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Abstract

Objective

Alzheimer disease (AD) is a common and costly neurodegenerative disorder. A large proportion of AD risk is heritable, and many genetic risk factors have been identified. The objective of this study was to test the hypothesis that cumulative genetic risk of known AD markers contributed to odds of dementia in a population-based sample.

Methods

In the US population-based Health and Retirement Study (waves 1995–2014), we evaluated the role of cumulative genetic risk of AD, with and without the APOE ε4 alleles, on dementia status (dementia, cognitive impairment without dementia, borderline cognitive impairment without dementia, and cognitively normal). We used logistic regression, accounting for demographic covariates and genetic principal components, and analyses were stratified by European and African genetic ancestry.

Results

In the European ancestry sample (n = 8,399), both AD polygenic score excluding the APOE genetic region (odds ratio [OR] = 1.10; 95% confidence interval [CI]: 1.00–1.20) and the presence of any APOE ε4 alleles (OR = 2.42; 95% CI: 1.99–2.95) were associated with the odds of dementia relative to normal cognition in a mutually adjusted model. In the African ancestry sample (n = 1,605), the presence of any APOE ε4 alleles was associated with 1.77 (95% CI: 1.20–2.61) times higher odds of dementia, whereas the AD polygenic score excluding the APOE genetic region was not significantly associated with the odds of dementia relative to normal cognition 1.06 (95% CI: 0.97–1.30).

Conclusions

Cumulative genetic risk of AD and APOE ε4 are both independent predictors of dementia in European ancestry. This study provides important insight into the polygenic nature of dementia and demonstrates the utility of polygenic scores in dementia research.
Dementia is characterized by progressive cognitive decline, leading to a loss of independence. The population of those aged 65+ years is estimated to grow from 55 million in 2019 to 88 million in 2050, and the number of people with dementia is predicted to increase.1 In 2019, the estimated health care, long-term care, and hospice costs associated with dementia were $290 billion.1 Heritability estimates for dementia attribute 50–80% of risk to genetic factors.2 The most common dementia genetic susceptibility locus is in the APOE gene, represented by multiple alleles (ε2, ε3, and ε4). In European and African ancestry samples, APOE ε4 alleles increase the risk of dementia, although they are neither necessary nor sufficient for disease.3,4 Understanding etiologic factors is essential for dementia prevention and potential treatment.
Alzheimer disease (AD) is implicated in 70% of dementia cases,5 and late-onset AD is the more common and sporadic form of AD. Late-onset AD genome-wide association studies (GWASs) have identified many single nucleotide polymorphisms (SNPs).6 Based on this a priori knowledge of AD genetics, cumulative genetic risk of AD can be summarized using polygenic scores (PGSs). In a recent familial AD study of non-Hispanic White participants, an unweighted PGS constructed using 19 genome-wide significant AD SNPs, observed a 1-SD unit increase in the PGS was associated with 1.29 times increased odds of clinically diagnosed late-onset AD (95% confidence interval [CI]: 1.21–1.37), relative to unaffected family members.7 The utility of a weighted AD PGS, independent of APOE, has not been tested in multiple ancestries or with population-based dementia outcomes.
We investigated whether cumulative genetic risk of AD—over and above the risk established by the APOE ε4 allele—was associated with dementia or cognitive impairment in 2 genetic ancestries. In a large, population-based study, the Health and Retirement Study (HRS), we characterized the utility of AD PGS in European ancestry as well as African ancestry, where the PGS may have value, albeit as a less informative instrument given the European-based GWAS weights. We provide important insight into the genetic correlates of dementia and demonstrate the utility of AD PGSs in dementia research across ancestries.

Methods

Health and Retirement Study

The HRS is a US nationally representative, longitudinal panel cohort study of adults older than 50 years (n ∼ 22,000, per wave).8 The HRS was collected biennially beginning in 1992, through face-to-face interviews, mail-in surveys, and leave-behind questionnaires, with samples obtained for genotyping (2006–2012). This analysis included 10 waves (1995–2014). The HRS is sponsored by the National Institute on Aging (U01AG009740) and is conducted by the University of Michigan. Informed consent was obtained from all participants.

Standard Protocol Approvals, Registrations, and Patient Consents

The Health and Retirement Study is sponsored by the National Institute on Aging (NIA U01AG009740) and is conducted by the University of Michigan. Before each interview, participants are provided with a written informed consent information document. At the start of each interview, all respondents are read a confidentiality statement and give oral consent by agreeing to do the interview. The University of Michigan Institutional Review Board approved the collection of these data (HUM00061128 and HUM00056464). This secondary data analysis was exempt and not regulated as determined by the University of Michigan Institutional Review Board (HUM00128220).

Cognitive Outcome

Cognition status at each wave was assigned using the Langa-Weir method.9 Cognition status at 3 levels (dementia, cognitive impairment–no dementia [CIND], and normal cognition) was assigned using survey instruments. For missing values, multivariate, regression-based imputation and variance estimation were used.10 The Langa-Weir method was validated against a clinically evaluated subsample of the HRS where 76% of self-respondents and 84% of proxy respondents were correctly classified with dementia.11
Cognition can fluctuate between waves. We were interested in cumulative cognitive status; thus, we constructed summary cognition measures based on all available wave-specific values (ranging from 3 to 10 waves). Observations were excluded if the participant was less than 60 years old at cognitive assessment. Participants were assigned 1 of 5 possible summary cognitive statuses: dementia, CIND, borderline CIND, cognitively normal, and unclassified (table e-1, links.lww.com/NXG/A393). Unclassified summary cognitive status participants were excluded.

Genetic Variables

Genetic data (waves 2006, 2008, and 2010) were downloaded from dbGap (phs000428.v2.p2). Saliva DNA was genotyped on the Illumina Human Omni-2.5-4v1 and Illumina Human Omni-2.5-8v1 Quad BeadChips12 at the Center for Inherited Disease Research. Autosomal SNPs were filtered on missing call rate >5% and minor allele frequency <5%. The highly variant lactase gene, HLA gene, 8p23, and 17q21.31 regions were excluded.
Genetic ancestry was identified through principal component (PC) analysis on independent genome-wide SNPs. The European ancestry sample included all self-reported non-Hispanic White participants within 1 SD of the mean for eigenvector 1 (n = 9,991 non-Hispanic White/European ancestry). The African ancestry sample included all self-reported non-Hispanic Black participants within 2 SDs of the mean of eigenvector 1 and 1 SD of the mean for eigenvector 2 (n = 2,279 non-Hispanic Black/African ancestry). In the HRS analytic sample, self-reported race/ethnicity and genetic ancestry are perfectly correlated by selection, eliminating our ability to test for effects in discordant or mixed racial/ancestral groups. To create sample eigenvectors for population stratification covariates, PC analysis was performed again within each ancestry sample.
Two genetic variants (rs7412 and rs429358) contribute to 3 APOE alleles (ε2, ε3, and ε4). One of 6 APOE genotypes (ε2/ε2, ε2/ε3, ε2/ε4, ε3/ε3, ε3/ε4, and ε4/ε4) was assigned using 1000 Genomes Project imputation.13 Primary analyses indicated the binary presence of the ε4 allele. Sensitivity analyses included additional APOE genotype categorizations.
Cumulative genetic risk of AD was calculated using PGS12 with the following formula:
where i is individual (i = 1 to N), j is SNP (j = 1 to J), and W is the GWAS meta-analysis effect size for SNP j. G is the number of variant alleles genotyped (0, 1, or 2), for individual i at SNP j. Effect estimates were derived from an AD meta-analysis GWAS (stage 1) in European ancestry with 21,982 cases and 41,944 controls.6 Summary statistics were downloaded from the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (niagads.org/datasets/ng00075). We identified SNPs that overlapped between the HRS measured genotypes and the AD GWAS summary statistics,6 after removing the region on chromosome 19 containing the linkage disequilibrium block of the APOE gene (chr19:45384477-45432606, GRCh37/hg19) from the summary statistics. After evaluating PGS developed across multiple p value thresholds for outcome association,14 SNPs with p < 0.01 in the summary statistics were included in the PGS.
Although the AD GWAS was conducted in individuals of European ancestry, we conducted our analyses in both European and African ancestry samples. We note the HRS recommendation12 that “PGSs for other ancestry groups may not have the same predictive capacity” and “users (should) perform analyses separately by ancestral group and adjust for PCs.” We emphasize the need for large GWAS on non-European ancestries with available summary statistics to advance the knowledge in this field. AD PGSs were z-score standardized within ancestry.

