Apolipoprotein E Genotype and Sex Risk Factors for Alzheimer Disease: A Meta-analysis

Key Points

Question  Are female carriers of the apolipoprotein E ε4 allele at greater risk of developing Alzheimer disease than men?

Findings  In this meta-analysis of 27 independent research studies with 58 000 participants, women and men with 1 copy of apolipoprotein E ε4 did not show a difference in risk of Alzheimer disease from age 55 to 85 years. However, these women were at increased risk vs men between ages 65 and 75 years.

Meaning  Sex-specific treatments for cognitive decline and Alzheimer disease may need to be initiated a younger age, especially in those who carry an apolipoprotein E ε4 allele.

Importance  It is unclear whether female carriers of the apolipoprotein E (APOE) ε4 allele are at greater risk of developing Alzheimer disease (AD) than men, and the sex-dependent association of mild cognitive impairment (MCI) and APOE has not been established.

Objective  To determine how sex and APOE genotype affect the risks for developing MCI and AD.

Data Sources  Twenty-seven independent research studies in the Global Alzheimer’s Association Interactive Network with data on nearly 58 000 participants.

Study Selection  Non-Hispanic white individuals with clinical diagnostic and APOE genotype data.

Data Extraction and Synthesis  Homogeneous data sets were pooled in case-control analyses, and logistic regression models were used to compute risks.

Main Outcomes and Measures  Age-adjusted odds ratios (ORs) and 95% confidence intervals for developing MCI and AD were calculated for men and women across APOE genotypes.

Results  Participants were men and women between ages 55 and 85 years. Across data sets most participants were white, and for many participants, racial/ethnic information was either not collected or not known. Men (OR, 3.09; 95% CI, 2.79-3.42) and women (OR, 3.31; CI, 3.03-3.61) with the APOE ε3/ε4 genotype from ages 55 to 85 years did not show a difference in AD risk; however, women had an increased risk compared with men between the ages of 65 and 75 years (women, OR, 4.37; 95% CI, 3.82-5.00; men, OR, 3.14; 95% CI, 2.68-3.67; P = .002). Men with APOE ε3/ε4 had an increased risk of AD compared with men with APOE ε3/ε3. The APOE ε2/ε3 genotype conferred a protective effect on women (OR, 0.51; 95% CI, 0.43-0.61) decreasing their risk of AD more (P value = .01) than men (OR, 0.71; 95% CI, 0.60-0.85). There was no difference between men with APOE ε3/ε4 (OR, 1.55; 95% CI, 1.36-1.76) and women (OR, 1.60; 95% CI, 1.43-1.81) in their risk of developing MCI between the ages of 55 and 85 years, but women had an increased risk between 55 and 70 years (women, OR, 1.43; 95% CI, 1.19-1.73; men, OR, 1.07; 95% CI, 0.87-1.30; P = .05). There were no significant differences between men and women in their risks for converting from MCI to AD between the ages of 55 and 85 years. Individuals with APOE ε4/ε4 showed increased risks vs individuals with ε3/ε4, but no significant differences between men and women with ε4/ε4 were seen.

Conclusions and Relevance  Contrary to long-standing views, men and women with the APOE ε3/ε4 genotype have nearly the same odds of developing AD from age 55 to 85 years, but women have an increased risk at younger ages.

For nearly 20 years, the prevalent view has been that women who carry copies of the ε4 allele of the apolipoprotein E (APOE) gene have a greater risk of developing Alzheimer disease (AD) than men with the same number of copies.¹ The ε4 allele is the main genetic risk factor for late-onset Alzheimer disease (AD),² and sex-based differences in AD risk have important implications for treatment trials, diagnostics, and therapeutics.³ Additionally, the sex-dependent relationship between APOE and mild cognitive impairment (MCI), which is often a transitional phase from cognitively normal (NL) aging to dementia,⁴ is unclear. Studies are in general agreement that the APOE ε4 allele is a risk factor for developing MCI,^5-11 but there is controversy as to whether it increases^10,12-14 or does not increase^9,11,15,16 the risks of transitioning from MCI to AD or dementia. The 3 most common alleles of the APOE gene are ε2, ε3, and ε4; whereas carrying the ε4 allele increases one’s risk of developing AD, the ε2 allele conversely has a putative protective effect that is associated with longevity and a lower AD risk.¹⁷

