=6), after excluding 45 participants with missing data.

Mild Cognitive Impairment (MCI) and Dementia

As described in detail in a previous publication (12), neurocognitive disorder (MCI and dementia) among study participants was ascertained from initial screening using MMSE and self or informant reports of subjective cognitive decline, using the IQCODE (13) followed by subsequent assessment using Clinical Dementia Rating Scale (14) and neuropsychological evaluation for cognitive domains: memory (RAVLT immediate recall, RAVLT delayed recall, Visual Reproduction immediate recall, Visual Reproduction delayed); executive function (Symbol Digit Modality Test; Design Fluency; Trail Making Test B, language (Categorical Verbal Fluency); visuospatial skills (Block Design); attention (Digit Span Forwards, Digit Span Backwards, Spatial Span Forwards and Backwards), before final clinical assessments including MRI and additional blood tests and consensus diagnosis by a panel of geriatricians and psychiatrists.
MCI was defined according to published criteria (15, 16): cognitive concern expressed by the participant or informant; cognitive impairment in one or more domains (executive function, memory, language, or visuospatial); normal functional activities; and absence of dementia, and operationalized as follows:
1) Subjective cognitive complaints from a single question asking whether the subject had more problems with memory than most, or an informant report of memory decline, “Do you think your family member’s memory or other mental abilities have declined?” or IQCODE ≥3.0.
2) Cognitive deficits defined as Modified Chinese MMSE score of 24-27, or decline of 2 more points from baseline, a neurocognitive domain score that was 1 to 2 SD below the age-and-education adjusted means, or decline from baseline of 0.5 SD from serial measurements;
3) No functional dependency in performing instrumental daily living activities (Lawton et al);
4) Clinical Dementia Rating Scale score of 0 or 0.5;
5) No dementia.

Dementia was diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders, DSM-4R (American Psychiatric Association, 1994) (17), with evidence of cognitive deficit from objective assessment (MMSE ≤23 or neuropsychological domain scores below 2 SD of age-education-adjusted mean), and evidence of social or occupational function impairment (dependency in one or more Instrumental ADL or Clinical Dementia Rating score ≥1).
Participants who did not meet these criteria for MCI or dementia were classified as cognitive normal.

Predictor variables

We developed the risk prediction model from stepwise selection of a parsimonious set of significant predictor variables from among 20 known variables that represent known psychosocial, lifestyle and health risk factors for the onset and development of MCI and dementia.
1. Age, gender, education (none, 6 or less years, and more than 6 years), marital status (single, widowed, divorced), APOE- ε4 genotype carrier status;
2. Physical, social or productive activities based on the number and frequencies on a 3-point Likert scale (0=never or less than once a month; 1=sometimes (once a month or more but less than once a week); 2=often (at least once a week) of usual participation in 18 different categories of physical, social and productive activities; level of physical, social or productive activities was scored as the sum of frequencies across all activities, with higher score denoting higher level of participation; hearing loss (self-reported or whisper test);
3. Psychosocial variables: living alone, loneliness, life satisfaction as reported in a previous publication, depression (Geriatric Depression Scale ≥5) (18);
4. Smoking (current or past smoking versus never smoking); alcohol consumption (one or more drinks daily);
5. Cardio-metabolic and vascular risk factors: body mass index (BMI)≥27.5kg/m2, waist circumference ≥90cm for male and ≥80cm for female; pre-diabetes and diabetes (raised fasting plasma glucose (FPG) (FPG ≥ 100 mg/dL (5.6 mmol/L), or on treatment for previously diagnosed type 2 diabetes); raised triglyceride (TG) level ( ≥ 150 mg/dL (1.7 mmol/L) or on anti-lipid treatment); reduced high density lipoprotein (HDL) cholesterol (< 40 mg/dL (1.03 mmol/L) in males and < 50mg/dL (1.29 mmol/L) in females or on anti-lipids treatment; hypertension (raised blood pressure(BP) (systolic BP ≥ 130 or diastolic BP ≥ 85 mm Hg, or on treatment of previously diagnosed hypertension).

Field version questionnaire and validation

We created a field version of the risk prediction tool by designing a simple self-administered checklist questionnaire of the 11 risk factor items that respondents endorse, and derive a summary risk score for each individual. (Table 1 and Figure 1) We deployed the risk screening questionnaire in a community-based National Innovation Challenge trial project in which individuals with high risk scores were identified and enrolled for multi-domain lifestyle interventions for dementia prevention. A total of 437 community-living older persons aged ≥55 years without ADL disability, visual impairment, or known neurodegenerative disorders (dementia, Parkinson’s and other diseases) were administered the risk screening questionnaire at neighourhood senior activity centres. A total of 194 participants who scored ≥6 points on the risk index were enrolled into the 6-month multi-domain intervention or control programmes. At the same time, they were assessed at baseline (pre-intervention) with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (19, 20). Raw scores were standardized to T scores with a mean of 100 and standard deviation of 15 in the global index score, with higher scores indicating better performance.

Table 1. SLAS Index of Risk Prediction for Pre-dementia and Dementia

Figure 1. Predicted risk of MCI/Dementia and risk prediction score


Statistical Analyses

In the SLAS-1 developmental cohort, we examined a total of 20 available demographic, psychosocial, lifestyle behavior and health risk variables known from the literature review or demonstrated in our previous published reports to be risk or protective factors for the risk of MCI or dementia. We modeled the cumulative incidence (rather than time-to-event) of new cases of MCI or dementia that develop within the 4.5-year period in logistic regression as it was considered to be more informative and relevant for the purpose of predicting the risk or probability of MCI, and it would be more interpretable for lay users of the information. We used stepwise procedures to select a parsimonious set of 7 surrogate variables in the final logistic model that predict incident MCI or dementia. The outcome variable combined MCI and dementia since the follow up of these cognitive normal participants identified incident cases of MCI as well as a small number of dementia cases (N=14, diagnosed without known progression from MCI). Results are presented using complete case analysis as they were similar to results using imputation methods for missing data. Estimated regression coefficients in the logistic model were used as weights to derive simple point scores (from 0 to 3) associated with each risk indicator, and a summed risk score. In the SLAS-2 validation cohort, we used the same prediction algorithm to calculate the summary risk score for each participant and estimated the risks of prevalent and incident MCI-dementia associated with each risk score category. The predictive accuracy of the risk index was assessed by the area under the curve (AUC) using receiving operating curve techniques. In the NIC community –based interventional trial, the risk scores (ranging from 6 to 11) of 194 participants were evaluated on the trend of association with mean RBANS T-score of global cognitive performance using ANOVA with significance tests for linear trend.



SLAS1 development cohort

The baseline characteristics and risk factors of cognitively normal participants who presented with incident MCI or dementia (N=163) at follow up and their counterparts (N=1438) are shown in Table 2. Older age, female sex, low education, hearing loss, depression and low life satisfaction were significant predictors of incident MCI-dementia. Diabetes was the only cardio-metabolic risk factor among the metabolic syndrome components that was significantly associated with MCI-dementia, but the number of metabolic syndrome components was more strongly predictive of MCI-dementia.

Table 2. Baseline characteristics and risk factors of SLAS-1 development cohort of cognitive normal participants by MCI-dementia follow-up status


Table 3 shows the estimates of the strength of association of individual risk factors with incident MCI-dementia in the final risk prediction model, and associated weighted risk scores. The summed risk scores for individual participants ranged from 0 to 11, and was associated respectively with cumulative incidence of MCI-dementia ranging from 0% to 50%, (Figure 2) or 45% increased odds of incident MCI-dementia per point score increase. The AUC was 0.73, 95% confidence interval (95%CI): 0.70-0.75. Using an optimum risk score of ≥6, the sensitivity was 0.748 (95%CI: 0.677-080), specificity was 0.672 (0.647-0.695) and positive predictive value was 0.169 (95%CI: 0.144-0.199), based on the observed cumulative incidence of MCI-dementia of 10.2%
SLAS-2 validation cohort

Figure 2. Predicted cumulative probabilities of incident MCI-dementia by risk index score in SLAS-1 developmental cohort

Table 3. Estimates of association of SLAS risk factors with and risk score predictors of MCI-dementia and ROC measure of discriminant accuracy


The baseline characteristic and risk profile of participants overall and among cognitive normal participants who were followed up are shown in Table 4. In cross-sectional cohort, the risk index ranged from 0 to 12 (mean of 5.7 and SD of 2.4) and were associated with increasing prevalence of MCI-dementia ranging from 0% to 45% (Figure 3A). The AUC was 0.74 (95%CI: 0.72-0.77), sensitivity was 0.755 (95%CI: 0.707-0.797), specificity was 0.675 (95%CI: 0.576-0.613) and PPV was 0.209, 95%CI: 0.175-0.217) based on the overall prevalence of 11.5%.
In the follow up cohort of cognitive normal participants, risk scores from 0 to 12 were associated with increasing cumulative incidence of MCI-dementia from 0% to 36%. (Figure 3B) The AUC was 0.74 (95%CI: 0.69-0.79); sensitivity=0.720 (95%CI: 0.609-0.809), specificity=0.672 (95%CI: 0.644-0.698), PPV=0.124 (95%CI: 0.097-0.159), based on the observed overall cumulative incidence of 6.1%.

Figure 3a. Predicted probabilities of prevalent MCI-dementia by risk index score in SLAS-2 validation cohort


Figure 3b. Predicted cumulative probabilities of incident MCI-dementia by risk index score in SLAS-2 validation cohort

Table 4. SLAS-2 validation cohort baseline characteristics


Field version questionnaire risk score validation

The risk scores range in values from 6 to 11 (mean of 8.0 and SD of 1.1), and were significantly associated with decreasing trend of mean RBANS T-score from 105 to 95 (p for linear trend <0.001) (Figure 4).

Figure 4. RBANS T-scores by risk score among persons with risk scores of six and above who are selected for targeted intervention



We describe in this report the development of a risk index that predicts average 4.5-years risk of MCI or dementia in an Asian population. It comprises established personal, lifestyle and health factors that are easily measured and commonly used in primary care. The risk index showed good predictive accuracy both in the development cohort (AUC=0.73) and the external validation cohort (AUC=0.74). Equally important is that the scores predict a wide range of cumulative risk estimates of MCI-dementia with the highest risk score predicting a probability of 50% in the development cohort. In the validation cohort, the highest risk score predicted 45% probability of prevalent MCI-dementia and 36% probability of incident MCI-dementia. A lower prediction performance is usually demonstrated in external validation samples compared to original development samples (21, 22).
Several other risk prediction tools for MCI have been previously described. They include the Mayo Clinic Study of Aging (MCSA) basic risk score comprising age, education, marital status, diabetes, hypertension, body mass index (BMI), which has a AUC of 0.60 (7). The Australian National University-Alzheimer’s Disease Risk Index (ANU-ADRI) (8) is computed from 11 to 15 predictive variables, including self-reports of age, education, BMI, diabetes, depressive symp¬toms, high cholesterol, head trauma, smoking, alcohol consumption, social engagement (marital status, size of social network, quality of social network, level of social activities), physical activity (number of hours performing mild, moderate and vigorous activities), cognitively stimulating activities (number of cognitive activities undertaken), BMI, and (as available) cholesterol, fish intake and pesticide exposure. It also showed a AUC of 0.61 for incident MCI/dementia (5). In comparison, the SLAS risk index has higher predictive accuracy. While these studies have evaluated the predictive accuracy of their risk scoring tools within the development cohort, there are no reports of calibrating the performance of these tools in external validation cohorts. In our study, the external validity and generalizability of the SLAS risk prediction tool was demonstrated in an independent population sample (SLAS-2). This shows that the risk index is transportable to another population that differed in geographical location and other characteristics.

Furthermore, we re-designed and created a brief questionnaire version of the risk prediction index for field application in a community interventional trial. The questionnaire was self-administered or administered by a healthcare or community service provider within 2 minutes. In Singapore, basic health screening is easily accessible in primary care clinics at no or minimal costs for older residents, and include blood tests for fasting glucose and lipids, blood pressure and body size measurements. Given the advantage of ease of collection of self-reported data, there was no material loss of accuracy of information ascertainment, as shown by the RBANS cognition data. We demonstrated that the risk index questionnaire was able to discriminate individuals with varying levels of cognitive function. Using a pre-determined cutoff of 6 and above (associated with less than 10% predicted risk of MCI, or equivalent to the combined scores for non-modifiable risk factors of age, sex and education), the RBANS T-score stands at about 105 for the threshold risk scores of 6 and 7 and are close to local population norms (17), but dropped to 97 and 95 for higher score of 8 and above. The 10-point difference is more than the minimum clinically important difference of 8 for RBANS total score (18). Although the data empirically suggests a critical threshold risk score of 8 and above, practically a lower screening threshold of 6 or 7 is optimal. As yet, we found no other studies that have validated the field performance of a risk index for MCI in a pragmatic intervention trial in the community setting. Only a Portuguese questionnaire version of the ANU-ADRI was assessed in Brazil in a primary care study to be reliable and valid for assessing risk for AD (not MCI), by showing a moderate negative linear relation between the ANU-ADRI and MMSE scores (23). Our data thus show the feasibility of using the risk index to screen and identify individuals with prevalent MCI or at risk of MCI who are likely to benefit from early targeted multi-domain lifestyle intervention in a randomized controlled community trial which is ongoing.

Strengths and limitations

Our risk index for predicting MCI was based on risk factors from a single study. Other approaches of modelling with data from meta-analyses of risk factors from multiple studies may produce more predictive accuracy. A Shanghai risk model of 10 risk predictors was derived from a meta-analysis of 38 Chinese population studies and reported higher AUC from multivariate logistic regression model of 0.77. Notably, the predictive performance of the risk index was verified on a cross-sectional study predicting prevalent cases of MCI (6). Interestingly, the Shanghai study showed that better AUC of 0.86 was obtained using the same data using the Rothman-Keller model with additional model parameterizations. Further work should explore this new approach in developing risk prediction models of MCI and dementia.
Our risk index was derived from available data on 20 candidate risk and protective factors in a single population study. However, they are not much less wide and diverse in coverage as those reported in published meta-analyses. In general, risk prediction is improved with more independent predictors that contribute additionally to the model, but at greater costs of data collection. This may be done by adding information from cognitive, MRI or other testing, which may be desirable in clinical settings but does not suit the purpose of a risk index for population-based prevention interventions in the community. Our risk index was intentionally designed to be brief, inexpensive and non-invasive. It can be self-administered or administered by non-highly trained staff, hence based on a minimalist set of risk factors predicting MCI or dementia. It turns out to compare quite favorably or outperform relatively lengthy risk prediction instruments. It is possible that the weights derived for the SLAS risk index are study specific. Studies in other heterogeneous populations using the same risk prediction model may have optimal weights that differ for specific populations. More studies in ethnically similar or different populations elsewhere are needed. This risk prediction model appears to be successful in predicting risks of MCI-dementia in a Chinese population in Singapore. Whether it will be equally successful in ascertaining risks in Chinese populations in other places such as Taiwan or China, or to non-Chinese populations elsewhere needs to be verified independently with further validation studies.
The relatively short length of follow up of this cohort is notable, but is not a limitation. Risk prediction requires a specification of the time span, and in this case, a 3 to 5-year time span for predicting the risk of MCI or early dementia appears to be appropriate. The relatively young age of the study cohorts is also notable. A diversity of risk factors has their own age-developmental trajectories in predicting future risk of dementia, hence risk prediction instruments are of necessity applicable at different ages. Hypertension, obesity and cholesterol are known to be risk factors for late-life dementia when they are measured at middle age. The SLAS risk index is therefore optimal in identifying individuals at increased risk of dementia at a younger age and suits the primary purpose of early population-level interventions to prevent late-life dementia.



We developed and validated the SLAS risk index for predicting the risk of MCI or dementia over 4.5 years. We observed a high level of predictive accuracy which was replicated in an external validation population, and demonstrated the feasibility of applying a field version self-reported questionnaire of the risk index in a pilot community intervention project for dementia prevention.


Declarations: The study was approved by the National University of Singapore Institutional Review Board. Written informed consent was obtained from all participants.

Potential Conflict of Interest: The authors declare that they have no competing interests.

Availability of data: The datasets generated and analysed during the current study are not publicly available due to local data regulations and institutional policies but may be available from the corresponding author on reasonable request with permission from relevant authorities.

Funding: The study was supported by the Agency for Science Technology and Research (A*STAR) Biomedical Research Council (BMRC) [grant number 08/1/21/19/567] and the National Medical Research Council [grant number: NMRC/1108/2007].

Authors’ contributions: TPN reviewed the literature, designed the study, analyzed the data, drafted and revised the manuscript. SON provided additional data analysis. TSL, WSL, MSC, KBY, PY, CYC, IR, TML, QG, XYG, and NPEM contributed to the study design and data collection. All authors reviewed the results and drafts, and approved the final manuscript.

Acknowledgments: We thank the following voluntary welfare organizations for their support: Geylang East Home for the Aged, Presbyterian Community Services, St Luke’s Eldercare Services, Thye Hua Kwan Moral Society (Moral Neighbourhood Links), Yuhua Neighbourhood Link, Henderson Senior Citizens’ Home, NTUC Eldercare Co-op Ltd, Thong Kheng Seniors Activity Centre (Queenstown Centre) and Redhill Moral Seniors Activity Centre.

Ethical standards: The National University of Singapore (NUS) Institutional Review Board (IRB) approved the study, and all participants gave informed consent before participating.



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K. Pun1, C.W. Zhu1,2, M.T. Kinsella1, M. Sewell1, H. Grossman1,2, J. Neugroschl1, C. Li1, A. Ardolino1, N. Velasco1, M. Sano1,2


1. Icahn School of Medicine at Mount Sinai, New York, NY, USA; 2. James J. Peters VA Medical Center, Bronx, NY, USA

Corresponding Authors: Carolyn W. Zhu, PhD, Department of Geriatrics & Palliative Medicine, Icahn School of Medicine at Mount Sinai and JJP VA Medical Center, 130 West Kingsbridge Road, Bronx NY 10468, USA. Email:, Telephone: 718-584-9000 ext. 6146, Fax: 718-741-4211.

J Prev Alz Dis 2021;3(8):292-298
Published online April 24, 2021,



Objectives: This report describes the efficacy and utility of recruiting older individuals by mail to participate in research on cognitive health and aging using Electronic Health Records (EHR).
Methods: Individuals age 65 or older identified by EHR in the Mount Sinai Health System as likely to have Mild Cognitive Impairment (MCI) were sent a general recruitment letter (N=12,951). A comparison group of individuals with comparable age and matched for gender also received the letter (N=3,001).
Results: Of the 15,952 individuals who received the mailing, 953 (6.0%) responded. 215 (1.3%) declined further contact. Overall rate of expression of interest was 4.6%. Of the 738 individuals who responded positively to further contact, 321 indicated preference for further contact by telephone. Follow-up of these individuals yielded 30 enrollments (0.2% of 15,952). No differences in response rate were noted between MCI and comparison groups, but the comparison group yielded higher enrollment. 6 individuals who were not the intended recipients of mailing but nevertheless contacted our study were also enrolled.
Conclusions: Mailings to individuals identified through a trusted source, such as a medical center from which they have received clinical care, may be a viable means of reaching individuals within this age group as this effort yielded a low rejection rate. However, EHR information did not enhance study enrollment. Implications for improving recruitment are discussed.

Key words: Recruitment methods, electronic health records, cognitive health, mild cognitive impairment.



