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Original Contributions |

Longitudinal Change of Biomarkers in Cognitive Decline FREE

Raymond Y. Lo, MD, MS; Alan E. Hubbard, PhD; Leslie M. Shaw, PhD; John Q. Trojanowski, MD, PhD; Ronald C. Petersen, MD, PhD; Paul S. Aisen, MD; Michael W. Weiner, MD; William J. Jagust, MD; for the Alzheimer's Disease Neuroimaging Initiative
[+] Author Affiliations

Author Affiliations: Division of Epidemiology (Drs Lo, Hubbard, and Jagust) and Helen Wills Neuroscience Institute (Dr Jagust), University of California, Berkeley, Department of Neurosciences, University of California, San Diego, La Jolla (Dr Aisen), and Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center and Departments of Radiology, Medicine, Psychiatry, and Neurology, University of California, San Francisco (Dr Weiner); Department of Pathology and Laboratory Medicine and Institute on Aging, University of Pennsylvania School of Medicine, Philadelphia (Drs Shaw and Trojanowski); and Department of Neurology, Mayo Clinic, Rochester, Minnesota (Dr Petersen).


Arch Neurol. 2011;68(10):1257-1266. doi:10.1001/archneurol.2011.123.
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Published online

Objective To delineate the trajectories of Aβ42 level in cerebrospinal fluid (CSF), fludeoxyglucose F18 (FDG) uptake using positron emission tomography, and hippocampal volume using magnetic resonance imaging and their relative associations with cognitive change at different stages in aging and Alzheimer disease (AD).

Design Cohort study.

Setting The 59 study sites for the Alzheimer's Disease Neuroimaging Initiative.

Participants A total of 819 participants 55 to 90 years of age with normal cognition, mild cognitive impairment, and AD who were followed up during the period from 2005 to 2007.

Main Outcome Measures Rates of change in level of Aβ42 in CSF, FDG uptake, hippocampal volume, and the Alzheimer Disease's Assessment Scale–cognitive subscale score during up to 36 months of follow-up by diagnostic group as well as prediction of cognitive change by each biomarker.

Results Reductions in the level of Aβ42 in CSF were numerically greater in participants with normal cognition than in participants with mild cognitive impairment or AD; whereas both glucose metabolic decline and hippocampal atrophy were significantly slower in participants with normal cognition than in participants with mild cognitive impairment or AD. Positive APOE4 status accelerated hippocampal atrophic changes in participants with mild cognitive impairment or AD, but did not modify rates of change in level of Aβ42 in CSF or FDG uptake. The Alzheimer Disease's Assessment Scale–cognitive subscale scores were related only to the baseline level of Aβ42 in CSF and the baseline FDG uptake in participants with normal cognition, which were about equally associated with change in FDG uptake and hippocampal volume in participants with mild cognitive impairment and best modeled by change in FDG uptake in participants with AD.

Conclusion Trajectories of Aβ42 level in CSF, FDG uptake, and hippocampal volume vary across different cognitive stages. The longitudinal patterns support a hypothetical sequence of AD pathology in which amyloid deposition is an early event before hypometabolism or hippocampal atrophy, suggesting that biomarker prediction for cognitive change is stage dependent.

Figures in this Article

Using biomarkers for the early detection of Alzheimer disease (AD) is crucial for developing potential treatment. Previous studies have shown that Aβ42 and tau protein levels in cerebrospinal fluid (CSF),1 region-specific fludeoxyglucose F18 (FDG) uptake using positron emission tomography (PET),2 and hippocampal volume using magnetic resonance imaging (MRI)3 were markers associated with AD. Postmortem examinations further demonstrated that the burden of AD pathology was reflected by the antemortem Aβ42 level in CSF,4 the region-specific FDG uptake using PET,5 and the hippocampal volume using MRI,6 which suggests that these markers are indicative of the altered biological states in AD.

Although lower levels of Aβ42 in CSF are associated with the risk of incipient AD,7 CSF biomarkers appear to be relatively stable over time within individuals.8,9 Greater hippocampal atrophy rates measured by serial MRI correlated with faster cognitive decline in normal aging and early conversion to dementia in mild cognitive impairment (MCI) in previous studies.1013 Several longitudinal FDG-PET studies also suggested that regional hypometabolism predicted clinical progression or conversion to AD.1417 Because these time-varying biomarkers as well as the APOE4 gene are all associated with AD or cognitive impairment, it is conceivable that they are correlated with one another.1821 However, very few studies have examined the dynamic change of 2 or more biomarkers simultaneously.22,23 Longitudinal comparison of biomarker change is an important approach to assess the relative importance and pathological significance of each biomarker.

