The Influence of BDNF Val66Met Polymorphism on Cognition,Cerebrospinal Fluid,and Neuroimaging Markers in Non-Demented Elderly

Abstract

Background:

The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism emerged as a risk factor for Alzheimer’s disease (AD). However, little was known about its effects on the process of potential AD.

Objective:

To explore the effects of the Val66Met polymorphism on cognition, cerebrospinal fluid (CSF), and neuroimaging markers in non-demented elderly individuals.

Methods:

A total of 1,081 adults without dementia (375 healthy subjects and 706 individuals with mild cognitive impairment) were recruited from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to test the influence of BDNF Val66Met polymorphism on cognitive impairment, brain structure atrophy, and change in the levels of CSF biomarkers. Moreover, we also conducted our study in abnormal amyloid-β (A+) subgroup and normal amyloid-β (A–) subgroup, as well as in APOE ɛ4 carriers and non-carriers.

Results:

The BDNF Val66Met polymorphism had significant association with atrophy of the entorhinal cortex and Mini-Mental State Examination (MMSE) scores in the non-demented elderly and A + subgroup, while no association was found in A–subgroup. What is more, there was a significant effect of interaction between BDNF Val66Met and amyloid-β load in MMSE. In addition, significant associations of BDNF Val66Met with the entorhinal cortex and ventricular volumes were found among APOE ɛ4 non-carriers, but not APOE ɛ4 carriers.

Conclusions:

The BDNF Val66Met polymorphism is associated with cognitive impairment and brain atrophy among the non-demented elderly, APOE ɛ4 non-carriers and A + subgroup, implying the potential of the Val66Met polymorphism as an important genetic factor for AD-related neurodegeneration.

Keywords

Alzheimer’s disease amyloid-β brain-derived neurotrophic factor polymorphism

INTRODUCTION

The people living with Alzheimer’s disease (AD) account for 50% to 70% of the 47 million dementia individuals worldwide and the prevalence of the disease is increasing dramatically [1, 2]. Amyloid-β (Aβ) plaques and neurofibrillary tangles that are composed of hyperphosphorylated tau proteins have been considered as two hallmark features of this disease [3, 4]. It has been documented that AD, a neurodegenerative syndrome, starts with extensive neuronal dysfunction and synaptic loss, resulting in cognitive impairment and progressive atrophy [5, 6]. Research found that neurotrophins (NTs) were necessary for neuronal survival, synaptic plasticity, and signal transmission, thus paving the way for AD therapeutic strategies [7 –9]. Brain-derived neurotrophic factor (BDNF), for example, the most widely distributed type of NTs in adult brain, together with its main receptor tropomyosin-related kinase B (TrkB), is a key component implicated in neurogenesis and memory storage of hippocampus [10]. In addition, impairment of BDNF-TrkB signaling pathway is considered as an important risk factor of AD for its influence on Aβ deposition, tau hyperphosphorylation, and cognition decline [11].

A functional single-nucleotide polymorphism (rs6265, Val66Met, G>A) in the BDNF gene can lead to a valine (Val) to methionine (Met) substitution at codon 66, consequently resulting in aberrant intracellular trafficking, abnormal packaging of pro-BDNF and reduced depolarization-induced secretion of mature BDNF [10]. A series of studies suggested the deleterious effects of Met carriage led to atrophy of hippocampal lobe, memory loss, and cognitive impairment [12, 13]. However, other studies suggested Val/Val homozygotes are linked to worse memory performance and increased risk of AD [14]. Since genetic association studies reported inconsistent results, the primary objective of our study was to determine the effects of BDNF Val66Met polymorphism on cognitive decline, brain atrophy, and cerebrospinal fluid (CSF) biomarkers in non-demented elderly.

The results of previous studies generally indicated BDNF may have a neuroprotective effect against Aβ-related neurotoxicity and BDNF may downregulate the accumulation of aberrant Aβ [12, 13]. And the level of BDNF decreased after intracerebroventricular injection of Aβ_1 - 42 oligomers into the mice [14, 15]. However, the relationship between the Val66Met polymorphism and Aβ burden in the pathological process remains to be determined, especially the underlying mechanism. Thus, we further explored the extent to which the variation of Val66Met acted with Aβ load to impact on the disease process in potential AD. In addition, although previous studies have observed additive effects of APOE ɛ4 alleles and Met variation on altered cognitive performance among healthy older adults [19], the subsequent studies that attempted to replicate these results showed a lack of consensus [20]. Therefore, we also explored whether there are potential effects of APOE ɛ4 allele on relationship between the BDNF Val66Met polymorphism and AD-related neuronal degeneration.