Covariates

Information on sex (female and male) and years of education was collected at the first HRS visit. At last cognitive assessment, we considered respondent age and year in our analyses.
In sensitivity models, we included additional dementia risk factors: smoking behavior,15 alcohol use,16 body mass index (BMI),17 and history of hypertension,18 diabetes,19 depressive symptoms,20 and stroke.21 History of hypertension (no/yes), diabetes (no/yes), smoking (never, former, or current), and alcohol use (never/yes) were assessed at the last cognitive visit using variables from the RAND Center for the Study of Aging, which is supported by the National Institute of Aging and Social Security Administration.22 If the last cognitive visit was face to face, we preferentially used the concurrent measured BMI (kg/m2), followed by the participant's self-reported BMI at that wave. If these 2 values were missing, we selected prior wave measured BMI followed by prior wave self-reported BMI. Depressive symptoms, measured by the 8-item Center for Epidemiological Scales—Depression questionnaire, were averaged across all waves concurrent to and prior to the last cognitive measure. This value was dichotomized at the ancestry-specific mean of depressive symptoms. At each wave, participants were queried on their history of stroke or TIA. This information was used to construct a summary variable for ever having a stroke (no/yes) at the last year of cognitive assessment.

Statistical Analysis

Analyses were performed using SAS 9.4 (SAS Institute, Cary, NC) and R (version 3.5.1). We calculated univariate and bivariate descriptive statistics. Multiple covariates (sex, age, APOE ε4, and education) violated the proportional odds assumptions. Thus, we used separate logistic regressions to model the odds of impaired cognition (dementia, CIND, or borderline CIND) with normal cognition as the reference. Analyses were stratified by ancestry (European and African). Stratification is essential given genetic architecture varies by ancestry, the PGSs were created using European ancestry weights,6 and risk factor profiles vary across groups.23
Model 1 included age and year of last visit, sex, educational attainment, and 2 ancestry-specific genetic PCs. Model 2 added one of the AD genetic components (model 2a: AD PGS; model 2b: APOE ε4 status) to model 1. Model 3 added both genetic components to model 1.

Sensitivity Analyses

To assess robustness to methodological and analytic decisions, we conducted several sensitivity analyses. First, to assess potential linear deviations among pairs of AD PGSs, we tested correlation (Pearson) between an AD PGS without variants in the APOE gene region (removing 444 variants from chr19:45384477-45432606, GRCh37/hg19 from the summary statistics; table e-2, links.lww.com/NXG/A393), to a PGS including APOE gene region variants. Second, to examine the effect of different p value cutoffs for variants included in the AD PGS, we compared the performance of AD PGS developed using variable GWAS p value thresholds (pT = 1, 0.3, 0.1, 0.05, 0.01, 0.001) in model 2a. Third, to account for potential survival bias, logistic models were repeated after removing the oldest HRS cohorts, Assets and Health Dynamics (AHEAD: birth year <1924) and Children of the Depression Era (CODA: birth years 1923–1930). Fourth, to test whether the effect of AD PGS was different in the presence of APOE and vice versa, we tested for multiplicative interaction between APOE and the AD PGS. Fifth, to characterize the APOE locus, a set of logistic regression models examined a 3-level APOE variable, based on the number of ε4 copies (0, 1, and 2). We did not have enough individuals in the protective ε2/ε2 haplotype (<1% of the total sample in each ancestry) to assess. Sixth, to assess the potential role of health behaviors on the relationships presented, we included model 3 variables as well as history of hypertension, diabetes, smoking, alcohol use, stroke, and depressive symptoms (model 4).

Receiver Operating Characteristic Curve

To evaluate classification capability, receiver operating characteristic (ROC) curves were estimated using areas under the curve (AUC) of logistic models of dementia vs normal cognition. C-statistics from models 2a, 2b, 3, and 4 were compared with model 1 to assess whether the addition of AD PGS, APOE ε4 status, or the combination of AD PGS and APOE ε4 status improved the classification ability over that of the model 1. We further evaluated the AUC of model 3 (with both AD PGS and APOE ε4 status) relative to model 2b (only APOE ε4 status) to determine whether the addition of AD PGS improved the classification of dementia and normal cognition over and above APOE ε4 status.

Attributable Fraction

To determine the proportion of the dementia burden that would be reduced in the absence of elevated cumulative genetic risk, we calculated the attributable fraction for adjusted impaired cognition vs normal cognition regression models. We first compared those in the highest 20th percentile of AD PGS with those in the lowest 20th percentile of AD PGS. Next, we compared those with at least 1 copy of APOE ε4 with those without the allele. We calculated the population attributable fractions and confidence intervals using the AF package with the case-control option.

Data Availability

All HRS survey data are publicly available (hrs.isr.umich.edu/data-products). The genetic data are available from the dbGaP database (accession phs000428.v2.p2). Analytic code is also publicly available (github.com/bakulskilab).

Results

Sample Description

Analyses were performed in both European (n = 8,399) and African (n = 1,605) ancestry samples (figure e-1, links.lww.com/NXG/A393). In the European ancestry sample, 8.6% were classified with dementia, 8.5% with CIND, and 15.0% with borderline CIND (table e-3). In the African ancestry sample, 16.9% were classified with dementia, 21.8% with CIND, and 19.4% with borderline CIND. The proportion of cases in each cognitive status differed by ancestry (p < 0.001).
The majority of the participants in the samples were female (European = 57.0%; African = 63.1%). The average age at the participant's last cognition visit was 75.3 years (SD = 9.04) in the European ancestry group and 72.2 years (SD = 8.83) in the African ancestry group. Sex, age, education, year of last visit, and APOE ε4 status differed by ancestry (p < 0.01). The prevalence of APOE ε4 was lower in the European sample (26.3%) than in the African sample (36.9%). Hypertension, diabetes, smoking, alcohol use, and stroke status also differed by ancestry (p ≤ 0.001). AD PGSs were normally distributed within ancestries.