It is increasingly recognized that the pathophysiological process of Alzheimer’s disease (AD) begins years and maybe decades prior to the onset of clinical symptoms (1-7). Over the past several decades, both pharmacological and non-pharmacological lifestyle interventions have been studied for the prevention of cognitive decline or dementia in older adults with or without risk factors for AD. While important innovations in ongoing trials include identification of novel targets, development of multidomain interventions, identification and validation of biomarker or genetic targets, and improving outcome measures, the biggest challenge remains the recruitment of participants, espcially for long studies of non or minimally affected individuals (8-11).
One of the critical components for the success of these studies is identifying and recruiting individuals at high risk of developing dementia, both for observational studies investigating the natural history of prodromal and early disease stages and for interventional studies aimed at disease prevention or modification. Individuals with Mild Cognitive Impairment (MCI) have an increased risk of developing dementia compared with their cognitively normal peers (12). However, outreach to older adults for studies in these areas is often difficult because disease may be undetected in its mildest forms and awareness of future problems may be low. Studies have used diagnostic codes in administrative data or medical records to identify cases, however accuracy of diagnostic codes for cognitive impairment is limited (13-16). Identification of non-demented individuals with MCI from electronic health records (EHR) can be challenging, often depending on unstructured text for detection but several algorithms have been reported for such case ascertainment (17-19). Machine learning algorithms that have been developed to increase precision of identification have yet to be used in outreach efforts (20).
Campaigns to improve outreach and recruitment have often used mass mailings, defined as letters without a specific addressee, as they permit an inexpensive way to reach large numbers of potential participants and do not require technologies that may be less available to older cohorts. In general, rates of recruitment by this method can be low but the large numbers reached can allow studies to achieve needed sample sizes. For example, the Lifestyle Interventions and Independence for Elders (LIFE) Study reported that directly mailing a study brochure to households with age-eligible residents obtained from commercial databases and voter registration lists yielded 59.5% of randomized cases (21). However, the number of contacts needed to achieve this recruitment is quite high.
We made the assumption that trusted sources such as a medical center from which individuals have received clinical care may increase their interest in research participation and improve response to outreach efforts. The Alzheimer’s Disease Research Center (ADRC) at the Icahn School of Medicine at Mount Sinai has been continuously funded by the National Institutes of Health and has more than 30 years of experience that is highly recognized in the community. The ADRC offers ongoing opportunities to participate in a variety of clinical studies ranging from observational studies with and without imaging to pharmacological and non-pharmacological intervention studies. Building on that reputation, we undertook a focused mailing outreach effort using EHR, to engage individuals in research on cognitive health and aging. This report describes our experience and evaluates the efficacy of implementing a mass mailing within a trusted healthcare system to a cohort likely to have MCI based on their EHR.



Using EHR to Identify MCI and Comparison Groups

The EHR was used to create a group of individuals likely to have MCI. The MCI group included those age 55 to 90 who received care from the Mount Sinai Health System between spring 2013 and 2018. Inclusion criteria were: presence of an International Classification of Diseases, Ninth Revision (ICD-9) diagnosis code consistent with memory loss (MCI, memory loss, or dementia), or a similar Tenth Revision (ICD-10) diagnosis code of MCI or other amnesia. Exclusion criteria included presence of Alzheimer’s disease, Parkinson’s disease, schizophrenia, Huntington’s Chorea, epilepsy, multiple sclerosis, substance use disorders, tobacco use disorders, bipolar and major depressive disorders, and use of anti-dementia medications. Full inclusion/exclusion criteria are available upon request.
To evaluate whether the MCI group provided a recruitment benefit over that from an unselected comparison group, a comparison group was selected from individuals age 65 years or older, seen in the same health care system over the same time interval. They were further matched by sex to the MCI group. Those with a dementia diagnosis were excluded. The sample size of the comparison group was approximately 20% of the MCI group.
The ADRC staff were blinded to group status when contacting interested individuals, but group status was available for analysis at the end of recruitment efforts.

Mailing Process

A letter printed on institutional letterhead without specific salutation was sent to all individuals in the MCI and comparison groups. The letter 1) acknowledged the individual’s connection to the health system; 2) invited the individual to learn more about research in cognitive health and aging; 3) provided options to contact the ADRC via telephone, mail, or email; 4) informed the individual that they could “opt out” of further contact; and 5) informed the individual that the ADRC may reach out to them in the future if interest in further contact was expressed.
The letter included a returnable mailing slip on which individuals could confirm or deny interest in further contact by the ADRC and specify their preferred method of contact. A prepaid envelope to return the mailing slip was also included. Mailing materials are shown in Supplemental Materials Figure 1.

Response to Mailing and Expressions of Interest

Individuals who received the mailing were able to actively express interest in learning more about research programs at the ADRC either by 1) contacting the ADRC directly by phone; 2) contacting the ADRC directly by email; or 3) returning the included mailing slip, denoting interest for further contact, and specifying interest in further contact by telephone, email, or mail. A small number of individuals contacted us directly by email, all of whom also contacted us by telephone and expressed interest in further contact by telephone. Because the majority of responses across all contact methods preferred telephone, the current analysis describes our outreach efforts to those individuals regardless of initial response method. These included individuals who called the ADRC directly as well as those who emailed us directly or returned the mailing slip and expressed interest in further contact by telephone.

Recruitment Efforts

ADRC staff proceeded to call all individuals who expressed interest in further contact by telephone. Interested individuals were offered the opportunity to participate in observational and interventional studies available at the ADRC at the time of contact. ADRC staff explained study details and then offered to schedule initial screening appointments for all individuals who remained interested.

Estimating Staff Time and Effort

ADRC staff attempted to contact interested individuals by telephone using the following protocol: When individuals answered calls or responded to voice messages left by ADRC staff, calls continued until a decision regarding research participation was reached. If there was no response to a voice message within two weeks or it was not possible to leave a message, staff made up to two additional follow-up calls. A total of three call attempts were made to minimally or non-responsive individuals, with attempts made to vary the day and time of the call. Staff time and effort were estimated as follows: scheduling visits: 20 minutes, handling requests for more information or time: 15 minutes, determining ineligibility: 15 minutes, determining not interested: 10 minutes, and leaving a voice message: 5 minutes.



Response to Mailing and Expression of Interest

Of the 15,952 individuals who were sent the mailing, 114 contacted the ADRC by telephone directly and expressed interest, 839 sent returnable mailing slips, and the remaining 14,999 did not respond (Figure 1). Of the 839 who returned mailing slips, 624 (3.9% of all individuals who were sent the mailing) expressed interest in further contact, while 215 (1.3%) declined further contact. Among the 624 mailing slip respondents who expressed interest in further contact, 207 (1.3% of all individuals who were sent the mailing) indicated preference for further contact by telephone, 240 (1.5%) by email, and 177 (1.1%) by mail. Taken together, overall rate of expression of interest from the mailing was 4.6%.

Figure 1. Response to Mailing and Expression of Interest


This analysis focused on the 321 individuals who expressed interest in further contact by telephone (i.e., 2% of the entire mailing). Therefore, the 417 individuals who expressed interest in further contact through mail and email are not described in this report, and follow up on this cohort was left for future efforts.

Recruitment Outcomes

Of these 321 individuals who expressed interest for further contact from the ADRC by telephone, 30 (9.3%) enrolled in a study, 57 (17.8%) were not eligible due to medical comorbidities or study contraindications, 82 (25.6%) were not interested in research participation after speaking with ADRC staff, 137 (42.7%) did not definitively respond after at least three contact attempts, and 15 (4.7%) were not eligible for currently enrolling studies but remained interested in future participation (Table 1).

Table 1. Recruitment Outcomes for Individuals who Expressed Interest in Further Contact by Telephone (N=321)

Study Participation by MCI and Comparison Group

Among the 321 individuals who expressed interest in further contact by telephone, 227 (70.7%) individuals were from the MCI group (Table 2). This represents 1.8% of the 12,951 individuals from the MCI group. 53 of the 321 individuals (16.5%) were from the comparison group, representing 1.8% of the 3,001 individuals in the comparison group. An additional 41 (12.8%) individuals were incidental contacts who were not the intended recipients of the mailing but nevertheless contacted the ADRC. Group status for these individuals is by definition unknown.

Table 2. MCI and Comparison Group Status of Individuals who Expressed Interest and Individuals who Enrolled


Of the individuals who enrolled in studies, 15 of 30 were from the MCI group, representing a 0.1% enrollment rate from the total MCI group, while 9 of 30 were from the comparison group, representing a 0.3% enrollment rate from the total comparison group. A two-sample test of proportions shows that the difference in enrollment rate between MCI and comparison groups is statistically significant (p=0.019). An additional 6 of the 30 enrolled individuals were incidental contacts whose group status was unknown.

Enrollment by Study Type

Of the 30 individuals who enrolled, 7 (23.3%) enrolled in observational studies which did not include imaging, 21 (70.0%) enrolled in observational studies which included imaging, and 2 (6.7%) enrolled in an intervention trial (Table 3). Of the 7 individuals enrolled in observational studies without imaging, 5 (71.4%) were from the MCI group and 2 (28.6%) were from the comparison group. Among the 21 individuals who enrolled in observational studies with imaging, 9 (42.9%) were from the MCI group, 6 (28.6%) were from the comparison group, and 6 (28.6%) were from the incidental contact group. Finally, of the 2 individuals who enrolled in interventional trials, 1 (50.0%) was from the MCI group and 1 (50.0%) was from the comparison group. Detailed descriptions of these studies are included in Supplemental Materials Table 1.

Table 3. Enrollment by Study Type

Staff Time and Effort

ADRC staff logged 463 calls and spent an estimated total of 87 hours communicating with the 321 individuals who expressed interest in further contact with the ADRC by telephone (Table 4). Distribution of call logs and estimated time spent by recruitment outcome are also reported. On average, three hours (177 minutes) of staff time were required to enroll one participant. These estimates are limited to communication with individuals by telephone and offer a conservative summary of the total time and effort dedicated to this recruitment effort.

Table 4. Staff Time and Effort Required to Contact Individuals who Expressed Interest for Further Contact by Telephone



In this report we described our experience with a mailing outreach effort to engage individuals in research on cognitive health and aging. Individuals who had recent contact with the medical center were identified through the Mount Sinai Health System’s Electronic Health Record. Despite recent contact with the medical center and proximity to the site (over 95% of mailing addresses were within New York, New Jersey, and Connecticut), overall rate of expression of interest was approximately 5%, which is somewhat higher than comparable efforts of targeted mailing in similar age groups. For example, the Medical, Epidemiologic and Social Aspects of Aging (MESA) Study used a commercial mailing list to recruit women age 55-80 for a trial of behavioral techniques for the prevention of urinary incontinence that reached over 48,000 individuals and reported a 3.3% initial positive response rate (22). Efforts to recruit for dementia prevention trials using Medicare beneficiary lists and voter registration polls reported response rates between 0.4 to 2.0%.23 In the AD Anti-inflammatory Prevention Trial (ADAPT), over 3.5 million mailings were sent to Medicare beneficiaries 70 years or older in specified zip codes. The trial enrolled 2,518 volunteers at 6 sites over 44 months. Across trial sites, enrollment ranged from 0.4 to 1.9 participants per 1,000 mailings (24, 25). Unlike our study, this report included calls made to all individuals who received the mailing and did not opt out. The Ginkgo Evaluation of Memory (GEM) study to evaluate ginkgo biloba to prevent dementia mailed brochures to approximately 243,000 individuals age 75 and over who were assumed to be dementia free from purchased lists, voter registration lists, and university lists. Using telephone follow up to those who received the mailing, the study team attempted to reach 14,603 mail recipients, reached 12,186 and enrolled 1.3% of those who received the initial mailing (26).

Higher response rates have been reported in the literature when mailings came directly from primary care providers and when research activity was home-based and required no travel. For example, the Screening, Recruitment, and Baseline Characteristics for the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) Study for fall prevention found that approximately 12% of mailings resulted in expressions of interest (27). Higher response rates have been noted outside the US. Andersen et al mailed a questionnaire examining self-reported cognitive function to more than 11,807 community residents in Norway. 3,767 (31%) responded to the mailing. Of these, 438 met criteria for cognitive impairment and 292 were willing to undergo clinical evaluation (28). Notably, the cohort was invited to join a study for symptomatic individuals which may be of greater interest than studies of disease prevention.
In our study, about one third of the individuals who responded to the mailing contacted us by telephone. Of note, while 1.5% (240) used the returnable mailing slip and indicated preference for future contact by email, those who contacted the ADRC directly by email also contacted us by telephone, which may indicate less confidence in initiating email connections in this age group. Those who used the returnable mailing slip to provide telephone contact were nearly twice as likely to be lost to follow up relative to individuals who expressed interest by telephone directly. Future work might attempt to determine if requiring telephone follow-up would identify a more specifically interested group. Optimizing approaches to identifying sufficient numbers of interested individuals is critical to efficient and cost effective outreach.
Contrary to our expectations, we found greater interest and higher enrollment among those in the comparison group than in the MCI group. Of note, among those who enrolled, the MCI group was more likely to participate in observational studies and less likely to enroll in imaging or interventional studies. There are several possible reasons for this difference. Cognitive impairment and dementia in health records may be associated with other serious health problems (29). These health problems may be more prominent than cognitive impairment for these individuals and may reduce their interest in research on this topic. Meanwhile, the comparison group may be less likely to suffer from comorbid conditions and may be motivated by interest in prevention or protection against cognitive decline. Only 2 participants enrolled in clinical trials, highlighting challenges in recruitment to prodromal AD drug trials. Given these low frequencies, results from this study should be replicated. Understanding these themes will be important topics for future research.
While this mailing did not specifically invite non-recipients to join, the recruitment effort was prepared to accept them. We identified several individuals who described the mailing but were unsure of the source from which it came. This incidental interest is obviously a positive outcome for improving recruitment. Some mailing efforts encourage anyone to reply and this approach may be worthy of consideration. In particular, these contacts may be a source of “high interest” individuals, and efforts to identify features to improve outreach to them could provide an important contribution to recruitment science.
Several studies report the use of an opt-out approach to recruitment. In general those who opt-out prior to contact are low but often not reported. In our study, very few mailing recipients refused further contact (<2% of total number of mailings). Others have reported similarly low rates (23). The opt-out option can be executed in different ways including the “pre-mailing” requirement or a delay in outreach before initiating contact to those who received mail (23). These options require significant expense and time delay. In our study we reached out only to those who contacted us. Without an opt-out option, when unsolicited telephone calls followed the initial mailing, rate of no-interest is high, with reports of over 50% in several studies (24-26). The discordance between the low opt-out rate and the high no-interest rate reported suggest that opt-out resources may be effective in allowing studies to call more people. However, the resources and expenses needed to call those who do not demonstrate any initial interest are high. Future work may focus on identifying criteria for more efficient recruitment of individuals who do not opt out.
There are several limitations to fully appreciate this report in the context of recruitment to studies of cognitive health and aging. While there is growing use of algorithms to identify undetected dementia using EHR, little work has been done on identifying MCI (30, 31). Accuracy of diagnostic codes for dementia and cognitive impairment in medical records is limited (13-15). The study aimed to target a wide group of potential participants, however, it is possible that suitable participants were excluded if they were prescribed cholinesterase inhibitors for their MCI but did not have a formal diagnosis of MCI recorded in their medical records. Furthermore, our algorithm was based on EHR data collected across a five year period, and key patient information may not be the most up to date. We also did not have important variables such as time since diagnosis to examine their usefulness as potential predictors of response. We have little data on the accuracy of our mailing as “return to sender” information was not available to us. Recruitment efforts served multiple studies which may not have been open throughout the entire outreach period. However, the center had a variety of observational and interventional studies open at any time, and the option to be contacted for future studies was always provided. Ongoing efforts include study-specific mailing outreach to individuals who did not decline further contact, including those who expressed their preference for future contact by mail or email. Finally, while response to this mailing effort was slightly higher than expected, our experience is limited to a single site.



Focused mailing outreach efforts continue to represent a valuable means of engaging older adults in research on cognitive health and aging. However, using EHR to identify individuals who likely have cognitive impairment did not appear to increase response or participation rates. As demonstrated among the incidental contacts, a written letter has the potential to spark interest beyond its intended recipients. Mailings from known healthcare systems build upon an established relationship and may foster the development of a preliminarily defined, trial-ready cohort.


Acknowledgments and funding: Funding for this initiative was provided by the Alzheimer’s Disease Research Center (ADRC) at the Icahn School of Medicine at Mount Sinai (P50AG005138), the Alzheimer’s Therapeutic Research Institute (R01AG047992), and the National Center for Advancing Translation Sciences (UL1TR001433). Drs. Sano, Grossman, and Zhu also are supported by the Department of Veterans Affairs, Veterans Health Administration. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. 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.

Conflict of Interest: No conflict of interest has been declared by the authors.

Ethics standards: The study was approved by the Icahn School of Medicine Institutional Review Board (IRB). All participants gave informed consent before participating.





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M. Kosaner Kließ1, R. Martins1, M.P. Connolly1,2

1. Health Economics, Global Market Access Solutions Sarl, St-Prex Switzerland; 2. Unit of Pharmacoepidemiology & Pharmacoeconomics, Department of Pharmacy, University of Groningen, Groningen, The Netherlands

Corresponding Author: Mark Connolly PhD, Unit of Pharmacoepidemiology & Pharmacoeconomics, Department of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands,,

J Prev Alz Dis 2021;3(8):362-370
Published online April 24, 2021,



Background: Alzheimer’s Disease is the most common cause of dementia, affecting memory, thinking and behavior. Symptoms eventually grow severe enough to interfere with daily tasks. AD is predicted to increase healthcare spending and costs associated with formal and informal caregiving. The aim of this study was to identify and quantify the contribution of the different cost components associated with AD.
Methods: A structured literature review was conducted to identify studies reporting the economic burden of Alzheimer`s Disease beyond the healthcare setting. The search was conducted in Medline, Embase and EconLit and limited to studies published in the last 10 years. For each identified cost component, frequency weighted mean costs were calculated across countries to estimate the percentage contribution of each component by care setting and disease severity. Results obtained by each costing approach were also compared.
Results: For community-dwelling adults, the percentage of healthcare, social care and indirect costs to total costs were 13.9%, 17.4% and 68.7%, respectively. The percentage of costs varied by disease severity with 26.0% and 10.4% of costs spent on healthcare for mild and severe disease, respectively. The proportion of total spending on indirect costs changed from 60.7% to 72.5% as disease progressed. For those in residential care, the contribution of each cost component was similar between moderate and severe disease. Social care accounted on average for 85.9% of total costs.
Conclusion: The contribution of healthcare costs to the overall burden was not negligible; but was generally exceeded by social and informal care costs.

Key words: Indirect costs, healthcare costs, Alzheimer’s Disease, societal perspective, economic evaluation.



Many chronic diseases pose significant economic and humanistic burden for patients, families, and society as a whole. For example, it has been estimated that the indirect costs of lost economic productivity of people with chronic diseases are almost 300% greater than the direct costs of healthcare (1). The economic consequences of health-related employment inactivity of people with chronic conditions can also extend to the government due to increased spending on support programs and lost tax revenues (2, 3). Fewer people working, earning income and paying taxes generates lost tax revenue for the government and increasing dependency on public benefits support (4). The externalities of poor health can further extend to family members or friends who may reduce or discontinue their work in order to provide informal care (5-8). Furthermore, informal caregiving can impact the well-being of those providing care, which is shown to be proportional to the amount of care provided (9, 10) suggesting that as the Alzheimer’s disease (AD) population grows, the externalities of the condition also expand.
Researchers have increasingly studied the cost related to informal caregiving due to its significant impact on families as well as the overall contribution to the total economic burden of many chronic conditions (11). Studies have also examined how including the cost of informal care can influence findings of cost-effectiveness studies, where inclusion of the cost of informal care can determine the likelihood that interventions are considered cost-effective or not (12). Many determinants can influence the amount of informal care provided, including age, gender, geographic region, caregiver relationship, the level of dependence of the person requiring care and the amount of social services being provided (1, 13).
The importance of informal caregiving is exemplified by AD, which is a progressive chronic condition with increasing global prevalence (14). AD is a continuum with the first clinically recognizable stage being Mild Cognitive Impairment (MCI) (15). MCI refers to individuals who function similarly to their peers and suffer some cognitive impairment, but it is not sufficiently severe for it to be considered dementia (16). As the disease progresses, symptoms gradually worsen and in the later stages patients typically lose their independence and become dependent on formal or informal care. As a result, AD is predicted to increase healthcare spending and costs associated with formal and informal caregiving compared to an average aging population.
This is particularly important as AD progresses, and more intensive care is required (17-19). Increasing demands are placed on informal care at a time when the proportion of working aged adults is decreasing in many advanced economies, which could influence economies and labor markets (20). There is growing evidence of the significant economic burden that AD poses on the healthcare system as well as on patients and their families. To further understand the contribution of healthcare costs to overall costs attributed to AD, we have reviewed the literature to identify studies that provide comprehensive estimates of financial burden including productivity losses, informal care costs, institutionalization costs and other economic domains. We believe that dissecting the cost components can give a more complete picture of the overall burden of AD, emphasize the major cost drivers associated with AD, and in the end serve as a foundation for future policy frameworks.

