Higher levels of vascular endothelial growth factor are associated with slower rates of hippocampal atrophy.
Biomarker groups are for illustration only. Amyloid groups are based on a previously identified cutoff value for amyloid positivity (cerebrospinal fluid β-amyloid 42 ≤ 192 pg/mL).
A, Annual change in executive function. B, Annual change in hippocampal volume is along the y-axis. Biomarker groups are for illustration. Tau groups are based on a previously identified cutoff value for tau positivity (tau positive ≥93 pg/mL).
eFigure 1. Association Between VEGF and CSF Aß-42 Levels by Diagnosis
eFigure 2. Association Between VEGF and CSF Tau Levels by Diagnosis
eTable 1. Associations Between VEGF and Brain-Aging Variables (Normal)
eTable 2. Associations Between VEGF and Brain Aging Variables (MCI)
eTable 3. Associations Between VEGF and Brain-Aging Variables (AD)
Hohman TJ, Bell SP, Jefferson AL, for the Alzheimer’s Disease Neuroimaging Initiative. The Role of Vascular Endothelial Growth Factor in Neurodegeneration and Cognitive DeclineExploring Interactions With Biomarkers of Alzheimer Disease. JAMA Neurol. 2015;72(5):520-529. doi:10.1001/jamaneurol.2014.4761
A subset of older adults present post mortem with Alzheimer disease (AD) pathologic features but without any significant clinical manifestation of dementia. Vascular endothelial growth factor (VEGF) has been implicated in staving off AD-related neurodegeneration.
To evaluate whether VEGF levels are associated with brain aging outcomes (hippocampal volume and cognition) and to further evaluate whether VEGF modifies relations between AD biomarkers and brain aging outcomes.
Design, Setting, and Participants
Biomarker analysis using neuroimaging and neuropsychological outcomes from the Alzheimer’s Disease Neuroimaging Initiative. This prospective longitudinal study across North America included individuals with normal cognition (n = 90), mild cognitive impairment (n = 130), and AD (n = 59) and began in October 2004, with follow-up ongoing.
Main Outcomes and Measures
Cerebrospinal fluid VEGF was cross-sectionally related to brain aging outcomes (hippocampal volume, episodic memory, and executive function) using a general linear model and longitudinally using mixed-effects regression. Alzheimer disease biomarker (cerebrospinal fluid β-amyloid 42 and total tau)–by–VEGF interactions evaluated the effect of VEGF on brain aging outcomes in the presence of enhanced AD biomarkers.
Vascular endothelial growth factor was associated with baseline hippocampal volume (t277 = 2.62; P = .009), longitudinal hippocampal atrophy (t858 = 2.48; P = .01), and longitudinal decline in memory (t1629 = 4.09; P < .001) and executive function (t1616 = 3.00; P = .003). Vascular endothelial growth factor interacted with tau in predicting longitudinal hippocampal atrophy (t845 = 4.17; P < .001), memory decline (t1610 = 2.49; P = .01), and executive function decline (t1597 = 3.71; P < .001). Vascular endothelial growth factor interacted with β-amyloid 42 in predicting longitudinal memory decline (t1618 = −2.53; P = .01).
Conclusions and Relevance
Elevated cerebrospinal fluid VEGF was associated with more optimal brain aging in vivo. The neuroprotective effect appeared strongest in the presence of enhanced AD biomarkers, suggesting that VEGF may be particularly beneficial in individuals showing early hallmarks of the AD cascade. Future work should evaluate the interaction between VEGF expression in vitro and pathologic burden to address potential mechanisms.
Vascular endothelial growth factor (VEGF) is involved in neural development,1 angiogenesis,1 and blood production1 and appears to play an essential role in the homeostasis of the adult vasculature.2 It has been investigated as a drug target for cancer3 but has also been implicated as a neuroprotective factor in Alzheimer disease (AD).4 Relative to control individuals, patients with AD have lower levels of serum VEGF in vivo5 and lower levels of cerebral capillary VEGF expression in the superior temporal cortex, hippocampus, and brainstem.6 In transgenic AD mice, the transplantation of stem cells overexpressing VEGF into the hippocampus reduces cognitive deficits and reverses memory defects.7 Similarly, treating APP transgenic mice with VEGF results in reduced memory impairment and reduced β-amyloid (Aβ) deposition.8 One possibility is that VEGF elevations are neuroprotective by counteracting the damaging effects of the AD pathological cascade through improvements in vascular survival.9
Vascular endothelial growth factor has also been evaluated as a potential biomarker for AD, although results are not entirely concordant. One study evaluating intrathecal cerebrospinal fluid (CSF) levels of VEGF found that patients with AD and vascular dementia had higher levels than healthy control individuals (ie, no neurological disease or deficit).10 A second study found that CSF VEGF levels did not differ between AD and cognitively normal control individuals, further confounding the issue.11 Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) appears to be more consistent with the serum results previously reported5 and found that lower levels of VEGF in CSF distinguish AD from healthy control individuals with 76% sensitivity and 84% specificity.12
Exploration into relations between VEGF and the phenotypic presentations of AD is just beginning and may be necessary to uncover potential mechanisms of neuroprotection in older individuals at risk for AD. One study evaluated more than 80 CSF analytes in relation to brain aging outcomes and found that lower levels of CSF VEGF were related to smaller hippocampi, larger ventricles, and faster decline on the Mini-Mental State Examination over 12 months.13 Interestingly, these observations were only present in amyloid-positive individuals. It is not yet clear whether an interaction between VEGF and such AD biomarkers is specific to amyloid or whether similar interactions are also present with tau, another primary pathology in AD. More importantly, each of these outcomes (CSF biomarkers, hippocampal volume, and cognitive performance) are highly correlated with diagnostic status, leaving open the possibility that the predictive power of VEGF may differ across the dementia spectrum.