Bivariate Analyses

In the European ancestry sample, there were differences in demographic characteristics by summary cognitive status (dementia, CIND, borderline CIND, and normal) (table 1). The mean age at last visit of those with dementia (84.2 years; SD = 7.73) was higher than those with normal cognition (72.9 years; SD = 8.17). The mean years of education were lower in those with dementia (11.9 years; SD = 2.90) compared with those with normal cognition (13.7 years; SD = 2.31). Among participants with impaired cognition, there were higher proportions of APOE ε4 carriers and history of hypertension, diabetes, stroke and depression, compared to those with normal cognition (p < 0.001). Lower mean BMI, less alcohol use, and lower proportions of current smokers were observed in those with impaired cognition compared with normal cognition (p < 0.001). PGS for AD, with and without the APOE gene region, was higher with impaired cognition (p < 0.003) (figure e-2, links.lww.com/NXG/A393).
Table 1 Bivariate Analyses of Covariates by Cognition Status Stratified by Ancestry Among Health and Retirement Study (HRS) Participants With Core Measurements Taken From 1995 to 2014
In the African ancestry sample, the mean age at last visit of those with dementia (78.8; SD = 9.16) was higher than those with normal cognition (68.4; SD = 6.84). The mean years of education were lower among those with dementia (9.49 years; SD = 3.82), relative to those with normal cognition (13.3 years; SD = 2.35). Among those with impaired cognition, there was a higher proportion of participants with history of hypertension, diabetes, stroke, and depression compared to participants with normal cognition (p < 0.01). Higher mean BMI and more alcohol use were observed in those with normal cognition relative to impaired cognition (p < 0.01). PGS for AD, with and without the APOE gene region, was higher with impaired cognition (p < 0.005).

Adjusted Association of Genetics With Dementia Outcomes

Logistic regression models were run separately by ancestry (table 2). In the European ancestry sample, age and education (model 1) were associated with each impaired cognitive status relative to normal cognition (p < 0.0001). AD PGS, without the APOE gene region, was associated with dementia compared with normal cognition (model 2a: OR = 1.13, 95% CI: 1.03–1.24). APOE ε4 status was also associated with dementia compared with normal cognition (model 2b: OR = 2.46, 95% CI: 2.02–2.99). In model 3, both AD PGS and APOE ε4 status were significantly and independently associated with dementia relative to normal cognition. A 1 SD increase in AD PGS was associated with a 1.10 (95% CI: 1.00–1.20) times higher odds of dementia relative to normal cognition in European ancestry. Carrying an APOE ε4 allele was associated with 2.42 (95% CI: 1.99–2.95) times higher odds of dementia, relative to normal cognition in European ancestry.
Table 2 Odds Ratios (ORs) of Cognitive Status, Relative to Normal Status, Explained by 1 SD Increase of Polygenic Score and the Presence of an APOE ε4 Allele in Participants in the Health and Retirement Study (HRS), of European and African Ancestries, Adjusted for Age, Sex, Education, Year of Last Visit, Genetic Principal Component (PC) 1, and Genetic PC 2
In the African ancestry sample, age and education (model 1) were associated with each abnormal cognitive status relative to normal cognition (p < 0.003). AD PGS, without the APOE gene region, was not associated with dementia compared with normal cognition (model 2a: OR = 1.29, 95% CI: 0.98–1.70). APOE ε4 status was associated with dementia compared with normal cognition (model 2b: OR = 1.77, 95% CI: 1.20–2.61). In model 3, APOE ε4 status remained significantly associated with dementia relative to normal cognition (OR = 2.10, 95% CI: 1.34–3.28).

Sensitivity Analyses

We compared the primary AD PGS excluding the APOE gene region to an AD PGS including the APOE gene region using a correlation test. These 2 AD PGSs were highly correlated (rEuropean = 0.9772, p < 0.0001; rAfrican = 0.9981, p < 0.0001).
To examine the effect of GWAS p value threshold selection for SNP inclusion in the PGS, we compared PGSs developed using variable p value thresholds. Correlations between PGSs at different p value threshold cutoffs ranged from 0.30 to 0.98 in the European ancestry sample and from 0.41 to 0.99 in the African ancestry sample (table e-4, links.lww.com/NXG/A393). In the European ancestry sample in model 2a, all AD PGSs were associated with the odds of borderline CIND (p < 0.05), whereas no AD PGSs were associated with the odds of CIND (table e-5). The relationship between AD PGS and dementia was sensitive to pT. The AD PGS was associated with dementia status at pT = 0.01 and pT = 0.001 (p < 0.01), but not associated at pTs > 0.01. In the African ancestry sample, AD PGS was not associated with odds of impaired cognition, relative to normal cognition at any pT.
To account for potential survival bias in our study sample, the oldest HRS cohorts (AHEAD and CODA) were removed, dropping the sample size from n = 10,004 to n = 7,913 (nEuropean = 6,492; nAfrican = 1,421). In this European ancestry subset in model 3, a 1 SD increase in AD PGS was not associated with the odds of dementia OR = 1.05 (95% CI: 0.92–1.20), relative to normal cognition (table e-6, links.lww.com/NXG/A393). The presence of APOE ε4 remained associated with the odds of dementia relative to normal cognition OR = 2.60 (95% CI: 2.00–3.38). In the African ancestry subset when AHEAD and CODA were removed, the APOE ε4 relationship attenuated (from 1.77 to 1.55) but remained associated with dementia.
We tested for a multiplicative interaction between having any copies of the APOE ε4 allele and the AD PGS. There was not an interaction between APOE ε4 and the PGS excluding APOE region (ORinteraction = 1.10, p = 0.30). There was also not an interaction between APOE ε4 and the PGS including the APOE region (ORinteraction = 1.09, p = 0.34). This suggests that the effect of the APOE ε4 allele is the same as the APOE region.
We assessed alternative APOE categories (table e-7, links.lww.com/NXG/A393). In European and African ancestry models, having 1 copy and having 2 copies of APOE ε4 compared with no copies both increased the odds of impaired cognition over normal cognition. Although this may indicate utility in modeling APOE ε4 as 2 indicators for 1 or 2 copies of an ε4 allele, the relative prevalence of 2 ε4 copies limits the power (prevalence < 5% in each ancestry).
To assess the robustness of our findings, we additionally adjusted for dementia risk factors (BMI, hypertension, depression, diabetes, smoking, alcohol use, and stroke) (table 2; model 4). In European ancestry, the association between APOE ε4 and the odds of dementia relative to normal cognition remained (model 4; OR = 2.30 95% CI: 1.86–2.85). The effect of AD PGS was consistent in magnitude, but nonsignificant (OR = 1.10 95% CI: 0.99–1.21). Similar associations were observed in African ancestry (APOE ε4 OR = 1.74, 95% CI: 1.15–2.63; AD PGS OR = 1.25, 95% CI: 0.95–1.65).

ROC Curve

To address the differences in dementia prediction ability and potential clinical relevance, we assessed AUC using ROC curves. In European ancestry, using model 1 as a reference (c-statistic = 0.87), adding AD PGS did not improve model discrimination (cdifference = 0.001, 95% CI: −0.0006 to 0.0018, p = 0.30). However, adding APOE ε4 status increased classification (cdifference = 0.0075, 95% CI: 0.0037 to 0.0114, p = 0.0001). Adding the AD PGS did not improve classification over the model already including APOE ε4 (model 2b: cmodel2b = 0.87; cdifference = 0.0001, 95% CI: −0.0006 to 0.0008, p = 0.77). In African ancestry, no models performed more accurately than model 1 (table e-8, links.lww.com/NXG/A393; figure 1).
Figure 1 Classification of Dementia vs Normal Cognition Status Using Demographics, APOE, and Polygenic Score
(A) European ancestry. (B) African ancestry. Receiver operating characteristic (ROC) curves for logistic regression models, looking at the association between Alzheimer disease polygenic risk score (PGS) and presence of APOE ε4 allele (APOE), with summary cognition statuses (dementia only) relative to normal status. This is among a subset of participants in the Health and Retirement Study (HRS) with more than 2 waves of cognition measured and in the top 20% PGS or bottom 20% PGS, by ancestry (nEuropean = 3,359; nAfrican = 642). AUC = area under the curve.