Study aims

The aim of this literature review was to provide an overview of the different cost components associated with AD and estimate the proportion of overall costs of AD that are attributable to healthcare in comparison with all other attributable costs incurred by individuals, households and society.



Search strategy

A comprehensive search strategy was constructed using controlled vocabulary and free-text terms relating to the population, outcomes and study designs of interest. Population terms included those related to AD and mixed dementia, as well as neurocognitive disorders other than AD, and those defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) and recognized patient societies, in order to reduce irrelevant studies. Outcome terms were clustered around five concepts: labor force participation and income, disposable income, social security, disability allowances and indirect costs. These measures are typically not included in randomized trials; or are reported as secondary outcomes for which studies are not powered to analyze. Additionally, when these data are collected alongside randomized trials they are intervention-specific, restricted to shorter follow-up periods and of limited generalizability due to strict trial inclusion criteria (21).Therefore, a search filter for observational studies formed the last search concept. The search was limited to humans and to studies published in the last 10 years. No language limitations were predefined. The full strategy provided in Supplement 1 was used for searching MEDLINE (PubMed) and adapted for searching EMBASE (OVID) and EconLIT. Backwards snowballing was conducted on eligible studies to identify further relevant research.

Study eligibility


Individuals identified with MCI likely due to AD or AD with or without another form of dementia were included along with their caregivers. Populations limited to a single gender or AD in combination with non-dementia health conditions were excluded.


Comparisons of AD to a cognitively normal population or between different stages of AD were of interest.


For the patient and caregiver, the outcomes of interest included direct and indirect healthcare costs; these including but not limited to income, labor force participation, economic (in)activity, work adaptation; disposable income; social insurance allowance or benefit; disability allowance and caregiver’s allowance. Studies assessing total societal costs which included health costs and the cost of each component as well as the total were included. However, studies reporting only on a single component of economic impact, e.g., only informal costs or health costs only, were excluded.

Study design

Non-interventional, observational studies providing an overview of AD were included. Interventional studies were kept in if they reported relevant outcomes; however, they were of less priority. Randomized or quasi-randomized clinical trials, traditional and systematic literature reviews, qualitative studies, methodological papers or study protocols, economic modeling studies, comments, editorials and letters were excluded. Studies with less than 10 subjects per arm were also omitted.

Study selection

References were downloaded into ENDNOTE version 9.3. Study titles and abstracts were screened against the eligibility criteria described above by a single reviewer. The full texts of relevant studies were subsequently obtained and screened by two independent reviewers. Posters of conference abstracts were sought if the material had not been published in a journal manuscript. Uncertainties between reviewers were resolved by discussion with a third reviewer.

Data extraction and synthesis

Data were extracted from each study by a single reviewer on study design and duration, country, care setting, sample size and age, disease diagnosis and disease severity; measurement and costing of resources (costing approach, costing year and currency), and the absolute mean and variance of each cost component and of the total costs. The resource items comprising each cost component were also recorded.
The percentage of total costs covered by each component was calculated for the overall AD population in each study and by disease severity. Outcomes from cross-sectional studies and at baseline from longitudinal studies were narratively synthesized. For each cost component, frequency weighted mean costs were calculated to summarize results across countries by disease severity, and per country when multiple studies were available. For this purpose, all costs were inflated to 2019 using country specific consumer price index values (22) and then converted to Euros. Primary analysis was based on studies that used the human capital approach for valuing indirect costs (23) and repeated for each care setting. When studies reported multiple analyses, results obtained with supervision time from a caregiver or family member were included. A separate assessment was conducted on studies that valued informal care using the labor replacement approach, i.e., by using the cost for hiring a professional caregiver. Results obtained with the two costing approaches for the community setting were compared. Economic elements not included in the estimation of the total societal costs, i.e., income, were narratively summarized.



The search yielded 2250 results. After removing duplicates, the titles and abstracts of 1740 records were screened of which 143 were considered relevant for full-text screening. Of these, 3 were conference abstracts for which journal publications were identified; 1 was a repeated publication; 10 provided an insufficient description of methods or results and 108 met at least one exclusion criteria. 21 publications were included in a narrative synthesis. Five publications were further included in synthesis after backward snowballing. Study selection is depicted in Figure 1.

Figure 1. Flow of study selection


Characteristics of individual studies

Ten publications reported results from the GERAS I (18, 24-28), GERAS II (29, 30), and extensions of the GERAS to Japan (31) and the USA (32). The remaining 20 publications included the ECO, EVOCOST and Codep-AD studies from Spain (33-36); the ECAD from Ireland (17, 37); one multinational study (38); a cluster-randomized observational study from China (39, 40); and others from France (41), Germany (42), Sweden (43), and the USA (44-48). Together there were 17 studies with unique methodologies.
One retrospective case-control study from the USA used a claims database to assess patient and caregiver medical costs in comparison to a cognitively healthy spouse-patient dyad (47-50). Based on population survey data also from the USA, Ton (46) assessed the relationship between cognitive decline (MCI and AD) and household income in addition to patient medical costs.
The total socioeconomic burden was estimated in 15 studies. The characteristics of these are summarized in Table 1. Two studies used random sampling to identify study sites (33, 39, 40). In the remaining studies, participants were conveniently sampled from their healthcare settings by their local healthcare providers. Longitudinal studies (9 studies) limited their sample to community-dwelling adults, with exception of the ECO study that also included individuals from a residential setting. Three studies further restricted their sample by disease severity: the EVOCOST study focused on adults with moderate disease severity (34); the GERAS-US study (32) compared mild AD against MCI; and Zhu (45) compared adults with MCI against cognitively healthy adults. Cross-sectional studies (6 studies) included a broad sample from the community and residential setting, except for Gervès et al (2014) who studied community-dwelling adults; and most did not specify an age-limit for inclusion (35, 36, 38, 42, 43). Disease severity was defined by the Mini-mental State Exam (MMSE) scores in 14 studies; and by the Clinical Dementia Rating (CDR) in the ECO and Codep-AD studies (33, 35, 36). Discrepancy was observed between studies in the diagnostic criteria for AD and disease staging based on MMSE scores. Two studies staged disease severity by dependency level (36, 44).

Table 1. Characteristics of studies assessing total socioeconomic burden of AD

1. National Institute of Neurological and Communicative Disorders, and Stroke and Alzheimer’s Disease and Related Disorders Association criteria; 2. National Institute on Aging and Alzheimer’s Association Alzheimer’s criteria; 3 .International Working Group on Mild Cognitive Impairment (J Intern Med 2014; 256(3):240-246) ; AD: Alzheimer`s Disease. CN: China. DE: Germany. ESP: Spain. FR: France. IE: Ireland. IT: Italy. JPN: Japan. MCI: Mild cognitive impairment. NR: Not reported. NA: Not available. SWE: Sweden. UK: United Kingdom. USA: United States of America. MMSE: Mini-Mental State examination. FAQ: Functional Activities Questionnaire. CDR: Clinical Dementia Rating. GDS: Global Deterioration Scale.


Overall, adults with MCI likely to be due to AD were included in 3 studies (17, 32, 37, 42); their outcomes were reported separately from adults with AD in the GERAS-US (32).
All 16 studies included patient health care, social care and informal care in their estimation of total socioeconomic burden. There were minimal differences across studies in the resource items assessed as most studies used the Resource Utilization in Dementia (RUD) (52) or RUD-Lite (53)instruments for measuring resource utilization. The case-control study by Zhu (45) differed from the others by using the Resource Use Inventory (54) to capture resource utilization and not valuing the use of informal care in MCI. It is also noteworthy that Reese (42) conducted their economic evaluation from the perspective of the German statutory health insurance; formal and informal care were assessed together as a component of social care. This evaluation also estimated productivity losses of the patient and caregiver. Productivity loss of the caregiver was evaluated independently from informal care in one other study where informal care was accounted as lost leisure time (35). Informal care was accounted as productivity loss in one study each from the USA (44) and China (39, 40). The Chinese study further considered intangible costs which accounted for 4.2% of total costs. Additionally, healthcare costs of the caregiver were evaluated by GERAS I, GERAS II-Spain and GERAS-US.
The contribution of patient health and social care and indirect costs to total societal costs, without caregiver health care and intangible costs, were calculated across all studies. Indirect costs related to informal care and productivity loss when evaluated separately.

Cost components by setting

The cost components attributed to the MCI population were obtained from a single study where the largest component of overall costs was patient health care costs (50.9%) followed by informal care costs (40.1%) when using the human capital approach. The case-control study by Zhu (45) found hospitalization to be the largest component of medical costs and that adults with MCI required significantly more informal care than cognitively healthy adults.
In community-dwelling adults with AD, the weighted mean contribution of health care costs was 26.0%, 15.7% and 10.4% for mild, moderate and severe forms of AD, respectively; and averaged 13.9% across all severity levels. Results summarized in Table 2 show that the weighted mean contribution of indirect cost to the overall cost burden was substantially high and increased as disease progressed representing 60.7%, 67.1% and 72.5% for mild, moderate and severe AD, respectively. Country-level data presented in Supplement 2 show that patient health care costs formed a greater component of total costs in the USA compared to European countries at all disease severity levels; and the least in Italy where informal care costs exceeded 80% of total costs. Further, social care costs composed a larger amount of the total costs in Japan and Sweden, and even exceeded the contribution of informal care in Sweden.

Table 2. Weighted mean (min-max) contribution of each cost component to total costs across countries

† only one MCI study identified.


For adults living in residential care, the weighted mean contribution of cost components was similar between moderate and severe AD, as shown in Table 2. Across severity levels, patient social care formed 85.9% of total costs and patient health care was slightly larger than that of informal care (8.6% vs. 5.5%). Further, the percentage contribution of each cost component was similar between countries. The difference in minimum and maximum values between Germany, Spain, Sweden, UK and USA were 3.1%, 8.5% and 5.8% for patient health care, social care and indirect costs, respectively between Germany, Spain, Sweden, UK and USA. Country-level data are tabulated in Supplement 2.
In studies that assessed both community and residential care settings, the percentage contribution of cost components varied between countries in terms of social care (15.6%-83.9%) and informal care (9.4%-67.8%). Looking at country-level data (Supplement 2), this outcome was heavily influenced by high social care costs and little informal care in Sweden. Additionally, social care constituted a smaller component of total costs than patient healthcare in China (15.6% vs. 32.5%) than in European countries.

Comparison of costing approaches

The choice of method for costing informal caregiving time had a substantial impact on the distribution of cost components in the early stages of cognitive decline. Using the labor replacement approach increased the weighted mean contribution of patient healthcare to total costs for MCI (79.9% vs. 50.9%) and mild AD (39% vs. 26%), as shown in Figure 2. Country-level results provided in Supplement 2 show that this was especially true for the USA where the contribution of patient healthcare almost doubled (36.2% to 65.4%). Smaller, but observable changes also occurred in Spain, Germany and Italy. Data for these countries came from analyses that excluded supervision time from informal care. Additional analysis was carried out using results from the GERAS studies to explore how the inclusion of supervision time influences results. Across France, Germany, UK, Spain and Italy, the weighted mean contribution of patient health and social care were equally elevated by 5% to 6% with the exclusion of supervision time from informal care calculations. Results are presented in Supplement 3.

Figure 2. Comparison of labor replacement method and human capital approach for valuing indirect costs and influence on percentage contribution of each cost component


Contribution of caregiver healthcare to overall costs

Across the GERAS-I countries, Spain and the USA, caregiver healthcare costs accounted for 6.9% of total costs in adults with MCI likely due to AD (32) and 3.7% of total costs in those with AD. As shown in Table 3, the percentage contribution of this component to the total cost decreased substantially from mild (11.5%) to moderate AD (4.4%) and reached 2.3% for severe AD. Across AD severity levels, the contribution of caregiver healthcare costs showed little variation between countries (3% – 4.2%).

Table 3. Weighted mean (min-max) contribution of caregiver healthcare costs

AD: Alzheimer`s Disease. MCI Mild Cognitive Impairment. HC: Healthcare. SC: Social care.


Impact of AD on other socioeconomic aspects

Ton et a (2017) (46) demonstrated that in the USA not only adults with AD but also those with MCI had greater medical expenditure and less household income than cognitively healthy adults (<0.001). This result remained highly significant after adjusting for age, sex, race, education, marital status, residential region and comorbidities (<0.015). Another study demonstrated that, compared to MCI, significantly more individuals with mild AD were pushed to an income below the federal poverty level. Patients’ employment rates were found to significantly drop from 21.4% to 9.4%; and the number of employed adults who reduced their work significantly rose from 3.2% to 13.8% (32). In the broader AD population, a significant relationship between dependency and household income has not been found (44).
When examining the impact of AD on household expenditure (47, 48), an US study indicated that annual health care costs were double the amount of costs of a cognitively healthy household ($6,028 vs. $2,951). Patient health care costs were significantly higher than age, sex and comorbidity-matched adults ($4408 vs. $1473, p<0.001). Spousal caregivers accumulated significantly higher costs for AD-related and mental health prescription; but on average were not significantly different from spouses of cognitively health adults.



The rising costs of treating AD and the impact on households and caregivers has been a topic of concern for researchers, policy-makers and planners for many years (55). The work described here helps to put expenditure into perspective to understand major cost drivers in the delivery of care to people with AD. This review has illustrated that in community-dwelling adults with AD, patient healthcare costs constitute the smallest component of the total cost burden representing, on average, 13.9% across all AD severity levels. Furthermore, the contribution of healthcare costs to the overall cost burden decreases as disease progresses and as informal care needs increase. As described here, the costs of informal care represent approximately 60% of total costs, and reach 72.5% of the total cost burden in severe AD. The difference between the contribution of patient healthcare and indirect costs was substantially reduced in early stages of AD when using the replacement labor approach to valuing informal care. This may be due to higher employment rates of the caregiver of adults with MCI and mild AD compared to the later stages; and that this is disregarded with the use of a uniform cost to value caregiving time. Robinson (32) reported employment rates of 48.3% and 43.4% respectively for patients with MCI and mild AD; with later stages of AD this tends to drop below 30% (18, 30).
Variation in the distribution of the cost components in the community and residential professional care settings emphasize the importance of studying each setting separately. When costs were pooled across settings, results were heavily influenced by residency care and showed high variability between countries. It is important to put the informal care costs into perspective as these represent lost earnings for individuals with significant economic consequences (56). Therefore, interventions that delay progression can offer economic benefits due to reduced need for informal and formal care.
We observed that the distribution of cost components was relatively similar between European countries. In Italy, however, there was a heavy reliance on informal care and little utilization of medical care which became even more apparent with increasing disease severity. The provision of long-term care by the family may be due to differences in the formalization of and access to healthcare compared to other European countries (29). The greater contribution of community care in Japan, compared to European countries, may be due to the caregiver being an adult-child of the person with AD (31), and in Sweden due to the availability of different social care structures (38). Such factors have been considered in other comparisons of country-level data (24, 26).
This review identified few studies evaluating the broader economic burden of MCI likely due to AD, probably because of the recent introduction of this term and the difficulty to establish this diagnosis (57). These studies demonstrated that individuals with MCI likely due to AD require social care and informal care more than their age-matched peers; and that this is further increased in those with mild AD dementia (32, 45). A similar trend is seen with caregiver health care costs when they are included in the estimation of total costs. These results highlight the importance of reporting disaggregated outcomes across early stages of cognitive decline. As more sensitive diagnostic methods become available to detect changes in cognition and more therapies become available to slow down progression early in the AD continuum, the need to explore the wide socioeconomic impact of cognitive decline will become more pertinent.
The results of this review should be interpreted with caution as a small number of studies were included. A larger number of studies might have been identified by removing the search limit on publication dates. The intention of this search limit was to identify studies reflecting current treatment practices. As part of a rapid review, study screening and data extraction were carried out mostly by a single reviewer, and the quality of the included studies were not assessed due to limited time and resources. The exclusion of quality appraisal is justifiable as a meta-analysis of study results was not possible. The analysis was nonetheless quantitative in nature and would not have benefited from the inclusion of qualitative evidence. Calculation of a frequency-weighted mean cost across countries was seen as a descriptive method for summarizing estimated costs per person. Differences in criteria for disease diagnosis and staging were not considered in data synthesis. Only the extensions of the GERAS study applied the more recent diagnostic criteria from the National Institute on Aging and Alzheimer’s Association Alzheimer’s (NIA-AAA) (58). Study-level results differed more between diagnostic criteria than between disease staging based on MMSE scores. Differences in AD severity categorization are likely to generate cost data somewhat different in absolute terms. There is a clear trend in the data showing that a reduction in the proportional contribution of healthcare costs is accompanied by an increase in the contribution of indirect costs, as severity progresses (Figure 2). The authors believe that this overall trend is unlikely to be substantially altered were AD categories more homogeneous.
NIA-AAA criteria distinguish AD dementia from earlier stages of cognitive decline, not limited to memory loss alone, and from other dementing conditions. They also recognize the additional use of imaging methods or biomarker analysis in increasing certainty in diagnosis, particularly for the differential diagnosis of MCI likely due to AD. However, at time of publication ancillary testing was described as optional clinical tools, advocating more investigational research on their use and standardization (57, 58). The Alzheimer`s Disease Neuroimaging Initiative has played an important role in the quest to find sensitive biomarkers and diagnostic tests; and have developed standardized methods for clinical tests, magnetic resonance imaging, positron emission tomography and cerebrospinal fluid biomarkers (59). Multi-modal use of neuroimaging and biological markers has been recommended as the way forward for detecting changes in cognition throughout the AD pathophysiology (60), and for predicting future decline (59). Blood biomarkers have also been developed as a non-invasive, low-cost alternative to cerebrospinal fluid biomarkers; and have shown to be effective in differentiating AD, MCI and cognitively normal controls (59, 61). These recent advances will likely impact the incidence of MCI due to AD and AD dementia and their associated health care costs. Study-level results from this review suggest the contribution of patient health care costs to be lower and that of social care costs to be higher with NIA-AAA criteria compared to older diagnostic criteria. Future observational studies reflecting the use of modern methods are needed to explore this hypothesis.



Healthcare costs can cover up to 30% of the overall burden of AD; but is generally exceeded by the costs associated with social care and informal care in the community setting the contribution of indirect costs to overall costs increases and that of patient healthcare decreases as disease progresses. As people transition from community care to residential care, the proportion of spending on social care increases and that of indirect costs substantially decreases. Such a transition allows some caregivers to regain independency and rejoin the labor force. The reliance on informal care in the community setting is likely due to the differing availability and organization of social care between countries particularly in the earlier, less dependent stages of AD.


Funding: This work was funded by Biogen.

Disclosures: During the peer review process, Biogen had the opportunity to review and comment on the manuscript. The authors had full editorial control of the manuscript and provided their final approval on all content to be published. The authors hold no financial interests in the sponsoring company.

Conflict of interest: The authors report no further conflicts of interest in relation to the work described here.

Ethical standards: The analysis reported here is based on previously reported literature. No individual patient data has been collected for this study and no ethics approval was required.

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.





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X.-X. Zhang1, Y. Tian1, Z.-T. Wang1, Y.-H. Ma1, L. Tan1, J.-T. Yu2

1. Department of Neurology, Qingdao Municipal Hospital, Qingdao University, China; 2. Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.

Corresponding Author: Dr. Jin-Tai Yu, Department of Neurology, Huashan Hospital, Fudan University, No. 12 Wulumuqi Road, Shanghai, China; or Dr. Lan Tan, Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China, E-mail addresses:
(J.T. Yu); (L. Tan), Tel: +86 21 52888160; Fax: +86 21 62483421.