Studies of participants with a family history of late-onset AD have reported that women with 1 copy of ε4 have a greater risk than male heterozygote ε4 carriers, who in turn have about the same risk as male ε3 homozygotes.^18,19 This sex dependence was also found in first-degree (parents and siblings) relatives of individuals with AD,^20,21 and in the meta-analysis of Farrer et al,¹ which aggregated data from 40 independent research studies. Among studies of residents in city suburbs and communities, there is general agreement that elderly female ε4 carriers have an increased risk of AD, dementia, and cognitive decline vs male ε4 carriers.^22-25 However, when participants are randomly recruited from hospitals, retirement homes, and aging consortiums, most studies have found no sex-specific difference between men and women in the risks of AD and dementia associated with the APOE ε4 allele.^26-29 The sex-dependent role of APOE ε4 in the risks of developing MCI and in MCI conversions to AD has been recently investigated,^3,30 and there is evidence that women are at greater risk than men.

We collected data sets from 27 independent research studies totaling nearly 58 000 participants. Information was collected on each participant’s APOE genotype, sex, race, ethnicity, diagnosis (NL, MCI, or AD), and age at diagnosis. From these data sets we included only white participants who were mostly non-Hispanic. The Global Alzheimer’s Association Interactive Network receives coded information and does not distribute data.

Prospective participants for this meta-analysis were identified using resources³¹ from the Global Alzheimer’s Association Interactive Network.^32,33 As shown in Table 1,^34-58 we used multiple data sets from 12 research institutions in the Global Alzheimer’s Association Interactive Network, with 2 institutions (National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site and Coalition Against Major Diseases) managing data from several independent studies. Details of the data sets obtained through the Global Alzheimer’s Association Interactive Network are given in the eAppendix in the Supplement.

We did not receive information about clinical diagnoses for all participants, and in some cases the ages of elderly participants were truncated downward to 90 years to protect their identities. We excluded patients with missing information and/or 90-year truncated ages from all data sets. In many data sets, birth dates were rounded to the nearest year as an extra measure to protect patient confidentiality. We excluded data from participants in the National Alzheimer’s Coordinating Center (NACC) data set who were also known to have participated in the Alzheimer’s Disease Neuroimaging Initiative Study; however, the full extent of the participant overlap between NACC and the Alzheimer’s Disease Neuroimaging Initiative has not currently been established but is estimated to be at most 3%. Across data sets, most participants were white, and for many participants, racial/ethnic information was either not collected or not known. Owing to insufficient numbers of other races/ethnicities, we only included white participants (along with participants from the Fundació ACE and Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing data sets) with non-Hispanic or unknown ethnicities. Through our correspondences with data set providers, we estimate that Hispanic participants make up no more than 5% of all white participants with unknown ethnicities. After applying exclusion criteria, these data sets were representative of non-Hispanic white individuals in North America and Europe.

The descriptions of the clinical diagnoses we received were unstandardized³³ and the levels of detail varied across different data sets according to how each disease was defined (eg, mild or moderate AD) and how it was recorded (eg, AD associated with cerebrovascular disease). We worked directly with each data set provider to translate each set of diagnoses into our 3 general preplanned diagnoses: NL, MCI, and AD. In addition, we excluded all patients with a clinical history of stroke, cerebrovascular disease, Lewy bodies, amyloid precursor protein or presenilin gene mutations, or comorbidity with any other known neurological disease. All subtypes of MCI (eg, amnestic and nonamnestic) were combined into a single MCI diagnosis.