Attributable Fraction

Attributable fraction analyses were restricted a sample of the top 20th percentile AD PGS and the bottom 20th percentile AD PGS by ancestry (nEuropean = 3,359, nAfrican = 642). In European ancestry model 3, having at least 1 copy of APOE ε4 was attributed to 21.4% (95% CI: 11.9%–30.8%; p < 0.0001) of dementia cases and 9.6% (95% CI: 0.9%–18.2%; p = 0.03) of borderline CIND cases. The AD PGS (top 20%) was attributed to 19.0% (95% CI: 8.5%–29.5%; p < 0.0001) of borderline CIND cases, relative to the bottom 20% of the AD PGS distribution. In African ancestry model 3, 53.9% (95% CI: 35.7%–72.0%; p < 0.0001) of dementia cases were attributed to being in the top 20% of the AD PGS distribution. All other attributable fractions were not significantly different than zero (table e-9, links.lww.com/NXG/A393).

Discussion

In the large US population–based HRS panel cohort, we observed that AD PGS and APOE ε4 status had independent associations with dementia and cognitive impairment compared with normal cognition in European and African ancestry samples. These genetic factors were associated with significant attributable fractions of impaired cognition. APOE ε4 status improved classification of dementia cases; however, the AD PGS did not improve classification. Together, these findings confirm that APOE ε4 and AD PGS are powerful predictors of cognitive impairment in population-based studies, although only APOE ε4 currently provides sufficient improved classification for potential clinical utility. It should be noted that the African ancestry analysis had a smaller sample size and used weights from a European-based study of Alzheimer disease to build the PGS. Our study replicates previous APOE results and expands to also consider cumulative genetic risk, providing greater understanding of the genetic etiology of dementia.
APOE ε4 is a consistent genetic risk factor associated with AD and dementia, but it accounts for only a portion of the heritability.24 In the Rotterdam study, those with a single copy of APOE ε4 had 1.7 times higher odds of dementia (95% CI: 1.0–2.9) and those with 2 copies had 11.2 times higher odds of dementia (95% CI: 3.6–35.2) compared with ε3/ε3.3 In our supplemental analysis, we also detected APOE ε4 dose-increasing odds for dementia relative to normal cognition (1 copy [n = 237]: OR = 2.29, 95% CI: 1.87–2.81; 2 copies [n = 23]: OR = 4.93, 95% CI: 2.82–8.62) in the European ancestry sample. To compare the highest genetic risk to the lowest genetic risk, we note that the attributable fraction analysis was performed on a sample that was 40% of the size of the primary analytic sample. This sample size and selection may have modified the relationship between APOE and cognitive status in the sensitivity analysis. We saw a similar APOE ε4 dose-effect pattern in the African Ancestry sample, although the observed effect size was smaller and the sample was more limited (1 copy [n = 104]: OR = 1.68, 95% CI: 1.12–2.51; 2 copies [n = 14]: OR = 2.65, 95% CI: 1.11–6.33). In an independent African-American sample, those with a single copy of APOE ε4 had 2.6 higher odds of dementia (95% CI: 1.8–3.7) and those with 2 copies had 10.5 higher odds of dementia (95% CI: 5.1–21.8), relative to ε3/ε3.4 Consistent with prior research, we observed that APOE ε4 is associated with impaired cognition in multiple ancestries and APOE ε4 status can improve classification of dementia cases in population based-samples.
Studies of AD PGS and AD or cognitive status have largely been restricted to participants of European ancestry or identifying as non-Hispanic White,2429 and these studies have varied in their PGS development techniques and modeling decisions. A non-APOE PGS study (constructed from 19 SNPs outside the APOE gene) found those with the highest PGS had 62% increased late-onset AD risk over the lowest PGS.30 Non-APOE PGS have also been used for AD-patient classification.3135 One reported AD classification when including age, sex, APOE ε4, APOE ε2, and a PGS with AD associated SNPs (p < 0.5) (AUC: 0.78, 95% CI: 0.77–0.80).25 This study used PGS weights from a GWAS that included the study sample. In our European ancestry sample independent of the GWAS weights, our estimate of the AUC for dementia with similar covariates (any APOE ε4, APOE ε2/ε2, and PGS with AD-associated SNPs p < 0.1) was higher (AUC: 0.85, 95% CI: 0.83–0.86). This difference is likely due to the broader definition of our dementia phenotype. Other PGS analyses have reported AD-subtype discrimination from PGS created for AD,36 revealing multiple biological mechanisms underlying AD subtypes.
A previous longitudinal analysis in the HRS (n = 8,253) featuring a 21 SNP AD PGS excluding APOE observed that a 0.1 unit increase in PGS was associated with 0.016 decreased memory score units (95% CI: −0.036 to 0.005) in European ancestry (n = 7,172) and 0.049 decreased memory score units (95% CI: −0.12 to 0.023) in African ancestry (N = 1,081) samples.37 Consistent with this prior study on impaired memory, in our analysis, we observed AD PGS was associated with increased odds of dementia. We further extended our analysis, by accounting for APOE and adjusting for additional dementia risk factors.
In this study, we assessed the utility of transferring PGS weights from one population to another population. Allele frequencies, linkage disequilibrium patterns, and the genetic architecture can vary by ancestral populations38 based on recombination and demographic histories.39 Participation or inclusion in the discovery GWAS is influenced by social and behavioral factors, which relates to the applicability of the discovery GWAS results to another population.40 Also, the background of nongenetic risk factors differs across populations, likely affecting the observed genetic signal.40 Furthermore, in population-based studies such as the HRS, accuracy of dementia classification algorithms varies across racial/ethnic groups41 and the algorithm used in the current study11 has higher accuracy in non-Hispanic White participants. Future studies in other samples may examine heritability in diverse populations to assess whether genetic or environmental factors explain different proportions of the variance in cognitive status. With systemic differences in the outcome classification and differences in the genetic exposure (PGS) classification by ancestry in our study, it is essential to not directly compare the genetic associations across groups (European ancestry and African ancestry). We focused on the within-ancestry findings and are cautious not to overstate our results. Although trans-ancestry genetic analyses are challenging, it remains important to perform studies in multiple ancestries to demonstrate these fundamental differences, refine methods that generate AD PGSs, and call for more inclusive ancestry GWAS.
Older participants with cognitive impairments may be more likely to die of other comorbid causes, and capturing cognitively impaired cases in older age groups may be difficult.42 Individuals surviving to age 90 years with no cognitive impairment may skew estimates. The HRS began genetic sample collection in 2006 and APOE or other risk genotypes may have already influenced survival for inclusion in our analytic sample. In sensitivity analyses, we removed the older cohorts (AHEAD and CODA) to observe if our results were robust to mortality selection. Removing AHEAD and CODA in the European ancestry sample, we dropped 58.3% of dementia cases with 1 substantive change in findings: the PGS association with odds of dementia (model 3) attenuated from 1.10 (95% CI: 1.00–1.20) to 1.05 (95% CI: 0.92–1.20), whereas the APOE ε4 association increased in magnitude from 2.42 (95% CI: 1.99–2.95) to 2.60 (95% CI: 2.00–3.38). The ORs did not significantly differ between the models, indicating that mortality selection did not critically bias these results.
Dementia ascertainment in population cohorts can be highly variable. We used the Langa-Weir method that incorporates the Telephone Interview of Cognitive Status and assigns a cognition status using cut points mirroring clinical diagnosis. This method has a comparable sensitivity and specificity (sensitivity: 75%; specificity: 83%) to related methods and provides balanced accuracy with prior clinical validation.41 Outcome misclassification can introduce bias into the study, likely toward the null, and may have contributed to the null findings with the ROC analysis. Future studies may improve dementia classification. New and larger studies may increase power with greater numbers of participants with impaired cognition, particularly in diverse ancestries. Importantly, our broad outcome of dementia did not involve direct imaging or biomarker measures or clinician diagnostic evaluation and did not allow for dementia subtyping, such as AD. Studies with specific clinical AD diagnoses may have higher predictive values of AD PGS. Other challenges such as time-varying biases and nonlinear cognitive trajectory43 were addressed by constructing a summary cognition status based on multiple visits. There are documented learning effects—where scores are higher the second time a participant sees a similar examination—for these cognition tests.43 Our summary cognition status excluded those with less than 3 visits, strengthening the cognitive status designation for each individual, and reducing practice effects.
Assessing outcome classification allows researchers to better understand how genetic factors can potentially contribute to clinical diagnosis of complex disease outcomes. The utility of PGS has helped to identify (for example) risk of MS,44 Parkinson disease,45 and cardiovascular disease.46 AD PGSs may also predict age at AD onset, where individuals with a top quartile AD PGS had an age at onset of 75 vs an age at onset of 95 for the lowest quartile.31 Despite the motivation to use AD PGS in clinical practice, scientists are understandably hesitant to encourage PGS use outside of research. The risk conferred by AD PGS is calculated at a population level and may not be appropriate to predict individual risk. In addition, current AD PGSs are not created with diverse populations as the reference weights, which may provide inaccurate risk for individuals of diverse ancestries,47 and clinical use in their present form may exacerbate health disparities.48
In summary, AD PGS and APOE ε4 were independently associated with dementia and cognitive impairment in the large, US population–based longitudinal HRS in European and African ancestry samples. APOE ε4 was associated with improved dementia classification; however, the current AD PGS did not improve classification. There are many opportunities to improve and apply AD PGS, especially in non-European ancestries. We may eventually be able to use PGSs for primary prevention (quantifying the genetic burden in subpopulations), secondary prevention (detecting high-risk individuals for disease screening), and tertiary prevention (a potential biomarker for optimizing treatment stratification).49 However, current AD PGSs, though associated with odds of dementia, are not sufficiently accurate enough for clinical diagnosis, particularly across ancestries.