J Prev Alz Dis 2021;3(8):313-321
Published online April 9, 2021,



Mild Alzheimer’s disease is the leading cause of dementia, accounting for 50-70% of cases. Alzheimer’s disease is an irreversible neurodegenerative disease, which affects daily life activities and social functioning. As life expectancy increases and demographic ageing occurs, the global prevalence of Alzheimer’s disease is expected to continue to rise especially in developing countries, leading to a costly burden of disease. Alzheimer’s disease is a complex and multifactorial disorder that is determined by the interaction of genetic susceptibility and environmental factors across the life course. Epidemiological studies have identified potential modifiable risk and protective factors for Alzheimer’s disease prevention. Moreover, Alzheimer’s disease is considered to start decades earlier before clinical symptoms occur, thus interventions targeting several risk factors in non-demented elderly people even middle-aged population might prevent or delay Alzheimer’s disease onset. Here, we provide an overview of current epidemiological advances related to Alzheimer’s disease modifiable risk factors, highlighting the concept of early prevention.

Key words: Alzheimer’s disease, epidemiology, modifiable risk factors, prevention.



Alzheimer’s disease (AD), as the most prevalent cause of dementia, is defined by deterioration in cognition, function and behavior, which typically begins in memory loss about recent events (1). The decisive pathological features in AD patients’ brain tissues are raised levels of both amyloid-β (Aβ) composing of extracellular senile plaques and hyperphosphorylated tau (p-tau) aggregating intracellularly as neurofibrillary tangles (NFTs) (2). About 50 million people are living with dementia around the world, due to the aging population, the number of patients is predicted to triple by 2050, which increases the risk of disability, burden of illness and health care costs (3). Moreover, current treatment strategies only ameliorate symptoms and there is no effective cure for AD. However, AD has a long prodromal period during which early prevention appears to be particularly important to slow down the progression of AD. Therefore, epidemiological investigations are essential to identify risk and protective factors that strongly influence cognitive status. In fact, one-third of AD cases worldwide are attributable to underlying modifiable risk factors (4), which might modulate an individual’s risk of developing AD. In this review, we classified these factors into several categories including psychosocial factors, pre-existing diseases, lifestyles and others, exploring the potential effects on cognition to provide better implications for AD prevention.


Descriptive epidemiology of Alzheimer’s disease

The number of dementia patients is projected to reach 152 million by mid-century worldwide, with the greatest increase expected in low-and middle-income countries (3). According to 2020 Alzheimer’s disease facts and figures, the number of AD patients (≥ 65 years) might increase greatly from 5.8 million to 13.8 million by 2050 in America (5). The obviously increased AD prevalence was found in community-dwelling investigations of Japan and China over the last few decades (6, 7). Particularly, age-specific global prevalence in women was 1.17 times larger than in men and the age-standardized mortality rate of women was also higher than men, suggesting the longer lifespan was not the only determinant of the women dominance (8). In addition, death tolls with AD increased 146.2% from 2000 to 2018 and AD became the fifth-largest cause of death in American old people (5). Notably, caregivers would experience more mental stresses and negative emotional influences (5). Therefore, the social and family burden of caring for AD population will be huge and unsustainable.
Wellbeing is the aim of much of AD care. The AD patients would have perplexing problems and symptoms in many domains. And some epidemiological investigations have provided robust evidence that behavioral and environmental factors have key roles in disease pathogenesis and progression. Especially, pre-existing disease is more common in AD patients than others of the same age, it is essential to keep physically healthy to protect the cognition. In addition, many risk factors could contribute to the development of AD and also be regarded as the symptoms of AD simultaneously, the reverse causality might account for this. Therefore, the accurate diagnosis is so important for those individuals suffering from cognitive dysfunction. Though AD is indicated by Aβ and tau biomarkers, some cognitively normal individuals having only these biomarkers never develop AD (9), it means that the pre-symptomatic diagnosis is more difficult to obtain. Future challenges would include discovering less-invasive and more-sensitive biomarkers or methods that can also be used for early screening and diagnosis purposes. Anyway, evidence-based prevention strategies, in line with the potential link between modifiable risk factors and late-onset AD, need to be explored in future studies.


Putative modifiable risk factors and prevention for late-onset Alzheimer’s disease

Evidence from observational studies has accumulated during the past few years and shown several potentially modifiable risk factors (Figure 1), concerning AD prevention some potential feasible suggestions are provided (Figure 2).

Figure 1. Potential modifiable risk factors for Alzheimer’s disease

Risk factors mainly included pre-existing diseases, unhealthy lifestyles and environmental exposures, while some factors concerning psychosocial conditions as well as healthy lifestyles might protect against AD. In addition, some factors appeared to be risk factors as well as symptoms of AD, possibly due to the reverse causality, these factors were highlighted in bold. Abbreviation: BP = blood pressure, DASH = Dietary Approach to Stop Hypertension, MIND = Mediterranean-DASH diet Intervention for Neurodegeneration Delay, PUFA = polyunsaturated fatty acid, HDL- cholesterol = high-density lipoprotein cholesterol.

Figure 2. Implications for preventing Alzheimer’s disease and slowing its progression

It is imperative to increase the cognitive reserve mainly via enhancing education attainment and promoting social contact. Additionally, good conditions of body health and healthy lifestyles as well as reducing environmental exposures might be favorable to reduce the neuropathological damage for AD prevention.


Psychosocial factors

Some prospective cohorts have studied whether these psychosocial factors might affect the cognition specifically (Supplemental Table 1).

Educational attainment

Continuing adult education could benefit language processing and intellectual capacities (10), long-term education might also have favorable influences on total brain volume such as elevated cortical surface area and thickness in the prodromal stage of AD (11, 12). Mendelian randomization studies confirmed causal associations between education and reduced AD risk [ odds ratio (OR) 0.64, 95% confidence intervals (CI) 0.56-0.74] as well as delayed AD onset [hazard ratio (HR) 0.76, 95% CI 0.67-0.85] (12), which could be mediated by intelligence in part (13). These causal relationships should be delineated profoundly in future studies.

Cognitive activity and bilingualism

Active engagements in cognitive activity were likely to have 46% reduction of AD risk in the Swedish study (14). Specifically, reduced decline in memory ability and cognitive speed among those playing more analog games was reported in the Lothian cohort study (15), which could be explained in part by intensive connectivity between the hippocampus and superior frontal cortex (16). Bilingualism, as a constituent of cognitive reserve, could enhance neural efficiency by increased functional connectivity in the frontoparietal control network for executive control and the default mode network for behavior control (17), certainly, bilinguals had stronger executive and visual-spatial functions than monolinguals (18). Specifically, compared with those only speaking Cantonese or Mandarin, lifelong bilinguals manifested the first symptoms of AD markedly later (19).
Higher educational attainment could delay the onset of AD by building cognitive reserve and brain volume (11). Similarly, cognitive activity and bilingualism were also beneficial to preserve the healthy cognitive functioning (14, 15, 18). Therefore, it is needed to increase access to education especially language and promote cognitive activities in the general population to protect against AD.

Social engagement

Frequent social contact with friends was related to a modestly decrease in dementia risk, owing to the creation of cognitive reserve at the early-stage via regular heathy social engagement (20). Particularly, community cultural activities might also confer benefits to the whole cognitive function (21). A healthy couple relationship might exert greater protective effects on cognition, whereas the widowed, especially those APOE ε4 carriers, had higher risk of AD (OR 7.67, 95% CI 1.6-40.0) than those married people (22). Indeed, among cognitively intact older people, widowed individuals were at higher likelihood of suffering serious Aβ-related cognitive deterioration (23). More social engagements could exercise memory and language which might further increase cognitive reserve (20). Of note, the widowed or the singled seemed to have less communication with others, it is encouraging to establish healthy social relationships and engage more regular social activities.

Depression and stress

Depression was a significant risk for AD (OR 1.65, 95% CI 1.42-1.92) (24). The finding of Australian study further proposed that the dementia odds ratios of mild, moderate to severe depressive symptoms were 1.2 (95% CI 1.0-1.4), 1.7 (95% CI 1.4-2.2) and 2.1 (95% CI 1.4-3.2). In addition, treatment with the citalopram for more than 4 years was strongly linked to a 3-year delay in progression from mild cognitive impairment (MCI) to AD (25). Notably, citalopram could reduce cerebrospinal fluid (CSF) amyloid plaque in experimental transgenic AD mice and healthy volunteers (26), conferring great significance to clinical practice. Besides, greater microglial activation, a symbol of neurodegenerative inflammation, existed in depressive patients not receiving medications (27), thus, reasonable treatment of depression might mean significant to prevent from neurodegeneration. However, depression and AD might have common causes such as inflammation and neurodegeneration, or similar symptoms in the prodromal stage of AD (28), thus depressive symptoms appeared to be a prodromal marker of AD rather than a causal risk factor. Further, the causal association of depression with AD was not supported in a mendelian randomization study (12), therefore, this association should be explored more in detail in future studies.
Higher risk of AD incidence (HR 1.36, 95% CI 1.12-1.67) was found among those individuals with stress-related disorders (29). Chronic work-related stress was also an emerging risk factor for AD (30). Notably, oxidative stress and neuroinflammation could be induced by stress, which could further promote the production of amyloid plaques (31). Regulating stress and adjusting emotions would ameliorate detrimental psychological effects on cognition.

Pre-existing diseases

We concluded detailed discoveries of prospective studies concerning the relationships between all these pre-existing diseases and AD development (Supplemental Table 2).

Diabetes, hypertension and dyslipidemia

Evidence supporting the higher risk of affecting with AD among the diabetics [relative risks (RR) 1.43, 95% CI 1.25-1.62] was based on 24 longitudinal studies (32). Specifically, a steeper fall in perceptual speed and verbal abilities among the diabetics was also found in the Swedish study (33). Interestingly, the interaction of Aβ with islet amyloid polypeptide exacerbating AD pathogenesis might explain the molecular association of diabetes with AD (34).The diabetics without proper treatment had higher CSF p-tau level than those using antidiabetic drugs or euglycemic adults, suggesting tau pathology might be ameliorated with the antidiabetic drugs (35). Moreover, metformin use and treatment with anti-diabetic drugs might slow down the development of cognitive decline and reduce the risk of dementia (36, 37).
A remarkably increase in AD risk (HR 1.73, 95% CI 1.02-2.94) among those with midlife and late-life high systolic pressure has also been reported in the Framingham Offspring Study (38). However, decreased systolic pressure from mid-to late life also contributed to higher risk of AD (HR 2.12, 95% CI 1.12-4.00) (38). Individuals with large blood pressure variability exhibited a larger than 2-fold risk of AD (39), indicating excessive blood pressure variation might partially be responsible for AD deterioration. Fortunately, the AD risk was decreased (RR 0.78, 95% CI 0.66-0.91) because of the treatment of antihypertensives, longer use of these drugs might have significantly protective effects on cognition (40).
Blood lipids such as total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) might play important parts in the process of AD. The Three-City Study reported a significant increase in AD risk among those with hypercholesterolemia, particularly, this association would be weakened after adjusting for APOE ε4 (41). In addition, the cholesterol was divided into two subtypes including HDL-C and non-HDL-C in the Adult Changes in Thought (ACT) Study, the finding revealed a U-shaped association of non-HDL-C level with risk of AD among 60-69 aged individuals, compared with 160 mg/dl non-HDL-C, the HR was 1.29 (95% CI 1.04-1.61) at 120 mg/dl and 1.16 (95% CI 1.01-1.33) at 210 mg/dl (42). Notably, hypercholesterolemia could compromise the integrity of the blood-brain barrier, increase Aβ deposition, as well as cause neuroinflammation, all of which could exacerbate the development of AD (43). In addition, increased LDL-C levels were associated with higher AD risk (41), while HDL-C was likely to be a protective factor against AD (44). There existed no significant association between AD risk and TG level (41). Encouragingly, a meta-analysis indicated that uses of stain drugs were likely to lower the risk of AD (RR 0.86, 95% CI 0.80-0.92) among those with hyperlipidemia in prospective studies, but these beneficial effects were not stronger in randomized controlled trials (40).
Young adults had better stay away from diabetes, hypertension, hypercholesterolemia via healthier lifestyles. Patients could manage these diseases via regular uses of pharmaceuticals. Taking regular antidiabetics was associated with less tau dysfunction delaying the progression of AD (35). The antihypertensive drugs and stain drugs might be favorable to lower the risk of AD among those patients (40, 45). More studies are required to investigate whether particular medications would effectively lower the risk of developing AD.


Consistent evidences have proposed a higher risk of developing AD among middle-aged patients with raised body mass index (BMI) whereas overweight or obesity in late-life might have protective effects on cognition (46, 47). Particularly the HR for AD was 0.89 (95% CI 0.81-0.98) among those with high late-life BMI, in contrast, there was 20% increased risk for AD (95% CI 1.09-1.33) among those with greater loss of BMI from midlife to late-life (46). Consistently, the obese elderly tended to have less Aβ load and larger hippocampus volume (47). The underlying mechanism might be partially attributed to the increased leptin produced mainly by adipocytes, leptin could robustly facilitate the neurogenesis of hippocampus in AD mouse models (48). Of note, we should emphasize the role of age in evaluating the association of obesity with AD. Young adults should maintain or lose weight under the healthy range (18.5-24.9 kg/m2) while older individuals should avoid greater weight loss otherwise attention should be focused on this situation.

Cardiovascular diseases

The HR of AD following atrial fibrillation was 1.31 (95% CI 1.20-1.43) in the Korean study (49). In addition, mild reductions in cardiac index were also related to a marked increase in AD risk (HR 2.87, 95% CI 1.21-6.80) over a long period of 7.7 years (50). Cerebral hypoperfusion, caused by lower cardiac output in atrial fibrillation patients, could explain the deteriorations of cortical atrophy and neuropathological features (51). Encouragingly, individuals with atrial fibrillation undergoing oral anticoagulants were at a lower risk of developing AD (HR 0.61, 95% CI 0.54-0.68) (49). Interestingly, compared with medical therapy, the risk of AD was decreased among atrial fibrillation patients treated with catheter ablation (HR 0.77, 95% CI 0.61-0.97) (52).
About 59% elevated risk of AD among stroke patients was well documented in a meta-analysis incorporating six studies (53).Consequently, effective strategies to reduce the extent of brain injury after stroke may help delay or prevent the progression of AD. A markedly increase in AD risk among individuals with multiple cerebral microbleeds has been reported in the Rotterdam Study (HR 2.10, 95% CI 1.21-3.64) (54). Meanwhile, severe cerebral atherosclerosis (OR 1.33, 95% CI 1.11-1.58) and arteriolosclerosis (OR 1.20, 95% CI 1.04-1.40) were also regarded as strong risk factors for AD (55). Neurovascular unit disorder, due to cerebrovascular diseases, could cause decreased cerebral blood flow, induce blood-brain barrier disruptions as well as selective brain atrophies, all of which could result in direct damage to neurons and trigger Aβ accumulation indirectly (56). Thus, better understanding of these relationships may acquire cognitive benefits from appropriate treatment.
Early screening and intervention of vascular risks as well as maintaining good cardiovascular conditions should become the top priority for AD prevention. Pharmaceuticals uses like oral anticoagulants and catheter ablation would significantly lower the risk of AD among atrial fibrillation patients (49, 52).

Traumatic brain injury

Participants exposed to traumatic brain injury (TBI) had increased risk of developing AD in the Danish study (HR 1.16, 95% CI 1.12-1.22) and in a Swedish study (OR 1.58, 95% CI 1.49- 1.69), which could be strengthened by some detailed discoveries of TBI such as severe and multiple TBIs, the first few months since the trauma occurrence, younger individuals with the injury as well as TBI involving the skull or spine (57, 58). Particularly, athletes experiencing many years of head injury were more susceptible to die from AD according to their death certificates (59). Furthermore, among the American military veterans, women having TBI were at higher risk of developing dementia (60). Encouragingly, stain medication especially the rosuvastatin, owing to the potential neuroprotective benefits, might reduce the dementia risk among those individuals having a concussion (45). Thus, more studies are required to focus on the risk of TBI individuals to have AD and formulate therapeutic strategies to mitigate the risk and impact of AD. Considering the deleterious effects of TBI on cognition, the public had better take measures to protect the head properly from injuries when engaging in dangerous activities or work.


A meta-analysis incorporating 5 prospective studies found a linear dose-response relationship between blood homocysteine (Hcy) levels and risk of AD (HR 1.15, 95% CI 1.04-1.26, per 5 µmol/L increment) (61). However, in some findings of longitudinal studies such dose-dependent relationship only existed in the range of high serum Hcy concentrations (about ≥10 µmol/l) (62). These inconsistent exposure-response associations should be further assessed in large-scale prospective studies. In addition, the elevation of Aβ deposition and tau hyperphosphorylation could be modulated by high Hcy levels via γ-secretase pathway and cdk5 kinase in mouse models (63). Additionally, hyperhomocysteinemia could be alleviated via folic acid supplementation (64), improving total homocysteine metabolism may also represent a viable strategy for AD prevention.

Hearing loss and oral diseases

And in a case-control study the OR was 1.39 for AD (95% CI 1.05-1.84) following hearing loss (65). Owing to the awful listening conditions, individuals might have difficulty in understanding speech and experience communication disorders even social isolation, contributing to reduced cognitive stimulation from the acoustic environment (66), which could aggravate cognitive impairment mediated by accelerated brain atrophy. Indeed, individuals with midlife hearing impairment tended to have prominent temporal lobe volume loss (67). Encouragingly, hearing aids and cochlear implants could mitigate some worse listening status, slowing down the rate of cognitive decline (68), early screening and correction of hearing loss might hold significant influence on AD prevention.
Oral diseases particularly tooth loss and chronic periodontitis were great concerns for cognitive dysfunction, probably mediated by local and systemic inflammatory responses (69), tooth loss was a strong risk factor for AD in the Hisayama Study (69). Specifically, individuals with chronic periodontitis were more susceptible to AD (HR 1.707, 95% CI 1.152-2.528) (70), periodontitis might induce peripheral inflammation through byperiodontal pathological bacteria directly or proinflammatory cytokines indirectly (71). Much attention should be attached to oral care especially in developing counties to prevent AD.


We identified plenty of prospective studies regarding lifestyles related to the risk of cognitive decline and AD (Supplemental Table 3, Supplemental Table 4).

Physical activity

Higher participation in daily physical activity was related to about half decreased risk of AD in the Hisayama Study (72). In addition, cognitive benefits of regular resistance exercise and choreographic intervention could delay the neurodegenerative process especially in those domains related to the conversion to dementia (73, 74). Besides, aerobic exercise was also beneficial to some cognitive domains including executive function and oral fluency (75), possibly due to the protective effects on hippocampus volume and neuronal health (76). After one-year aerobic exercise training, improvements of cardiorespiratory function particularly cerebral perfusion and memory ability were found in a prospective study (77). The neurotrophic effects of active exercise are needed to be further investigated, especially its type, intensity, duration and timing might have greater implications on AD prevention. However, in the Whitehall II study physical activities appeared to have no neuroprotective effects on cognitive functioning, individuals in the preclinical stage of AD tended to have lower physical activity levels than the healthy elderly (78), in other words, reverse causality might account for the relationship between active exercise and reduced risk of AD. It still remains controversial whether reverse causality could explain the favorable effects of physical activity on cognition.

Sleep disturbances

Sleep disturbances (insomnia and sleep disordered breathing) were risk factors for AD, and those with sleep disorders were more likely to experience more accumulation of neurotoxic substances as a result of the decreased metabolite clearance ability (79). There was a 66% (95% CI 1.03-2.68) increased AD risk in participants having severe obstructive sleep apnea (≥30 vs.<5 apnea-hypopnea events/hour) (80). Additionally, individuals with longer sleep length (> 9 hours) showed a greater than 2-fold risk of AD (81), the RR of AD following habitual shorter sleep duration was 1.25 (95% CI 0.88-1.76) (80). Moreover, both short duration (≤6 hours) and long duration (≥8 hours) all had detrimental effects on cognitive function, it further offered a V-shaped association of daily sleep duration with cognitive decline and subsequent risk for dementia (82). Notably, greater amyloid deposition was more common among those who had insufficient or excessive nocturnal sleep time (83). And there existed a causal relationship between sleep length and elevated cortical thickness in a mendelian randomization study (12). There may also exist a bidirectional relationship between sleep dysregulation and AD pathology, sleep disorders could stimulate the accumulation of Aβ and tau, meanwhile the enhanced aggregation of Aβ and tau may exacerbate the progression of sleep disturbances (84). More long-term longitudinal studies are needed to further explore the potential role of sleep dysregulation as a biomarker of AD and the potential bidirectional relationship. High quality sleep is extremely important in maintaining cognition, when having sleep problems patients should consult a doctor and receive therapy in time.