For longitudinal data sets (eg, NACC and Framingham Heart Study) that had multiple diagnoses per participant, we assigned each participant a single diagnosis. Each participant without a history of MCI or AD was assigned an NL diagnosis, each participant with a history of MCI and no history of AD was assigned an MCI diagnosis, and each participant with a history of AD and no history of MCI was assigned an AD diagnosis. Participants with a history of both MCI and AD were randomly assigned either an MCI or AD diagnosis. We used the latest examination age for the diagnosis age of participants with NL and the earliest recorded age of MCI or AD for participants with MCI and AD, respectively. With the exception of the FHS data set, no participants were followed up more than 10 years; therefore, our NL diagnosis ages were not significantly skewed toward very old ages. We used these diagnosis assignments to form 3 case-control study groups containing 22 AD-NL, 10 MCI-NL, and 7 AD-MCI data sets.

Meta-analyses of the case-control study groups were conducted using the Mantel-Haenszel fixed-effects method to calculate odds ratios for each sex and APOE genotype, using the APOE ε3/ε3 genotype as the referent. We imputed missing NL data in the ACE, Coalition Against Major Diseases, Translational Genomics Research Institute series 2, and Religious Orders Study and Rush Memory and Aging Project data sets using available NL participant data as follows. The Mann-Whitney U test was used to compare the age distributions of participants with normal cognition from each research study, and dissimilar NL participant data was excluded. In particular, we excluded the Alzheimer’s Disease Repository Without Borders and Wisconsin Registry for Alzheimer’s Prevention data sets because the median age of their participants with NL was relatively young (mid-50s to mid-60s) and that of the Adult Changes in Thought data set was comparatively older (lower 80s). Variations in the total numbers of ε2, ε3, and ε4 alleles of participants with NL were then compared using the χ² test of homogeneity to exclude correspondingly heterogeneous data sets. The resultant NL participant data contained men (χ² of homogeneity = 0.84) and women (χ²of homogeneity = 0.90), with NL diagnoses from the Alzheimer’s Disease Neuroimaging Initiative, Australian Imaging, Biomarker and Lifestyle Flagship Study of Aging, NACC, and Washington University, St Louis, data sets, respectively. The participants with NL used for imputation were in Hardy-Weinberg equilibrium (men: χ² = 3.0; P = .39; women: χ² = 1.2; P = .75), their ages were normally distributed (men, mean [SD], 73.5 [7.0] years; women, mean [SD], 74.6 [7.1] years), and their APOE genotype frequencies were consistent with those reported for the general population of the United States.⁵⁹ Forest plots of the log odds ratios (ORs) for the APOE ε3/ε4 genotype by sex are shown in eFigures 1-3 in the Supplement. Separate meta-analyses were also performed in 3 age ranges (55-65 years, 65-75 years, and 75-85 years).

The meta-analyses were repeated after removing ascertainment-biased studies from the case-control study groups. Community-based studies (ACE, Alzheimer’s Disease Repository Without Borders, and Framingham Heart Study) that recruited participants in localized geographic regions and disease-biased studies (National Institute on Aging Late-Onset Alzheimer’s Disease Family Study and TGEN2) that recruited participants with family histories of AD were excluded. The Religious Orders Study and Rush Memory and Aging Project were also excluded because we did not have enough information to definitively remove participants with comorbidities from its data set.

Data from each ascertainment-adjusted case-control study group were then pooled together, and logistic regression was used to calculate ORs for each sex and APOE genotype (Table 2). For each sex, a continuous age variable and 5 indicator variables (values of 1 or zero) representing the 5 APOE genotypes (ε2/ε2, ε2/ε3, ε2/ε4, ε3/ε4, and ε4/ε4) were used, with the APOE ε3/ε3 genotype as the referent. We also conducted another pooled analysis where we added a sex indicator variable and 5 additional covariates that were products of the sex variable with each APOE genotype variable to test for sex interactions. The age-dependent curves shown in Figure 1 were derived by adding several quadratic covariate products to the logistic regression that were created by combining APOE genotype, sex, and age. Because the NACC data set was predominantly larger (48% to 85%) than other data sets in the pooled analysis, we separated it from the pooled data and repeated the analyses without it and exclusively with it. Results of all these analyses are listed in the eMethods and eTables 1-3 in the Supplement for the APOE ε3/ε4 genotype.