Glossary

AD
Alzheimer dementia
AUC
area under the curve
BMI
body mass index
CI
confidence interval
CIND
cognitive impairment—no dementia
df
degrees of freedom
GWAS
genome-wide association study
HRS
Health and Retirement Study
PC
principal component
PGS
polygenic score
ROC
receiver operating characteristic
SNP
single nucleotide polymorphism

Acknowledgment

The authors thank the participants in the Health and Retirement Study and the staff who catalogued their information.

Appendix Authors

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Information & Authors

Information

Published In

Neurology® Genetics
Volume 7Number 2April 2021

Publication History

Received: June 18, 2020
Accepted: October 29, 2020
Published online: March 5, 2021
Published in print: April 2021

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Disclosure

The authors report no disclosures. Go to Neurology.org/NG for full disclosures.

Study Funding

This study was funded by the NIH (grant numbers R01 AG055406, R01 AG055654, R25 AG053227, R01 AG053972, R03 AG048806, and P30 AG053760). The Health and Retirement Study is sponsored by the National Institute on Aging (U01AG009740) and is conducted at the Institute for Social Research, University of Michigan.

Authors

Affiliations & Disclosures

From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
Scientific Advisory Boards:
1.
NONE
Gifts:
1.
NONE
Funding for Travel or Speaker Honoraria:
1.
NONE
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1.
NONE
Patents:
1.
NONE
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1.
NONE
Employment, Commercial Entity:
1.
NONE
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1.
NONE
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1.
NONE
Other Activities:
1.
NONE
Clinical Procedures or Imaging Studies:
1.
NONE
Research Support, Commercial Entities:
1.
NONE
Research Support, Government Entities:
1.
National Institute on Aging: Grant Numbers R01 AG067592, R25 AG053227, R01 AG055406, P30 AG053760 National Institute for Environmental Health Sciences: Grant Numbers R35 ES031686, P30 ES017885, P42 ES017198, R01 ES030049, R01 ES025574, R01 ES025531 National Institute on Minority Health and Health Disparities: Grant Numbers R01 MD013299 National Institute of Health Office of the Director: Grant Numbers UG3 OD023285, UH3 OD023285
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1.
University of Michigan Rogel Cancer Center Grant University of Michigan MCubed Pilot Grant Program
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Amyotrophic lateral sclerosis association (ALSA): Grant Number 20-IIA-532
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Harita S. Vadari, MPH [email protected]
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
Scientific Advisory Boards:
1.
NONE
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1.
NONE
Funding for Travel or Speaker Honoraria:
1.
NONE
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1.
NONE
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1.
NONE
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1.
NONE
Employment, Commercial Entity:
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NONE
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NONE
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1.
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1.
NONE
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NONE
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(1) NIH/NIA, R01 AG055406, Research Associate, 2018-2019
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Jessica D. Faul, PhD, MPH [email protected]
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
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NORC at the University of Chicago - Scientific Advisor for the NHATS cohort study
Gifts:
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NONE
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NONE
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Biodemography and Social Biology, Assocaite Editor, 2019- current
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(1)Tufts University, (2) Centers for Disease Control and Prevention
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(1) NIA/NIH, R01 NS116741 01, Co-Investigator, 04/01/20 - 03/31/25, (2) NIA/NIH, R01 AG068937 01, Co-Investigator, 08/01/20 - 07/31/25 (3) NIA/NIH, R25 AG053227-04S1, MPI, 09/15/16 - 04/30/21 (4) NIA/NIH, 5 U01 AG009740-30, Co-Investigator, 01/01/18 - 12/31/23 (5) NIEHS/NIH, R01 ES028694-01, Co-Investigator, 09/01/18 - 08/31/22 (6) NIA/NIH, R01 AG055406-03, Co-Investigator, 09/15/17 - 05/31/21 (7) NIMHD/NIH, R01 MD011716-03, Co-Investigator, 08/16/17 - 05/31/22 (8) NIA/NIH, 5 R25 AG053227-03, MPI, 09/15/16 - 04/30/21 (9) NIA/NIH, 5 U01 AG058499-02, Co-Investigator, 01/01/18 - 12/31/23 (10) NIA/NIH, 1 RF1 AG055654-01A1S1, MPI, 09/15/17 - 06/30/22 (11) NIA/NIH, 1 R01 AG060110-01, Co-Investigator, 09/01/18 - 08/31/20 (12) NIA/NIH, R24 AF065182-01, Co-Investigator, 09/01/19 - 08/31/24
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Steven G. Heeringa, PhD [email protected]
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
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1.
NONE
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1.
NONE
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1.
NONE
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1.
Editorial Board, Chapman and Hall, Social and Bahvioral Science Series
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1.
NONE
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1.
(1) Applied Survey Data Analysis, Second Edition, Chapman and Hall, Royalties, 2017-2020. (2) Multiple Imputation in SAS, SAS Institute, Royalties, 2013-2020
Employment, Commercial Entity:
1.
NONE
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1.
NONE
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NONE
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1.
NONE
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1.
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1.