Accumulating evidence demonstrated that AD intervention should be given probably in the early stage when the brain still has the capacity for self-repair to effectively prevent the progression of AD-related neurodegeneration [8, 16]. In our research, we explored whether the polymorphism of Val66Met correlated with cognitive deficit, neuroimaging abnormalities, and change in levels of CSF biomarkers of neuronal injury among the non-demented elderly. Furthermore, we stratified the participants by Aβ load levels to explore the extent to which it acted with the Val66Met polymorphism to impact AD process. And we also performed our analysis after we divided the cohort by APOE ɛ4 variation.

METHODS

Alzheimer’s Disease neuroimaging initiative

Data were downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). Launched in 2003, the ADNI was as a public-private partnership [17] led by principal investigator Michael W. Weiner, MD. It is a highly innovative longitudinal study on AD progression using data about clinical assessments, cognition, magnetic resonance imaging (MRI) of brain structures, CSF measurements, amyloid positron emission tomography (PET), and other biomarkers of AD pathologic features. To date, it has recruited more than 1500 adults aged 55 to 90 years from the United States and Canada.

Participants

The details concerning the ADNI cohort can be found in previous publications [18, 19]. Briefly speaking, all the participants were required to have at least 6 years of education and be fluent in Spanish or English, without a history of substance dependence in the past 2 years, active depression, and other psychiatric and neurological disorders. The study was approved by institutional review boards of all participating institutions, and written informed consent was obtained from all participants or authorized representatives.

With the aim to identify AD in its very early stage for timely intervention therapy, a number of studies on BDNF Val66Met polymorphisms focused on non-demented elderly [20, 21]. Therefore, in our study, 375 cognitively normal (CN) individuals and 706 subjects diagnosed with mild cognitive impairment (MCI) were recruited, while patients with dementia were excluded. The inclusion criteria for CN in ADNI were global Clinical Dementia Rating (CDR) [22] score of 0, and Mini-Mental State Examination (MMSE) [23] scores of 24r–30. Individuals with MCI in ADNI were required to have MMSE scores between 23 and 30, global CDR score of 0.5, objective memory loss, and preserved activities of daily living. Additionally, only individuals with available BDNF Val66Met polymorphisms were included.

Genotyping

The genotype data for the participants were obtained from the ADNI database. Genetic assessment for BDNF Val66Met (rs6265) was completed using the Illumina Human610-Quad BeadChip. However, genotyping of the two APOE SNPs (rs429358, rs7412) that define the ɛ2, ɛ3, and ɛ4 alleles was performed in another way. Genotypes for these SNPs were obtained separately from blood sample taken in ethylenediaminetetraacetic acid (EDTA)-containing vacutainer tubes, using allelic discrimination technology or equivalent techniques [24].

Florbetapir PET

In our analysis, the standardized uptake value ratio (SUVR) as mean florbetapir uptake was downloaded from the ADNI database (http://adni.loni.usc.edu). It was acquired via dividing florbetapir uptake from gray matter within lateral and medial frontal anterior, posterior cingulate, lateral parietal, and lateral temporal regions by the whole cerebellum.

CSF biomarker concentrations

Data of CSF biomarkers used in our analysis were obtained from the ADNI dataset. The method for data acquisition was described previously [25]. In sum, CSF was collected by lumbar puncture and then carried to ADNI Biomarker Core laboratory on dry ice. CSF Aβ_1 - 42, CSF total (T)-tau, and CSF phosphorylated (P)-tau were measured using Innogenetics (INNOBIA AlzBio3; Ghent, Belgium) immunoassay kit-based reagents.

Cognition

The Rey Auditory Verbal Learning Test (RAVLT), the CDR, the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-cog), and the MMSE were performed to assess the cognitive functions of ADNI participants. All the assessments were completed at baseline and during the follow-up period.