Statistical analyses were performed in R, version 3.3.1, using the metafor meta-analysis package, version 1.9-9, along with the glm generalized linear model function (R Programming).⁶⁰ Mathematica,⁶¹ version 10.0, was used for curve fitting and plotting. The P value level of significance was .05, and P values were 2-sided.

From an aggregation of 27 independent research studies with a total of 57 979 participants (Table 1), meta-analyses were performed on 31 340 non-Hispanic white individuals, with clinical diagnoses between ages 55 and 85 years in 3 case-control analyses (Figure 2). After excluding ascertainment-biased studies, the data in each analysis were pooled, and ORs for each sex and APOE genotype (Table 2) were calculated. In all case-control analyses, between-study heterogeneity was reduced after the removal of ascertainment-biased study data. However, P values from the Tarone⁶² test of heterogeneity (Table 2) still detected significant study heterogeneity in the female APOE ε3/ε4 data (OR, 3.31; 95% CI, 3.03-3.61; P = .03) and in the APOE ε4/ε4 data (men, OR, 11.7; 95% CI, 9.24-14.7; P = .02; women, OR, 9.67; 95% CI, 8.07-11.6; P < .001) of the AD-NL analysis. On further investigation (eTable 1 in the Supplement), we found that the heterogeneity in the female APOE ε3/ε4 data was localized to ages 75 to 85 years (OR, 3.28; 95% CI, 2.92-3.68; P = .003). This determination was supported after comparing the ORs in that age range from analyses without the NACC data set (OR, 2.67; 95% CI, 2.23-3.21) and with the NACC data set exclusively (OR, 4.12; 95% CI, 3.41-4.98). Otherwise, between the ages of 55 and 85 years, the 95% confidence intervals of the ORs calculated from pooled data without the NACC data set overlapped the confidence intervals of the ORs calculated using the NACC data set alone.

As shown in Table 2, men and women with the APOE ε3/ε4 genotype had the same risks of developing AD (men, OR, 3.09; 95% CI, 2.79-3.42; women, OR, 3.31; 95% CI, 3.03-3.61; P = .47) between the ages of 55 and 85 years. Men with APOE ε3/ε4 had an increased risk of AD compared with men with ε3/ε3 (P < .001). The APOE ε2/ε3 genotype decreased the risk of AD more for women than for men (women, OR, 0.51; 95% CI, 0.43-0.61; men, OR, 0.71; 95% CI, 0.60-0.85; P = .01). Men and women with the APOE ε3/ε4 genotype had the same risks of developing MCI between ages 55 and 85 years (men, OR, 1.55; 95% CI, 1.36-1.76; women, OR, 1.60; 95% CI, 1.43-1.81; P value = .82).

Odds ratio curves for men and women with the APOE ε3/ε4 genotype are shown in Figure 1 between age 55 and 85 years. The ORs calculated from the pooled data analyses in 3 age ranges (55-65 years, 65-75 years, and 75-85 years) are plotted for each sex, with error bars indicating their 95% confidence intervals. As shown in Figure 1A between ages 65 and 75 years, women with APOE ε3/ε4 had an increased risk of AD compared with men with ε3/ε4 (women, OR, 4.37; 95% CI, 3.82-5.00; men, OR, 3.14; 95% CI, 2.68-3.67; P = .002). In Figure 1B, the OR curves suggested that women with APOE ε3/ε4 were at higher risk for developing MCI than men between ages 55 and 70 years, which was confirmed in a separate analysis in that age range (women, OR, 1.43; 95% CI, 1.19-1.73; men, OR, 1.07; 95% CI, 0.87-1.30; P = .05). No significant risk differences between men and women for MCI to AD transitions were found in Figure 1C, but the OR curves parallel a previous study that found that APOE ε4 increased the risk of transitioning from MCI to AD between the ages of 70 to 85 years, but not between the ages of 55 to 69 years.¹⁶

