Motoric cognitive risk syndrome

Abstract

Objectives:

Our objective is to report prevalence of motoric cognitive risk syndrome (MCR), a newly described predementia syndrome characterized by slow gait and cognitive complaints, in multiple countries, and its association with dementia risk.

Methods:

Pooled MCR prevalence analysis of individual data from 26,802 adults without dementia and disability aged 60 years and older from 22 cohorts from 17 countries. We also examined risk of incident cognitive impairment (Mini-Mental State Examination decline ≥4 points) and dementia associated with MCR in 4,812 individuals without dementia with baseline Mini-Mental State Examination scores ≥25 from 4 prospective cohort studies using Cox models adjusted for potential confounders.

Results:

At baseline, 2,808 of the 26,802 participants met MCR criteria. Pooled MCR prevalence was 9.7% (95% confidence interval [CI] 8.2%–11.2%). MCR prevalence was higher with older age but there were no sex differences. MCR predicted risk of developing incident cognitive impairment in the pooled sample (adjusted hazard ratio [aHR] 2.0, 95% CI 1.7–2.4); aHRs were 1.5 to 2.7 in the individual cohorts. MCR also predicted dementia in the pooled sample (aHR 1.9, 95% CI 1.5–2.3). The results persisted even after excluding participants with possible cognitive impairment, accounting for early dementia, and diagnostic overlap with other predementia syndromes.

Conclusion:

MCR is common in older adults, and is a strong and early risk factor for cognitive decline. This clinical approach can be easily applied to identify high-risk seniors in a wide variety of settings.

Predementia syndromes based on cognitive tests, biomarkers, or neuroimaging have been proposed to identify dementia risk in older adults,^1,2 but have limitations in many settings. For instance, an estimated two-thirds of persons with dementia live in low- and middle-income countries where there is often no access to complex neuropsychological testing or neuroimaging.^2,3 Hence, there is a need to optimize and increase accessibility of clinical dementia risk assessments in order to institute preventive measures and curtail health care costs.

There is increasing evidence that gait slowing occurs early in dementia and may precede declines in cognitive tests.^4–6 Hence, incorporating gait into dementia risk assessments is a novel approach that can be used even in resource poor settings.⁷ The motoric cognitive risk syndrome (MCR), a recently described predementia syndrome characterized by cognitive complaints and slow gait, avoids the need for complex cognitive tests or other burdensome investigations.^7,8

Our goal is to report prevalence of MCR in 26,802 older adults from 17 countries. We predicted that participants with MCR would have higher disease burden and worse cognitive status than non-MCR participants.⁸ Older adults with MCR in our validation study⁸ were at increased risk of dementia even after accounting for potential confounders and diagnostic overlap with mild cognitive impairment (MCI) syndrome. To further explore this finding, we studied the association of MCR with risk of cognitive decline in 4,812 cognitively normal individuals without dementia and with Mini-Mental State Examination (MMSE) scores ≥25 from 4 well-established cohorts.

METHODS

The MCR consortium includes data from 22 cohorts from 17 countries: 7 North American,^9–15 6 European,^16–19 5 Asian,^15,20–22 2 African,¹⁵ 1 Israel,²³ and 1 Australia.²⁴ Sixteen studies were community-based, 4 memory clinics, and 2 recruited from clinic and community. Eligible cohorts contained baseline information on cognitive complaints, gait speed, cognitive tests, mobility disability, and dementia. Main features are summarized in table 1. From 62,215 available individuals, we excluded 27,882 who were younger than 60 years because our focus was the geriatric population, and few studies enrolled younger participants. From the remaining 34,333 participants, we excluded those missing gait speed (n = 4,508) and cognitive complaints (n = 517). We also excluded 881 participants with mobility disability (inability to ambulate with or without assistive devices) and 1,625 with clinically adjudicated dementia at baseline. Table 1 lists diagnostic procedures in individual studies. After exclusions, the final sample included 26,802 individuals aged 60 years and older.

Table 1.

MCR consortium: Summary of studies, procedures, and tests

Standard protocol approvals, registrations, and patient consents.

The institutional review board of the Albert Einstein College of Medicine approved this analysis. Each site obtained approval from their local ethics committee.

MCR.