The present study conducted a focused candidate analysis of CSF VEGF in relation to brain aging outcomes. First, we evaluated whether a main effect of VEGF was present cross-sectionally and longitudinally in relation to hippocampal volume and 2 domains of cognitive performance (episodic memory and executive function). Consistent with the theory that elevations in VEGF are neuroprotective, we hypothesized that higher VEGF levels would relate to larger hippocampal volumes and better cognitive performances. Next, we tested whether the relation between VEGF and brain aging outcomes differed between cognitive diagnostic categories. Finally, we tested the interaction between VEGF and continuous measures of CSF AD biomarkers (Aβ-42 and total tau) to test whether the role of VEGF depends on the level of CSF amyloid, CSF tau, or both. Our hypothesis was that the neuroprotective effect of VEGF on brain aging outcomes (hippocampal atrophy and cognitive decline) would not be specific to one pathologic process but would be strongest in the presence of either AD biomarker (Aβ-42 or total tau).
Participants were drawn from the ADNI launched in October 2004 (http://adni.loni.ucla.edu/). The original ADNI study enrolled approximately 800 participants aged 55 to 90 years, excluding serious neurological disease other than AD, history of brain lesion or head trauma, and history of psychoactive medication use (for full inclusion/exclusion criteria, see http://www.adni-info.org). Informed written consent was obtained from all participants at each site, and analysis of ADNI’s publically available database was approved by the Vanderbilt University Medical Center institutional review board prior to data analysis.
We accessed publicly available data from the ADNI on June 1, 2014. Participants were enrolled in the ADNI based on criteria outlined in the ADNI protocol (http://www.adni-info.org/Scientists/ADNIOverview.aspx). For the present analyses, we included all participants with CSF multiplex data that passed ADNI’s quality-control procedures (defined further on), CSF measurement of Aβ-42 and total tau, and the neuroimaging or cognitive outcome of interest. For the neuroimaging analyses, participants had to have a FreeSurfer measure of hippocampal volume derived from 1.5-T magnetic resonance imaging data, yielding 279 participants. For the cognitive analyses, participants had to have a composite measure of memory and executive function, yielding 306 participants. Participant characteristics are presented in Table 1.
The ADNI CSF protocol, including the quantification of Aβ-42 and total tau, has been detailed elsewhere.14,15 For the present analyses, we compiled a data set across the UPENN1-UPENN5 data sources available for download and used the first measure of total tau and Aβ-42 available for each participant.
Vascular endothelial growth factor was calculated as part of a CSF multiplex proteomic processing stream using an xMAP multiplex panel (MyriadRBM),16 details of which are available on the ADNI website (http://adni.loni.usc.edu/wp-content/uploads/2012/01/2011Dec28-Biomarkers-Consortium-Data-Primer-FINAL1.pdf). Test-retest validation was performed across 16 participant samples to ensure reliability. Analytes were removed during ADNI’s quality control if the test-retest sample was less than 7, if the mean percentage difference was greater than 35%, if the mean absolute percentage difference was greater than 60%, or if the Bland-Altman slope and intercept significantly differed from zero. Analytes were natural log transformed to better approximate a gaussian distribution. The VEGF analyte included in this study passed each of these quality-control procedures.
The ADNI neuropsychological protocol, including calculation of episodic memory and executive function composite measures, has been reported previously.17,18 We leveraged a memory (ADNI-MEM) and executive function (ADNI-EF) composite score in the present analyses. The ADNI-MEM included a composite z score based on item-level data from the Rey Auditory Verbal Learning Test, the AD Assessment Scale–Cognitive Test, the Mini-Mental State Examination, and Logical Memory I and II. The ADNI-EF included item-level data from the Trail Making Test Parts A and B, Digit Span Backward, Digit Symbol, Animal Fluency, Vegetable Fluency, and Clock Drawing Test.