When examining the entire age span from 55 to 85 years, men and women with the APOE ε3/ε4 genotype had nearly the same odds of developing MCI and AD, both in comparisons between data sets and in data set aggregation. Notably, women had an increased risk of MCI between ages 55 and 70 years and an increased risk of AD between ages 65 and 75 years. These results are consistent with a previous study that found a significant association between APOE ε4 and cognitive decline between ages 70 and 80 years in women only²⁴ and with another study that found that episodic memory was more impaired in women with APOE ε3/ε4 than in men with ε3/ε4 between ages 70 and 74 years.²⁵ Mechanisms that underlie these sex differences may be linked to physiologic changes associated with menopause and estrogen loss that begins at a mean age of 51 years⁶³ just prior to our risk groups. Studies in animals and humans have reported an interaction between APOE ε4, menopause, and cognitive decline (for a review, see Riedel et al⁶⁴). Furthermore, other evidence suggests that carrying 1 copy of APOE ε4 shifts the age at onset in women, but not in men.¹⁸ Collectively, our findings, along with previous work, warrant further investigation into a likely complex set of risk factors with consideration of sex-specific treatments for cognitive decline and AD. For example, if women are at increased risk for AD at younger ages, it is plausible that treatments for women may need to be initiated earlier, especially in those who carry an APOE ε4 allele. Both men and women with APOE ε3/ε4 had an increased risk of AD compared with men and women with ε3/ε3, respectively. The APOE ε2/ε3 genotype conferred more of a protective effect on women, decreasing their risk of AD more than men. No significant sex-dependent differences were found for transitioning between MCI and AD. Our ORs for developing MCI are consistent with other studies.^6,65

After adjusting for NL participant differences between AD studies by replacing participants with NL with the data set we used for imputation, there was significant variation of AD risk between data sets; the male and female ε3/ε4 ORs were near 1 for the ACE data set and nearly 7 for the National Institute on Aging Late-Onset Alzheimer’s Disease Family Study data set. In retrospect, high ORs were not remarkable for the National Institute on Aging Late-Onset Alzheimer’s Disease Family Study, which recruited families with 2 or more affected siblings with AD because family history of AD is an AD risk factor and the probability of carrying a genetic mutation in a recognized AD gene increases with the number of first-degree relatives affected with AD.⁶⁶ The lowest ORs tended to be associated with community-based studies (eg, ACE, ARWIBO, and FHS) that ascertained participants from geographically specific cities and suburbs. As shown in eFigure 4 in the Supplement, most data points clustered around the NACC data point; these studies primarily recruited random participants who were unrelated to each other.

These results are notably different from those of Farrer et al,¹ who found that the relative odds of women with ε3/ε4 compared with men with ε3/ε4 for developing AD were about 1.5, and that men with ε3/ε3 and ε3/ε4 had the same AD risks when participants were ascertained from clinics/hospitals and autopsies/brain banks (n = 6305). Many of the participants in their meta-analysis had family histories of AD, they noted differences with population-based studies, and they aggregated participants with early-onset AD. Inclusion of the latter participants could help explain why their AD ORs curves for individuals with ε3/ε4 reached their maxima around ages 60 to 65 years, as opposed to ours, which reached their maxima around ages 73 to 80 years. These results are in closer agreement with studies that have found ε3/ε4 carriers to have a mean age at clinical onset of 76 years, and the risk for developing late-onset AD to occur primarily between ages 60 and 79 years.²⁶ We note that between the ages of 65 to 75 years, the ORs of women and men with APOE ε3/ε4 differed by a factor of about 1.5, which is consistent with the results of Farrer et al¹ across all ages. Our result that the APOE ε2/ε3 genotype decreased the risk of AD more for women than for men is the opposite of what they found; this is likely owing to the fact that our analysis (n = 1482) used more than 3 times the number of participants than they used (n = 447).

