Val66Met Polymorphism in BDNF Has No Sexual and APOE ε4 Status-Based Dimorphic Effects on Susceptibility to Alzheimer’s Disease: Evidence From an Updated Meta-Analysis of Case–Control Studies and High-Throughput Genotyping Cohorts

Abstract

Some studies showed that Val66Met polymorphism of brain-derived neurotrophic factor (BDNF) conveys susceptibility to Alzheimer’s disease (AD) in females only. However, the confounding effects of some risk factors for AD were omitted in these studies. The aim of this meta-analysis comprising 19 604 patients with AD and 26 333 controls was to reexamine the association between Val66Met and AD by conditioning the effects of age, sex, and/or apolipoprotein E (APOE) ε4 status. In agreement with the previous meta-analysis, Val66Met was associated with AD in females without confounding adjustment (odds ratio [OR], 1.08; 95% confidence interval [CI], 1.03-1.14; P = .003). Nevertheless, after adjusting for age and APOE ε4 status, Val66Met was not associated with AD in females (OR, 1.02; 95% CI, 0.94-1.11; P = .57). This comprehensive meta-analysis with the largest sample size demonstrated no association could be observed between Val66Met and AD in general or by dividing samples based on sex or APOE ε4.

Keywords

Alzheimer’s disease brain-derived neurotrophic factor Val66Met polymorphism meta-analysis high-throughput genotyping

Introduction

Alzheimer’s disease (AD) is the most common form of dementia and pathologically characterized by senile plaques, comprising amyloid β-peptide (Aβ), and neurofibrillary tangles, which in turn is consisted of hyperphosphorylated tau. These pathological changes are accompanied by deficits in axonal transport and neuronal loss.

Neurotrophins, such as brain-derived neurotrophic factor (BDNF), can promote the development, regeneration, survival, and functioning of neurons.¹ Reduced BDNF messenger RNA (mRNA) level and BDNF protein level were observed in cerebral cortices of patients with AD.^2,3 Moreover, BDNF/Tropomyosin receptor kinase B (TrkB) neurotrophic signaling pathway is selectively decreased in frontal cortex and hippocampus of patients with AD.^4,5 Arancibia et al showed potential protective effect of BDNF against Aβ-induced neurotoxicity in vitro and in vivo.⁶ Furthermore, social interaction can rescue memory deficit in an AD mouse model by increasing mRNA and protein levels of BDNF in the hippocampus.⁷ Recently, BDNF gene therapy was shown to prevent neuronal degeneration and to stimulate neuronal function in patients with AD.⁸ The body of evidence demonstrates dysfunction of BDNF is critical in the development of AD and suggests polymorphisms of BDNF may confer risk of AD.

Val66Met is a functional single-nucleotide polymorphism (SNP) of BDNF. G>A substitution at nucleotide 196 of BDNF results in the Val66-to-Met amino acid change in the human BDNF protein.⁹ Since a decade ago, many studies have been performed to evaluate the association between Val66Met and AD. Except a few case–control studies that showed either Val or Met allele of the SNP was associated with AD,^10
-12 most studies reported no association.^13
-15 One recent review argued that population stratification and uncontrolled gene–gene or gene–environment interactions were likely to account for the inconsistency.¹⁶ Therefore, these conflicting results may be ascribed to the omitted confounding factors, such as age, sex, and apolipoprotein E (APOE) ε4, the strongest genetic risk factor for AD. Furthermore, some studies examined interactive effects of either sex or APOE ε4 with Val66Met,^17

-20 but the findings are still mostly negative and conflicting. One probable explanation is that the results were underpowered and biased by limited sample sizes.

Fukumoto et al conducted a sex-based meta-analysis on the association between Val66Met and AD with a large sample size (4711 cases and 4537 controls).²¹ They revealed sexually dimorphic effect of Val66Met in AD—Met allele confers susceptibility to AD in females but not in males. Recently, 1 study also reported female-specific effect of Val66Met on susceptibility to AD in 1 of their 2 independent Chinese Han cohorts.²⁰ However, these studies neglected to adjust for other confounding factors for AD, such as age and APOE ε4.

In recent years, a few genes were reported to interact with APOE ε4 on AD risk. Jun et al revealed PICALM, 1 of genome-wide association study (GWAS) identified genes, confers risk predominantly in APOE ε4 noncarriers.²² Reiman et al reported GAB2 modifies late onset AD risk in APOE ε4 carriers only.²³ In addition, some other genes have interactive effects with APOE ε4.²⁴ Therefore, it is necessary to evaluate whether Val66Met can confer AD risk by interacting with APOE ε4.