The ADNI neuroimaging protocol has been reported in detail elsewhere.19 Images for the current study included original uncorrected 1.5-T T1-weighted high-resolution 3-dimensional structural data. Cortical reconstruction and volumetric segmentation were performed with the FreeSurfer image analysis suite version 4.3 (http://surfer.nmr.mgh.harvard.edu/).20- 22 FreeSurfer processing in the ADNI has been described previously.23 An early version of the longitudinal image-processing framework was used to process the sequential scans.24 Left hippocampal volume was the primary outcome measurement and intracranial volume was included as a covariate in all volumetric analyses, both of which were defined by FreeSurfer.25
All statistical analyses were performed in R version 2.15.2 (http://www.r-project.org/). Covariates included age, sex, education, cognitive diagnosis, and intracranial volume (for neuroimaging analyses). Significance was set a priori as α = .05.
Baseline effects were estimated using a general linear model for each of the 3 outcomes (left hippocampal volume, ADNI-MEM, and ADNI-EF). Longitudinal analyses were performed using mixed-model regression with time modeled as days from baseline for each participant. Time was then rescaled so that slopes would represent annual change (days from baseline/365.25). The time-by-VEGF interaction term tested whether VEGF levels were associated with change in the given outcome (left hippocampal volume, ADNI-MEM, and ADNI-EF) over the follow-up period. We evaluated the main effects of Aβ-42 and tau in separate models for comparison with VEGF. Correlations among VEGF, CSF Aβ-42, and tau were evaluated using Pearson correlations.
Next, we evaluated the interaction between VEGF and CSF AD biomarkers (Aβ-42 or total tau) on the 3 brain aging outcomes to test the neuroprotective effect of VEGF in the presence of enhanced AD. Predictors included VEGF level, biomarker level (either Aβ-42 or total tau), and a VEGF-by-biomarker interaction term. Longitudinal analyses included a time-by-VEGF-by-biomarker interaction term to evaluate whether VEGF level interacted with biomarker level in association with change in hippocampal volume, memory, or executive function over the follow-up period. All lower-order interactions of this 3-way interaction term were included in the model. The CSF AD biomarkers were treated as continuous variables for all analyses; however, biomarker groups were also identified for illustration purposes based on previously reported cut points (Aβ-42 positive ≤192 and tau positive ≥93 pg/mL).15
Finally, we evaluated all identified significant main effects and interactions of VEGF to determine whether the effect of VEGF differed across diagnostic categories. For all models, the normal cognition group was set as the referent.
In baseline analyses, increased VEGF was associated with larger hippocampal volume (t277 = 2.62; P = .009; Table 2) but was not associated with episodic memory (t305 = 0.44; P = .66) or executive function performance (t305 = 1.38; P = .17).
In longitudinal analyses, increased VEGF level was associated with less hippocampal atrophy (t858 = 2.48; P = .01; Figure 1), less episodic memory decline (t1629 = 4.09; P < .001), and less executive function decline over time (t1616 = 3.00; P = .003). In all cases, a high VEGF level was associated with more optimal brain aging.
Vascular endothelial growth factor was correlated with CSF Aβ-42 (r = 0.22; n = 279; P < .001; eFigure 1 in the Supplement) and tau (r = 0.29; n = 279; P < .001; eFigure 2 in the Supplement).
At baseline, VEGF did not interact with Aβ-42 in relation to hippocampal volume (t274 = 0.31; P = .76), baseline memory performance (t302 = −0.06; P = .95), or baseline executive function performance (t302 = 0.92; P = .36).
In longitudinal analyses, there was a VEGF–by–Aβ-42 interaction in relation to memory performance changes (t1618 = −2.53; P = .01). As seen in Figure 2, a high VEGF level was associated with better memory performance in the presence of a low Aβ-42 level (amyloid positive). There was no VEGF–by–Aβ-42 interaction in relation to hippocampal atrophy (t850 = −0.53; P = .60) or changes in executive function performance over the follow-up interval (t1605 = −0.58; P = .56).
At baseline, VEGF did not interact with tau in relation to hippocampal volume (t271 = −1.11; P = .27), memory performance (t299 = 1.30; P = .19), or executive function performance (t299 = 0.62; P = .54).
In longitudinal analyses, there was a VEGF-by-tau interaction in relation to hippocampal atrophy (t845 = 4.17; P < .001), memory performance changes (t1610 = 2.49; P = .01), and executive function performance changes (t1597 = 3.71; P < .001) across the follow-up interval. As illustrated in Figure 3, in all cases, a high VEGF level was associated with better outcomes in the presence of a higher tau level (tau positive).
At baseline, diagnostic status interacted with VEGF in relation to hippocampal volume (F2,274 = 3.33; P = .04), whereby the protective effect of VEGF was only apparent in those individuals with mild cognitive impairment (MCI) compared with normal cognition individuals. Longitudinally, the observed effects of VEGF did not differ across diagnostic categories. However, given the observed baseline modification of diagnostic status, we present stratified results across all models in eTables 1 through 3 in the Supplement.