In agreement with previous studies,^1,67 we found that individuals with 2 copies of the APOE ε4 allele were at greater risk for developing AD than individuals with only 1 copy. No significant differences between men and women with ε4/ε4 were seen in their risks for developing AD, which is consistent with the results reported by Farrer et al.¹ Apolipoprotein ε4 homozygotes also had increased risks compared with ε4 heterozygotes for MCI and for transitioning from MCI to AD.

Ascertainment biases are known to modify the true effects of APOE on the risks of developing AD, and they may have played a role in the variations we found between data sets. Men have higher rates of cardiovascular disease and stroke than women, so men who live to old age may be healthier than women of the same age and therefore have lesser risks of developing AD.^68,69 On average, women live longer than men, which makes it difficult to locate older men with AD in sufficient numbers to study. There may be increased study participation rates among individuals with a family history of AD,⁷⁰ which is an established risk factor for developing AD.^71-73 Population-based studies can oversample participants from families in areas where widows outnumber widowers.²³ Nonresponders are generally burdened with higher rates of illness than responders to surveys, and they require extra effort to participate.⁷⁴ Biases may occur when recruitment and dropout occur continuously throughout studies,²⁹ or when individuals do not consent to or are not available for genotyping. A notable example of ascertainment bias occurred in a study that compared participants sampled from a research clinic with participants recruited through a health maintenance organization; they found that the research-based cohort contained younger participants, more severe AD cases, and a higher APOE ε4 allele frequency.⁷⁵

Variability in the methods used to define AD and MCI across data sets could have affected our results. We relied on the expertise of each data set provider to translate their diagnostic definitions into our general AD and MCI diagnoses independently of other data set providers. Although it would have been preferable to use MCI subtypes (eg, amnestic and nonamnestic), that level of diagnostic detail was mostly unavailable. We could not adjust for known AD risk factors, such as the number of years of education and family history of AD/dementia, because in many data sets that information was not provided. Nor could we account for sex-dependent differences owing to factors such as cigarette smoking, hormonal changes with age, and alcohol use.⁷⁶ As previously mentioned, in some data sets the birth dates of participants were rounded to the nearest year, and that limited the accuracy in determining the onset ages of AD and MCI. Finally, we were not able to fully exclude all Hispanic participants from our meta-analysis because in many cases information about race/ethnicity was not collected. Although we believe the percentage of Hispanic participants to be less than 5%, this could have affected our results because the odds of developing AD is different among Hispanic individuals than in white individuals.¹ Considering these limitations, our results should not be generalized beyond white non-Hispanic individuals in North America and Europe. Taken together, limited information on risk factors were not modeled in our analysis owing to our large pooled cohort approach. Of particular note, lifestyle factors, such as lower educational attainment and vascular risk factors, are well-documented contributors to Alzheimer risk⁷⁷ and could have influenced our findings.

In this meta-analysis of 27 independent research studies with 58 000 participants, women and men with 1 copy of APOE ε4 did not show a difference in risk of Alzheimer disease across the lifespan of 55 to 85 years. However, these women were at increased risk vs men between ages 65 and 75 years.

Corresponding Author: Arthur W. Toga, PhD, Laboratory of Neuro Imaging, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA 90033 (toga@loni.usc.edu).

Accepted for Publication: May 10, 2017.

Published Online: August 28, 2017. doi:10.1001/jamaneurol.2017.2188

Author Contributions: Dr Neu had full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis.

Concept and design: Neu, Pa, Romero, Toga.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Neu, Gangadharan, Romero, Hsu, Chen, Toga.