Brain structures on MRI

The MRI data of brain structures can be found the ADNI dataset (https://ida.loni.usc.edu/pages/access/studyData.jsp). All available data at baseline and at 3, 6, 12, 18, 24, 36, 48, 60, 72, 84, and 96 months were used in our analysis. Structural MRI brain scans were acquired via 1.5-T or 3.0-T MRI imaging systems with T1-weighted scan and average examination time for each person was 45 minutes [26]. Based on the 2010 Desikan-Killany atlas, the FreeSurfer version 5.1 was used for cerebral image segmentation and analysis. Here, we selected the most associated brain regions with AD, such as hippocampus, entorhinal cortex, and ventricles. These regions were widely explored and known to be associated with memory deficits.

Grouping of subjects

The subjects were categorized into abnormal amyloid-β (A+) subgroup and normal amyloid-β (A–) subgroup to determine whether the effects of BDNF Val66Met polymorphism on AD biomarkers were related to Aβ burden. The classification depended on CSF and PET levels at baseline, first using the a priori cutoffs (CSF Aβ_1 - 42 192 ng/L and global PET florbetapir SUVR 1.11). Since cases close to the cutoffs may be easily misclassified for the variable measurements, we excluded them and used±5% confidence intervals around original cutoffs in our study to avoid drawing conclusions based on discordant borderline cases. This approach has already been used in previous publications [27r –29]. Participants were then divided into APOE ɛ4 allele carriers and non-carriers to detect the potential effects of APOE ɛ4 allele on relationship between the BDNF Val66Met polymorphism and the levels of AD biomarkers.

Statistical methods

Multiple linear regression models were employed to explore the association between genotypes and these various endophenotypes at baseline. To further test the association over the follow-up period, linear mixed-effects models were carried out as the approach to longitudinal data analysis. These models specified a random subject-specific intercept and a random subject-specific slope. We also repeated our analysis including an Aβ×BDNF Val66Met interaction effect in pooled sample to explore the impact of their relationship on the disease process.

To facilitate comparisons between modalities, we standardized all our outcome variables to z scores. All analyses were adjusted for age, sex, educational level, and APOE ɛ4 genotype, as well as intracranial volume for hippocampus, entorhinal cortex, middle temporal cortex, and ventricles. All statistical analyses were performed by R 3.12 and PLINK 1.07 (http://pngu.mgh.harvard.edu/wpurcell/plink/). The primary statistical significance was defined as p < 0.05. The cohort was then separated into two groups based on Aβ levels. To reduce false positive rates, as described previously [30], the Bonferroni correction was applied in analysis in each subgroup. A Bonferroni-corrected p of 0.025 was used to determine the significance of results, which was adjusted for the 2 multiple comparisons. The adjusted p of 0.025 was also applied after we stratified all participants into APOE ɛ4 carriers and non-carriers.

RESULTS

Group means and standard deviations on biomarker measures for full sample and each group at baseline are listed in Table 1. In total, 375 CN and 706 MCI were recruited in this study. Regarding BDNF Val66Met genotypes, 318 were Val/Met heterozygous, 725 Val66 homozygous, and 38 Met66 homozygous. The classification of subjects resulted in 429 A+(190 women, 74.08±6.84 years) and 322 Arrr–(142 women, 72.13±7.27 years). With regard to APOE genotypes, there were 631 ɛ4 non-carriers versus 450 ɛ4 carriers.

There were no significant differences found in cognitive assessments, CSF biomarkers, and MRI measures of different Val66Met genotypes among non-demented elderly at baseline. Analyses were then performed in A + group and Arrr–group, and only entorhinal cortex volume in A + group showed a significance at baseline (β= rrr–0.183, p = 0.036), assessed at a threshold of uncorrected p < 0.05. In non-carriers of the APOE ɛ4 allele, we found significant association of the Val66Met polymorphism with entorhinal cortex volume (β= rrr–0.148, p = 0.046). However, none of these differences remained statistically significant after adjustment for multiple comparisons through Bonferroni correction. In addition, there were no significant effects of interaction between Aβ and BDNF Val66Met on any outcome measure at baseline.