MCR diagnosis builds on MCI criteria,¹ and is defined as presence of cognitive complaints and slow gait in older individuals without dementia or mobility disability.⁸ Table e-1 on the Neurology® Web site at Neurology.org lists tests and procedures in individual cohorts. Cognitive tests were not used to assign MCR. Presence of cognitive complaints was ascertained by participant responses on standardized questionnaires administered by interviewers (table e-1).⁸ Informant reports were not used because no cohort had this as a requirement. Gait speed (cm/s) was measured quantitatively or timed over a fixed distance. Slow gait was defined as walking speed 1 SD below age- and sex-specific means individualized to each cohort (table e-1).⁸

To account for population and procedural differences, we examined an alternate MCR definition by deriving global slow gait cutscores pooling gait data from all sites. We also examined MCR prevalence in 2,753 individuals (10.2%) from 9 studies in which gait speed was measured with similar equipment (GAITRite²⁵; CIR Systems, Inc., Sparta, NJ) (table 1) and walking protocols.

Other covariates.

Scores on tests of global mental status, memory, attention, executive function, and depressive symptoms in each cohort were standardized to facilitate comparisons. Because not all studies diagnosed MCI,¹ we classified participants as MCI if they had cognitive complaints and memory or nonmemory test scores 1.5 SDs below age- or sex-specific means in each cohort.¹

Presence of self-reported vascular diseases (any one of angina, hypertension, diabetes, and strokes), depression, and arthritis was recorded. Information on myocardial infarctions, TIA, and Parkinson disease was available for <15% of the sample, and is not reported.

Cognitive outcomes.

Incident cognitive impairment was defined as a change during follow-up in MMSE scores of ≥4, a reliable indicator of significant cognitive decline.²⁶ Four studies with 4,812 participants with longitudinal data were included. The Chicago-based Memory and Aging Project (MAP) is a clinical-pathologic aging study (mean age 79.9 years, 74.0% women, 93.1% Caucasian, 6.2% African American).¹⁴ The Religious Orders Study (ROS) is also based in Chicago, but enrolled religious clergy from across the United States (mean age 75.1 years, 69% women, 91.7% Caucasian, 7.2% African American).¹¹ The Hispanic Established Populations for Epidemiologic Studies of the Elderly (H-EPESE) is a representative sample of community-dwelling Mexican American elderly from 5 southwestern US states (mean age 72.3 years, 56% women).¹² The Invecchiare in Chianti (InCHIANTI) Study is a representative population-based study in Chianti, Italy (mean age 74.1 years, 55% women).¹⁷

Dementia was diagnosed prospectively using individual study procedures (tables 1 and e-1) in MAP,¹⁴ ROS,¹¹ and H-EPESE cohorts,¹² and examined as a secondary cognitive outcome.

Analysis.

Random-effects meta-analysis was used to pool individual and summary estimates. Heterogeneity between studies was tested using the χ² test and quantified by the I² statistic. We report MCR prevalence by age and sex. Associations with 95% confidence intervals (CIs) of MCR with medical and cognitive variables were examined using linear regression for continuous variables and logistic models for binary variables, adjusted for age, sex, and education.

To confirm MCR as a dementia risk factor, we used Cox models to compute hazard ratios (HRs) with 95% CIs adjusted for age, sex, education, cohort source, baseline MMSE scores, and vascular disease for developing cognitive impairment and dementia. All participants were clinically adjudicated to be dementia-free at baseline using individual study procedures (table 1). In addition, we excluded participants with MMSE scores <25. We examined associations in the pooled sample using meta-analysis. We conducted sensitivity analyses to account for early dementia and diagnostic overlap with MCI.

RESULTS

The ages of the 26,802 participants ranged from 60 to 114 years (mean 71.6), with 55.7% women, and mean education 6.9 years. Mean gait speed was 81.8 ± 32.7 cm/s. Participants with MCR had worse performance on all cognitive tests than participants without MCR (table 2). Participants with MCR had higher prevalence of vascular and nonvascular diseases. High education (≥12 years) was associated with reduced risk of MCR (estimate −0.47, 95% CI −0.65 to −0.29, p < 0.001). Older age (≥75 years) had a borderline association (estimate 0.14, 95% CI −0.00 to 0.28, p = 0.05) and sex was not associated with MCR (estimate 0.03, 95% CI −0.06 to 0.11, p = 0.55).

Table 2.

Motoric cognitive risk syndrome (MCR) baseline characteristics

Prevalence.