In this study, we incorporated Val66Met data from high-throughput genotyping data, which is different from ordinary meta-analysis on polymorphism. This is the first meta-analysis with the largest sample size to date to comprehensively examine the association between Val66Met and AD by introducing age, sex and APOE ε4 status as the confounding factors.

Methods

Search Strategy

PubMed and EmBase databases were searched for all published case–control association studies of the Val66Met polymorphism with AD. Various combination of these search terms were used: “BDNF,” “brain-derived neurotrophic factor,” “polymorphism,” “Val66Met,” “rs6265” and “Alzheimer.” Furthermore, reference list of Val66Met from the AlzGene database (www.alzgene.org) were referred to.²⁵

Inclusion Criteria

The titles and abstracts of all articles identified by the search strategy were retrieved for further review. Inclusion criteria for all potentially relevant articles were (1) diagnosis of AD according to the Diagnostic and Statistical Manual of Mental Disorders and the National Institute of Neurological Disorders and Stroke–Alzheimer Diseases and Related Disorders working group criteria,²⁶ (2) case–control studies reporting genotype or allele frequencies of the BDNF Val66Met polymorphism in patients with AD and healthy controls, and (3) genotype frequencies in Hardy-Weinberg equilibrium (HWE) for the healthy controls (P > .05).

Val66Met Data From High-Throughput Genotyping Data

In this study, we used Val66Met data extracted from case–control high-throughput genotyping data for AD. Twelve unrelated high-throughput genotyping cohorts were collected from NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS). For detailed information on the 12 cohorts from NIAGADS, please refer to Supplementary Table. We also collected genotyping data from Alzheimer’s Disease Neuroimaging Initiative (ADNI; the ADNI database http://www.loni.ucla.edu/ADNI/).²⁷ The ADNI genotyping data were generated as previously described We did not include high-throughput genotyping cohorts of which Val66Met data were imputed.

Data Extraction

Two investigators (Q.Z. and Y.S.) performed the literature search and reviewed all the results independently. Full articles were examined for further assessment if the information in the title or abstract suggested the study is possibly eligible. Data from each study were extracted independently by 2 investigators (Y.Z. and L.S.), using a standardized protocol. In case of disagreement of study inclusion, a third investigator (S.J.) was involved. The following information were extracted: first author name, year of publication, ethnicity, sex, presence or absence of APOE ε4, and full genotyping data of Val66Met of the studied patients. Both S.J. and Y.Q. extracted the Val66Met data from the high-throughput genotyping cohorts.

Statistical Analysis

We used R package meta (version 4.8-4) to perform the meta-analysis.²⁸ The odds ratio (OR) and 95% confidence interval (CI) of AD for the Met allele compared with the Val allele were assessed in each study using logistic regression. The study-specific ORs were then pooled with adjustment for study. Between-study heterogeneity was examined using the Cochran’s Q-test by calculating the I ² statistics. A fixed-effects model using the Mantel-Haenszel method was applied when no statistically significant heterogeneity was detected. Otherwise, a random effects DerSimonian and Laird model was applied. For ORs after adjusting for covariates, the inverse variance weighting is used for pooling.

To explore the possible sex-specific or APOE ε4 status-specific effect of Val66Met polymorphism on AD, 4 subgroups (female, male, APOE ε4 carrier, and APOE ε4 noncarrier) were created. Pooled OR and 95% CI with or without adjusting for covariates were calculated for each subgroup after excluding studies of genotype frequencies that are not in HWE in healthy controls.

Sensitivity analysis was performed by sequential exclusion of individual studies (leave-one-out analysis) for meta-analysis of total samples and subgroup meta-analyses. Publication bias was evaluated graphically using funnel plot. Begg’s rank correlation test was conducted to evaluate the publication bias quantitatively.

Results

Characteristics of Published Studies Included and High-Throughput Genotyping Cohorts

After literature search and review, 31 published association studies of AD with Val66Met were included. Characteristics of the 31 studies are shown in Table 1. Characteristics of the 13 unrelated high-throughput genotyping cohorts genotyped Val66Met are shown in Table 2. Finally, 19 604 cases and 26 333 controls were included.

Table 1.

Characteristics of Included Published Studies.