The present study evaluated whether VEGF relates to reduced neurodegeneration and cognitive decline in older adults. A higher VEGF level was associated with larger baseline hippocampal volume, less hippocampal atrophy over time, and less cognitive decline over time. Interestingly, the neuroprotective effect of VEGF appeared strongest in the presence of enhanced AD biomarkers, consistent with previous reports in Aβ-42 participants,13 suggesting that angiogenic factors may be particularly important in those individuals showing early hallmarks of the AD cascade. In further support of this theoretical pathway, the baseline effect of VEGF was also strongest in participants with MCI and, when performing stratified analyses, most associations were driven by effects in the MCI group.
The neuroprotective effect of VEGF in CSF is consistent with previous reports that high serum VEGF is associated with a decreased risk for AD5 and previous findings in the ADNI that VEGF differentiates AD cases from control individuals.12 Yet the mechanisms of this effect remain elusive. Our findings add to previous literature associating VEGF with various brain aging outcomes13 and suggest that the observed effects may have strong implications for potential interventions. For example, VEGF plays a large role in maintaining neural perfusion homeostasis.4 In mice, suppressed VEGF levels result in a reduction in perfusion even in the absence of angiogenic deficits.26 In humans, cerebral hypoperfusion is common in AD,27 appearing initially in the posterior cingulate and precuneus regions and later in medial temporal regions including the hippocampus.28 The protective effects of VEGF, if mediated through alterations in perfusion, may be particularly beneficial in older adults who are AD biomarker positive before the onset of clinical symptoms. Additional work is needed that teases apart the complex interplay between VEGF and neurodegeneration, particularly targeting mechanisms, such as neural perfusion, to clarify the pathway of the observed protective effects presented here. Future work leveraging arterial spin labeling–magnetic resonance imaging data and measures of VEGF would be useful in clarifying the role of cerebral blood flow alterations as a possible mediator of VEGF effects on brain aging.
The observed VEGF-by-biomarker interactions suggest the protective effect of VEGF is strongest in AD biomarker–positive individuals, particularly those adults who are tau positive. We observed a robust interaction between VEGF and tau in predicting longitudinal change in hippocampal volume, memory, and executive function. However, more importantly, our results suggested that the effect is not specific to one aspect of AD pathogenesis, as we also observed an interaction between VEGF and Aβ-42 in predicting longitudinal change in memory performance. It is interesting that we did not observe a VEGF–by–Aβ-42 interaction in relation to hippocampal volume, particularly given the reported protective effect of VEGF in the hippocampus of APP transgenic mice,6,8,9 and the previously reported effect of VEGF in Aβ-42–positive individuals.13 Our models treated both VEGF and Aβ-42 as continuous variables rather than binary (positive vs negative) variables and explicitly tested for an interaction, which may explain the discordant results. We confirmed a strong effect of VEGF in Aβ-42–positive participants in relation to both baseline and longitudinal hippocampal volume (results not shown); therefore, the difference in outcomes between the studies is likely owing to the statistical model applied. While interaction effects do explain some of the association between VEGF and neurodegeneration, it appears that there is a strong underlying main effect of VEGF that is present whether biomarker positive or negative.
We also observed an interesting interaction between VEGF and diagnosis whereby the effect of VEGF on baseline hippocampal volume was driven by a strong association in participants with MCI. Moreover, when we stratified results across diagnostic categories, the observed effects appeared to be driven primarily by the MCI group, although the study was somewhat underpowered to fully investigate the VEGF-by-biomarker-by-diagnosis interaction models given the sample size and number of model parameters. However, the identified diagnostic interaction and the stratified results certainly suggest that VEGF may have the most relevance in individuals at highest risk for future neurodegeneration.
There are a few potential mechanisms by which VEGF may reduce the risk for neurodegeneration. One possibility is that VEGF causes actual reductions in the pathological hallmarks of AD. In support of such an explanation, prior work has demonstrated that treating AD mice with cells secreting VEGF yielded reductions in both tau and amyloid burden at autopsy.29 Another possibility is that the observed statistical interaction is driven by a physical interaction between VEGF and AD pathology. Prior work has demonstrated that VEGF binds to Aβ-40 and Aβ-42 in in vitro experiments, and such binding results in increased neural vulnerability to future insult by reducing the availability of VEGF throughout the brain.30 That is, in individuals with low levels of VEGF, the binding of VEGF to Aβ-42 could result in a measurable cognitive or neurodegenerative effect due to large net reductions in VEGF activity throughout the brain. Such an effect may not be observed in individuals with high levels of VEGF at baseline who have sufficient reserves to endure the reduced presence of VEGF throughout the brain as it binds to Aβ-42. It is also possible that non–Aβ-42 sequestration effects, perhaps via VEGF receptors, determine the region-specific expression and activity of VEGF throughout the brain. The current results make it difficult to decipher how VEGF, tau, and Aβ-42 interact at various stages of the AD process; however, regardless of the mechanism, VEGF may be particularly relevant to the long-term clinical manifestation of AD in biomarker-positive individuals.