Of the 26,802 participants, 2,808 met MCR criteria. Pooled MCR prevalence among individuals aged 60 years and older was 9.7% (95% CI 8.2%–11.2%). Age- and sex-adjusted pooled MCR prevalence was 9.6% (95% CI 8.7%–10.6%). MCR prevalence in 6 low- or middle-income countries with 14,011 participants ranged from 5.3% to 15.5% (figure 1). MCR prevalence was higher in the 8,651 individuals aged 75 years and older (10.6%, 95% CI 9.0%–12.3%) than in the 18,151 individuals aged 60 to 74 years (8.9%, 95% CI 7.1%–10.7%). Prevalence of MCR was similar in 11,881 men (9.5%, 95% CI 7.9%–11.1%) and 14,921 women (9.6%, 95% CI 7.8%–11.3%).

Figure 1. Prevalence of motoric cognitive risk syndrome.

Prevalence estimates (ES) for each study are graphically represented by small diamonds and 95% CIs by horizontal bars. Gray boxes surrounding the diamonds graphically represent study weighting in the analysis, which is also shown in the last column. The large diamond is the pooled prevalence estimate. Pooled effect estimate was similar for random- and fixed-effects model. The vertical dotted line represents the prevalence estimates of the pooled result. CCMA = Central Control of Mobility in Aging; CI = confidence interval; MAP = Memory and Aging Project; ROS = Religious Orders Study; SAGE = Study on Global Ageing and Adult Health.

MCR prevalence using alternate criteria (global slow gait cutscores) was 9.2% (95% CI 7.7%–11.0%, I² 90.0%). MCR prevalence in 2,753 individuals with similar GAITRite walking protocols²⁵ was 8.0% (95% CI 5.0%–1.1%, I² 80.1%). MCR prevalence was similar in the 11 studies that used similar assessments for gait speed (timed walk) and cognitive complaint (self-report questionnaire): 9.0% (95% CI 8.1%–10.7%, I² 65.0%).

Figure 1 shows MCR prevalence in the individual cohorts (I² 94.2%). The lowest MCR prevalence was seen in the Australian (2%) and UK (2%) studies that recruited ambulatory seniors with high walking speeds.^18,24 The highest MCR prevalence was in French (16%) and Indian (15%) cohorts, which enrolled seniors with cognitive complaints.^19,20 When these 4 outliers with lowest and highest prevalence rates were excluded, pooled MCR prevalence was 10.0% (95% CI 8.7%–11.3%). When the Canadian, French, and Belgian cohorts that only enrolled participants with cognitive complaints were excluded,^10,19 MCR prevalence was 9.1% (95% CI 7.5%–10.8%).

Cognitive outcomes.

Mean follow-up varied from 5.1 to 9.3 years. MCR predicted incident cognitive impairment in all cohorts with adjusted HRs (aHRs) ranging from 1.48 in the H-EPESE to 2.74 in InCHIANTI (table 3).

Table 3.

Motoric cognitive risk syndrome and risk of cognitive impairment

To examine whether MCR mainly identifies individuals with early dementia, we repeated the analysis excluding 668 participants who met incident cognitive impairment criteria in the first 3 years. MCR still predicted incident cognitive impairment in this subgroup (aHR 1.71, 95% CI 1.31–2.24). MCR also predicted incident cognitive impairment when the analysis was restricted to 3,289 participants with baseline MMSE scores ≥28 (aHR 1.65, 95% CI 1.36–2.01). The results were similar in individual cohorts (data not shown).

While MCR is diagnosed independent of cognitive tests used to define MCI, 39% of individuals with MCR also met MCI criteria.¹ Both MCR (aHR 1.63, 95% CI 1.39–1.89) and MCI (aHR 1.36, 95% CI 1.19–1.56) predicted cognitive impairment when examined together in the same model.

There were 212 incident dementias in MAP, 265 in ROS, and 417 in H-EPESE. MCR predicted dementia in MAP (aHR 2.10, 95% CI 1.43–2.09), ROS (aHR 1.98, 95% CI 1.44–2.74), and H-EPESE (aHR 1.79, 95% CI 1.31–2.44) cohorts as well as in the pooled sample (aHR 1.93, 95% CI 1.59–2.35). MCR was associated with increased risk of Alzheimer disease dementia^11,14,27 in MAP (aHR 2.21, 95% CI 1.49–3.28) and ROS (aHR 1.97, 95% CI 1.41–2.74) cohorts. There were insufficient cases of vascular dementia to examine as an outcome. Dementia subtyping was not done in H-EPESE.