First Author, Year^{reference number}	Ethnicity	Cases/Controls	Female (%)	Presence of APOE ε4 (%)
Bagnoli et al, 2004²⁹	Caucasian	128/97	NA	NA
Bodner et al, 2005³⁰	Caucasian	256/195	NA	NA
Combarros et al, 2004³¹	Caucasian	237/218	69.50	37.10
Cozza et al, 2008³²	Caucasian	251/97	NA	NA
Desai et al, 2005³³	African American	64/45	72.50	NA
Desai et al, 2005³³	Caucasian	995/671	64.80	NA
Fehér et al, 2009³⁴	Caucasian	160/164	NA	NA
Fukumoto et al, 2010²¹	Asian	657/525	61.90	NA
Giedraitis et al, 2009³⁵	Caucasian	84/385	NA	NA
Huang et al, 2007³⁶	Caucasian	220/128	NA	52.30
Li et al, 2005¹⁷ (UCSD)^a	Caucasian	188/361	57	36.40
Li et al, 2005¹⁷ (WashU)^b	Caucasian	388/349	62.80	41.50
Li et al, 2005¹⁷ (UK)^c	Caucasian	359/396	70.90	41.60
Li et al, 2008³⁷	Caucasian	692/682	NA	NA
Li et al, 2017²⁰	Asian	715/760	58.23	47.48
Nacmias et al, 2004³⁸	Caucasian	83/97	66.10	NA
Reiman et al, 2007²³	Caucasian	859/551	NA	NA
Saarela et al, 2006¹¹	Caucasian	97/101	62.60	NA
Ventriglia et al, 2002⁹	Caucasian	130/111	NA	NA
Vepsäläinen et al, 2005¹³	Caucasian	372/464	NA	NA
Zhang et al, 2006³⁹	Caucasian	295/250	NA	NA
Akatsu et al et al, 2006¹⁸	Asian	95/108	70.90	NA
Bian et al, 2005⁴⁰	Asian	203/239	48.20	29.40
He et al, 2007¹⁴	Asian	513/575	59.70	NA
Matsushita et al, 2005¹⁰	Asian	487/471	69	NA
Nishimura et al, 2005⁴¹	Asian	172/275	NA	NA
Tsai et al, 2006¹²	Asian	175/189	50.80	NA
Forero et al, 2006⁴²	Mixed	101/168	69.90	NA
Lee et al, 2005⁴³	Unknown	95/70	60	NA
Pivac et al, 2011¹⁵	Caucasian	211/402	60.70	NA
Boiocchi et al, 2013¹⁹	Caucasian	191/408	57.60	NA

Abbreviations: NA, not available; APOE, apolipoprotein E.

^aUCSD samples from the University of California, San Diego.

^bWashU samples from the Washington University.

^cUK samples from Cardiff University, Wales College of Medicine and King’s College London.

Table 2.

Characteristics of Included High-Throughput Genotyping Cohorts.

Abbreviated Cohort Name^a	Ethnicity	Cases/Controls	Female, %	Presence of APOE ε4, %
NIA-LOAD	Mixed	993/884	62.30	52
ADC1	Caucasian	1574/527	55.30	57.90
ADC2	Caucasian	745/165	54.20	55.10
ADC3	Caucasian	862/618	54.20	40.70
UPITT	Mixed	1424/996	63.80	42.20
TGEN II	Caucasian	1013/585	54.20	48.50
ROSMAP	Caucasian	368/1326	69.10	23.30
WashU1	Caucasian	403/225	58.10	43.60
MIRAGE	Caucasian	603/885	59.70	38.90
ACT	Unknown	567/1701	57.70	26.10
UMVUMSSM	Unknown	1240/1230	62.50	37.90
MAYO	Caucasian	841/1253	53.40	42.70
ADNI	Mixed	213/347	47	41.60

Abbreviation: APOE, apolipoprotein E.

^aCohort full names: NIA-LOAD, National Institute on Aging Genetics Initiative for Late-Onset Alzheimer’s Disease; ADC1, Alzheimer’s Disease Center Dataset 1; ADC2, Alzheimer’s Disease Center Dataset 2; ADC3, Alzheimer’s Disease Center Dataset 3; UPITT, University of Pittsburgh; TGEN II, Translational Genomics Research Institute II; ROSMAP, Religious Orders Study and Memory and Aging Project; WashU1, Washington University Dataset 1; MIRAGE, Multi Institutional Research on Alzheimer Genetics Epidemiology; ACT, Adult Changes in Thought; UMVUMSSM, University of Miami (UM), Vanderbilt University (VU) and Mount Sinai School of Medicine (MSSM); MAYO, Mayonnaise; ADNI, Alzheimer’s Disease Neuroimaging Initiative.