In our study, we aimed to delineate the trajectories of the Aβ42 level in CSF, FDG uptake, and hippocampal volume as well as the influence of the APOE4 gene, and then evaluated their relative associations with cognitive function in participants with normal cognition (NC), MCI, or AD.

STUDY POPULATION

A total of 819 research participants (229 with NC, 397 with MCI, and 193 with AD) were enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) from 59 centers in the United States and Canada during the period from 2005 to 2007. Full inclusion and exclusion criteria are detailed at http://www.adni-info.org. Briefly, screening criteria for entry into our study included the Mini-Mental State Examination score, the Clinical Dementia Rating scale, and an education-adjusted cutoff score on delayed recall of 1 paragraph from the Logical Memory subtest of the Wechsler Memory Scale–Revised.24 All participants were recruited between the ages of 55 and 90 years and had at least 6 years of education. Participants who took specific psychoactive medications or who had other neurological disorders were excluded. After the baseline visit, subsequent visits occurred at 6- or 12-month intervals. Participants with NC or MCI were followed up for 3 years, whereas those with AD were followed up for 2 years at maximum.

STANDARD PROTOCOL APPROVALS, REGISTRATIONS, AND PATIENTS’ CONSENT

The study procedures were approved by the institutional review boards of all participating institutions. Written informed consent to obtain blood samples and to perform lumbar puncture, neuropsychological testing, and neuroimaging were obtained from all research participants or their representatives.

GENETIC MARKER

Blood samples at baseline were collected, and APOE genotyping was performed at the University of Pennsylvania AD biomarker laboratory in Philadelphia. APOE4 gene carriers were participants who had at least 1 APOE4 allele.

CSF PROTEINS

Cerebrospinal fluid samples were collected in the morning after overnight fast, shipped to the University of Pennsylvania AD biomarker laboratory, and analyzed using a standardized protocol.25 Levels of Aβ42, total tau, and phosphorylated tau were measured (in units of picograms per milliter) in each of the CSF aliquots using the multiplex xMAP Luminex (Luminex Corp, Austin, Texas) platform with Innogenetics (INNO-BIA AlzBio3; Ghent, Belgium; for research use–only reagents) immunoassay kit–based reagents. About 50% of all participants underwent lumbar puncture at baseline, after which 106 participants underwent a lumbar puncture every year for 3 years.

FDG UPTAKE USING PET

The protocol to acquire ADNI PET data at sites nationwide is detailed at http://adni.loni.ucla.edu/research/protocols/pet-protocols/, and methods for FDG-PET analysis have been described previously.26 Briefly, PET images were acquired 30 to 60 minutes after injection. Images were averaged, spatially aligned, interpolated to a standard voxel size, intensity normalized, and smoothed to a common resolution of 8-mm full-width at half-maximum. The PET volumes were intensity normalized to a single region comprising the cerebellar vermis and the pons defined by the Montreal Neurological Institute template. We used predefined regions of interest to reflect glucose metabolism. Mean FDG uptake was extracted and averaged from 5 regions of interest (right and left temporal gyrus, right and left angular gyrus, and posterior cingulate gyrus) for each participant. Baseline PET images were available for 404 participants, and more than 60% of these participants were followed up for 2 additional years with repeated PET scans.

MRI HIPPOCAMPAL VOLUME

The 1.5-T MRI protocol, which was described elsewhere,27 was standardized across all sites: 2 T1-weighted MRI scans, using a sagittal volumetric magnetization-prepared rapid gradient echo sequence, with an echo of 4 milliseconds, a repetition time of 9 milliseconds, a flip angle of 8°, and an acquisition matrix size of 256 × 256 × 166 in the x, y, and z dimensions with a nominal voxel size of 0.94 × 0.94 × 1.2 mm. The images were aligned, skull-stripped, and segmented. A quality-control center was designated to exclude scans with serious motion artifacts. FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) was applied to obtain bilateral hippocampal volumes in units of cubic millimeters from this segmentation. Baseline MRI images were available for 811 participants, and more than 60% of these participants were followed up for 2 more years with multiple MRI scans.