Figure 2A graphically presents risk of incident cognitive impairment over 12 years’ follow-up associated with MCR (HR adjusted for age, sex, education, and cohort source 1.69, 95% CI 1.44–1.98), slow gait alone (aHR 1.40, 95% CI 1.20–1.65), and self-reported cognitive complaints alone (aHR 1.09, 95% CI 0.94–1.27) compared with healthy controls with neither subjective cognitive complaints nor slow gait. Figure 2B shows risk of dementia over 12 years’ follow-up associated with MCR (aHR 2.47, 95% CI 1.93–3.17), slow gait alone (aHR 1.77, 95% CI 1.38–2.27), and cognitive complaints alone (aHR 1.27, 95% CI 0.99–1.63) compared with healthy controls.

Figure 2. MCR and risk of incident cognitive impairment (A) and dementia (B).

Kaplan-Meier survival curves with 95% confidence interval over 12 years’ follow-up in pooled samples. MCR = motoric cognitive risk syndrome.

DISCUSSION

In this multicountry study of 26,802 older adults, pooled prevalence of MCR was 9.7%. MCR prevalence was higher in persons aged 75 years and older, paralleling the greater prevalence of cognitive complaints and dementia in this age segment. There were no sex differences in MCR prevalence. Our findings show that MCR criteria can be easily applied in clinical settings with simple questions about cognitive complaints and timing gait; 90% of participants had walking timed over fixed distances without requiring major resource commitments.

MCR was associated with a 2-fold increased risk of developing incident cognitive impairment (aHR 2.0) in 4,812 participants without dementia and MMSE scores ≥25, even after accounting for vascular disease and baseline cognitive status.⁸ MCR predicted dementia in our current (aHR 1.9) and previous (aHR 3.3) analyses.⁸ MCR was a stronger predictor of cognitive outcomes than its individual components of cognitive complaints or slow gait in the current and previous validation study.⁸ The association of MCR with cognitive impairment remained even when the analysis was conservatively restricted to participants with baseline MMSE scores ≥28 or when incident cognitive impairment cases in the first 3 years were excluded. These findings support MCR as an early clinical marker for cognitive decline.

MCR predicted vascular dementia in our validation cohort.⁸ However, MCR predicted Alzheimer disease dementia in more than 2,000 participants from 2 cohorts in this study.^11,14 This finding may be explained by the higher frequency of mixed brain pathology with advancing age.²⁸ Alternatively, Alzheimer pathology can manifest in early gait dysfunction as suggested by reports of gait slowing in the years leading to MCI.^4–6,29 The association of MCR with clinical dementia subtypes needs to be further verified. The inverse association noted between education and MCR needs further scrutiny to gain insights into potential interventions. Three of 4 cohorts in the cognitive analysis were from the United States, as was our initial study.⁸ While insights into dementia gained from these 4 well-established cohorts have been generalizable to other populations,^{8,11,12,14,25,30} MCR findings should be cross-validated in other countries.

The gait-based MCR offers several benefits in detecting cognitive risk. Gait speed has a common metric, high reliability between different protocols, and excellent validity in predicting health outcomes.^12,25,31,32 A universal test or metric is lacking for cognitive test–based predementia syndromes, and may account for variance in reported prevalence.^1,33 Unlike MCR criteria that can be easily and inexpensively applied, neuropsychological tests to diagnose MCI or laboratory and imaging tests for biomarker-based predementia syndromes may not be practical in many settings.^1,2 MCR is diagnosed independent of cognitive tests minimizing diagnostic circularity in using the same test to define predementia and dementia syndromes.

A key strength is that our study is based on well-established cohorts with reliable cognitive and motor protocols from multiple nations, 52% from low- or middle-income countries. Because MCR is newly proposed, there are no other comparative studies.⁸ There are few global studies of predementia syndromes, and fewer still in low- or middle-income countries.³³ A low prevalence of amnestic MCI was reported in 15,376 seniors from 8 low- or middle-income countries: from 0.8% in China to 4.3% in India.³³ Lack of specificity in MCI criteria was proposed to account for the low prevalence.³³ MCR prevalence was higher in the 14,011 participants from our 6 low- or middle-income countries. HRs for cognitive impairment were similar for MCR and MCI.

To our knowledge, this is one of the largest prevalence studies of a predementia syndrome, yet it shares limitations of prior studies in not being global.³³ While representing all countries may not be feasible, MCR needs to be examined elsewhere because etiologies for core criteria such as cognitive complaints and slow gait might vary regionally. Then again, modifiable risk factors for cardiovascular disease and strokes,^34,35 major contributors to gait and cognitive declines, were remarkably consistent globally. Although detailed vascular or neuroimaging studies may reveal underlying etiologies for MCR, resource constraints will not permit screening all patients with these tests. The MCR approach can help streamline high-risk individuals for further investigations, especially in resource-poor settings.