Val66Met Polymorphism and AD Risk

For the overall association between Val66Met and AD, phenomenal heterogeneity (I ² = 43%) was observed across studies (Supplementary Figure S1). Therefore, random effects meta-analysis was used. Val66Met was not associated with AD before (OR = 1.02, 95% CI = 0.97-1.07; P = .40; Supplementary Figure S1) and after adjusting for age, sex, and APOE ε4 status (OR, 1.00; 95% CI, 0.94-1.06; P = .97; Figure 1). Sensitivity analysis showed OR and P value were not statistically altered after each leave-one-out analysis. Funnel plot and Begg’s test showed no evidence of publication bias (Supplementary Figure S2).

Figure 1.

Forest plot of meta-analysis on the association between Val66Met polymorphism and AD after adjusting for age, sex, and APOE ε4 status. AD indicates Alzheimer’s disease; APOE, apolipoprotein E.

Interaction Between Val66Met Polymorphism and Sex on AD Risk

We divided samples into male and female subgroups and assessed the associations separately between Val66Met and AD in the 2 subgroups. In agreement with previous meta-analysis findings,²¹ Met allele is significantly associated with AD in females (OR = 1.08, 95% CI = 1.03-1.14; P = .003; Supplementary Figure S3) without confounding adjustment. However, after adjusting for age and APOE ε4 status, Met allele is not associated with AD in females (OR, 1.02; 95% CI, 0.94-1.11; P = .57; Figure 2A). Met allele is not associated with AD in males before (OR, 0.96; 95% CI, 0.90-1.02; P = .17; Supplementary Figure S4) and after adjusting for age and APOE ε4 status (OR, 0.94; 95% CI, 0.86-1.04; P = .22; Figure 2B). Sensitivity analysis showed OR and P value were not statistically altered after each leave-one-out meta-analysis for each subgroup. Funnel plots and Begg’s tests showed no evidence of publication bias in either female (Supplementary Figure S5) or male (Supplementary Figure S6) subgroup.

Figure 2.

Forest plots of subgroup meta-analyses on the associations between Val66Met polymorphism and AD in females (A) and males (B) after adjusting for age and APOE ε4 status. AD indicates Alzheimer’s disease; APOE, apolipoprotein E.

Interaction Between Val66Met Polymorphism and APOE ε4 Status on AD Risk

Likewise, we divided samples into APOE ε4 carrier and APOE ε4 noncarrier subgroups and assessed the associations between Val66Met and AD in the 2 subgroups separately. Val66Met is not associated with AD in APOE ε4 carriers before (OR = 1.02, 95% CI = 0.93-1.10; P = .72; Supplementary Figure S7) and after adjusting for age and sex (OR = 0.97, 95% CI = 0.88-1.07; P = .56; Figure 3A). Similarly, Val66Met is not associated with AD in APOE ε4 noncarriers before (OR = 1.05, 95% CI = 0.98-1.12; P = .18; Supplementary Figure S8) and after adjusting for age and sex (OR = 1.02, 95% CI = 0.94-1.11; P = .64; Figure 3B). Sensitivity analysis showed OR and P value were not statistically altered after each leave-one-out meta-analysis for each subgroup. Funnel plots and Begg’s tests showed a slight publication bias in APOE ε4 noncarrier subgroup meta-analysis without adjusting for age and sex (Supplementary Figure S9A) but not in APOE ε4 noncarrier subgroup meta-analysis after adjusting for age and sex (Supplementary Figure S9B). No evidence of publication bias was observed in APOE ε4 carrier subgroup (Supplementary Figure S10).

Figure 3.

Forest plots of subgroup meta-analyses on the associations between Val66Met polymorphism and AD in APOE ε4 carriers (A) and APOE ε4 noncarriers (B) after adjusting for age and sex. AD indicates Alzheimer’s disease; APOE, apolipoprotein E.

Discussion

We conducted, for the first time, a comprehensive meta-analysis to assess the association between the Val66Met of BDNF and AD by introducing age, sex, and APOE ε4 as confounding factors. We included published studies and high-throughput genotyping cohorts with Val66Met data in this meta-analysis.