This study had several strengths. First, the large sample size made this study among the largest to evaluate VEGF in humans to date. The availability of CSF data in the ADNI cohort expands prior human work relying on serum VEGF. The longitudinal follow-up and the combination of cognitive and neuroimaging data allowed us to test for a protective effect of VEGF in relation to several commonly used brain aging phenotypes. Further, the availability of CSF AD biomarker data allowed us to demonstrate that the VEGF protective effect is particularly relevant in AD biomarker–positive older adults.
Despite these strengths, a larger sample would have allotted us the opportunity to properly evaluate the VEGF-by-biomarker interaction across diagnostic groups. Our results suggest the protective effect of VEGF is strongest in the MCI group but future work is needed to confirm such an observation. Moreover, it would be worthwhile to study VEGF in a cohort with pathological confirmation of AD to evaluate whether the beneficial effect of VEGF is related to a reduction in comorbidities at autopsy, particularly vascular comorbidities that frequently co-occur in clinical AD cases.31 Such comorbidities are especially relevant to differences in cognitive profiles.32 We also performed 18 comparisons in this primary analysis, so the possibility of false-positives cannot be overlooked. When correcting for multiple comparisons using the Bonferroni procedure, 4 of our 8 findings remained statistically significant (Table 2). Finally, although CSF tau and Aβ-42 are well-established AD biomarkers15 included in the updated diagnostic criteria used in clinical trials,33 they are not perfect surrogates for pathological burden at autopsy. Future work should evaluate the interaction between VEGF expression and pathologic burden to clarify the specificity of the interaction effects observed here.
Corresponding Author: Timothy J. Hohman, PhD, Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, 2525 W End Ave, 12th Floor, Ste 1200, Nashville, TN 37203 (email@example.com).
Accepted for Publication: December 23, 2014.
Published Online: March 9, 2015. doi:10.1001/jamaneurol.2014.4761.
Author Contributions: Dr Hohman had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: All authors.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: All authors.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Hohman.
Obtained funding: Hohman, Jefferson.
Administrative, technical, or material support: Hohman.
Study supervision: Jefferson.
Conflict of Interest Disclosures: None reported.
Funding/Support: This research was supported in part by the Pharmaceutical Research and Manufacturers of America Foundation Fellowship in Translational Medicine and Therapeutics (Dr Hohman); the Eisenstein Women’s Heart Fund (Dr Bell); grants K12-HD043483 (Dr Bell), K24-AG046373 (Dr Jefferson), R01-AG034962 (Dr Jefferson), and R01-HL111516 (Dr Jefferson) from the National Institutes of Health through Vanderbilt University Medical Center; Alzheimer’s Association grant IIRG-08-88733 (Dr Jefferson); and the Vanderbilt Memory & Alzheimer’s Center. Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health grant U01 AG024904) and Department of Defense ADNI (Department of Defense award W81XWH-12-2-0012). The 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; Araclon Biotech; BioClinica Inc; Biogen Idec Inc; Bristol-Myers Squibb Co; Eisai Inc; Elan Pharmaceuticals Inc; Eli Lilly and Co; 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 and Johnson Pharmaceutical Research and Development LLC; Medpace Inc; Merck and Co Inc; Meso Scale Diagnostics LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corp; Pfizer Inc; Piramal Imaging; Servier; Synarc Inc; and Takeda Pharmaceutical Co. 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 (http://www.fnih.org). 