ASSESSMENT OF COGNITIVE FUNCTION

The Alzheimer's Disease Assessment Scale–cognitive subscale (ADAS-cog) score was used as a dependent measure to examine relationships between biomarkers and cognitive change. This test contains 11 items covering language, memory, praxis, and comprehension function. The total score ranges from 0 to 70, and higher scores indicate poorer cognitive function. Baseline and multiple follow-up ADAS-cog assessments were available for all participants.

STATISTICAL ANALYSES

Participants with 2 or more repeated measures had their data entered into the analyses. We first delineated the trajectories of different biomarkers and used repeated measures linear regression (an exchangeable, working-within-subject correlation model via a generalized estimating equation)28 to estimate population average rates of change in levels of CSF proteins, FDG-PET regions of interest, hippocampal volume, as well as ADAS-cog scores for participants with NC, MCI, or AD. To account for the residual correlation due to repeated measures on the same subject, we could have also used a more parametric, mixed-model approach. However, given that our focus was on the average rate of change in biomarkers (and not on the variance components), and because we wanted to derive a robust inference (standard errors not sensitive to the specified correlation model), we chose the generalized estimating equation approach rather than a parametric maximum likelihood approach.29 Time-varying biomarkers were treated as the outcome and modeled by time and baseline age in the regression. In these models, a significant time coefficient indicated a nonzero rate of change. We also made intergroup comparisons of rates of change. In a separate analysis, we included APOE4 allele carrier status in the model to evaluate its influence on the rate of change for each biomarker, reflected by the coefficient of the interaction term (APOE4 × time).

We then examined the relation between the change of cognitive function and the change of different biomarkers. Time-varying ADAS-cog scores were treated as the outcome of interest and modeled by time and the change in biomarkers after adjusting for baseline age and baseline biomarker value. Values of R2 were calculated for each longitudinal model to represent the goodness of fit or the extent to which the marginal variance of cognitive function was explained by the model. Models differed by biomarker of interest and sample size because only a limited number of participants had all 3 biomarkers available. We conducted model comparisons by restricting participants to those with 2 biomarkers available (Aβ42 level in CSF and FDG uptake; Aβ42 level in CSF and hippocampal volume; or FDG uptake and hippocampal volume) so as to make models more comparable. All statistical analyses and graphics were performed in R version 2.11.1 (R Foundation for Statistical Computing, Vienna, Austria).

The demographic features of all participants are summarized in Table 1. The sample size declined over time, and the number of repeated measures available for longitudinal analysis varied across different biomarkers and diagnostic groups (Table 2). The Aβ42 level in CSF (measured in units of picograms per milliliter per month) appeared to decrease faster in participants with NC (−0.46 pg/mL/mo) than in participants with MCI (−0.26 pg/mL/mo) or AD (−0.29 pg/mL/mo), but intergroup differences were not significant; changes in total tau and phosphorylated tau levels in CSF for the most part were not significantly different from zero (Table 2). Brain regional glucose metabolic decline (measured in units of normalized intensity per month) was significantly slower in participants with NC (−7.4 × 10−4 normalized intensity per month) than in participants with MCI (−1.9 × 10−3 normalized intensity per month) or AD (−4.2 × 10−3 normalized intensity per month) and slower in participants with MCI than in participants with AD (Table 2) Quiz Ref IDThe rate of MRI hippocampal atrophy (measured in units of cubic millimeters per month) was also significantly slower in participants with NC (−2.95 mm3/mo) than in participants with MCI (−5.52 mm3/mo) or AD (−8.01 mm3/mo) and slower in participants with MCI than in participants with AD . (Table 2). Cognitive function assessed by the ADAS-cog declined (increased in ADAS-cog score) in participants with MCI and declined even faster in participants AD, but it improved (decreased in ADAS-cog score) a little in participants with NC. The hypothetical average changes of these biomarkers and the ADAS-cog scores for a 75-year-old person in the 3 diagnostic groups are illustrated in our Figure.