Neurotrophins, of which BDNF is a member,⁴⁴ are evolutionarily young and do not exist in invertebrate species.⁴⁵ The late evolutionary appearance of neurotrophins suggests that these molecules are necessary for both the development and functioning of a more complex nervous system.^46,47 Transgenic mice deficient in either neurotrophins or neurotrophic receptors can result in neonatal death.^45,48

Moreover, BDNF has also emerged as an important regulator of synaptogenesis and synaptic plasticity underlying learning and memory in adult central nervous system.⁴⁹ These evidences demonstrate BDNF is critical in the development and functioning of nervous system neonatally and in adults. However, no GWAS signal can be identified in region encompassing BDNF for neurological or psychiatric diseases, suggesting variants in region encompassing BDNF could not disturb its function significantly or their consequences can be compensated. We hypothesize that fatal variants in region encompassing BDNF were discriminated against by natural selection because of its indispensable role in nervous development.

Biased samplings involving confounding factors may explain the heterogeneous results in previous association studies. In the present study, overall meta-analysis assessing association between Val66Met and AD showed an extraordinary heterogeneity across studies. However, heterogeneity was profoundly reduced after adjusting for age, sex, and APOE ε4 status. After subdividing samples based on sex or APOE ε4 status, no cross-study heterogeneity was observed even in confounding effect-unadjusted subgroup meta-analysis. For female subgroup meta-analysis, in agreement with the previous meta-analysis and the recent study on Chinese Han population that omitted confounding adjustment, ^20,21, Met allele was associated with AD in females without adjusting for covariates. Nevertheless, after adjusting for age and APOE ε4 status, Met allele was not associated with AD in females. It suggests that the female-specific association between Val66Met and AD identified in the previous meta-analysis may be ascribed to the effects of age and APOE ε4 status. These facts underlie the necessity of confounding adjustment for research on Val66Met and even other polymorphisms in AD.

In conclusion, we showed Val66Met polymorphism was not associated with and had no sexual or APOE ε4 status-based dimorphic effect on susceptibility to AD. Our study demonstrates that confounding adjustment is necessary for research of Val66Met and even other polymorphisms on AD or AD-related trait.

Footnotes

Authors’ Note

The authors Qingnan Zhao and Yaqi Shen contributed equally and share the first authorship. Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689), funded by the National Institute on Aging.

Acknowledgments

We thank contributors who collected samples used in this study as well as patients and their families whose help and participation made this work possible. The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA funded ADCs: P30 AG019610 (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). NIAGADS datasets NG00022, NG00023, and NG00024 contain ADC samples. The studies included were supported by grants UO1 AG006781, UO1 HG004610, UO1 HG006375, and U01 HG008657 for ACT (NG00034); P30AG10161 and R01AG15819 for ROS, R01AG17917 for MAP (NG00029); U24 AG026395, U24 AG026390, and R01AG041797 for NIA-LOAD (NG00020); R01AG09029 and R01AG025259 for MIRAGE (NG00031); R01 AG032990, U01 AG046139, R01 NS080820, RF1 AG051504 and P50 AG016574 for Mayo (NG00043); 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 (NG00042); P50 AG005681, P01 AG03991, P01 AG026276 for Washington University St. Louis (NG00030); P50 AG005133, AG030653, AG041718, AG07562, AG02365 for University of Pittsburgh (NG00026). The TGen series was also funded by NIA grant AG041232 to AJM and MJH, 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 (HEFCE), Alzheimer’s Research Trust (ART), BRACE as well as North Bristol NHS Trust Research and Innovation Department and DeNDRoN), The Netherlands Brain Bank (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona (NG00028).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The analysis of the data and the writing of the report were supported by National Natural Science Foundation of China (No. 81460284, No.81460051), and Natural Science Foundation of Qinghai Provincial (No. 2014-ZJ-944Q, No. 2015-ZJ-744). The Alzheimer’s Disease Genetics Consortium (ADGC) supported the collection of samples used in this study through National Institute on Aging (NIA) grants U01AG032984 and RC2AG036528. Samples from the National Cell Repository for Alzheimer’s Disease (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc; Biogen Idec Inc; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc; GE Healthcare; Innogenetics, N.V.; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Medpace, Inc; Merck & Co, Inc; Meso Scale Diagnostics, LLC; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc; Piramal Imaging; Servier; Synarc Inc; and Takeda Pharmaceutical Company. 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 Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 and K01 AG030514.

Supplemental Material

Supplementary material for this article is available online.

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