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. Data from the ADNI are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Group Information: The Alzheimer’s Disease Neuroimaging Initiative investigators are follows: ADNI I, GO, and II: Principal Investigator (PI):Michael W. Weiner, MD, University of California (UC) San Francisco. ADCS PI and Director of Coordinating Center Clinical Core: Paul Aisen, MD, UC San Diego. Executive Committee: Michael Weiner, MD, UC San Francisco; Paul Aisen, MD, UC San Diego; Ronald Petersen, MD, PhD, Mayo Clinic, Rochester; Clifford R. Jack Jr, MD, Mayo Clinic, Rochester; William Jagust, MD, UC Berkeley; John Q. Trojanowki, MD, PhD, University of Pennsylvania (UPenn); Arthur W. Toga, PhD, University of Southern California (USC); Laurel Beckett, PhD, UC Davis; Robert C. Green, MD, MPH, Brigham and Women’s Hospital/Harvard Medical School; Andrew J. Saykin, PsyD, Indiana University; John Morris, MD, Washington University, St Louis; and Leslie M. Shaw, UPenn. ADNI External Advisory Board (ESAB): Zaven Khachaturian, PhD, Prevent Alzheimer’s Disease 2020 (chair); Greg Sorensen, MD, Siemens; Maria Carrillo, PhD, Alzheimer’s Association; Lew Kuller, MD, University of Pittsburgh; Marc Raichle, MD, Washington University, St Louis; Steven Paul, MD, Cornell University; Peter Davies, MD, Albert Einstein College of Medicine of Yeshiva University; Howard Fillit, MD, AD Drug Discovery Foundation; Franz Hefti, PhD, Acumen Pharmaceuticals; Davie Holtzman, MD, Washington University, St Louis; M. Marcel Mesulam, MD, Northwestern University; William Potter, MD, National Institute of Mental Health; and Peter Snyder, PhD, Brown University. ADNI 2 Private Partner Scientific Board (PPSB): Adam Schwartz, MD, Eli Lilly (chair). Data and Publication Committee (DPC): Robert C. Green, MD, MPH, Brigham and Women’s Hospital/Harvard Medical School (chair). Resource Allocation Review Committee: Tom Montine, MD, PhD, University of Washington (chair). Clinical Core Leaders: Ronald Petersen, MD, PhD, Mayo Clinic, Rochester (core PI); and Paul Aisen, MD, UC San Diego. Clinical Informatics and Operations: Ronald G. Thomas, PhD, UC San Diego; Michael Donohue, PhD, UC San Diego; Sarah Walter, MSc, UC San Diego; Devon Gessert, UC San Diego; Tamie Sather, MA, UC San Diego; and Gus Jiminez, MBS, UC San Diego. Biostatistics Core Leaders and Key Personnel: Laurel Beckett, PhD, UC Davis (core PI); Danielle Harvey, PhD, UC Davis; and Michael Donohue, PhD, UC San Diego. MRI Core Leaders and Key Personnel: Clifford R. Jack Jr, MD, Mayo Clinic, Rochester (core PI); Matthew Bernstein, PhD, Mayo Clinic, Rochester; Nick Fox, MD, University of London; Paul Thompson, PhD, UC Los Angeles School of Medicine; Norbert Schuff, PhD, UC San Francisco MRI; Charles DeCarli, MD, UC Davis; Bret Borowski, RT, Mayo Clinic; Jeff Gunter, PhD, Mayo Clinic; Matt Senjem, MS, Mayo Clinic; Prashanthi Vemuri, PhD, Mayo Clinic; David Jones, MD, Mayo Clinic; Kejal Kantarci, Mayo Clinic; and Chad Ward, Mayo Clinic. PET Core Leaders and Key Personnel: William Jagust, MD, UC Berkeley (core PI); Robert A. Koeppe, PhD, University of Michigan; Norm Foster, MD, University of Utah; Eric M. Reiman, MD, Banner Alzheimer’s Institute; Kewei Chen, PhD, Banner Alzheimer’s Institute; Chet Mathis, MD, University of Pittsburgh; and Susan Landau, PhD, UC Berkeley. Neuropathology Core Leaders: John C. Morris, MD, Washington University, St Louis; Nigel J. Cairns, PhD, MRCPath, Washington University, St Louis; Erin Householder, Washington University, St Louis; and Lisa Taylor-Reinwald, BA, HTL (ASCP) (past investigator), Washington University, St Louis. Biomarkers Core Leaders and Key Personnel: Leslie M. Shaw, PhD, UPenn School of Medicine; John Q. Trojanowki, MD, PhD, UPenn School of Medicine; Virginia Lee, PhD, MBA, UPenn School of Medicine; Magdalena Korecka, PhD, UPenn School of Medicine; and Michal Figurski, PhD, UPenn School of Medicine. Informatics Core Leaders and Key Personnel: Arthur W. Toga, PhD, USC (core PI); Karen Crawford, USC; and Scott Neu, PhD, USC. Genetics Core Leaders and Key Personnel: Andrew J. Saykin, PsyD, Indiana University; Tatiana M. Foroud, PhD, Indiana University; Steven Potkin, MD, UC Irvine; Li Shen, PhD, Indiana University; Kelley Faber, MS, CCRC, Indiana University; Sungeun Kim, PhD, Indiana University; and Kwangsik Nho, PhD, Indiana University. Initial Concept Planning and Development: Michael W. Weiner, MD, UC San Francisco; Lean Thal, MD, UC San Diego; and Zaven Khachaturian, PhD, Prevent Alzheimer’s Disease 2020. Early Project Proposal Development: Leon