Smoking increased the risk of AD (RR 1.40, 95% CI 1.13-1.73), which was also prominent among non-APOE ε4 carriers in a meta-analysis including 37 longitudinal studies (85). Of note, smoking initiation was associated with lower cortical thickness (12). Smoking-related cerebral oxidative stress might facilitate the production of amyloid or tau pathology (86). In contrast, never-smokers had 18% risk reduction of AD than continual smokers (87), indicating that early smoking cessation would confer greater benefits on cognition.

Alcohol consumption

The effects of alcohol intake on cognitive function may remain controversial in many epidemiological findings. The Nord-Trøndelag Health study supported a 47% elevated AD risk (95% CI 1.00-2.16) among frequent alcohol drinkers (≥5 times/two weeks) vs. infrequently drinkers (1-4 times) (88). This relationship was thought to be J-shaped, suitable alcohol intake (<12.0 g/day) could protect against dementia (89, 90), similar with the relationship between coffee intake and AD. It is not advisable to take excessive drinking in daily life, as for the heavy alcohol drinkers, extracellular cold-inducible RNA-binding protein (eCIRP) might mediate tau phosphorylation, leading to the progression of alcohol-induced AD (91). After matured hop bitter acid supplementation from beer, improvement of cognitive status was confirmed in a randomized trial (92). Particularly, alcohol from wine appeared to be stronger inversely related to the risk of dementia (89, 90), polyphenolic and antioxidant contents in wine showed greater protective effects against neurodegeneration (93). More primary studies should be warranted to clarify the underlying mechanism explaining the AD risk related to alcohol intake. The only established causal relationship between earlier AD onset and alcohol consumption was found in a mendelian randomization study (94), which indicated that moderate alcohol intake might be harmful to the brain health but not beneficial. This study also stressed that potential confounding factors should be seriously taken into consideration, especially the survival bias (94), therefore future studies should delineate this relationship thoroughly and precisely. Coffee and tea A J-shaped association of coffee intake with AD was proposed, low (1-2 cups/day) but not high (>3 cups/day) coffee intake was related to a 18% reduction of AD risk (RR 0.82 , 95% CI 0.71-0.94, vs. <1 cup/day) (95), which was more pronounced among women (96). However, this gender characteristic was different from tea intake. The protective effects of green tea on cognition were more prominent in men (97, 98). Some neuroprotective components may exist in the coffee or green tea drinking like caffeine and L-theanine (99), anti-amyloid effects of green tea might protect against AD mainly including inhibition of Aβ aggregation and reduction of Aβ-induced oxidative stress (100). Moderate coffee consumption and green tea drinking should be encouraged to the public. There was reduced level of CSF total-tau protein among frequent green tea consumers, probably owing to improved abnormal tau metabolism (98). However, the black tea and oolong tea did not show cognitive benefits in the overall elderly Han study population (97).


Three dietary patterns including the Mediterranean diet (HR 0.46, 95% CI 0.26-0.79), the DASH (Dietary Approaches to Stop Hypertension) diet (HR 0.61, 95% CI 0.38-0.97) and the MIND (Mediterranean-DASH Intervention for Neurodegenerative Delay) diet (HR 0.47, 95% CI 0.26-0.76), were all inversely associated with the risk of AD (101), the protective effects of these dietary patterns on cognition might be attributed to anti-oxidant, anti-inflammatory, and anti-diabetic effects and enough mono-/poly-unsaturated fats (102). More interestingly, the Three-city study proposed the concept of novel diet pattern, a more diverse diet including vegetables, fresh fruits and seafood, might be particularly beneficial to the cognitive function (103). Particularly, ketogenic diets could reduce the AD risk via altering gut mycobiome (104), while following higher glycemic load intake the HR increased by 27% for AD (105). Additionally, a meta-analysis incorporating 21 cohort studies proposed that higher intake of fish (RR 0.93, 95% CI 0.90-0.95) and marine-derived dietary docosahexaenoic acid (RR 0.63, 95% CI 0.51-0.76) could protect against the risk of developing AD (106). Besides, habitual intake of seafood was associated with lower burden of AD brain pathology among APOE ε4 carriers, which was not affected by higher brain levels of mercury (107).
Severe 25-Hydroxyvitamin D [25(OH)D] deficiency (<25 nmol/L) was related to elevated risk of AD (HR 2.22, 95% CI 1.02-4.83), as was 25(OH)D inadequacy (25-50 nmol/L) (HR 1.69, 95% CI 1.06-2.69), using the level of more than 50 nmol/L as the refence category (108). Persons in early adulthood taking more B vitamins including niacin, folate, vitamin B-6, and vitamin B-12 were likely to acquire better cognitive performance in late life (109). In addition, the potential cognitive benefits of vitamin C were more remarkable among women APOE 4-carriers while higher blood vitamin E level might hold such benefits among APOE 4-negative men (110). Higher flavonoids intake from daily food might reduce the risk of AD incidence by 38% (95% CI 0.39-0.98), which was independent of other lifestyle factors and cardiovascular related diseases (111).Therefore, further studies are required to delineate the potential biologic explanations, and more mendelian randomization studies are needed to elucidate whether these associations of micronutrients intake with the cognition are causal. It is necessary to take certain vitamins and other micronutrients from daily diets properly.



Current or former nonsteroidal anti-inflammatory drugs (NSAIDs) exposure was associated with a 19% reduction in AD risk (95% CI 0.70-0.94) (112). However, in a randomized placebo-controlled trial, no robust evidence supported that aspirin could effectively lower the risk of AD (113), aligned with the meta-analysis conducted by Veronese et al (114). In this meta-analysis, low-dose aspirin did not seem to improve the cognitive function (114). Therefore, it still remains controversial whether NSAIDs would protect against AD.
The adverse impact of anticholinergic medications on cognition was underscored. Over a long period of 4 years, excess use of anticholinergics was considered as a risk for AD (HR 1.63, 95% CI 1.24-2.14) (115). Minimizing anticholinergic use over time might be important to preserve the cognition.


We identified several longitudinal cohorts studying some environment factors associated with the risk of cognitive decline and AD (Supplemental Table 3). Misfolding and abnormal aggregation of p-tau and Aβ were found in the brainstem of children and young adults exposed to Mexico City’s air pollution (116). In a study from Northern Sweden, there was a 38% elevated risk for AD among those exposed to residential traffic-related air pollution (95% CI 0.87-2.19) when comparing the highest with the lowest quartile of NOx (≥26 vs. ≤9 μg/m3) (117). Indeed, Particulate Matter2.5 (PM2.5) relevant with gray matter atrophy indicated higher risk of AD (HR 1.24, 95% CI 1.14-1.34) in older women (118). The adverse effects of air pollutants on cognition might be amplified by cardiovascular diseases including heart failure and ischemic heart disease (120). Crucially, long-term air pollution might accelerate the progression of neurodegeneration via vascular disease, Aβ deposition and neuroinflammation (116, 120). Therefore, reducing the exposures of air pollution seems particularly important for AD prevention. More studies are required to investigate the effects of environmental exposures on AD and the potential mechanisms underlying these relationships.



Many longitudinal studies have identified various risk and protective factors for AD, including some that could be targeted to reduce risk of AD or delay the onset of AD, suitable preventions might help slow down the progress of AD. More policies targeting education popularization and social or cognitive activities promotion should be put forward among the public. Managing the pre-existing disease reasonably and maintaining daily healthy lifestyles would protect against AD. Additionally, environment protection especially targeting air pollutants would be of great importance to AD prevention. If possible, more studies should focus on individuals at high risk of AD or in the prodromal stage of AD, among whom daily preventions and neuroprotective interventions are likely to exert greater favorable effects.


Acknowledgements: This study was supported by grants from the National Key R&D Program of China (2018YFC1314700), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.

Conflicting interests: None.

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.



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D. Kellar1, S.N. Lockhart1, P. Aisen2, R. Raman2, R.A. Rissman2,3, J. Brewer2,3, S. Craft1


1. Department of Internal Medicine–Geriatrics, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA; 2. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, USA; 3. Department of Neurosciences, University of California, San Diego, La Jolla, USA

Corresponding Author: Suzanne Craft, PhD, Department of Internal Medicine–Geriatrics, Wake Forest School of Medicine, One Medical Center Boulevard, Winston-Salem, NC 27157,
J Prev Alz Dis 2021;3(8):240-248
Published online April 7, 2021,



Background: Intranasally administered insulin has shown promise in both rodent and human studies in Alzheimer’s disease; however, both effects and mechanisms require elucidation.
Objective: We assessed the effects of intranasally administered insulin on white matter health and its association with cognition and cerebral spinal fluid biomarker profiles in adults with mild cognitive impairment or Alzheimer’s disease in secondary analyses from a prior phase 2 clinical trial (NCT01767909).
Design: A randomized (1:1) double-blind clinical trial.
Setting: Twelve sites across the United States.
Participants: Adults with mild cognitive impairment or Alzheimer’s disease.
Intervention: Participants received either twice daily placebo or insulin (20 IU Humulin R U-100 b.i.d.) intranasally for 12 months. Seventy-eight participants were screened, of whom 49 (32 men) were enrolled.
Measurements: Changes from baseline in global and regional white matter hyperintensity volume and gray matter volume were analyzed and related to changes in cerebral spinal fluid biomarkers, Alzheimer’s Disease Assessment Scale-Cognition, Clinical Disease Rating-Sum of Boxes, Alzheimer’s Disease Cooperative Study–Activities of Daily Living Scale, and a memory composite.
Results: The insulin-treated group demonstrated significantly reduced changes in white matter hyperintensity volume in deep and frontal regions after 12 months, with a similar trend for global volume. White matter hyperintensity volume progression correlated with worsened Alzheimer’s disease cerebral spinal fluid biomarker profile and cognitive function; however, patterns of correlations differed by treatment group.
Conclusion: Intranasal insulin treatment for 12 months reduced white matter hyperintensity volume progression and supports insulin’s potential as a therapeutic option for Alzheimer’s disease.

Key words: Alzheimer’s disease, clinical trial, intranasal insulin, white matter, CSF.



Alzheimer’s disease (AD) is the leading cause of dementia and, as there are currently no disease modifying treatments, its prevalence is expected to increase in response to an aging population (1). AD is characterized by aggregation of amyloid beta (Aβ) plaques and tau neurofibrillary tangles (NFT). Clinical trials have attempted to reduce accumulation of these proteins in the brain and prevent further cognitive decline; however, although some amyloid antibody trials have successfully reduced plaque load, none have been successful to date in halting the progression of AD symptoms (2, 3). Positron Emission Tomography (PET) measures AD pathology by quantifying the load of Aβ and NFT in the brain, and a number of studies have demonstrated relationships between PET measures and concentrations of these proteins in cerebral spinal fluid (CSF) (4, 5). CSF Aβ42 decreases as amyloid accumulation in brain increases, suggesting it is being sequestered in the brain parenchyma, while hyperphosphorylated tau (p-tau) in CSF increases with increased propagation of NFT (6). Utilizing ratios of Aβ42 to Aβ40, p-tau, and total tau (t-tau) has been found to further improve specificity and sensitivity to identify AD dementia (7, 8). There remains a clear need for a pharmacological intervention to prevent or slow AD progression.
As AD progresses, gray matter volume is reduced. While this is also true of non-pathological aging, adults with AD exhibit a far greater reduction in overall gray matter associated with a predictable pattern (9). The hippocampus and entorhinal cortex are perhaps the most easily detected regions affected by AD (10); however, an AD-defined meta region including the hippocampus and other regions such as entorhinal, inferior temporal, middle temporal, inferior parietal, fusiform, and precuneus has recently been utilized in order to increase sensitivity (11). Cortical thickness has also been postulated to be a more reliable marker of AD progression as it is less affected by total intracranial volume which can vary greatly between patients (11). While progression of the disease and corresponding gray matter reduction have been well established, its utility as a clinical trial endpoint needs to be validated. Interventions that have successfully removed amyloid or even allayed cognitive decline have also been associated with reduced gray matter volume (12, 13). These findings illustrate a complex relationship between brain health and gray matter volumetrics which is not fully understood.
White matter integrity can be reflected by the presence of white matter hyperintensities (WMH) detected with magnetic resonance imaging (MRI); however, the relationship of WMHs and AD progression has not been characterized in detail. WMHs are detected using fluid-attenuated inversion recovery (FLAIR) MRI, are presumed to indicate cerebrovascular pathology (14), and are associated with gliosis, demyelination, axonal loss, and arteriosclerosis (15). It is postulated that WMHs reflect a number of factors including hypoxia, amyloid angiopathy, blood brain barrier damage, degeneration, hypoperfusion, and inflammation (15). WMH volume (WMHV) increases with age and some studies have found WMHV to be independent of Aβ burden, leading to the proposal that WMHs should be considered a co-pathology that do not directly contribute to AD (16, 17). Other studies have found correlations between WMHs and cortical tau load in AD (18). It has been proposed that cerebrovascular pathology represented by WMHs precedes and therefore could initiate Aβ aggregation (19-21). Conversely, other investigators claim that Aβ induces vascular damage through neuroinflammation, formation of reactive oxygen species, and oxidative stress (22, 23). It is possible that vascular and AD-specific pathology form a vicious cycle giving rise to these differing viewpoints, or that the precise time course of the association differs for subgroups of patients with AD.
Identification of the nature of the association of WMHs and AD is hindered by lack of a commonly-accepted standardized approach to measurement of WMHs (24). Methods evaluating WMHs range from semi-quantitative visual reads using one of 3 established rating scales (Manolio, Fazekas and Schmidt, Scheltens) to fully automated lesion segmentation (25, 26). Representation of the data is also not consistent, with some studies reporting global ratings or volumes and others focusing on spatial patterns (27, 28). There are also many ways to spatially segment WMHs such as periventricular versus deep (29); however, deep can also be further split in to juxtacortical and non-juxtacortical[30]. Studies in which WMHs are segmented in classical lobular fashion have reported that AD is associated with temporal WMHs (31), or with global and parietal/occipital volumes (32). Volumes can also be displayed as raw volumes (33), log transformed values (33), or percent change ratios (34, 35). As the field progresses automated techniques generating quantitative spatially accurate information may prove the best way to track WMH progression in AD. It is clear that WMHs are associated with poor cognitive outcomes and preventing progression is a clinically relevant marker.
A promising area of research in the treatment and prevention of AD focuses on metabolism, inflammation and, in particular, the role of insulin in the central nervous system. With respect to metabolism, although insulin does not appear to impact global transport of glucose into the brain, it has been shown to increase glucose uptake via the glucose transporter GLUT4 in selected regions such as the hippocampus (36). Further, insulin increases glycogen storage in astrocytes, thereby providing an alternate energy source during glucose deprivation or intense neuronal activity (37). Insulin has long been implicated in AD and several reviews have highlighted both its importance and therapeutic potential (38-40). In short, insulin has been demonstrated to modulate both Aβ and pathological tau formation, and improves neuronal health, dendritic spine proliferation, and white matter integrity. Insulin can be administered intranasally where it is detectible in perivascular spaces with PET imaging (41) and in the CSF in less than 30 minutes (42). A promising pilot trial documented improvements in delayed memory recall, preserved Alzheimer’s Disease Assessment Scale (ADAS-Cog) scores, and functional abilities assessed by the Alzheimer’s Disease Cooperative Study–Activities of Daily Living Scale (ADCS-ADL) after 4 months of treatment with intranasal insulin compared to placebo (43). A recent large 18-month phase II clinical trial of INI treatment in AD and MCI patients found differing patterns of results depending on the device used to administer the insulin (13). For the device used by the primary intent-to-treat cohort, no significant differences in rates of decline measured by the ADAS-Cog13, Clinical Disease Rating-Sum of Boxes (CDR-SOB), ADCS-ADL, or CSF Aβ and tau were observed between placebo and insulin groups. In a secondary cohort, a different device showed better performance on ADAS-Cog13 in the insulin-treated group compared to placebo at 6 months with a similar trend at 12 months. In open-label analyses, the early-start secondary device cohort treated with insulin performed better on the ADAS-Cog13 and ADL-MCI at 18 months than the delayed start secondary group. The insulin-treated group using this device also demonstrated an improvement in CSF Aβ42/Aβ40 and Aβ42/t-tau ratios at 12 months. This study highlights the need for additional investigation to definitively determine the potential for intranasal insulin as a therapeutic for AD.
In the present study, we assessed the effects of INI on white matter health in the secondary cohort of participants using the device associated with improved cognition and AD biomarker profiles. There are several mechanisms through which insulin could act directly to improve white matter health and prevent WMH progression (44). Reduced insulin levels or activity impair oligodendrocyte myelin survival and maintenance, and increase ceramides and decreases sulfatides, leading to oxidative stress, inflammation, and lipid peroxidation. These factors all contribute to myelin damage and subsequent WMHs. Insulin resistance impairs vascular responsiveness, causing luminal narrowing and fibrosis, which cause decreased blood flow and blood brain barrier damage. These effects lead to ischemia and inflammation and promote the formation of WMHs. As mentioned previously, insulin reduces Aβ and p-tau levels in the brain, both of which can cause inflammation, neuronal and glial damage, and vascular impairment (45). These distinct pathways could all culminate in the formation and progression of WMHs, thus poising insulin at a convergence point in several potential cascades, and raising the possibility that providing insulin to the brain to overcome deficient insulin availability or activity may have therapeutic benefit in AD.
Based on this evidence, we examined the effect of 12 months of INI treatment vs. placebo on change in WMHs. We also examined the relationships among changes in WMHs, cognition, and AD CSF biomarkers.



The study was overseen by the Alzheimer’s Therapeutic Research Institute (P. Aisen, Director) together with the Principal Investigator (S. Craft). Eligibility and recruitment for this study have been described previously (13). Two devices were used in the parent study; however, only one device demonstrated cognitive benefits or changes in AD CSF biomarkers across the 18-month long study. For this reason, we evaluated only the group using the device that showed potential beneficial effects. Briefly, participants with AD (n=31) or amnestic MCI (n=18) were recruited from 12 sites. Participants received baseline testing including CDR, MMSE, ADAS-Cog13, a lumbar puncture, and an MRI, then were randomized on a 1:1 basis to receive either 20 IU intranasal insulin (n=24) or placebo (n=25) twice daily for 12 months. After 12 months the cognitive battery was readministered, a lumbar puncture was performed, and another MRI was obtained. There were a total of 40 participants (insulin n=20; placebo n=20) with MRI data that passed quality control measures at baseline and month 12.
T1 and Fluid Attenuated Inversion Recovery images were collected with 1.5 or 3T MRI. T1 weighed images were processed using FreeSurfer 6.0.0 to produce participant specific gray matter volume, thickness, and area. FLAIR images were segmented by the lesion growth algorithm[46] as implemented in the LST toolbox version 3.0.0 ( for SPM. The algorithm first segments the T1 images into the three main tissue classes (CSF, GM and WM). This information is then combined with the coregistered FLAIR intensities in order to calculate lesion belief maps. By thresholding these maps with a pre-chosen initial threshold (κ= 0.3) an initial binary lesion map is obtained which is subsequently grown along voxels that appear hyperintense in the FLAIR image. The result is a lesion probability map. The lesion probability maps were then warped to MNI space and lobular volume was extracted using Mayo Clinic Adult Lifespan Template (47). A temporal-parietal volume meta-ROI was created to examine volume and was defined as bilateral entorhinal, inferior temporal, middle temporal, inferior parietal, fusiform, and precuneus (11). Cortical thickness was similarly defined by Jack (48) as the surface area weighted thickness of the entorhinal, inferior temporal, middle temporal, and fusiform.
CSF was collected in the morning after an overnight fast and was immediately placed on dry ice and was shipped overnight to the central biomarker laboratory. AD biomarkers Aβ42, Aβ40, total tau, and tau phosphorylated at threonine 181 were quantified with the Meso Scale Discovery platform (Meso Scale Diagnostics). Blood was collected for APOE genotyping using established protocols.
Cross sectional analysis was performed to assess group differences at baseline in age, cognitive status, sex, baseline surface weighted cortical thickness, AD signature region volume, total WMHV, and regional WMHV at baseline using general linear models or chi squared tests when appropriate. Change variables for gray matter and WMHV were defined as percentage change from baseline as previously described (35). General linear modeling was performed in SAS v 9.4 with covariates age, ApoE4 status, study site, and sex included in all initial models. Baseline volumes were also included for GMV and WMHV models, and total intracranial volume was included in models analyzing data in native space. Non-contributing covariates (p>0.15) were dropped from the model. No adjustments were made for multiple comparisons; rather, results are reported as mean estimates and corresponding 95% confidence intervals. In exploratory analyses, we examined whether individual treatment groups showed reliable change in WMHV over time with within-group LSMEANS t-tests. Change variables for WMHV, cognitive scores, and CSF values were subjected to Pearson’s r correlations to determine inter-relationships.