Ongoing Research Support U24 DA041147, UC-San Diego/NIH/NIDA Heeringa (PI) 09/30/15-05/31/20 ABCD Coordinating Center The team at the University of Michigan will provide statistical design, sample development and cohort demographic analysis and monitoring support to the ABCD Coordinating Center (CC). Role: Principal Investigator R01 NS100687, NIH/NINDS Morgenstern (PI) 09/01/17-07/31/22 Cognitive Impairment in Mexican Americans: A population- Based Study The Mexican American population in the U.S. is rapidly growing and aging. This project seeks to determine the prevalence and outcomes of cognitive impairment and dementia in Mexican Americans. It also seeks solutions to help patients with cognitive impairment and dementia and their caregivers get the resources they need. Role: Co-Investigator U01 AG009740, NIH/NIA Weir (PI) 01/01/18-12/31/23 Health and Retirement Survey - Yrs 29-34 This project is for three additional waves of data collection on an established panel designed to study development throughout the second half of life. Special emphasis is given to the measurement of economic and health status variables. Role: Statistician U01 AG058499, NIH/NIA Langa (PI) 01/01/18-12/31/23 Health and Retirement Study: Harmonized Cognitive Assessment Protocol (HCAP) This project aims to conduct a follow-up to the 2016 Health and Retirement Study (HRS) Harmonized Cognitive Assessment Protocol (HCAP) sub-study by re-administering in 2020 the same HCAP in-home cognitive assessment and informant report to all surviving members of the original HCAP sub-sample of HRS participants (n ∼ 2,500), and to a new random sample of HRS participants aged 65 to 68 in 2020 (n ∼ 740). The HCAP 2020 follow-up study will provide extensive new data to support research on the causes, consequences, and trends of cognitive impairment and dementia in the US and around the world. Role: Statistician R01 HD041455, Univ. of Maryland/NIH/NICHD Chardoul (PI) 01/01/19-12/31/23 India Human Development Survey, Wave III The India Human Development Surveys (IHDS) form part of a collaborative research program between the University of Maryland and the National Council of Applied Economic Research (NCAER, India). The goal of the program is to document changes in the daily lives of Indian households in a society undergoing rapid transition. In documenting the way they live, work, educate their children, care for their aged parents, and deal with ill health, IHDS seeks to infuse the development discourse with the lived experiences of ordinary people. IHDS Wave 1 was a nationally representative survey of 41,554 households conducted in 2004-2005; IHDS Wave 2 re-interviewed 83% of these households in additional to new/split households in the village/city. The sample is spread across 33 states in both urban and rural areas of India. This proposal seeks funding to conduct a Wave 3 of the data collection, locating the interviewing the same households. Role: Senior Statistician R01 DA001411, NIH/NIDA Miech (PI) 05/01/17-03/31/22 Monitoring the Future: Drug Use and Lifestyles of American Youth This project seeks a five-year continuation of the Monitoring the Future (MTF) study, an ongoing epidemiological and etiological research and reporting project begun in 1975. In addition to being a basic research study, MTF has become one of the nation’s most relied upon sources of information on emerging trends in illicit drug, alcohol, and tobacco use among American adolescents, college students, and young and middle-aged adults. Nationally representative samples of 8th-, 10th-, and 12th-grade students (about 42,000 students in 415 schools per year) will be surveyed annually from 2017/18 to 2021/22. Role: Co-Investigator Completed Research Support R01 AG055406, NIH/NIA Ware (PI) 09/15/17-05/31/21 Characterizing Disparities in late-onset Alzheimer’s Disease Risk through Polygenic Risk and Epidemiologic Factors in the Health and Retirement Survey Using existing genomics data, well-characterized dementia phenotypes, and diverse risk factor data, we will analyze up to 16,000 aging participants in the Health and Retirement Study factors (HRS), attempt clinical confirmation in participants of the Aging, Demographics, and Memory Study (ADAMS) sub-cohort of the HRS, and test replication in other clinical and population-based samples. Our goal is to determine the joint genetic and environmental contributions to late- onset Alzheimer’s dementia (LOAD) risk that underlie health disparities, including sex, race/ethnicity, education, and residence. Role: Statistical Advisor R01 HD069609, NIH/NICHD Sastry (PI) 04/25/16-02/28/21 Family Dynamics, Fertility, and Investments in Children Across Generations This project will screen and recruit a new immigrant refresher sample for the Panel Study of Income Dynamics in 2016 and 2017, comprising approximately 750 immigrant households that migrated to the U.S. after 1997 (when PSID last added an immigrant refresher), and will interview these families for two rounds of the Core PSID survey in 2017 and 2019. Additionally, this project will collect core data for PSID 2017 and 2019 on family dynamics and background, education, and newborn children. Role: Co-Investigator U01 AG009740, NIH/NIA Weir (PI) 01/01/12-12/31/18 Health and Retirement Study Yrs 23-28 This project is for three additional waves of data collection on an established panel designed to study development throughout the second half of life. Special emphasis is given to the measurement of economic and health status variables. Role: Statistician U01 AG009740, NIH/NIA Weir (PI) 12/01/15-12/31/17 Health and Retirement Study Yrs 23-28: Population Dementia The primary aim of the Health and Retirement Study (HRS) cooperative agreement is to collect and distribute multi-disciplinary data to support research on aging. Cognitive impairment and dementia, for which Alzheimer’s disease is the principal cause either alone or in combination with other factors, impose enormous burdens on families and government policies. The proposed sample of 3,000 persons aged 65 and older drawn from HRS will create the potential for longitudinal follow-up to observe trajectories of decline and study their correlates in the rich data collected by HRS. We also propose to collect and store samples of venous blood from HCAP participants for future use. Role: Statistician R01 AG040213, NIH/NIA Freedman(PI) 09/01/16-05/31/21 Health, Wealth, and Time Use Over The Life Course and Across Generations This project will allow researchers to study behavioral and social influences on health and economic well-being over the course and across generations including: how childhood experiences shape health and economic wellbeing at retirement age; life course experiences associated with dementia in later life and the adequacy of economic resources of families of older adults with dementia; the role of measured and unmeasured family background in multi-generational transmission of wealth and health; and life course and genealogic influences on time use across the adult lifespan. Role: Co-Investigator