Critical revision of the manuscript for important intellectual content: Neu, Pa, Kukull, Beekly, Kuzma, L. Wang, Romero, Arneric, Redolfi, Orlandi, Frisoni, Au, Devine, Auerbach, Espinosa, Boada, Ruiz, Johnson, Koscik, J. Wang, Toga.

Statistical analysis: Neu, Pa, Romero, J. Wang, Toga.

Obtained funding: Au, Toga.

Administrative, technical, or material support: Pa, Kukull, Beekly, Kuzma, L. Wang, Arneric, Redolfi, Orlandi, Au, Devine, Auerbach, Boada, Ruiz, Johnson, Koscik, J. Wang, Chen, Toga.

Supervision: Romero, Frisoni, Devine, Espinosa, Boada, Toga.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by the Global Alzheimer’s Association Interactive Network initiative of the Alzheimer’s Association (GAAIN-14-244631) and National Institutes of Health grants U54-EB020406 and P41-EB015922. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (National Institutes of Health grant U01 AG024904) and Department of Defense Alzheimer’s Disease Neuroimaging Initiative (Department of Defense award W81XWH-12-2-0012). The Alzheimer’s Disease Neuroimaging Initiative is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc; Biogen; Bristol- Myers Squibb Company; CereSpir Inc; Cogstate; Eisai Inc; Elan Pharmaceuticals Inc; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech Inc; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research and Development LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck and Co Inc; Meso Scale Diagnostics LLC; NeuroRx Research; Neuro-track Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. Alzheimer’s Disease Neuroimaging Initiative data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data used in the preparation of this article were obtained from the Australian Imaging Biomarkers and Lifestyle (ABIL) flagship study of aging funded by the Commonwealth Scientific and Industrial Research Organization which was made available at the ADNI database. The AIBL researchers are listed at http://www.aibl.csiro.au. The National Alzheimer Coordinating Center database is funded by grant U01 AG016976 from the National Institute of Aging. National Alzheimer Coordinating Center data are contributed by the National Institute of Aging–funded Alzheimer’s Disease Cooperative Studies: P30 AG019610 (Principal Investigator [PI], Eric Reiman, MD), P30 AG013846 (PI, Neil Kowall, MD), P50 AG008702 (PI, Scott Small, MD), P50 AG025688 (PI, Allan Levey, MD, PhD), P50 AG047266 (PI, Todd Golde, MD, PhD), P30 AG010133 (PI, Andrew Saykin, PsyD), P50 AG005146 (PI, Marilyn Albert, PhD), P50 AG005134 (PI, Bradley Hyman, MD, PhD), P50 AG016574 (PI, Ronald Petersen, MD, PhD), P50 AG005138 (PI, Mary Sano, PhD), P30 AG008051 (PI, Steven Ferris, PhD), P30 AG013854 (PI, M. Marsel Mesulam, MD), P30 AG008017 (PI, Jeffrey Kaye, MD), P30 AG010161 (PI, David Bennett, MD), P50 AG047366 (PI, Victor Henderson, MD, MS), P30 AG010129 (PI, Charles DeCarli, MD), P50 AG016573 (PI, Frank LaFerla, PhD), P50 AG016570 (PI, Marie-Francoise Chesselet, MD, PhD), P50 AG005131 (PI, Douglas Galasko, MD), P50 AG023501 (PI, Bruce Miller, MD), P30 AG035982 (PI, Russell Swerdlow, MD), P30 AG028383 (PI, Linda Van Eldik, PhD), P30 AG010124 (PI, John Trojanowski, MD, PhD), P50 AG005133 (PI, Oscar Lopez, MD), P50 AG005142 (PI, Helena Chui, MD), P30 AG012300 (PI, Roger Rosenberg, MD), P50 AG005136 (PI, Thomas Montine, MD, PhD), P50 AG033514 (PI, Sanjay Asthana, MD, FRCP), P50 AG005681 (PI, John Morris, MD), and P50 AG047270 (PI, Stephen Strittmatter, MD, PhD). Data for this study were prepared, archived, and distributed by grant U24-AG041689-01 from the National Institute on Aging Alzheimer’s Disease Data Storage Site at the University of Pennsylvania, funded by the National Institute on Aging. The Alzheimer’s Disease Genetics Consortium supported the collection of samples used in this study through National Institutes of Aging grants U01AG032984 and RC2AG036528. Samples