For the parent study secondary cohort that utilized the device associated with cognitive benefit, 78 participants were screened, of whom 49 (32 men [65.3%]) were enrolled. Twenty-four were randomized to the insulin arm and 25 were randomized to the placebo arm (Figure 1). Of those 49, 40 participants (insulin n=20, placebo n=20) had usable MRI data at both time points and were analyzed for this study. There were no demographic or other notable clinical differences between participants with usable and unusable data. There were also no differences in demographic characteristics between arms at baseline (Table 1).

MRI Results

The temporal-parietal meta-ROI decreased in volume over time as did the surface weighted cortical thickness (both ps<.001, Figure 2). There was no interaction between treatment arm and rate of decline of gray matter or cortical thickness (Supplementary Table 1).

Figure 1. CONSORT diagram


Figure 2. Changes in gray matter A) volume and B) surface weighted thickness in the temporal-parietal meta-ROI

There were no significant differences between treatment group and placebo. Error bars represent 95% confidence intervals.


An interaction between treatment arm and global WMHV was observed such that the insulin-treated arm tended to have less global WMH volume increase over the 12 month intervention compared to the placebo group (insulin lsmeans [95% CI]=18.98 [-1.38,39.33] and placebo 42.21 [21.70,62.72], p=0.064, Figure 3). Given this trend, exploratory analyses were conducted for comparisons of individual ROIs between treatment arms. Insulin significantly reduced WMHV change over the 12 month intervention in both the frontal lobe and deep white matter compared to placebo (frontal insulin lsmeans [95% CI]=15.14 [-3.84,34.12] and placebo=39.18 [20.05,58.30], p=0.042; deep WM insulin lsmeans [95% CI]=56.94 [-20.20,134.08] and placebo=161.37 [81.68, 241.05], p=0.042, Figure 3). Change in WMHV was less in the insulin arm than the placebo arm for all other regions, although these comparisons did not reach statistical significance.

Figure 3. White Matter Hyperintensity Volume (WMHV) as percent change from baseline both globally and regionally split by MCALT (excluding cerebellum and midbrain regions)

There were significant differences between the degree of change for insulin and placebo groups in the deep white matter and frontal regions with a similar trend for global change (+ p<0.10, * p<0.05). The placebo group showed significantly increased change from baseline WMHV in all regions, whereas the insulin group showed significant change only in temporal lobe with a trend for global change (# <0.10, ## p<0.05). Error bars represent 95% confidence intervals.


When we examined whether individual treatment groups showed reliable change in WMHV over time with within-group LSMEANS t-tests, the placebo group showed significantly increased WMHVs across all regions (all ps<0.05, Figure 3; raw means for baseline and month 12 for all regions are presented in Supplementary Table 2), whereas WMHV was unchanged following insulin treatment in the deep white matter, corpus callosum, occipital, parietal, and frontal regions (all p>0.1, Figure 3). Temporal WMHV increased slightly over the 12 month intervention with insulin treatment with a similar trend in global WMHV (temporal p=0.033; global p=0.066), although to a lesser degree than with placebo.

Correlation between MRI and Cognitive Outcomes

For the combined cohort including both insulin and placebo groups, increased global WMHV correlated with lowered memory composite scores (r=-0.38, p=0.024, Figure 4A) and similar trends were observed for the ADAS-Cog13 and CDR-SOB (r=0.297, p=0.062; r=0.278, p=0.081, figure 4A). Regional analysis revealed a significant correlation between the memory composite score and parietal and occipital WMHVs and a trend correlation for the corpus callosum (r=-0.536, p=0.001; r=-0.405, p=0.015; r=0.31, p=0.069, Figure 4A). Increased temporal WMHV was associated with worsened (higher) scores for the ADAS-Cog13 (r=0.313, p=0.049, Figure 4A). A similar trend correlation was observed between frontal WMHV and ADCS-ADL scores (r=-0.267, p=0.095, Figure 4A).

Figure 4. Changes in global and regional White Matter Hyperintensity Volume (WMHV) correlate with changes in ADAS-cog, CDR-SB, ADCS-ADL, and a memory composite

Analyses were performed for both insulin and placebo groups combined (A) and for the insulin treatment arm (B) and placebo (C) groups independently. Light colors represent correlations with lower p values (ps range from <0.001 to 0.10 from light to dark). Exemplar scatterplots are shown that demonstrate relationships between change in frontal WMHVs (which differed between insulin and placebo groups) and change in (D) CDR-SB, (E) ADCS-ADL, and (F) memory composite scores.


When analyzed by treatment group, the insulin group showed a significant correlation between change in ADAS-Cog13 scores and WMHVs change in the corpus callosum, and trending correlations for the deep white matter and temporal regions (r=0.459, p=0.041; r=0.409, p=0.073; r=0.368, p=0.092, Figure 4B). Increased (worsened) CDR-SOB scores correlated significantly with increased frontal and global WMHV with trends noted for deep white matter, temporal, and the corpus callosum regions (r=0.535, p=0.014; r=0.537, p=0.014; r=0.439, p=0.052; r=0.426, p=0.059; r=0.409, p=0.061, Figure 4B). Smaller increases in frontal WMHV also correlated with smaller increases in ADCS-ADL scores (r=-0.475, p=0.034). In the placebo group, declines in memory composite scores only correlated with increased WMHV in global, parietal, and occipital regions with trends for frontal, corpus callosum, and temporal regions (r=-0.639, p=0.005; r=-0.773, p=0.001; r=-0.585, p=0.013; r=-0.48, p=0.05; r=0.435, p=0.08; r=-0.425, p=0.088, Figure 4C).

Correlation between MRI and CSF outcomes

For the combined cohort including both insulin and placebo groups, global WMHV increase correlated with a decrease in CSF Aβ42 and with a similar trend for the Aβ42/Aβ40 ratio (r=-0.375, p=0.028; r=-0.328, p=0.058, Figure 5A). Frontal WMHV increases also correlated with decreases in both Aβ42 and Aβ42/Aβ40 ratio (r=-0.355, p=0.039; r=-0.44, p=0.009, Figure 5A). Increases in both corpus callosum and deep white matter WMHV correlated with decreases in Aβ42/tau ratio (r=-0.397, p=0.001; r=-0.734, p=0.001, figure 5A).

Figure 5. Changes in global and regional White Matter Hyperintensity Volume (WMHV) correlate with changes in CSF AD biomarkers

Analyses were performed for both insulin and placebo groups combined (A) and for the insulin treatment arm (B) and placebo (C) groups independently. Light colors represent correlations with lower p values (ps range from <0.001 to 0.10 from light to dark). Exemplar scatterplots are shown that demonstrate relationships between change in frontal WMHVs (which differed between insulin and placebo groups) and change in CSF (D) Aβ42, (E) Aβ42/Aβ40 ratio, and (F) Aβ42/T-tau ratio.


When analyzed by treatment group, there was a significant correlation between increased parietal WMHV and decreased Aβ40 in the insulin group (r=-0.525, p=0.036, figure 5B). There were trending relationships between increased global WMHV and decreased Aβ42/Aβ40 ratio, increased frontal WMHV and decreased Aβ42/Aβ40 ratio and Aβ42/t-tau ratio changes, and increased corpus callosum and decreased Aβ42 levels (r=-0.443, p=0.085; r=-0.467, p=0.067; r=-0.477, p=0.061; r=-0.446, p=0.083, figure 5B). In the placebo group, increases in both corpus callosum and deep white matter WMHV correlated with decreases in Aβ42/t-tau ratio (r=-0.733, p=0.001; r=-0.724, p=0.001, figure 5C). Temporal WHV change positively correlated with ptau-181/t-tau ratio (r=0.481, p=0.043, figure 5C). There was a trend for both global and frontal WMHV change to negatively correlate with Aβ42 change (r=-0.431, p=0.073; r=-0.408, p=0.092, figure 5C).



The present study found that increased WMHV correlated with greater declines in cognition and worsening of CSF AD biomarker profiles, and that INI treatment for 12 months reduced WMHV progression in key brain regions. White matter hyperintensities represent cerebral small vessel disease and white matter damage resulting from degraded myelin, and have been shown to increase with aging. Some studies have suggested that regional, and not global WMHV may best predict, or correlate, with AD progression (49). While there are numerous ways to segregate WMHs the most promising research has suggested a division between frontal and posterior regions in differentiating normal versus pathological aging, with lobular divisions further increasing the regional specificity (32, 50). We observed that INI treatment for 12 months slowed the progression of WMHs globally and in deep WM and frontal regions compared to placebo assignment. This finding supports previous studies linking frontal WMHs and pathological aging and suggests that INI may reduce AD-related WMH progression in these key areas. Exploratory analysis also demonstrated that for all other regions, with the exception of the temporal lobe, progression of WMHs were stabilized following insulin treatment. These findings may be considered clinically significant as growing research has shown that WMHVs correlate with numerous pathological outcomes such as ependymal loss, cerebral ischemia, and demyelination (51). Insulin could be acting through a number of pathways to preserve white matter health (44). Insulin resistance impairs oligodendrocyte myelin maintenance and survival, while also leading to reduced vascular smooth muscle responsiveness. Increasing insulin availability could ameliorate these deficiencies thereby reducing WMH progression.
Previous research has shown that higher baseline WMHV predicts worse cognitive outcomes measured by the ADAS-Cog (52) and CDR (53). We demonstrated in the combined cohort that over 12 months, longitudinal increases in WMHV correlate with decreases in cognition measured by the ADAS-Cog, CDR-SOB, ADCS-ADL, and a memory composite. We also found that correlations differed depending on treatment group. The placebo group showed correlations only between WMHV and memory composite scores, while the insulin treated group showed associations between WMHs and both the ADAS-Cog13 and CDR-SOB, as well as the ADCS-ADL. Of note, in the parent study, insulin treatment was shown to benefit performance on the ADAS-Cog13, CDR, and ADCS-ADL for the secondary cohort (13). The present findings raise the possibility that insulin’s effects on these measures are mediated in part by factors associated with WMHV. These findings suggest that preventing or even delaying progression of white matter damage could prevent global cognitive and functional decline.
Increased CSF Aβ42 levels and decreased hyperphosphorylated tau concentrations have been proposed as markers demonstrating improvement in AD pathology (54, 55). Studies have shown that greater Aβ load, indicated by lower CSF Aβ42 concentration and increased Aβ PET, correlates with greater WMH burden (56-59); however, the findings regarding p-tau and total tau are less consistent, with some studies reporting correlations (18, 60) while other do not (59, 61, 62). In the parent study, insulin treatment was associated with improved (increased) Aβ42/Aβ 40 and Aβ42/tau ratios (13) for the secondary cohort treated with the device which was associated with cognitive benefit. The present study adds to these findings by demonstrating that WMHV changes inversely correlate with changes in Aβ40, Aβ42, Aβ42/Aβ40 ratio, and Aβ42/t-tau ratio; however, these associations differ by region and CSF metric. The relationship between WMHV and tau and Aβ may change throughout the course of the disease as Aβ pathology is stabilized and tau pathology increases; however, this hypothesis is still in contention (59). Insulin affects the regulation of both Aβ and hyperphosphorylated tau (63). Both Aβ40 (64) and Aβ42 (65) cause inflammation via production of reactive oxygen species. Aβ42 aggregates and starts seeding points which grow to plaques leading to death in several cell types including oligodendrocytes (66, 67), while Aβ40 is more prone to form deposits on vessels walls where it damages pericytes (68, 69). Hyperphosphorylated tau also leads to neurovascular dysfunction resulting in reduced energy supply due to aberrant vessel dilation (70). Insulin may act indirectly to stop WMH progression by preventing damage caused by hyperphosphorylated tau, Aβ40, and Aβ42 to both the blood brain barrier and myelin producing oligodendrocytes. Further research needs to be done to elucidate patterns behind regional WMH load and CSF AD biomarkers of Aβ and tau.
Numerous studies have sought to identify the temporal and spatial patterns of gray matter volume loss in typical AD progression (71). While there is not a consensus across all studies, several vulnerable regions have been highlighted (11, 72). Measures of gray matter volume and thickness were unchanged by 12 months of INI administration. It is possible that our region of interest, while widely accepted, failed to capture subtle differences between treatment groups. Other regions of the brain that were excluded from our analysis may be more sensitive to changes specific to this intervention. Previous interventions have shown decreased rates of atrophy in patients with MCI treated with other non-insulin interventions; however, these findings were over a 2-year period (73). It is possible that our intervention was not long enough to detect subtle changes in gray matter volume. Cognitive improvement has been observed without halting gray matter atrophy. A phase II clinical trial testing daily administration of resveratrol showed greater reduction in brain volume in the treatment group compared to placebo after one year of treatment, but the resveratrol-treated group also showed less decline on the ADCS-ADL suggesting interventions can still be beneficial independent of apparent reduction of gray matter volume (74).
Our study had several limitations. Our small sample size may have contributed to our inability to detect preservation of gray matter volume and cortical thickness by INI treatment, and may also have reduced our ability to detect relationships between WMHVs and other measures. Our cohort was predominantly white (93%) and male (62%) and thus results may not generalize to a more diverse population. A number of participants did not complete the trial or had usable data at either baseline or follow up. These missing datapoints could result in a completer bias; however, this is unlikely as those with incomplete data did not differ demographically or in any baseline measures from completers. It is also possible that a longer intervention may be needed to observe a divergence between groups on our measures. These results need to be replicated in a larger, longer study in order to determine the effects of insulin on the brain WM health and the mechanistic pathways underlying these effects.
In conclusion, we found that treating MCI and AD patients with 12 months of INI significantly reduced WMH progression without affecting gray matter volume or cortical thickness, and that increases in WMHV correlated with both worsening in AD CSF biomarker profile and cognitive/functional measures. These findings support insulin’s potential as a therapeutic option for AD; however, more research needs to be conducted to elucidate mechanism through which insulin may impact white matter integrity.


Funding and acknowledgments: This study was supported by the National Institute on Aging (NIA RF1AG041845). Eli Lilly provided diluent placebo for the blinded phase of the trial, and insulin for the open label portion at no cost; they had no input into the design, conduct or interpretation of trial results or in the preparation of this manuscript. The authors would like to thank the site personnel, and the following Site Prinicipal Investigators for their perseverance throughout the trial: Deniz Erten-Lyons, David Knopman, Joseph Kass, Rachelle Doody, Hillel Grossman, Neelum Aggarwal, Esther Oh, Gregory Jicha, Anton Porsteinsson, Aimee Pierce, Gaby Thai, Ruth Mulnard, Allan Levey, Jeffrey Burns, Neill Graff-Radford, Jared Brosch, Martin Farlow, Christopher van Dyck, Marek-Marsel Mesulam, Ian Grant, Raymond Scott Turner, Scott McGinnis, Thomas Obisesan, Alan Lerner, Allison Perrin, Laura Baker, Elaine Peskind, Henry Querfurth, Brian Ott, Ralph Richter, Jacobo Mintzer, Marwan Sabbagh, Jiong Shi, Daniel Press, Shauna Yuan, David Carr, Nupar Ghoshal, Amanda Smith, James Galvin, and Kyle Womack. We are particularly grateful to the trial participants for their dedication.

Conflict of interest: The authors have no conflict of interest to report.
Ethical Standards: The study was approved by the Institutional Review Boards of all participating institutions. All participants provided written informed consent to participate in the study.

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.


Supplementary Material 1

Supplementary Material 2


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E. Tsoy1, S. Zygouris2,3,4, K.L. Possin1,4

1. Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, California, USA; 2. School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece; 3. Network Aging Research, Heidelberg University, Heidelberg, Germany; 4. Global Brain Health Institute, University of California San Francisco, San Francisco, California, USA; Trinity College Dublin, The University of Dublin, Dublin 2, Ireland

Corresponding Author: Katherine L. Possin, PhD, Associate Professor in Residence, Department of Neurology, University of California San Francisco, Memory and Aging Center, Box 1207, 675 Nelson Rising Lane, Suite 190, San Francisco, CA 94158, Tel: 415-476-1889, E-mail:
J Prev Alz Dis 2021;
Published online March 24, 2021,



Early diagnosis of cognitive disorders in older adults is a major healthcare priority with benefits to patients, families, and health systems. Rapid advances in digital technology offer potential for developing innovative diagnostic pathways to support early diagnosis. Brief self-administered computerized cognitive tools in particular hold promise for clinical implementation by minimizing demands on staff time. In this study, we conducted a systematic review of self-administered computerized cognitive assessment measures designed for the detection of cognitive impairment in older adults. Studies were identified via a systematic search of published peer-reviewed literature across major scientific databases. All studies reporting on psychometric validation of brief (≤30 minutes) self-administered computerized measures for detection of MCI and all-cause dementia in older adults were included. Seventeen studies reporting on 10 cognitive tools met inclusion criteria and were subjected to systematic review. There was substantial variability in characteristics of validation samples and reliability and validity estimates. Only 2 measures evaluated feasibility and usability in the intended clinical settings. Similar to past reviews, we found variability across measures with regard to psychometric rigor and potential for widescale applicability in clinical settings. Despite the promise that self-administered cognitive tests hold for clinical implementation, important gaps in scientific rigor in development, validation, and feasibility studies of these measures remain. Developments in technology and biomarker studies provide potential avenues for future directions on the use of digital technology in clinical care.

Key words: Computerized cognitive assessment, early detection, mild cognitive impairment, dementia, psychometrics.