Because the inception of our studies antedated the MCR concept,⁸ heterogeneity is not unexpected given clinical and methodologic diversity. While some might preclude pooling data due to heterogeneity, we believe the summary data provide a global perspective on MCR. The variance in MCR prevalence in our different sites is in accord with the variability in MCI and dementia prevalence globally.^1,33 Moreover, our estimates show the same direction of effect in multiple sites, an equally important consideration in meta-analyses.³⁶ The high agreement in MCR prevalence defined multiple ways and excluding subgroups is reassuring. There was no significant heterogeneity in longitudinal analyses. Reasons for heterogeneity are not explained by subgroup analyses, but might include differences in methods. Memory complaints are subject to cultural bias,³³ a limitation of MCR common with MCI and dementia definitions.^1,27 However, exclusion of cohorts that recruited only participants with memory complaints or restriction to cohorts that used similar cognitive complaint ascertainment did not change the MCR prevalence estimates. Refinements of cognitive complaint ascertainment should be pursued to increase the specificity of the MCR criteria.

Given differences in source populations, sample sizes, and protocols, variability in gait cutscores is not unexpected. This situation is not unique to gait; cognitive test norms also vary by population. We used insular slow gait cutscores in each cohort, reflected in the relatively narrow MCR prevalence range of 7% to 13% in 17 studies (81% of sample). Furthermore, MCR prevalence rates were not materially changed when an alternate secular definition using global slow gait cutscores was used or when examined in the subgroup with uniform instrumented gait protocols, although heterogeneity remained high. However, global slow gait cutscores or arbitrary cutscores will not account for regional variations and bias against the oldest age groups. The slow gait criterion was age- and sex-adjusted, which accounts for similarity in the adjusted and unadjusted MCR prevalence. As is done for cognitive tests, we recommend developing local slow gait norms.

Gait speed may not be the strongest motoric predictor of dementia,²⁵ but there is limited information on comparative predictive validity of other motoric signs for dementia.^8,25 Substituting motoric signs such as tone or strength for slow gait in MCR did not improve dementia prediction.⁸ Gait markers, such as variability or rhythm, predict declines on specific cognitive domains.²⁵ The need for instrumentation will restrict accessibility of these quantitative markers by nonspecialists or in community settings.^7,8 Nonetheless, other motoric markers should be investigated in research settings to improve MCR definition to gain insights into early stages of dementia. Given the scale of data collection and retrospective design, a more limited analysis than is possible in single-population studies was done. For instance, APOE genotype information that may have shed light on cognitive and gait decline was not available.^37,38 Dementia prevalence may be underestimated with criteria used in our participating studies, particularly in low- and middle-income countries.³ However, informant reports and functional scales needed to apply alternate dementia criteria were not available in all of our cohorts.³ The agreement in predictive validity of MCR for cognitive outcomes in different well-established cohorts and after accounting for early dementia or overlap with MCI is reassuring.

This multicountry study supports MCR as a high risk of dementia phenotype that can be easily applied and identified in a variety of settings by clinicians. Further validation studies are required so that clinicians and researchers may utilize this clinical approach to improve dementia risk assessments, plan management, and develop novel interventions to prevent cognitive decline worldwide.

AUTHOR AFFILIATIONS

From the Departments of Neurology (J.V., E.A., R.H.), Medicine (J.V., N.B.), and Epidemiology (C.W.), Albert Einstein College of Medicine, Bronx, NY; Division of Geriatric Medicine and Memory Clinic (C.A., O.B.), Angers University Hospital, France; Rush Alzheimer's Disease Center (D.A.B., A.S.B.), Rush University Medical Center, Chicago, IL; Acute Geriatrics Department (S.A.B., R.W.K.), University Hospital Basel, University of Basel, Switzerland; Stroke and Ageing Research (M.L.C., V.S.), Department of Medicine, Southern Clinical School, Monash University, Clayton; Menzies Research Institute (M.L.C.), Hobart, Australia; Department of Medicine (R.C.), University of Alberta, Glenrose Rehabilitation Hospital, Edmonton, Canada; Division of Epidemiology & Community Health (B.C.), University of Minnesota, Minneapolis; Department of Health Statistics and Information Systems (S.C., P.K.), World Health Organization, Geneva, Switzerland; Department Geriatric Medicine (A.-M.D.C.), AZ ST Maarten Mechelen, Belgium; Longitudinal Studies Section (L.F.), Clinical Research Branch, Gerontology Research Center, National Institute on Aging, Baltimore, MD; Department of Neurology (N.G., J.M.H.), Tel-Aviv Sourasky Medical Center, Israel; Department of Epidemiology and Public Health (J.M.G.), Division of Gerontology, University of Maryland School of Medicine, Baltimore; Ferkauf School of Psychology (R.H.), Yeshiva University, Bronx, NY; Departments of Neuropsychiatry (K.W.K.) and Rehabilitation Medicine (J.-Y.L.), Seoul National University College of Medicine; Department of Brain and Cognitive Sciences (K.W.K.), Seoul National University College of Natural Sciences, Korea; Clinical Ageing Research Unit (S.L., L.R.), Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK; Department of Geriatric Behavioral Neurology (K.M.), Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Medicine (M.M.-O.), Epidemiology and Biostatistics (M.M.-O.), Division of Geriatric Medicine (M.M.-O.), Gait and Brain Lab (M.M.-O.), and School of Physical Therapy (S.W.M.-H.), University of Western Ontario, London, Canada; and Department of Neurosciences (M.L.N.), Baby Memorial Hospital, Kozhikode, India.