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NONE
Sharon L.R. Kardia, PhD [email protected]
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
Scientific Advisory Boards:
1.
None
Gifts:
1.
NONE
Funding for Travel or Speaker Honoraria:
1.
None
Editorial Boards:
1.
NONE
Patents:
1.
NONE
Publishing Royalties:
1.
NONE
Employment, Commercial Entity:
1.
NONE
Consultancies:
1.
NONE
Speakers' Bureaus:
1.
NONE
Other Activities:
1.
NONE
Clinical Procedures or Imaging Studies:
1.
NONE
Research Support, Commercial Entities:
1.
NONE
Research Support, Government Entities:
1.
(1) NIH NCI, R01-CA214829, PI, 02/01/18-01/31/22(2) NIH NIMHD, R01 MD011720, Co-I, 04/01/18-01/31/22(3) NIH NIA, U01 AG00974029, Co-I, 1/1/18 ? 12/31/23(4) NIH NHLBI, R01 HL122684, Co-I, 08/10/15 ? 04/30/21(5) NIH NIA, U01 AG017719, Co-I, 09/15/16 ? 08/31/21(6) NIH NHGRI, R01 HG010567, Co-I, 09/21/18 ? 06/30/21(7) NIH NIA, U01AG064948, MPI, 09/15/19-08/31/24(8) NIH NHLBI, R01 HL13322, Co-I, 07/01/16-04/30/21
Research Support, Academic Entities:
1.
University of Michigan School of Public Health
Research Support, Foundations and Societies:
1.
None
Stock/stock Options/board of Directors Compensation:
1.
NONE
License Fee Payments, Technology or Inventions:
1.
NONE
Royalty Payments, Technology or Inventions:
1.
NONE
Stock/stock Options, Research Sponsor:
1.
NONE
Stock/stock Options, Medical Equipment & Materials:
1.
NONE
Legal Proceedings:
1.
NONE
Kenneth M. Langa, MD, PhD [email protected]
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
Scientific Advisory Boards:
1.
DSMB for MINDSpeed (R01 AG052439) at Indiana University
Gifts:
1.
NONE
Funding for Travel or Speaker Honoraria:
1.
NONE
Editorial Boards:
1.
NONE
Patents:
1.
NONE
Publishing Royalties:
1.
NONE
Employment, Commercial Entity:
1.
NONE
Consultancies:
1.
Consultant to NIH-funded projects at Harvard, USC, and Johns Hopkins University.
Speakers' Bureaus:
1.
NONE
Other Activities:
1.
NONE
Clinical Procedures or Imaging Studies:
1.
NONE
Research Support, Commercial Entities:
1.
NONE
Research Support, Government Entities:
1.
(1) NIA U01 AG009740, Co-I, 2018-2020; (2) NIA R01 AG051125, Co-I, 2018-2020; (3) NIA U01 AG058499, PI, 2018-2020; (4) NIA R01 AG055406, Co-I, 2018-2020
Research Support, Academic Entities:
1.
NONE
Research Support, Foundations and Societies:
1.
Alzheimer's Association; HRS-18-586069C
Stock/stock Options/board of Directors Compensation:
1.
NONE
License Fee Payments, Technology or Inventions:
1.
NONE
Royalty Payments, Technology or Inventions:
1.
NONE
Stock/stock Options, Research Sponsor:
1.
NONE
Stock/stock Options, Medical Equipment & Materials:
1.
NONE
Legal Proceedings:
1.
NONE
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
Scientific Advisory Boards:
1.
NONE
Gifts:
1.
NONE
Funding for Travel or Speaker Honoraria:
1.
NONE
Editorial Boards:
1.
BMC Medical Genomics, Associate Editor, 2019-present
Patents:
1.
NONE
Publishing Royalties:
1.
NONE
Employment, Commercial Entity:
1.
NONE
Consultancies:
1.
NONE
Speakers' Bureaus:
1.
NONE
Other Activities:
1.
NONE
Clinical Procedures or Imaging Studies:
1.
NONE
Research Support, Commercial Entities:
1.
NONE
Research Support, Government Entities:
1.
R01 AG067506 Antonucci (PI) 06/01/20 – 03/31/25 NIH/NIA $1,000,000 (Annual Direct Costs) Racial/ethnic disparities in ADRD Risk: The impact of social relations Role: Co-Investigator U01 AG064948 Lee/Kardia (MPIs) 09/15/19 – 08/31/24 NIH/NIA $2,703,152 (Annual Direct Costs) Harmonized diagnostic assessment of dementia (DAD) for Longitudinal Aging Study of India (LASI) – Genomic study Role: Co-Investigator R01 AA026687 Birditt (PI) 09/20/19 – 08/31/22 NIH/NIAAA $411,863 (Annual Total Costs) Alcohol consumption and cardiovascular health among older couples: The roles of genetics and marital quality Role: Co-Investigator R01 AG057510-S2 Ajrouch (PI) 09/01/19 – 08/31/20 NIH/NIA $249,100 (Annual Direct Costs) Alzheimer’s Disease risk and ethnic factors: The case of Arab Americans – Administrative Supplement for saliva collection Role: Co-Investigator R01 AG061118 Liu (PI) 08/01/19 – 04/30/23 NIH/NIA $267,723 (Total Costs) Marriage, cognitive trajectories, and ADRD risk in late life Role: Consultant MCubed 3.0 Project Zhao/Smith/Sen (MPIs) 03/14/19 – 12/30/20 University of Michigan $60,000 (Total Direct Costs) Epigenetic Signatures of Stress and Mental Health Role: Principal Investigator R01 HL141291 Morrison/Wolberg (MPIs) 02/01/19– 01/31/23 NIH/NHLBI $12,000 (Total Direct Costs for UM Subcontract) Using genomics and functional biology to understand fibrinogen and its effect on thrombotic and atherosclerotic outcomes Role: Principal Investigator of UM Subcontract R01 HL141292 Smith (PI) 04/01/18 – 01/31/22 NIH/NHLBI $1,888,652 (Total Direct Costs) A Social Epigenomic Approach to Health Disparities in Cardiovascular Risk Factors Role: Principal Investigator 2U01 AG009740 Weir (PI) 01/01/18 – 12/31/23 NIH/NIA $17,306,462 (Annual Direct Costs) Health and Retirement Study Years 29-34 Role: Research Staff R01 AG055406 Ware/Bakulski (MPIs) 09/15/17 – 05/31/21 NIH/NIA $450,000 (Annual Direct Costs) Characterizing Disparities in Late-Onset Alzheimer’s Disease Risk through Polygenic Risk and Epidemiologic Factors in the Health and Retirement Study Role: Co-Investigator R01 MD011721 Needham/Rehkopf (MPIs) 08/16/17 – 05/31/22 NIH/NIMHD $500,000 (Annual Direct Costs) Race/Ethnicity, DNA Methylation, and Disparities in Cardiovascular Mortality: NHANES 1999-2002 Role: Co-Investigator R25 AG053227 Faul/Mitchell/Kardia (MPIs) 09/15/16 – 04/30/21 NIH/NIA $125,000 (Annual Direct Costs) Genomic Analysis for Social-Behavioral Scientists Role: Co-Investigator U01 AG017719 Harlow (PI) 09/15/16 – 05/31/21 NIH/NIA $914,234 (Annual Direct Costs) SWAN Repository IV Role: Co-Investigator R01 HL133221 Smith (PI) 07/01/16 – 04/30/21 (NCXT) NIH/NHLBI $1,636,482 (Total Direct Costs) Epigenetics of Arteriosclerosis in African American Hypertensive Sibships Role: Principal Investigator K99/R00 AG056599 Schmitz (PI) 09/01/17 – 08/31/22 NIH/NIA $245,000 (Total Direct Costs) Life Course Determinants of Epigentic Age Acceleration and Subsequent Dementia Role: Principal Investigator of UM Subcontract
Research Support, Academic Entities:
1.
NONE