from the National Cell Repository for Alzheimer’s Disease, which receives government support under cooperative agreement grant U24 AG21886 awarded by the National Institutes of Aging, were used in this study. The studies included were supported by grants UO1 AG006781, UO1 HG004610, UO1 HG006375, and U01 HG008657 for ACT; P30AG10161 and R01AG15819 for ROS, R01AG17917 for MAP; U24 AG026395, U24 AG026390, and R01AG041797 for NIA-LOAD; R01AG09029 and R01AG025259 for MIRAGE; R01 AG032990, U01 AG046139, R01 NS080820, RF1 AG051504 and P50 AG016574 for Mayo; R01 AG027944, R01 AG028786, R01 AG019085, the Alzheimer’s Association (IIRG09133827), and the BrightFocus Foundation (A2011048) for University of Miami, P50 AG005138, P01 AG002219 for Mount Sinai School of Medicine, R01 AG019085 for Vanderbilt University; P50 AG005681, P01 AG03991, P01 AG026276 for Washington University St. Louis; P50 AG005133, AG030653, AG041718, AG07562, AG02365 for University of Pittsburgh. The TGen series was also funded by National Institutes of Aging grant AG041232, The Banner Alzheimer’s Foundation, The Johnnie B. Byrd Sr Alzheimer’s Institute, the Medical Research Council, and the state of Arizona and also includes samples from the following sites: Newcastle Brain Tissue Resource (funding via the Medical Research Council, local NHS trusts and Newcastle University), MRC London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council),South West Dementia Brain Bank (funding via numerous sources including the Higher Education Funding Council for England, Alzheimer’s Research Trust, BRACE as well as North Bristol National Health Services Trust Research and Innovation Department, The Netherlands Brain Bank (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, and Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, and Universitat de Barcelona. Data used in preparation of this article were obtained from the Coalition Against Major Diseases database. A complete listing of Coalition Against Major Diseases members can be found at http://c-path.org/programs/camd/. Funding of the Coalition Against Major Diseases database is made possible by membership dues and by grant 1U18FD005320 from the US Food and Drug Administration’s Critical Path Public Private Partnerships Grant Program. ARWIBO, EDSD, and PharmaCOG (alias E-ADNI) data used in the preparation of this article were obtained from NeuGRID4You initiative (http://www.neugrid4you.eu) funded by grant 283562 from the European Commission under grant agreement. Data used in this study were supported by the Framingham Heart Study’s National Heart, Lung, and Blood Institute contract (N01-HC-25195), by grants R01-AG016495, R01-AG008122, R01-AG033040 from the National Institute on Aging, and by grant R01-NS017950 from the National Institute of Neurological Disorders and Stroke. Fundació ACE collaborates with the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, and is one of the participating centers of the Dementia Genetics Spanish Consortium. Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas is an Instituto de Salud Carlos III ISCIII Project. Dr Ruiz is supported by grant PI13/02434 from Acción Estratégica en Salud, Instituto de Salud Carlos III (ISCIII), Ministerio de Economía y Competitividad, Spain. Genetic Research project at Fundació ACE is funded by Fundación Bancaria La Caixa (Barcelona, Spain), GRIFOLS SA and intramural funds. Data used in this study were supported by the National Institutes of Health grant R01-AG027161. Dr Wang received support from Health Ageing Research Center, ChangGung University, and Center for Advanced Molecular Imaging and Translation, Chang Gung Memorial Hospital, Linkou. This work was supported by grants UL1TR001855 and UL1TR000130 from the National Center for Advancing Translational Science of the US National Institutes of Health.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.