GLOSSARY

aHR

adjusted hazard ratio

CI

confidence interval

H-EPESE

Hispanic Established Populations for Epidemiologic Studies of the Elderly

HR

hazard ratio

InCHIANTI

Invecchiare in Chianti

MAP

Memory and Aging Project

MCI

mild cognitive impairment

MCR

motoric cognitive risk syndrome

MMSE

Mini-Mental State Examination

ROS

Religious Orders Study

AUTHOR CONTRIBUTIONS

Verghese was responsible for study concept, acquisition of data, initial draft, and analysis of data. Ayers and Wang were responsible for revising draft for content and analysis of data. Annweiler, Barzilai, Beauchet, Bennett, Bridenbaugh, Buchman, Callisaya, Camicioli, Capistrant, Chatterji, De Cock, Ferrucci, Giladi, Guralnik, Hausdorff, Holtzer, Kim, Kowal, Kressig, Lim, Lord, Meguro, Montero-Odasso, Muir-Hunter, Noone, Rochester, and Srikanth were responsible for acquisition of data, interpretation of data, and revising draft for content.

STUDY FUNDING

No targeted funding is reported for the pooled analysis performed in this study. The individual studies were supported by the following agencies. The TASCOG Study was funded by the Australian National Health and Medical Research Council (403000). The Gait and Brain Study was funded by Canadian Institutes of Health and Research (MOP 211220). The SAGE Study was funded by NIH Interagency agreements with WHO (OGHA 04034785, YA1323-08-CN-0020, Y1-AG-1005-01, R01 AG034479, 1R21AG034263). The GAIT Study was funded by the French Ministry of Health (Projet Hospitalier de Recherche Clinique national 2009-A00533-54). The Kerala-Einstein Study was funded by NIH (R01 AG039330). The Israel studies were funded by NIH (AG-14100). The InCHIANTI Study was funded by the Italian Ministry of Health (grant ICS 110.1/RS97.71) and NIH (grants 263 MD 916413, 263 MD 821336, 1ZIAAG001050). Kurihara Project was funded by Kurihara, Miyagi, Japan (2008–2010) and by the Japanese Ministry of Health, Labour and Welfare (2009–2010). The KLoSHA project was funded by the Korean Health Technology R & D Project (grants A092077 and A070001) and the National Research Foundation of Korea grant (2012-0000999). The Newcastle Study was funded by a UK NIHR Biomedical Research Centre for Ageing and Age-Related Disease award to the Newcastle upon Tyne Hospitals NHS Foundation Trust, Department of Health, and a Parkinson's UK programme grant (J-0802). The LonGenity Study was funded by NIH (R00AG037574, 1P01AG034906, R01AG046949, 1R01AG042188, P30AG038072, and NIH R37AG18381), CTSA KL2TR000088, Einstein Glenn Center, Paul Glenn Foundation, and the American Federation for Aging Research. The CCMA study was funded by NIH (R01AG036921, RO1AGO44007-01A1). The MAP and ROS cohorts were funded by NIH (P30AG10161, R01AG15819, R01AG17917, R01AG34374, R01AG33678) and the Illinois Department of Public Health. The H-EPESE Study was funded by NIH (RO1 AG10939, RO3 AG026106).

DISCLOSURE

The authors report no disclosures relevant to the manuscript. Go to Neurology.org for full disclosures.