Thal, MD, UC San Diego; Neil Buckholtz, National Institute on Aging (NIA); Michael W. Weiner, MD, UC San Francisco; Peter J. Snyder, PhD, Brown University; William Potter, MD, National Institute of Mental Health; Steven Paul, MD, Cornell University; Marylyn Albert, PhD, Johns Hopkins University; Richard Frank, MD, PhD, Richard Frank Consulting; and Zaven Khachaturian, PhD, Prevent Alzheimer’s Disease 2020. NIA: John Hsiao, MD, NIA. Investigators by site: Oregon Health and Science University:Jeffrey Kaye, MD, Joseph Quinn, MD, Betty Lind, BS, Raina Carter, BA, and Sara Dolen, BS (past investigator). USC: Lon S. Schneider, MD, Sonia Pawluczyk, MD, Mauricio Beccera, BS, Liberty Teodoro, RN, and Bryan M. Spann, DO, PhD (past investigator). UC San Diego: James Brewer, MD, PhD, Helen Vanderswag, RN, and Adam Fleisher, MD (past investigator). University of Michigan: Judith L. Heidebrink, MD, MS, and Joanne L. Lord, LPN, BA, CCRC. Mayo Clinic, Rochester:Ronald Petersen, MD, PhD, Sara S. Mason, RN, Colleen S. Albers, RN, David Knopman, MD, and Kris Johnson, RN (past investigator). Baylor College of Medicine:Rachelle S. Doody, MD, PhD, Javier Villanueva-Meyer, MD, Munir Chowdhury, MBBS, MS, Susan Rountree, MD, and Mimi Dang, MD. Columbia University Medical Center: Yaakov Stern, PhD, Lawrence S. Honig, MD, PhD, and Karen L. Bell, MD. Washington University, St Louis: Beau Ances, MD, John C. Morris, MD, Maria Carroll, RN, MSN, Sue Leon, RN, MSN, Erin Householder, MS, CCRP, Mark A. Mintun, MD (past investigator), Stacy Schneider, APRN, BC, GNP (past investigator), and Angela Oliver, RN, BSN, MSG (past investigator). University of Alabama, Birmingham: Daniel Marson, JD, PhD, Randall Griffith, PhD, ABPP, David Clark, MD, David Geldmacher, MD, John Brockington, MD, and Erik Roberson, MD. Mount Sinai School of Medicine: Hillel Grossman, MD, and Effie Mitsis, PhD. Rush University Medical Center: Leyla deToledo-Morrell, PhD, and Raj C. Shah, MD. Wien Center:Ranjan Duara, MD, Daniel Varon, MD, Maria T. Greig, HP, and Peggy Roberts, CAN (past investigator). Johns Hopkins University: Marilyn Albert, PhD, Chiadi Onyike, MD, Daniel D’Agostino II, BS, and Stephanie Kielb, BS (past investigator). New York University:James E. Galvin, MD, MPH, Dana M. Pogorelec, Brittany Cerbone, Christina A. Michel, Henry Rusinek, PhD (past investigator), Mony J. de Leon, EdD (past investigator), Lidia Glodzik, MD, PhD (past investigator), and Susan De Santi, PhD (past investigator). Duke University Medical Center: P. Murali Doraiswamy, MD, Jeffrey R. Petrella, MD, and Terence Z. Wong, MD. UPenn: Steven E. Arnold, MD, Jason H. Karlawish, MD, and David Wolk, MD. University of Kentucky:Charles D. Smith, MD, Greg Jicha, MD, Peter Hardy, PhD, Partha Sinha, PhD, Elizabeth Oates, MD, and Gary Conrad, MD. University of Pittsburgh:Oscar L. Lopez, MD, MaryAnn Oakley, MA, and Donna M. Simpson, CRNP, MPH. University of Rochester Medical Center: Anton P. Porsteinsson, MD, Bonnie S. Goldstein, MS, NP, Kim Martin, RN, Kelly M. Makino, BS (past investigator), M. Saleem Ismail, MD (past investigator), and Connie Brand, RN (past investigator). UC Irvine:Ruth A. Mulnard, DNSc, RN, FAAN, Gaby Thai, MD, and Catherine McAdams-Ortiz, MSN, RN, A/GNP. University of Texas Southwestern Medical School: Kyle Womack, MD, Dana Mathews, MD, PhD, Mary Quiceno, MD, Ramon Diaz-Arrastia, MD, PhD (past investigator), Richard King, MD (past investigator), Myron Weiner, MD (past investigator), Kristen Martin-Cook, MA (past investigator), and Michael DeVous, PhD (past investigator). Emory University:Allan I. Levey, MD, PhD, James J. Lah, MD, PhD, and Janet S. Cellar, DNP, PMHCNS-BC. University of Kansas Medical Center: Jeffrey M. Burns, MD, Heather S. Anderson, MD, and Russell H. Swerdlow, MD. UC Los Angeles: Liana Apostolova, MD, Kathleen Tingus, PhD, Ellen Woo, PhD, Daniel H. S. Silverman, MD, PhD, Po H. Lu, PsyD (past investigator), and George Bartzokis, MD (past investigator). Mayo Clinic, Jacksonville: Neill R. Graff-Radford, MBBCH, FRCP (London), Francine Parfitt, MSH, CCRC, Tracy Kendall, BA, CCRP, and Heather Johnson, MLS, CCRP (past investigator). Indiana University:Martin R. Farlow, MD, Ann Marie Hake, MD, Brandy R. Matthews, MD, Scott