Dementia remains a widely underdiagnosed condition, both in Western countries (1-5) and globally (6). In light of projected increases in prevalence and burden (7) of dementia, innovative solutions in diagnosis and clinical care of dementia will be critical to alleviate the impact of these changes on public healthcare systems. Most experts agree that underdiagnosed dementia is a major gap in care because early detection of cognitive decline in older adults with cognitive symptoms (i.e., patient’s concerns, informant concerns, etc.) is beneficial for both patients and their caregivers (8-11). Targeted evaluation of cognitive impairment can facilitate early detection of cognitive disorders, which in turn can promote patient safety and wellbeing through more informed medication management, implementation of comprehensive care plans, introduction of lifestyle modifications, improved management of symptoms, and the opportunity to participate in clinical trials (8-11). Additionally, earlier diagnosis of dementia may reduce healthcare costs by decreasing long-term care expenditures with projected economic benefits to the affected individuals and to the public health care systems (12-14).
Primary care providers are in a position to first detect cognitive decline because of their established relationships with their patients. In the United States, cognitive impairment detection in primary care is supported by the Medicare Annual Wellness Visit and a new billing code for cognitive assessment and care planning (11). However, based on a survey of 1,000 PCPs and 1,954 older adults conducted by the Alzheimer’s Association, nearly all PCPs (94%) recognized that routine cognitive assessments were important but only 16% of the older adults said that they received routine cognitive assessments (7). Moreover, the survey found that when cognitive assessments are performed in primary care, paper and pencil assessments are almost always used (7). Thus, the most commonly used instruments for detection of cognitive impairment by the PCPs were the Mini Mental State Examination (MMSE; 80%), the Clock Drawing Test (64%), and the Mini-Cog (52%) (7). These conventional paper-based evaluation tools are accurate at detecting dementia but have poorer sensitivity to milder forms of cognitive impairment (15). An additional and important limitation of these conventional tools is that administration, scoring, interpretation, and documentation require substantial clinical staff time. Indeed, among the commonly reported reasons to not conduct the screening were insufficient time during visits and lack of confidence in performing the evaluation (7, 16).
Computerized tools present both exciting potential advantages and significant challenges for improving the detection of cognitive impairment. Older adults endorse both eagerness to use technology (17) and actual usage of technology in their daily lives (18), particularly touchscreen devices, which allow for direct interaction and have lower motor demands and relative ease of use (17, 19). Technology-based assessments offer enhanced precision of measurement and scoring, instant automated scoring and interpretation, standardized administration, enhanced stimulus presentation, availability of multiple alternate forms to minimize practice effects, and potential for adaptive capabilities and more sophisticated algorithms (20-23). Additionally, computerized tools have been discussed as being more cost-effective, particularly with respect to materials and supplies.
Computerized measures also present with challenges related to examinee variables (familiarity with technology, attitude and anxiety towards technology) (24) and technological issues (variability in hardware and software characteristics, data and privacy issues, data charges and internet access) (20, 22, 23). Additionally, a number of past reviews highlighted the lack of adequately established psychometric standards, limited or unfamiliar response modality, and poorly designed user interface (22, 25). A number of studies also demonstrated that computerized measures failed to demonstrate equivalence between the examinee’s experience of computer versus traditional test administration (e.g., participants performed worse on electronic version of the Montreal Cognitive Assessment [MoCA] compared to paper MoCA) (26).
Although most brief cognitive assessments designed for primary care use are examiner-administered, self-administered instruments have the potential to minimize costs and practice barriers related to training and staffing costs (15, 27), and to support social distancing requirements during the COVID-19 pandemic. Patients could complete a self-administered brief cognitive assessment at the clinic prior to or following a provider appointment, or at home on their own device. Additional benefits include minimization of examiner effects and greater accessibility of the tool for patients in remote locations (23). If the self-administered test is translated into different languages, accessibility may be increased for patients who do not speak English because interpreter may not be required. Finally, there is some evidence that absence of an examiner may reduce observer-related stress and increase respondents’ openness during administration, although these findings were reported only in studies on unsupervised surveys (27). While self-administered assessments share the same challenges with examiner-administered computerized assessments, additional challenges include lack of monitoring to ensure response validity (compliance, effort, motivation), lack of support should the patient need help with a task or runs into technological issues, and loss of qualitative data available from a conventional in-person evaluation (25). Additionally, a bring-your-own-device paradigm may present additional challenges related to potential technological differences to ensure consistent stimuli presentation and reaction time measurement, such as screen size and resolution, operating system, central processing unit capacity, etc. (23).
In this study, we conducted a systematic review of studies on self-administered computerized assessments designed to detect mild cognitive impairment (MCI) and dementia in older adults, discuss benefits and weaknesses related to their use, and provide practical recommendations and considerations regarding implementation of these measures into clinical practice.




A systematic search of published literature was conducted from February 1, 2020 to April 20, 2020. Databases searched included PubMed, EMBASE, Web of Science, and PsycINFO. Example search items used were “computerized” or “tablet,” and “cognitive assessment” or “cognitive screen,” and “older adult” or “geriatric,” and “dementia” or “cognitive impairment.” Specific search strings for each database are included in Supplementary Methods. Additional search filters included 1) original peer-reviewed articles (not book chapters, abstracts or conference papers, unpublished dissertations, or review studies), 2) studies published on or after January 1, 2000, and 3) studies published in English language.

Inclusion and exclusion criteria

Inclusion criteria were: 1) studies including a control sample and a clinical (MCI or all-cause dementia diagnosed based on published consensus criteria; 28-34) sample of older adults (ages 50 years and above); 2) studies reporting on a brief (administration time of 30 minutes or less) computerized cognitive assessment tool; and 3) studies reporting on psychometric characteristics of the measure, including reliability and validity indices.
Exclusion criteria were: 1) studies reporting on the use of the cognitive assessment tool in individuals with medical conditions other than MCI or dementia (e.g., schizophrenia, multiple sclerosis, etc.); 2) studies reporting on computerized instruments that were not cognitive assessment tools (e.g., informant surveys, functional questionnaires, etc.); 3) studies reporting on computerized instruments that required a dedicated hardware platform for use (e.g., virtual reality sets, hardware kiosks, etc.) due to potential barriers of implementing these modalities in clinical settings; 4) studies reporting on computerized cognitive measures that were not validated in English; 5) studies reporting on computerized cognitive measures that assess a single cognitive domain; and 6) studies reporting on instruments that required an examiner to administer the tests.

Data extraction

To minimize selection bias, two authors (ET and SZ) independently conducted database searches using same search terms and reviewed titles and abstracts for inclusion criteria. Same two authors then conducted a full-text review of screened-in studies for exclusion criteria. Data extracted from the final set of studies included in the review were: 1) platform; 2) level of supervision required; 3) administration time; 4) characteristics of the validation samples; and 5) psychometric indices. We extracted additional data on commercial availability of the tool, requirements for devices, automated reporting of results, available languages, and number of associated publications based on review of bibliography and dedicated websites of the measures when available. A brief e-mail survey (Supplementary Methods) was also sent out to test developers to collect additional information. Any disagreements were resolved via consensus discussions with a third reviewer (KLP).

Quality assessment

The quality of the studies was assessed using a scale specifically designed for this study in order to capture important considerations and factors for self-administered cognitive assessments (Table 1). Development of the criteria included in the scale was based on prior works on computerized cognitive tools (20). Specifically, we assessed the measures based on the following criteria: comprehensive assessment of the core cognitive domains, size of the validation sample and use of standard diagnostic criteria for identifying participants with cognitive impairment, reliability and validity indices, degree to which an examiner is involved in the testing process in relation to fully automated procedures, current availability for clinical use including any requirements for purchase of a dedicated device, availability of offered tests in multiple languages for participants/patients whose first language is other than English, presence or absence of feasibility studies in the intended settings, issues related to data security and compliance with regulations, and comprehensiveness of the delivery of test results (Table 1). Any ambiguity or disagreements were resolved via a consensus agreement with the third author (KLP).



The search identified 11,617 citations which, after removal of duplicates, resulted in 9,986 unique records. Seventy studies, which were selected from the initial screening process, were further assessed for eligibility based on full-text review. The overall trends in peer-reviewed published studies on computerized tools which were included in the full-text review are presented in Figure 1. In total, 17 studies reporting on 10 self-administered computerized tools were included in the review (for PRISMA flowchart, see Supplementary Results).

Figure 1. Number of published peer-review studies included in the full-text review from January 1, 2000 to April 20, 2020

Abbreviations: SA, self-administered. Blue bars represent the number of studies that met inclusion criteria but were not self-administered, while orange bars represent the number of studies that met inclusion criteria and were self-administered.

Table 1. Quality assessment scale


The measures included in this review were Computer Assessment of Memory and Cognitive Impairment (CAMCI; 35), Computer-Administered Neuropsychological Screen for Mild Cognitive Impairment (CANS-MCI; 36,37), Computerized Cognitive Screening (CCS; 38), CNS Vital Signs (CNSVS; 39,40), Computerized Self Test (COGSelfTest; 41), CogState (CogState; 42-44), CogState Brief Battery (CogState BB; 45-48), Cognitive Testing on Computer (C-TOC; 49), digitally translated Self-Administered Gerocognitive Examination (eSAGE; 50), and an untitled test developed by Kluger et al. (51). Results of the quality assessment ratings of the included tools are reported in Table 2.

Table 2. Quality assessment ratings of included measures

Abbreviations: CAMCI, Computer Assessment of Memory and Cognitive Impairment; CANS-MCI, Computer-Administered Neuropsychological Screen for Mild Cognitive Impairment; CCS, Computerized Cognitive Screening; CNSVS, CNS Vital Signs; COGSelfTest, Computerized Self Test; CogState BB, CogState Brief Battery; C-TOC, Cognitive Testing on Computer; eSAGE, digitally translated Self-Administered; Gerocognitive Examination.


Tool characteristics

Detailed characteristics of the measures included in the review are reported in Table 3. Of the 10 tools included in the review, 3 were available only on a personal computer (PC) platform (CNSVS, COSSelfTest, untitled test), 2 only on a tablet (CAMCI, CCS), and 4 on both platforms (CANS-MCI, CogState, CogState BB, C-TOC, eSAGE). CogState measures were available on an unrestricted set of devices, while most other tools had some restrictions, such as requiring touchscreen capabilities (CANS-MCI, CCS, eSAGE), keyboard input (CNSVS, COGSelfTest, untitled test), or a specific set of devices (CAMCI, C-TOC). Regarding level of supervision, CAMCI and CANS-MCI were designed to be administered in medical and research settings and test developers do not recommend at-home remote testing. Additionally, C-TOC and eSAGE require a trained examiner for scoring. Administration times varied across measures averaging at approximately 15-20 minutes across tools. Commercial availability was a common characteristic with 8/10 measures, except for C-TOC and untitled test, available for purchase. Finally, more than half (6/10) of the measures had fewer than 5 peer-reviewed published studies on the use of the measure across any age groups or clinical populations, while the remaining 4 tools (CAMCI, CNSVS, CogState, CogState BB) were researched more widely with at least 10 peer-reviewed published studies.

Table 3. Summary of the features of included measures

Abbreviations: CAMCI, Computer Assessment of Memory and Cognitive Impairment; CANS-MCI, Computer-Administered Neuropsychological Screen for Mild Cognitive Impairment; CCS, Computerized Cognitive Screening; CNSVS, CNS Vital Signs; COGSelfTest, Computerized Self Test; CogState BB, CogState Brief Battery; C-TOC, Cognitive Testing on Computer; eSAGE, digitally translated Self-Administered Gerocognitive Examination; ND, no data; PC, personal computer; *Includes peer-reviewed published journal articles across all age groups and clinical populations.


Validation samples

Characteristics of the validation samples are presented in Table 4. There was substantial variability in sample sizes across studies, and only 2/10 instruments (CAMCI, CogState BB) were validated in large cohorts with at least 50 or more participants in each diagnostic group. Studies on more than half of the instruments (6/10) used published criteria to classify participants into diagnostic groups, while 3 measures were validated in samples classified by scores on standard cognitive testing (CAMCI, CNSVS, CogState), and the study on the untitled test did not provide sufficient details regarding diagnostic criteria used. Additionally, studies were varied with regard to selection of cognitively normal participants ranging from recruitment of spouses of individuals with MCI and dementia as controls to comprehensive assessment of control subjects. With regard to demographic characteristics of validation samples, the vast majority of participants were non-Hispanic White with 12 or more years of education (educational attainment was not reported for CNSVS and untitled test).

Table 4. Summary of the psychometric properties of included measures

Abbreviations: AD, Alzheimer’s disease; CAMCI, Computer Assessment of Memory and Cognitive Impairment; CANS-MCI, Computer-Administered Neuropsychological Screen for Mild Cognitive Impairment; CCS, Computerized Cognitive Screening; CIND, cognitively impaired no dementia; CN, cognitively normal; CNSVS, CNS Vital Signs; COGSelfTest, Computerized Self Test; CogState BB, CogState Brief Battery; C-TOC, Cognitive Testing on Computer; DEM, dementia; EF, executive functions; eSAGE, digitally translated Self-Administered Gerocognitive Examination; h, hours; m, months; MCI, mild cognitive impairment; ND, no data; PC, personal computer; w, weeks; *Reported only in the dementia group


Psychometric properties

Test-retest reliability was reported on 6/10 tools (Table 4), and there was substantial variability in the reported indices both across and within individual measures by constituent subtests. Reported reliability coefficients of 5/10 measures (CANS-MCI, CNSVS, COGSelfTest, CogState, CogState BB) were consistently within ranges of moderate to high stability based on standard psychometric criteria (52). The range of time intervals for test-retest reliability studies also varied ranging from 2 hours to 12 months. Internal consistency estimates were reported on 3/10 tools (CANS-MCI, CCS, COGSelfTest) with coefficients ranging from .43-.97.
Table 4 presents concurrent validity estimates with either paper-and-pencil brief cognitive assessments or conventional neuropsychological tests which differed by site and study design for 9/10 measures (not reported for CAMCI). Similar to reliability findings, these indices varied significantly across and within measures with most tools demonstrating moderate degree of concurrent validity with standard tests. Across studies, highest concurrent validity estimates were mostly reported with standard brief global assessments (e.g., MMSE), while domain-specific concurrent estimates were more varied. Criterion validity estimates were reported for all measures including discriminant analyses results for 9/10 tools and mean group differences on 1 tool (Table 4). Studies on 6/10 instruments (CAMCI, CANS-MCI, CNSVS, CogState, CogState BB, eSAGE) reported on discriminant analyses between control and MCI groups with sensitivity indices ranging from .41 to .90 and corresponding specificity estimates of .64-.94. As expected, the indices for distinguishing cognitively normal and dementia groups reported on 3/10 tools (CCS, CNSVS, CogState BB) were slightly higher with a sensitivity range of .53-.94 and a specificity range of .50-.94. Finally, discrimination between cognitively normal and impaired (MCI and dementia combined) groups was reported for 3/10 instruments (COGSelfTest, eSAGE, untitled test) with the sensitivity range of .71-.99 and the specificity range of .72-.95. The remaining study on C-TOC reported on criterion validity in the form of mean group differences suggesting significantly lower performance on tasks of episodic memory, executive functions, and speed measures in cognitively impaired no dementia group compared to controls.

Delivery of results and available languages

Automated delivery of results is a feature of 6/10 tools (CAMCI, CANS-MCI, CNSVS, CogState, CogState BB, eSAGE), all of which include interpretation of results based on normative adjustments and differ in presentation of the results (Table 3). Automated reports on 2 measures (eSAGE, CANS-MCI) are reviewed by a trained professional prior to generation and the delivery of the results is thus not immediate. Additionally, CANS-MCI reports include recommendations to physicians on next steps and potential areas for intervention, and eSAGE has functionality to generate patient-facing reports. User-friendliness of the automated reports for non-specialist physicians was empirically examined only for CAMCI (53).
Data on available languages was reported on 6/10 tools (Table 3). Out of these, CAMCI, C-TOC, and untitled test are available only in English, eSAGE is available in English and Spanish, CANS-MCI is available in 4 languages, and CNSVS, CogState, and CogState BB are available in more than 40 languages. Additionally, availability of peer-reviewed studies on validation of non-English versions of these tools for detection of cognitive disorders in older adults varied substantially with the majority of published studies conducted in English-speaking samples.



In this systematic review, we evaluated 10 brief self-administered computerized cognitive assessment measures designed to detect cognitive disorders in older adults. Similar to past reviews of computerized cognitive tools (20, 21), we found significant variability across measures with regard to characteristics and design of the tools, sizes of validation samples, availability in different languages, and psychometric qualities, all of which are crucial considerations for potential widescale implementation of these measures in clinical care. Specifically, we found that few of the reviewed measures were validated in sufficiently large samples (CAMCI, CogState BB) and are available in multiple languages (CANS-MCI, CNSVS, CogState, CogState BB). Test-retest reliability, which is critical for self-administered tools aiming to monitor cognitive functions, was reported only on 60% of the tools, and internal consistency measures were reported on even fewer measures. While almost all reviewed measures reported data on concurrent validity, the estimates for several individual domain subtests within some tools were low. These findings are concerning, particularly when considering the need for a battery to distinguish among different types of MCI and dementia and inform differential diagnoses in non-specialty settings (16). On the other hand, we found that most measures required minimal involvement of an examiner in test administration and scoring of results and were available as standalone applications on several device types (e.g., PC, tablet computer, etc.). These features are important benefits of self-administered computerized tools, particularly if additional built-in functionality for integration of results into electronic medical records (EMR) systems is developed (54). In general, despite the promise that self-administered cognitive tests hold for clinical applications, important gaps in scientific rigor in development, validation, and feasibility studies of these measures remain. Below we discuss critical areas of need for future development and validation of self-administered cognitive measures that would facilitate their potential for widescale clinical implementation.
One of the most critical gaps identified in the current review is the size and demographic constitution of the validation samples. In particular, several studies included in this review included fairly small (<50 participants in each diagnostic group) validation samples, and the majority of validations samples were comprised of White, highly educated individuals. Because we did not identify systematic reporting of the power analyses for detecting main effects in the reviewed studies, we applied a generous estimate of 50 participants per group as part of our criteria. However, given the variability in statistical approaches used in these studies, reporting of robust power estimations would not only support the overall results but also ensure transparency, comparability, and generalizability of results across cohorts.
Another important finding of this review is the scarcity of feasibility and implementation studies of self-administered instruments in care settings. In contrast to highly standardized research settings, self-administration of cognitive assessments in the real world may be subject to interruptions and practical limitations such as time and space, which could be detrimental not only to feasibility but also to the validity of results (22). Some domains, such as orientation, may not be applicable for self-administration altogether, as it would be difficult to ensure the fidelity of responses on such tasks in absence of examiner. Given these considerations, research on development and validation of self-administered computerized measures must be supported by well-designed feasibility and implementation studies, which will critically inform the clinical utility of these measures in intended settings. Specifically, feasibility and implementation studies have the potential to identify facilitators and barriers to clinical applications, inform development of optimal diagnostic and care pathways, and, based on the insights from 2 measures (CAMCI, 53,55 and CogState BB, 56) studied in clinical settings, are critical for informing targeted solutions for individual practices.
The automated delivery of results is key to the clinical utility of computerized tools. To facilitate integration of self-administered tests in non-specialty settings, they should have easy-to-interpret, safe automated report delivery, which would ideally inform the provider on follow-up care and diagnostic considerations based on evidence-based practice guidelines (54). Out of the measures reviewed, only CANS-MCI features an automated report that provides such recommendations to physicians. Moreover, a study on CAMCI with primary care physicians (53) suggested that providers expressed a need for training in interpretation of the report, which highlights the need for refinement of automated reporting and empirical studies on non-specialty providers’ attitudes and perceptions of cognitive testing results.
With regard to patient-level characteristics, there are number of critical considerations, particularly given the dearth of normative or validation data in older adults who are racially/ethnically diverse and have low educational attainment. Importantly, one of the prior studies on CogState BB reported that older adults with lower education were less likely to meet the integrity criteria on 3/5 subtests of the battery (47). This is a major issue given that one of the most promising potentials of self-administered cognitive assessments is supporting services in remote areas and populations less likely to seek specialty evaluations. Moreover, numerous studies suggest that older adults in the U.S. who report Hispanic ethnicity, non-White race, or low education are at a higher risk for neurodegenerative diseases (7) and experience significant disparities in healthcare access and delivery (57). Well-validated self-administered assessments may help substantially reduce these disparities given their potential to deliver tests in different languages (23) but only if they undergo rigorous scientific and cross-cultural validation development. In addition to language and education variables, it is important to validate computerized tools across socioeconomic groups, as past evidence suggests that older adults with lower socioeconomic status reported lower levels of intention to use computerized cognitive testing (24). Finally, successful clinical implementation of even the most well-validated tools would likely require continuous efforts for education and outreach to patients belonging to underrepresented groups as well as their medical providers and families.
Another important variable to consider for self-administration of computerized cognitive measures is the impact of familiarity with technology on test results. While some studies (CCS; 38) reported no differences in test scores between older adults with and without technology experience, these variables do appear to play a significant role through interactions with age (CogState BB; 48) and diagnostic status (C-TOC; 49). Moreover, comparisons between content-equivalent paper and electronic version measures revealed that older adults with no technology experience performed worse on the electronic version of the measure compared to those with digital proficiency (eSAGE; 50). Finally, while most studies examine the associations between familiarity with technology and computerized cognitive testing results on a group-based level, systematic research on the impact of these variables for individual patients is necessary to support utility of self-administered assessments in clinical practice.
Regarding technical considerations, the practice parameters on optimal development and validation of computerized cognitive tools, including issues related to end-user agreements, privacy, data security and reporting (23, 25), are highly relevant to self-administered paradigms. Of particular relevance are challenges related to the use of bring your own device (BYOD) model and dependence on broadband connection, which pose a threat for timing and measurement error and may thus lead to inaccurate interpretation of results. Built-in integrity measures designed to address this challenge are features of some self-administered tools (e.g., CogState; 47,48), but are not widely available across reviewed instruments. Moreover, because of rapidly evolving hardware and operating systems across both PC and tablet platforms, computerized assessments require continuous quality assurance testing and software maintenance investments, and these challenges are greater when many devices (i.e., BYOD models) are supported. Additionally, availability of a measure on multiple devices also requires supporting research to establish the equivalence of normative and psychometric data across different platforms and input parameters, such as touchscreen vs. keyboard response, screen size, etc. Finally, past studies highlighted concerns regarding underreporting of privacy and security safeguards and their limitations on currently available computerized measures (22), and test developers should strive to explicitly disclose any potential consequences of data loss or breaches, particularly for individual patients in clinical settings. As such, collaborative efforts among researchers, funding bodies, industry, policy regulators, and consumers are necessary to develop robust, sustainable platforms supporting optimal levels of security, privacy, confidentiality, and potential functionality of data sharing across sites in order to promote and maintain successful implementation of computerized tools into everyday clinical practice while meeting programming cost demands.
This study has a number of limitations. First, while all attempts were made to conduct a comprehensive search of available literature, our results were limited to studies available in databases searched. Second, due to variability in study design and test statistics, quantitative summary of the findings was not possible. Finally, our review was limited to inclusion of studies that reported on instruments available at least in English language, and a number of promising self-administered computerized cognitive measures validated in non-English cohorts were not considered.
At the same time, a major strength of this study is the scope of reviewed characteristics of the included measures, including not only psychometric qualities but also functional and technological features critical for clinical implementation considerations. Additionally, our review is conducted at a point in time when the need for self-administered cognitive assessments has never been so dire in both clinical and research settings. In light of rapidly developing technologies for identifying disease biomarkers, future studies should examine the associations of a variety of self-administered cognitive assessments with biomarkers of neurodegenerative diseases, particularly given promising existing studies within this line of research (CogState; 58 and Computerized Cognitive Composite [C3]; 59). Additionally, future studies on self-administered cognitive measures in clinical settings should explore optimal implementation paradigms and provider behavior patterns which would be valuable for informing public healthcare policy and efforts to support earlier diagnosis of cognitive disorders in older adults.