Table 1

Demographic and neuropsychological characteristics of included subjects at baseline

Characteristics	All participants		APOE ɛ4 non-carriers		APOE ɛ4 carriers		Abnormal amyloid-β		Normal amyloid-β
Age (y)	1081	73.78±6.99	631	74.39±6.99	450	72.93±6.91	429	74.08±6.84	322	72.13±7.27
Gender (male/female)	1081	611/470	631	356/275	450	255/195	429	239/190	322	180/142
Education (years)	1081	16.00±2.83	631	16.10±2.82	450	15.87±2.84	429	15.97±2.82	322	16.19±2.70
APOE ɛ4 (0/1/2)	1081	631/370/80	631	631	450	370/80	429	177/199/53	322	269/53
CDR (scores)	1081	0.99±1.00	631	0.80±0.91	450	1.27±1.05	429	1.21±1.05	322	0.67±0.81
ADAS-cog (scores)	1080	8.84±4.53	631	7.97±4.12	449	10.07±4.79	429	9.80±4.76	322	7.27±3.58
MMSE (scores)	1081	28.09±1.75	631	28.33±1.64	450	27.75±1.83	429	27.84±1.83	322	28.61±1.45
ICV (mm3)	1075	1539219.67±162973.94	628	1537169.90±160330.60	447	1542099.42±166574.19	424	1542378.37±168619.03	321	1518775.92±154680.12
Hippocampus (mm3)	945	6965.36±1088.14	551	7121.17±1067.19	394	6747.46±1079.69	374	6803.37±1038.56	288	7348.20±1040.91
Ventricles(mm3)	1049	38913.70±22130.76	613	38570.86±21598.35	436	39395.73±22849.64	412	40776.57±22728.58	312	34003.29±19446.56
Middle Temporal(mm3)	948	19828.39±2883.28	551	20020.50±2814.43	397	19561.77±2955.57	376	19694.19±2822.65	289	20502.01±2667.76
Entorhinal (mm3)	948	3609.37±729.24	551	3718.81±710.57	397	3457.49±727.61	376	3531.58±694.09	289	3800.13±675.87
CSF T-tau (ng/L)	744	82.54±48.33	449	70.25±37.78	295	101.24±56.02	411	101.22±53.98	308	59.70±25.99
CSF P-tau (ng/L)	748	35.84±20.83	452	30.75±16.49	296	43.61±24.10	414	44.47±22.55	309	25.39±11.72
CSF Aβ (ng/L)	748	181.14±53.79	452	200.99±50.66	296	150.83±43.28	414	139.09±25.58	309	236.60±25.91

CDR, Clinical Dementia Rating; ADAS-cog, Alzheimer’s disease Assessment Scale Cognition; MMSE, Mini- Mental State exam; ICV, intracranial Volume; CSF, cerebrospinal fluid; Aβ, amyloid-beta; T-tau, total tau; P-tau, phosphorylated tau. Data are given as mean±standard deviation unless otherwise indicate.

Longitudinally, there were significant effects of the Val66Met polymorphism on MMSE (β= rrr–0.065, p = 0.015) and entorhinal cortex volume (β= rrr–0.096, p = 0.046) among non-demented elderly (Fig. 1). In the analysis after stratifying the whole sample by Aβ deposition levels, we also observed distinct time-dependent differences in MMSE (β= rrr–0.113, p = 0.006) and entorhinal volume (β= rrr–0.177, p = 0.017) in the A + subgroup, while no significant differences were found in the Arrr–group using uncorrected p < 0.05. We then assessed the association between the Val66Met polymorphism and AD pathogenesis among APOE ɛ4 carriers and non-carriers. The Val66Met polymorphism showed significant influences on hippocampus (β= rrr–0.123, p = 0.0497), entorhinal cortex (β= rrr–0.166, p = 0.013), ventricles (β= 0.135, p = 0.012), and tau levels (β= 0.164, p = 0.044) among APOE ɛ4 non-carriers. None of the volumetric parameters or other AD-related biomarkers showed any correlation with Val66Met genotype when tested across APOE ɛ4 carriers, assessed at a significance threshold of p < 0.05, and uncorrected for multiple comparisons. All the differences in most parameters remained significant after the Bonferroni correction except hippocampus and tau levels in non-carriers of APOE ɛ4 allele (Fig. 2). In addition, a significant time-dependent interaction between Aβ and BDNF Val66Met was observed in MMSE (β= rrr–0.140, p = 0.027).