Herring, RN, CCRC, and Cynthia Hunt, BS, CCRP. Yale University School of Medicine:Christopher H. van Dyck, MD, Richard E. Carson, PhD, and Martha G. MacAvoy, PhD. McGill University, Montreal-Jewish General Hospital: Howard Chertkow, MD, Howard Bergman, MD, and Chris Hosein, MEd. Sunnybrook Health Sciences, Ontario: Sandra Black, MD, FRCPC, and Bojana Stefanovic Curtis Caldwell, PhD. UBC Clinic for AD and Related Disorders:Ging-Yuek Robin Hsiung, MD, MHSc, FRCPC, Howard Feldman, MD, FRCPC, Benita Mudge, BS, and Michele Assaly, MA (past investigator). Cognitive Neurology, St. Joseph’s, Ontario: Andrew Kertesz, MD, John Rogers, MD, and Dick Trost, PhD. Cleveland Clinic Lou Ruvo Center for Brain Health: Charles Bernick, MD, and Donna Munic, PhD. Northwestern University: Diana Kerwin, MD, Marek-Marsel Mesulam, MD, Kristine Lipowski, BA, Chuang-Kuo Wu, MD, PhD (past investigator), and Nancy Johnson, PhD (past investigator). Premiere Research Institute (Palm Beach Neurology): Carl Sadowsky, MD, Walter Martinez, MD, and Teresa Villena, MD. Georgetown University Medical Center:Raymond Scott Turner, MD, PhD, Kathleen Johnson, NP, and Brigid Reynolds, NP. Brigham and Women’s Hospital: Reisa A. Sperling, MD, Keith A. Johnson, MD, Gad Marshall, MD, and Meghan Frey (past investigator). Stanford University:Jerome Yesavage, MD, Joy L. Taylor, PhD, Barton Lane, MD, Allyson Rosen, PhD (past investigator), and Jared Tinklenberg, MD (past investigator). Banner Sun Health Research Institute: Marwan N. Sabbagh, MD, Christine M. Belden, PsyD, Sandra A. Jacobson, MD, and Sherye A. Sirrel, MS. Boston University:Neil Kowall, MD, Ronald Killiany, PhD, Andrew E. Budson, MD, Alexander Norbash, MD (past investigator), and Patricia Lynn Johnson, BA (past investigator). Howard University: Thomas O. Obisesan, MD, MPH, Saba Wolday, MSc, and Joanne Allard, PhD. Case Western Reserve University: Alan Lerner, MD, Paula Ogrocki, PhD, and Leon Hudson, MPH (past investigator). UC Davis–Sacramento: Evan Fletcher, PhD, Owen Carmichael, PhD, John Olichney, MD, and Charles DeCarli, MD (past investigator). Neurological Care of CNY: Smita Kittur, MD. Parkwood Hospital:Michael Borrie, MBChB, T.-Y. Lee, PhD, and Rob Bartha, PhD. University of Wisconsin:Sterling Johnson, PhD, Sanjay Asthana, MD, and Cynthia M. Carlsson, MD. UC Irvine–BIC: Steven G. Potkin, MD, Adrian Preda, MD, and Dana Nguyen, PhD. Banner Alzheimer’s Institute: Pierre Tariot, MD, Adam Fleisher, MD, and Stephanie Reeder, BA. Dent Neurologic Institute:Vernice Bates, MD, Horacio Capote, MD, and Michelle Rainka, PharmD, CCRP. Ohio State University:Douglas W. Scharre, MD, Maria Kataki, MD, PhD, and Anahita Adeli, MD. Albany Medical College:Earl A. Zimmerman, MD, Dzintra Celmins, MD, and Alice D. Brown, FNP. Hartford Hospital, Olin Neuropsychiatry Research Center: Godfrey D. Pearlson, MD, Karen Blank, MD, and Karen Anderson, RN. Dartmouth-Hitchcock Medical Center: Robert B. Santulli, MD, Tamar J. Kitzmiller, and Eben S. Schwartz, PhD (past investigator). Wake Forest University Health Sciences: Kaycee M. Sink, MD, MAS, Jeff D. Williamson, MD, MHS, Pradeep Garg, PhD, and Franklin Watkins, MD (past investigator). Rhode Island Hospital: Brian R. Ott, MD, Henry Querfurth, MD, and Geoffrey Tremont, PhD. Butler Hospital: Stephen Salloway, MD, MS, Paul Malloy, PhD, and Stephen Correia, PhD. UC San Francisco:Howard J. Rosen, MD, and Bruce L. Miller, MD. Medical University South Carolina: Jacobo Mintzer, MD, MBA, Kenneth Spicer, MD, PhD, and David Bachman, MD. St Joseph’s Health Care:Elizabeth Finger, MD, Stephen Pasternak, MD, Irina Rachinsky, MD, John Rogers, MD, Andrew Kertesz, MD (past investigator), and Dick Drost, MD (past investigator). Nathan Kline Institute:Nunzio Pomara, MD, Raymundo Hernando, MD, and Antero Sarrael, MD. University of Iowa College of Medicine: Susan K. Schultz, MD, Laura L. Boles Ponto, PhD, Hyungsub Shim, MD, and Karen Elizabeth Smith, RN. Cornell University:Norman Relkin, MD, PhD, Gloria Chaing, MD, and Lisa Raudin, PhD. University of South Florida, USF Health Byrd Alzheimer’s Institute: Amanda Smith, MD, Kristin Fargher, MD, and Balebail Ashok Raj, MD.
Additional Contributions: Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.