In conclusion, this systematic review identified 10 self-administered brief computerized cognitive measures which have a potential for future clinical implementation. Continuous collaborative efforts of different stakeholders are necessary to address the gaps in scientific rigor of development, validation, and implementation studies of these measures.


Funding: This study was supported by the National Institute of Neurological Disorders and Stroke [UG3 NS105557] and the Global Brain Health Institute. 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.

Declarations of interest: Elena Tsoy declares no conflicts of interest. Stelios Zygouris declares no conflicts of interest. Katherine L. Possin has received research funding from the NIH, Quest Diagnostics, the Global Brain Health Institute, the Merck Foundation, and the Rainwater Charitable Foundation, consulting fees from ClearView Healthcare Partners and Vanguard, and a speaking fee from Swedish Medical Center.



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S. Zhou1, K. Wang2

1. Department of Respiratory and Critical Care Medicine, the Second People’s Hospital of Yibin/West China Yibin Hospital, Sichuan University, Yibin, Sichuan, China; 2. Department of Neurosurgery, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA, ORCID ID is 0000-0002-6958-7677

Corresponding Author: Kanran Wang, MD, Department of Neurosurgery, Harvard Medical School and Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, USA, 02115,
J Prev Alz Dis 2021;3(8):345-350
Published online March 24, 2021,



Background: This study aimed to investigate the associations between secondhand smoke exposure and dementia, Alzheimer’s disease (AD) and stroke.
Methods: This prospective study analyzed Framingham Offspring (FHS-OS) cohort participants with parents in the original Framingham Heart Study (FHS) cohort with known smoking status during offspring childhood. Surveillance for incident events, including dementia and stroke, among offspring participants exposed to parental smoking up to the age of 18 years commenced at examination 9 through 2014 and continued for approximately 30 years.
Results: At baseline, a total of 1683 (56.2%) subjects were not exposed to any secondhand smoke, whereas 670 (22.4%) subjects were exposed to 0-1 packs (20 cigarettes)/day, and 640 (21.4%) were exposed to over 1 pack/day. On follow-up (median: 31 years), 2993 patients developed dementia, including 103 with AD dementia and 315 with stroke. After adjusting for a wide range of established risk factors, participants with the highest exposure to secondhand smoke exhibited increased risks of all dementia, AD dementia and stroke compared with individuals with no exposure [HR 2.86 (2.00-4.09) for dementia; HR 3.13 (1.80-5.42) for AD dementia; HR 1.89 (1.37-2.61) for stroke]. The results remained comparable in the subgroup for individuals with median exposure to secondhand smoke.
Conclusion: Exposure to secondhand smoke may be associated with increased risks of dementia, AD dementia and stroke.

Key words: Secondhand smoke, Dementia, Alzheimer disease, stroke, cohort study.



Dementia is a common neurodegenerative disease, and it is estimated that the number of cases will reach approximately 81.1 million by 2040 with a complicated etiology underpinned by genetic and environmental components (1-3). In the past decade, the prevention and exploration of risk factors for dementia has been increasingly receiving attention in the field (4-6).

The smoking-dementia relationship has been investigated by many studies, and cigarette smoking remains one of the most important modifiable risk factors for incident dementia and AD (7, 8). Moreover, exposure to secondhand smoke has also been shown to alter the risk of cardiovascular and metabolic diseases (9-11). However, only a few reports have been published on the effects of secondhand smoke exposure on dementia and AD. A national cross-sectional study reported that exposure to secondhand smoke may be associated with increased odds of cognitive impairment, which relied on self-reported cigarette smoke exposure; therefore, recall bias may have played a role (12). Both stroke and dementia share common risk factors and etiologies (13), but the longitudinal relationship between exposure to secondhand smoke and the risk for stroke was also insufficiently verified. Thus, the potential effects of long-term and accurately evaluated exposure to secondhand smoke on the risk for dementia and stroke remain largely unexplored.
We therefore sought to leverage the multigenerational Framingham Heart Study to test the association between exposure to secondhand smoke and long-term risk (i.e., over 30 years) of dementia and AD and stroke with a detailed review of all medical records and precise assessment of secondhand smoke.



Study Design

We used data from the Framingham Heart Study (FHS), which commenced in 1948 with the enrollment of 5,209 original cohort participants in the Framingham, Massachusetts and Framingham Heart Study offspring (FHS-OS) cohort, which was established in 1970 and includes 5124 individuals who were the offspring of the FHS original cohort and their spouses (14, 15). These cohorts as well as their design and methods were described in greater detail elsewhere (16). Briefly, since their recruitment, participants from the FHS cohort have had serial examinations every 2-4 years and the FHS-OS cohort every 4-8 years, including standardized interviews, physician examinations and laboratory tests. For the present investigation, we included participants of the FHS-OS cohort with at least 1 parent in the FHS original cohort with a known smoking status at any point until his or her offspring reached the age of 18 years. The most recent examination period for both the FHS and FHS-OS cohorts concluded in 2014. This study complied with the Declaration of Helsinki; written informed consent was obtained from all study participants. This current study utilizing the Framingham Heart Study datasets was approved by the National Heart, Lung, and Blood Institute (NHLBI) of National Institutes of Health (NIH).

Smoking Assessment

Smoking was defined in both the FHS and FHS-OS cohorts as participants reporting smoking >1 cigarette daily during the year prior to their study examination. For those participants who reported smoking, the number of mean packs of cigarettes smoked per day was calculated based on the daily number of cigarettes (1 pack representing 20 cigarettes). Parental smoke exposure for the FHS-OS cohort was assessed and defined as the presence of parental smoking (either parent) of greater than 0 mean packs/day at any point in an examination period when his or her offspring participant was between 0 and 18 years of age. To account for a possible dose-response smoking relationship from variable exposure between individual parents, parental smoke exposure was also defined as the summation of the number of cigarettes smoked daily by both the mother and father, which was further categorized into a three-level variable: no exposure, 0-1 pack/day and >1 pack/day. Baseline offspring smoking status was defined as smoking of >0 mean packs per day in Exam 1 of the FHS-OS cohort. Details were further described in the FHS-OS protocol (

Ascertainment of Dementia and AD

Participants in the FHS-OS were under ongoing continuous surveillance for the onset of cognitive impairment and clinical dementia. We related childhood secondhand smoke exposure to the long-term risk of dementia and AD dementia. A diagnosis of dementia was made according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition. A diagnosis of Alzheimer’s disease (AD) dementia was based on the criteria of the National Institute of Neurological and Communicative Disorders and Stroke and the AD and Related Disorders Association for definite, probable, or possible AD (17, 18).

Ascertainment of Stroke

Stroke incidence was assessed through the continuous monitoring of hospital admissions in Framingham and by reviewing all available outside medical records and interim hospitalizations (19). Stroke was defined as focal neurological symptoms of rapid onset and presumed vascular origin lasting >24 hours or resulting in death within 24 hours. A committee comprising at least 3 FHS investigators, including at least 2 neurologists, adjudicated stroke diagnosis. The committee considered all available medical records, brain imaging, cerebrovascular imaging, and the assessment of the study neurologist who visited the participant.


The following dementia and/or stroke risk factors at Exam 1 in the FHS-OS cohort were utilized as baseline covariates in the study analysis: hypertension, diabetes mellitus, body mass index (BMI), waist circumference, alcohol consumption, current smoking status, blood lipids and education level. Hypertension was confirmed as systolic blood pressure>140 mm Hg, diastolic blood pressure>90 mm Hg, or use of antihypertensive medications. Individuals with fasting plasma glucose concentrations ≥7 mmol/L or who self-reported the use of antidiabetic medications were considered diabetic. BMI was calculated as weight in kilograms divided by the square of the participant’s height in meters at baseline examination. Waist circumference (in inches) was measured at the level of the umbilicus. Current smokers were defined as participants who smoked regularly during the year before the examination (yes or no) as assessed via questionnaire in all serial examination cycles. The educational level was assessed by medical interview. Total cholesterol and low-density lipoprotein cholesterol were measured after an overnight (>10 hours) fast.

Statistical Analysis

Descriptive statistics were performed for the 3 subgroups: participants with no exposure to secondhand smoke, 0-1 pack/day and >1 pack/day. Continuous and ordinal variables are expressed as the mean ± SD or median (interquartile range), respectively. The chi-square test was used to compare categorical variables, and Fisher’s exact test was used for categories with <5 observations. Follow-up for dementia and stroke was from the baseline examination to the time of incident event. For persons with no incident events, follow-up was censored at the time of death or the date the participant was last known to be dementia free. For survival analysis, Cox proportional hazards modeling was applied, and the following covariates were included: crude analysis; model 1 adjusted for age and sex; and model 2 adjusted for age, sex, and the following dementia risk factors: hypertension, smoking, diabetes and body mass index. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. The same analysis was performed for Alzheimer’s disease and stroke. Analyses were performed using SPSS 23.0 (SPSS Inc., Chicago, IL, USA) and Stata statistical software, version 15 (Stata Corporation, College Station, Texas, USA). A 2-sided P<0.05 was considered statistically significant.




Of the 3765 participants of the FHS Offspring Cohort attending the baseline exam until older than 18 years old, the condition of parental smoking was ascertained in 3545 subjects. Thus, these participants were included in this study. Therefore, 552 subjects were excluded because 498 subjects lacked assessment of dementia or stroke, 4 subjects were diagnosed with dementia at baseline, 3 with stroke, and 50 subjects were lost to follow-up for dementia or stroke. Finally, 2993 individuals could be included in the analyses with 1683 (56.2%) subjects with no secondhand smoke exposure, 670 (22.4%) subjects with exposure to 0-1 packs/day and 640 (21.4%) subjects with over 1 pack/day. (Figure 1)

Figure 1. Selection of Study Participants in the Framingham Heart Study


Table 1 shows the characteristics of those included in the analysis. The three groups descriptively differed with regard to age, sex, systolic blood pressure, diastolic blood pressure, smoking status and history of hypertension. In detail, in trends from the lowest to the highest exposure of secondhand smoking, those exposed to secondhand smoke were more likely to be slightly younger, female, with an increased incidence of blood pressure and hypertension but not diabetes and differ metabolic indicators, including LDL and TC. Finally, those exposed to secondhand smoke were more likely to be smokers themselves, and no difference was shown in alcohol consumption.

Table 1. Characteristics of the study sample of the Framingham Heart Study at baseline secondhand smoke exposure assessment

Abbreviations: BMI, body mass index; WC, waist circumference. LDL, low-density lipoprotein, TC: total cholesterol.


Secondhand smoke exposure and risk for dementia and stroke

Among the FHS-OS cohort with available smoking ascertainment, 239 (8.0%) developed dementia, including 103 with AD dementia over a median follow-up of 31 years (interquartile range, 28 to 32 years), with an overall incidence rate of 3.32 per 1,000 person-years. In addition, 315 (10.5%) cases of incident stroke were identified over a median follow-up of 31 years (interquartile range, 30 to 32 years) with an overall incidence rate of 3.53 per 1,000 person-years.
Risks of incident dementia, AD dementia and stroke according to secondhand smoke exposure are presented in Table 2. After adjusting for offspring age, sex, BMI, diabetes, smoking and hypertension, parental smoking greater than 1 pack/day was associated with significantly increased risks of offspring dementia, AD dementia and stroke [HR 2.86 (2.00-4.09) for dementia; HR 3.13 (1.80-5.42) for AD dementia; HR 1.89 (1.37-2.61) for stroke] compared with subjects without exposure for secondhand smoke. These trends remained the same for participants with parental smoking between 0 and 1 pack/day [HR 1.79 (1.21-2.64) for all dementia; 1.97 (1.09-3.58) for AD dementia; 1.60 (1.17-2.19) for stroke]. In an additional sensitivity analysis, all analyses in the subgroup of nonsmokers in offspring were repeated, and results consistent with the whole population were obtained. (eTable 1 in the supplement) The cumulative incidence curves for all dementia, AD dementia and stroke stratified by groups with different levels of exposure to secondhand smoke showed consistent results with the Cox regression after adjusting for age and sex. (Figure 2.)

Table 2. Cumulative hazards based on secondhand smoke exposure

Abbreviations: AD, Alzheimer’s disease; HR, hazard ratio; CI, confidence interval; Model 1 Sex and Age; Model 2 in addition for hypertension, smoking, diabetes and body mass index.


Figure 1. Adjusted cumulative incidence of dementia and stroke based on secondhand smoke exposure



In this prospective community-based and multigenerational sample with a median follow-up time of almost 30 years, exposure to secondhand smoke was associated with increased risks of offspring dementia, AD dementia and stroke after adjustment for conventional risk factors and accounting for dementia or stroke clustering within families.
These findings highlight potential new mechanistic pathways for dementia and stroke risk that begin during childhood and an association between secondhand smoke exposure and dementia and/or stroke risk. These observations may also provide new information pertinent to smoking cessation and avoidance more than relationship may be mediated by a greater tendency among their posterity of smoking parents to smoke themselves (20), highlighting the harms that may be associated not only to irrelevant strangers but to close and the most vulnerable members of the family.
Despite the published health hazards of smoking and public awareness campaigns to reduce smoking, 52.1% of Chinese men and 14% of US adults continue to smoke (21, 22). Smoking remains the primary cause of preventable death with the number of attributable annual deaths expected to increase to 8 million by 2030 (23). However, we often overlook that secondhand smoke exposure reached 54.3% in the workplace and 57.1% at home in 2015 in China and is responsible for at least 41,000 deaths annually in the US (22, 24). While there have been numerous published reports of the deleterious effects of secondhand smoke exposure on chronic kidney disease (25), cancer and other cardiovascular conditions, such as coronary heart disease (26, 27), the risk of dementia and stroke secondary to is less well defined. A national cross-sectional study from the UK demonstrated that exposure to secondhand smoke may be associated with increased odds of cognitive impairment after adjustment for a wide range of established risk factors for cognitive impairment. A secondary analysis including 970 US participants in the Cardiovascular Health Cognition Study found that exposure to high levels of secondhand smoke alone would increase the risk of dementia in elderly individuals with a history of carotid artery stenosis but not in the general population. A prospective cohort study including 7000 permanent residents from six regions within Zhejiang Province, China showed that passive smoking exposure increased the risk of cognitive impairment in older adults, especially nonsmokers (28). To our knowledge, this is the first study to demonstrate a significant association between secondhand smoke exposure and all dementia, AD dementia and stroke development in a prospective observation of a large-scale cohort with accurate assessments for secondhand smoke based on well-organized original and offspring cohorts.
Given that smoking is widely recognized a risk factor for dementia and stroke, we hypothesize that these mechanisms should be no less relevant during passive smoke inhalation and that this may be even more critical during early human growth and development in the childhood and adolescent years. First, secondhand smoke is highly noxious and contains greater than 250 chemicals known to be harmful or carcinogenic (29, 30). Therefore, such exposure could negatively impact the brain and nerves in a direct manner that could result in dementia and stroke. Moreover, exposure to secondhand smoke is a previously established risk factor for coronary heart disease and diabetes mellitus (31), each of which is an important risk factor for cognitive impairment, dementia and stroke and thus may indirectly affect the risk (32, 33). Finally, regarding basic research, secondhand smoke exposure adversely affects endothelial function and contributes to vasoconstriction, atherogenesis, and thrombosis and may therefore compromise the blood supply to the brain (34, 35). Moreover, endothelial dysfunction may lead to the reduced clearance of β-amyloid protein, which is considered to be related to the pathogenesis of Alzheimer’s disease (36). Taken together, there are several mechanisms through which secondhand smoke could directly and indirectly affect dementia risk. In a large, dementia-free multigenerational cohort, our results provide further evidence that secondhand smoke is associated with an increased risk of all dementia, AD dementia and stroke after adjustment for confounders, including sex, age, BMI, diabetes and hypertension.
The strengths of the study include a large heterogeneous sample size; a unique longitudinal, community-based cohort spanning generations; and long-term and robust follow-up for our outcomes of interest. Moreover, the FHS and FHS-OS cohorts were renowned for their precise ascertainment of relevant covariates that may be potential confounders used in multivariable adjustment.


First, parental smoking status was unclear in some potentially exposed offspring participants, and the number of incident events was small, which may lead to selection bias. Second, the observational nature of our study prevents us from inferring causal links between exposure to secondhand smoke and the risks of dementia and stroke. Finally, although we addressed confounding in numerous ways, we cannot exclude the possibility of residual confounding, especially for certain dementia and/or risk factors that were not available to us based on public databases, such as genotype ApoE4 (37). Therefore, larger studies are needed to replicate and verify our results.



Our study demonstrates that early-life exposure to secondhand smoke was significantly associated with increased risks of dementia, AD dementia and stroke development. International policy debate on exposure to secondhand is a topic of major public health significance. Dementia and stroke are two of the largest global public health challenges facing our aging population. These findings may provide new evidence to reduce the risk of secondhand smoke exposure by enhancing public smoking restriction policies and motivating current smokers to quit as well as motivating potential smokers to avoid smoking altogether to maintain better health.


Acknowledgments: The authors thank the National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, and the Second People’s Hospital of Yibin/West China Yibin Hospital, Yibin, Sichuan.

Data Availability: Data described in the manuscript, code book, and analytic code will not be made available because the authors are prohibited from distributing or transferring the data and codebooks on which their research was based to any other individual or entity under the terms of an approved NHLBI Framingham Heart Study Research Proposal and Data and Materials Distribution Agreement through which the authors obtained these data.

Author Contributions: All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.Study concept and design: All authors. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: SF Zhou. Critical revision of the manuscript for important intellectual content: KR Wang. Statistical analysis: KR Wang. Obtained funding: KR Wang. Supervision: KR Wang. Grant Support: None. All authors have read the journal’s authorship agreement, and the manuscript has been reviewed by and approved by all named authors.

Ethical Standards: The study procedures followed were in accordance with the ethical standards of the Institutional Review Board and the Principles of the Declaration of Helsinki.

Conflicts of Interest: The authors declare that they have no conflicts of interest.



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