Research Support, Foundations and Societies:
1.
NONE
Stock/stock Options/board of Directors Compensation:
1.
NONE
License Fee Payments, Technology or Inventions:
1.
NONE
Royalty Payments, Technology or Inventions:
1.
NONE
Stock/stock Options, Research Sponsor:
1.
NONE
Stock/stock Options, Medical Equipment & Materials:
1.
NONE
Legal Proceedings:
1.
NONE
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
Scientific Advisory Boards:
1.
Reimbursement of travel costs and AAIC registration associated with serving on the Medical and Scientific Advisory Board of the Alzheimer's Association
Gifts:
1.
NONE
Funding for Travel or Speaker Honoraria:
1.
NONE
Editorial Boards:
1.
NONE
Patents:
1.
NONE
Publishing Royalties:
1.
NONE
Employment, Commercial Entity:
1.
NONE
Consultancies:
1.
National Academy of Sciences, 2018 - present University of Mississippi, 2019 - present
Speakers' Bureaus:
1.
NONE
Other Activities:
1.
NONE
Clinical Procedures or Imaging Studies:
1.
NONE
Research Support, Commercial Entities:
1.
NONE
Research Support, Government Entities:
1.
(1) NIA R01AG016495, co-investigator, 09/15/2012- 06/30/2017 (2) NIDDK U01DK098246, co-investigator, 12/01/2012- 7/31/2017 (3) NIA R56AG034189, co-investigator, 09/01/2016 – 08/31/2017 (4) NINDS U01NS041588, co-investigator, 09/01/2001- 11/30/2017 (5) NIA R00AG47963, co-investigator, 09/01/2016- 08/31/2019 (6) NIA U01AG032984, co-investigator, 07/01/2015- 06/30/2020 (7) NIA U01DK048404, co-investigator, 08/15/1994- 01/31/2021 (8) NIA U54AG052427, co-investigator, 04/15/2016- 02/28/2021 (9) NIA R01AG049810, co-investigator, 03/15/2016- 02/28/2021 (10) NIA RF1AG054070, co-PI, 07/01/2016-03/31/2021 (11) NIA RF1AG056164, PI, 09/15/2016-06/30/2021 (12) NIA RF1AG054023, co-investigator, 09/01/2016- 06/30/2021 (13) NIA P30AG059303, co-PI, 04/01/18 - 03/31/23 (14) NIA U01AG009740, co-I, 01/01/2018 - 12/31/2023 (15) NIA U01AG058499, co-I, 01/01/2018 - 12/31/2023
Research Support, Academic Entities:
1.
NONE
Research Support, Foundations and Societies:
1.
NONE
Stock/stock Options/board of Directors Compensation:
1.
NONE
License Fee Payments, Technology or Inventions:
1.
NONE
Royalty Payments, Technology or Inventions:
1.
NONE
Stock/stock Options, Research Sponsor:
1.
NONE
Stock/stock Options, Medical Equipment & Materials:
1.
NONE
Legal Proceedings:
1.
NONE
Colter M. Mitchell, PhD [email protected]
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
Scientific Advisory Boards:
1.
NONE
Gifts:
1.
NONE
Funding for Travel or Speaker Honoraria:
1.
NONE
Editorial Boards:
1.
NONE
Patents:
1.
NONE
Publishing Royalties:
1.
NONE
Employment, Commercial Entity:
1.
NONE
Consultancies:
1.
NONE
Speakers' Bureaus:
1.
NONE
Other Activities:
1.
NONE
Clinical Procedures or Imaging Studies:
1.
NONE
Research Support, Commercial Entities:
1.
NONE
Research Support, Government Entities:
1.
(1)NIH/NIA, R01 AG055406, Co-Investigator, 2018-2020 (2)NIH/NIMHD, R01 MD011716, PI, 2017-2020 (3)NIH/NIMH, R01 MH121079, MPI, 2019-2020 (4)NIH/NICHD, R01 HD076592, Co-I, 2019-2020 (5) NIH/NICHD, R25 AG053227, MPI, 2016-2020 (6) NIH/NIMH, UG3MH11424901, Co-I, 2017-2020 (7) NIH/NIMH, R01 MH110872-01, Co-I, 2017–2020 (8) NIH/NIMH, R01 MH103761, Co-I, 2014-2020 (9) NIH/NIMH, R01 HD052646, Co-I, 2018-2020 (10) NIH/NIMHD, R01 MD 013299, Co-I, 2018-2020
Research Support, Academic Entities:
1.
NONE
Research Support, Foundations and Societies:
1.
Jacobs Foundation, Early Career Research Fellowship, PI, 2017- 2020
Stock/stock Options/board of Directors Compensation:
1.
NONE
License Fee Payments, Technology or Inventions:
1.
NONE
Royalty Payments, Technology or Inventions:
1.
NONE
Stock/stock Options, Research Sponsor:
1.
NONE
Stock/stock Options, Medical Equipment & Materials:
1.
NONE
Legal Proceedings:
1.
NONE
Kelly S. Benke, PhD [email protected]
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
Scientific Advisory Boards:
1.
NONE
Gifts:
1.
NONE
Funding for Travel or Speaker Honoraria:
1.
NONE
Editorial Boards:
1.
NONE
Patents:
1.
NONE
Publishing Royalties:
1.
NONE
Employment, Commercial Entity:
1.
NONE
Consultancies:
1.
NONE
Speakers' Bureaus:
1.
NONE
Other Activities:
1.
NONE
Clinical Procedures or Imaging Studies:
1.
NONE
Research Support, Commercial Entities:
1.
NONE
Research Support, Government Entities:
1.
NONE
Research Support, Academic Entities:
1.
NONE
Research Support, Foundations and Societies:
1.
NONE
Stock/stock Options/board of Directors Compensation:
1.
NONE
License Fee Payments, Technology or Inventions:
1.
NONE
Royalty Payments, Technology or Inventions:
1.
NONE
Stock/stock Options, Research Sponsor:
1.
NONE
Stock/stock Options, Medical Equipment & Materials:
1.
NONE
Legal Proceedings:
1.
NONE
From the Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Disclosure
Scientific Advisory Boards:
1.
NONE
Gifts:
1.
NONE
Funding for Travel or Speaker Honoraria:
1.
NONE
Editorial Boards:
1.
NONE
Patents:
1.
NONE
Publishing Royalties:
1.
NONE
Employment, Commercial Entity:
1.
NONE
Consultancies:
1.
NONE
Speakers' Bureaus:
1.
NONE
Other Activities:
1.
NONE
Clinical Procedures or Imaging Studies:
1.
NONE
Research Support, Commercial Entities:
1.
NONE
Research Support, Government Entities:
1.
(1) NIH/NIA, R01 AG055406, Multiple Principal Investigator, 2018-2020 (2) NIH/NIA, R01 AG067592-01, Multiple Principal Investigator, 2020 (3) NIH/NIA U01 AG009740-29, Co-Investigator, 2018-2020 (4) NIH/NIA, U24 AG058556, Pilot Award Principal Investigator, 2020 (5) NIH/NIA, R01 AA026687, Co-Investigator, 2019-2020 (6) NIH/NICHD, R01 HD099135, Co-Investigator, 2020 (7) NIH/NICHD, R01 HD076592, Co-Investigator, 2019-2020 (8) NIH/NICHD, R01 MD011716, Co-Investigator, 2018-2020 (9) NIH/NIA, R01 AG055654, Co-Investigator, 2018-2020 (10) NIH/NIA, R25 AG053227, Co-Investigator, 2018-2020 (11) NIH/NIA, R25 AG053227-S1, Co-Investigator, 2019-2020
Research Support, Academic Entities:
1.
NONE
Research Support, Foundations and Societies:
1.
NONE
Stock/stock Options/board of Directors Compensation:
1.
NONE
License Fee Payments, Technology or Inventions:
1.
NONE
Royalty Payments, Technology or Inventions:
1.
NONE
Stock/stock Options, Research Sponsor:
1.
NONE
Stock/stock Options, Medical Equipment & Materials:
1.
NONE
Legal Proceedings:
1.
NONE

Notes

Correspondence Dr. Bakulski [email protected]
Go to Neurology.org/NG for full disclosures. Funding information is provided at the end of the article.
The Article Processing Charge was funded by the NIH.

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