Fig.1

Effects of brain-derived neurotrophic factor Val66Met polymorphism on Alzheimer’s disease-related cognition, cerebrospinal fluid, and neuroimaging markers in linear mixed-effects analysis among all participants. Data from linear mixed-effects analysis adjusted for age, sex, educational level, APOE ɛ4 genotype, and intracranial volume indicating correlation of BDNF Val66Met with entorhinal cortex volume (A) and MMSE scores (B) in non-demented elderly.

Fig.2

Effects of brain-derived neurotrophic factor Val66Met polymorphism on Alzheimer’s disease-related cognition, cerebrospinal fluid, and neuroimaging markers in linear mixed-effects analysis in subgroups. Data from linear mixed-effects analysis adjusted for age, sex, educational level, APOE ɛ4 genotype, and intracranial volume indicating correlation of BDNF Val66Met with entorhinal cortex volume (A) and MMSE scores (B) in abnormal amyloid-beta participants, and entorhinal cortex (C) and ventricles volume (D) in APOE ɛ4 non-carriers.

DISCUSSION

Our data indicated that the presence of BDNF Met allele contributed to AD-related cognitive decline and brain atrophy independently or with Aβ deposition. It was also shown to contribute to brain atrophy among non-carriers of APOE ɛ4 allele. Moreover, there was an interaction between Aβ load and BDNF Val66Met in cognition. Our findings suggested that the Val66Met variant underlay the genetic susceptibility to AD via effects of it alone or in concert with Aβ burden on entorhinal cortex thickness and cognitive functions. In non-carriers of the APOE ɛ4 allele, the BDNF Val66Met polymorphism showed significant association with brain atrophy. Taken together, our results supported the hypothesis that Val66Met may play an important role in neuronal injury among non-demented elderly.

Several studies on the influence of BDNF Val66Met polymorphism involved the ADNI cohort. The meta-analyses of Zhao’s team and Li’s team found no significant association between Val66Met and AD after dividing samples based on APOE ɛ4 allele [31, 32]. But we observed significance in APOE ɛ4 non-carriers. The inconsistency may be due to ethnic difference and the adoption of different models for statistical analysis. In addition, Kim’s study observed no significant association of the Val66Met polymorphism with hippocampal volume and memory [33]. Lack of longitudinal analysis may contribute to the null results. Honea’s study showed the Val66Met polymorphism was associated with right hippocampal atrophy over two years in 175 CN subjects [34]. But the small sample size reduced the credibility of the result. In addition, the associations of the Val66Met polymorphism with the atrophy of other brain regions and change in cognitive measurements were not analyzed in these two studies. Thus, AD-related comprehensive phenotypes should be used to assess the effects of Val66Met polymorphism on process of potential AD. And longitudinal analysis is also very necessary in our research.

BDNF is critical to synaptic excitation, neuronal integrity, and long-time memory via its interaction with TrkB. It has been reported that increased secretion of BDNF can relieve AD-related memory impairment or brain atrophy and even prevent the occurrence of dementia [30, 35], whereas the precursor form of BDNF (pro-BDNF), which would be cleaved to form the mature protein, may play a negative role in the pathogenesis and progression of AD [36, 37]. Furthermore, pro-BDNF as a proapoptotic ligand can induce apoptosis by binding to p75 neurotrophin receptor at subnanomolar concentrations [38]. Thus, Met allele was shown to be associated with increased AD risk for its negative effects on the transport and secretion of BDNF [10].

Our study found that the Val66Met polymorphism was significantly related with cognitive impairments as well as entorhinal cortex atrophy among all participants. These findings are almost completely consistent with the results of ADNI studies by Gomar [39] in MCI/AD cohorts. To our knowledge, entorhinal cortex, a crucial memory-encoding region, acts as an indicator of postmortem medial temporal and neocortical AD pathology [40]. It has been reported that mature BDNF was crucial for spatial and working memory by facilitating persistent firing of pyramidal neurons and ameliorating long-term potential impairments in entorhinal cortex [41, 42]. In addition, another study suggested lesion-induced apoptosis of entorhinal cortical neurons could be prevented by BDNF treatment in adult mice and primates [43, 44]. Moreover, multiple lines of evidence supported that the levels of BDNF mRNA and its expression were reduced in hippocampus and temporal lobe among patients with AD [20 , 45rrr–48]. Taken together, it has been demonstrated that improper trafficking and/or lower secretion of mature BDNF in Met polymorphism may contribute to the development of AD through neuronal atrophy of the entorhinal cortex.