Place holder to copy figure label and caption
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Figure. Hypothetical longitudinal changes in the Aβ42 level in cerebrospinal fluid (CSF), fludeoxyglucose F18 (FDG) uptake using positron emission tomography, hippocampal volume using magnetic resonance imaging, and Alzheimer Disease's Assessment Scale–cognitive subscale (ADAS-cog) score for a 75-year-old person at different cognitive states. AD indicates Alzheimer disease; MCI, mild cognitive impairment; and NC, normal cognition.

Table Graphic Jump LocationTable 1. Demographic Features of 819 Participants in the Alzheimer's Disease Neuroimaging Initiative at Enrollment
Table Graphic Jump LocationTable 2. Monthly Change in Biomarkers Among Study Population and Intergroup Rate Comparisona

The associations between APOE4 status and the baseline value of biomarkers were significant in the NC group for Aβ42 level in CSF and FDG uptake and in the MCI group for all 3 biomarkers (Table 3). Positive APOE4 status did not appear to modify the rate of change in the Aβ42 level in CSF or glucose metabolism in all 3 groups, but it accelerated hippocampal atrophy in the MCI and AD groups.

Table Graphic Jump LocationTable 3. Influence of APOE4 Gene on Biomarkers Among Participants in the Alzheimer's Disease Neuroimaging Initiativea

For participants with NC, although changes in cognitive function were not captured by any of these time-varying biomarkers, Aβ42 level in CSF (R2 = 0.12) appeared to better explain the total variance of ADAS-cog scores over time than did FDG uptake (R2 = 0.07) or MRI hippocampal volume (R2 = 0.03) (Table 4). Quiz Ref IDFor participants with MCI, changes in cognitive function were associated with all of these biomarkers, such that cognitive decline (increase in ADAS-cog score) was associated with the decrease in the Aβ42 level in the CSF, FDG-PET regional metabolism, and MRI hippocampal volume. Cognitive function at the MCI stage was about equally well modeled by FDG uptake (R2 = 0.18) and hippocampal volume (R2 = 0.16). For participants with mild AD, cognitive decline was still captured by FDG uptake and hippocampal volume, but not by the Aβ42 level in CSF. The variance of the ADAS-cog score during the course of dementia seemed better modeled by FDG uptake (R2 = 0.36) than by hippocampal volume (R2 = 0.19). We further conducted head-to-head comparisons in sample-size matched groups (Aβ42 level in CSF vs FDG uptake; Aβ42 level in CSF vs hippocampal volume; and FDG uptake vs hippocampal volume), and their relative contributions to model cognitive decline remained largely unchanged (eTable).

Table Graphic Jump LocationTable 4. Goodness of Fit of Regression Analyses Modeling Cognitive Change by Biomarkersa

Annualized changes in the biomarkers of the Aβ42 level in CSF, FDG uptake, and hippocampal volume as well as cognitive function during the first 12 months of follow-up in ADNI have been reported.30 We extended the follow-up study to up to 36 months and found evidence of significant change in the biomarkers of Aβ42 level, glucose metabolism, and hippocampal volume in all 3 groups of participants: NC, MCI, and AD. These biomarker trajectories showed that rates of change in the Aβ42 level were not different among the groups, but changes in glucose metabolism and hippocampal volume accelerated as cognitive function deteriorated. For participants with NC, cognitive change was not related to change in any of these biomarkers, although a model that included the Aβ42 level in CSF captured more variance than models that contained other biomarkers. The lack of association between cognitive change and biomarker dynamics in participants with NC may be due to only a subtle functional difference at this stage or to the limitation of our cognitive measurement tool. For participants with MCI, all 3 categories of biomarkers were related to cognitive decline, whereas for participants with AD, only glucose metabolism and hippocampal atrophy, and not the Aβ42 level in CSF, were related to cognitive decline. These findings imply that the Aβ42 level in CSF declines prior to the onset of cognitive impairment, in relation to aging or preclinical AD, whereas measures of neuronal dysfunction and injury (glucose metabolism and hippocampal atrophy) change with disease severity and stage.

Previous studies showed that, prior to cognitive impairment, APOE4 carriers have accelerated memory decline,31 greater MRI hippocampal atrophy rates,32 and faster decline in regional FDG uptake using PET.33 Our data in Table 3 demonstrated that APOE4 was associated with a baseline Aβ42 level in CSF and a baseline FDG uptake (but not a baseline hippocampal volume) in participants with NC, whereas in participants with MCI or AD, the presence of APOE4 accelerated hippocampal atrophy (but not the Aβ42 level in CSF or FDG uptake). The influence of the APOE4 gene on Aβ42 level in CSF and FDG-PET regional metabolism appeared to begin earlier than on hippocampal atrophy. There is evidence from pathological examinations and amyloid PET imaging showing that the APOE4 gene increases the risk of AD through Aβ accumulation in the brain.34,35 Therefore, the effect of APOE4 on biomarkers at different stages may reflect the pathological sequence led by the pivotal event in AD, β-amyloid deposition.

The decrease in the Aβ42 level in CSF as an early event shown in our biomarker trajectories and the influence of APOE4 on hippocampal atrophy that occurred after Aβ42 deposition in CSF and FDG uptake both imply that the FDG-PET marker changes after Aβ42 deposition in CSF but before hippocampal atrophy. Quiz Ref IDOur study supports the hypothetical model of the AD pathological cascade proposed by Jack et al,36 in which brain Aβ deposition heralds the onset of the entire AD pathological process and is followed by regional synaptic dysfunction or glucose hypometabolism that eventually culminates in cell loss or brain atrophy.

One of the unique features in our study is that we have follow-up information on the biomarkers of the Aβ42 level in CSF, FDG uptake, and hippocampal volume from our study participants, as well as ADAS-cog scores, to address the dynamics of the pathological course of AD. These biomarker dynamics have been examined by the ADNI using a cross-sectional approach37; however, to translate cross-sectional results into actual patterns of change requires a strong assumption that all participants follow the same pattern of disease progression from normal all the way to dementia. We understand that this assumption may hold true for participants who developed MCI and for participants with AD, but it is unlikely for participants with NC who did not develop MCI. Nearly half of the participants with MCI developed AD during follow-up, but very few people changed from NC to MCI or AD in the ADNI. Participants with NC may be a very different group of people from those who used to be cognitively normal but currently have MCI or AD. Ideally, the longitudinal change of biomarkers could have been better delineated had our study continued with follow-up that was long enough to observe the same group of participants with NC transitioning to MCI and AD. Limited by this design, we might be observing biomarker dynamics in aging but not necessarily disease progression in AD; therefore, we should be conservative about making inferences from participants who remained cognitively intact.

Previous longitudinal CSF studies showed that the decrease in the Aβ42 level correlated with cognitive decline in a healthy elderly population38 but that the decrease might be too slight to detect later in the disease course,22,39,40 which suggests that the level of Aβ42 in CSF might stabilize long before symptomatic dementia. These longitudinal CSF studies were, however, limited by, at most, 2 repeated measures and relatively small sample sizes. Our longitudinal study of CSF biomarkers is based on up to 3 repeated measures, which is the minimum number of time points allowing us to evaluate the variance of change. A baseline measure and 1 follow-up measure can only generate 1 single slope or change for each individual, and therefore there is no variance of slope to evaluate. The 2-point difference may result from either actual change or simply measurement error. In addition, if CSF biomarker measurement error exists, which is very likely for all laboratory tests, the magnitude of difference can be subject to the “regression toward the mean” effect. In other words, the more the baseline value deviates from the population mean, the larger the change is likely to be.

We used the ADAS-cog score to monitor cognitive function and mapped the change of biomarkers to the ADAS-cog score as a way to assess the extent to which pathological markers correlated with clinical progression over time. Quiz Ref IDThere is no gold standard for measurement of cognitive function, particularly when our outcome of interest includes multiple stages of AD from normal to overt dementia. We noticed that ADAS-cog scores in participants with NC even improved over time, and we recognized that the possible learning effect might hinder us from using the ADAS-cog to track cognitive change among healthy elderly people. Nevertheless, ADAS-cog is still the standard tool in many clinical trials to assess AD, which allows our results to be more interpretable across different studies.