In our analysis, BDNF Met66 allele showed a significant relationship with AD via its impact on brain volume and cognitive function in A + subgroup, while no significance was observed in Arrr–subgroup. The results were in line with the observations in previous studies performed by Lim’s Team [49rrr –51]. Moreover, their team confirmed the necessity of Aβ positivity for cognitive decline, brain atrophy, and tau accumulation associated with Met66 carriage, of which the underlying mechanisms still need further investigation. Furthermore, our study observed a significant time-dependent interaction between Aβ and BDNF Val66Met in MMSE, which was consistent with the hypothesis that the BDNF Val66Met polymorphism would interact with Aβ toxicity to predict changes in cognitive performance during the preclinical stage of AD [50]. What is more, accumulating evidence showed that high level of Aβ is involved in regulation of BDNF expression, then accelerate the process of potential AD. The underlying mechanisms may be that Aβ_1 - 42 oligodeoxynucleotides may impair neuronal function by downregulating BDNF transcription and inhibiting the transport of intracellular BDNF through Ras-MAPK/ERK or PI3K-Akt-mTOR signaling pathway [52, 53]. Moreover, other findings indicated a series of mechanisms by which BDNF Val66Met can relieve the deleterious effects of Aβ deposition [54]. For example, the secretion of BDNF influenced by Val66Met polymorphism can alleviate reduction of cell signaling and impairment of synaptic function caused by amyloid-deposition, prevent Aβ-related loss and abnormality of neurons in hippocampus [44, 55]. In addition, abnormal accumulation of Aβ has been proven to be decreased by α-secretase of amyloid-β protein precursor (AβPP), which can be induced by BDNF-related expression of sorting protein-related receptors with A-type repeats (SORAL) [12 , 57]. Based on these studies, high Aβ levels coupled with Met carriage may be a useful prognostic marker of accelerated cognitive decline and accelerated atrophy rates of AD-related brain structures.

In this study, the Met allele had significant associations with time-dependent volume reduction of entorhinal lobes and volume increase of the ventricles among non-carriers of the APOE ɛ4, while the Gomar group [39] found positive results among APOE ɛ4 carriers. Our focus on older adults without dementia accounted for this distinction. Besides, the sample size of our research was larger compared to theirs (1,081 to 397) and the follow-up time of our study was much longer (8 years to 3 years). Moreover, we have analyzed the levels of CSF biomarkers while they have not. The significant differences shown by Voineskos’s research after excluding the APOE ɛ4 carriers [20] are consistent with our conclusion as well as the findings of Tsai SJ and Huang’s team [58, 59]. It is suggested that BDNF Val66Met may be a genetic susceptibility locus for accelerating neurodegeneration among adults with APOE ɛ4 deficiency.

Several advantages and limitations of our study warrant discussion. For example, both cross-sectional analysis and longitudinal analysis were used to process our data and the follow-up of 8 years was long enough to explore the hidden course of chronic neurodegenerative progression. Furthermore, the combination of data on florbetapir and CSF biomarkers provided useful insight into the assessment of the levels of Aβ deposition. Although our study strongly supported that the BDNF Val66Met polymorphism was associated with AD only in APOE ɛ4 non-carriers, larger sample size and new analysis methods are required for further investigation given the previous ambiguous conclusions. In addition, more studies are needed to explore the interactive mechanisms between Aβ load and BDNF Val66Met in the future.

Our findings suggested that the non-demented elderly with both Met allele and a high level of Aβ may have higher risk of cognitive deterioration and brain atrophy. As the BDNF polymorphism can promote synaptic growth to protect against Aβ neurotoxicity, more exploration in this area is needed to translate BDNF biology into potential therapeutic strategies.

Footnotes

ACKNOWLEDGMENTS

This study was supported by grants from the National Key R&D Program of China (2016YFC1305803). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous 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 & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack 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. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Authors’ disclosures available online ().

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