There are several limitations in our study. First, research participants in the ADNI were volunteers and from clinics, not from the general population. Although they all met the inclusion and exclusion criteria for NC, amnestic MCI, or mild AD, they were not newly diagnosed or incident cases. Within the same diagnostic group, participants were enrolled in our study at different stages in the disease course. Baseline evaluation did not adequately reflect their clinical states when they first had the disease. Therefore, we want to be clear that our target population is patients who come to the clinic rather than the general community; and we applied a generalized estimating equation approach to avoid the unverifiable assumption about their biological states at the beginning of cognitive impairment. Second, not all ADNI research participants underwent all biomarker examinations, especially lumbar puncture for CSF. Like many longitudinal studies, we had substantial missing data for biomarkers during the 36-month follow-up period. Although a generalized estimating equation approach can handle missing time points within individuals, there is no way that we can recover the actual biomarker profiles for those individuals who did not end up being in the analyses. The differences in sample size, particularly the smaller samples of individuals with longitudinal CSF samples compared with the other biomarkers, may limit our ability to draw inferences about the relative changes in these biomarkers. Participants whose data were used in the analyses might be different from those whose data were not included or who dropped out; we do not know whether this is informative censoring or random missing data. Nevertheless, we focused on the relative rates of change or associations with cognitive change but not true rates. The calculated biomarker values would be biased by informative censoring, but the interrelationship among these biomarkers might not be affected.

Quiz Ref IDIn sum, longitudinal patterns of biomarkers suggest that Aβ42 level in CSF, FDG uptake using PET, and hippocampal volume using MRI capture AD pathological states sequentially and that their predictive values for cognitive decline depend on the stage of the disease. Repeated measurement of these candidate biomarkers provides a potential approach for early diagnosis of AD.

Correspondence: Raymond Y. Lo, MD, MS, Division of Epidemiology, University of California, Berkeley, 118 Barker Hall MC3190, Berkeley, CA 94720-3190 (rlo@berkeley.edu).

Accepted for Publication: March 25, 2011.

Published Online: June 13, 2011. doi:10.1001/archneurol.2011.123

Author Contributions:Study concept and design: Lo, Trojanowski, Weiner, and Jagust. Acquisition of data: Trojanowski and Weiner. Analysis and interpretation of data: Lo, Hubbard, Shaw, Trojanowski, Petersen, Aisen, Weiner, and Jagust. Drafting of the manuscript: Lo. Critical revision of the manuscript for important intellectual content: Lo, Hubbard, Shaw, Trojanowski, Petersen, Aisen, Weiner, and Jagust. Statistical analysis: Lo, Hubbard, and Trojanowski. Obtained funding: Weiner and Jagust. Administrative, technical, and material support: Shaw, Trojanowski, Aisen, Weiner, and Jagust. Study supervision: Jagust.

Financial Disclosure: Dr Shaw has received funding for travel and speaker honoraria from Pfizer; serves on the editorial board of the journal Therapeutic Drug Monitoring ; may potentially receive revenue for patent pending (application number 10/192,193): O-methylated rapamycin derivatives for alleviation and inhibition of lymphoproliferative disorders, licensed by the University of Pennsylvania to Novartis; receives royalties from publication of Applied Pharmacokinetics and Pharmacodynamics: Principles of Therapeutic Drug Monitoring (Wolters Kluwer/Lippincott Williams & Wilkins, 2005); receives research support from the National Institutes of Health (NIH) (grant AG024904 [coprincipal investigator {co-PI}, Biomarker Core Laboratory]); and receives board of directors' compensation and holds stock options in Saladax Biomedical. Dr Trojanowski has received funding for travel and honoraria from Takeda to attend numerous conferences not funded by industry; serves as an associate editor of the journal Alzheimer's & Dementia ; may accrue revenue on patents regarding Modified Avidin-Biotin Technique, Method of Stabilizing Microtubules to Treat Alzheimer's Disease, Method of Detecting Abnormally Phosphorylated Tau, Method of Screening for Alzheimer's Disease or Disease Associated with the Accumulation of Paired Helical Filaments, Compositions and Methods for Producing and Using Homogeneous Neuronal Cell Transplants, Rat Comprising Straight Filaments in Its Brain, Compositions and Methods for Producing and Using Homogeneous Neuronal Cell Transplants to Treat Neurodegenerative Disorders and Brain and Spinal Cord Injuries, Diagnostic Methods for Alzheimer's Disease by Detection of Multiple MRNAs, Methods and Compositions for Determining Lipid Peroxidation Levels in Oxidant Stress Syndromes and Diseases, Compositions and Methods for Producing and Using Homogenous Neuronal Cell Transplants, Method of Identifying, Diagnosing and Treating α-synuclein Positive Neurodegenerative Disorders, Mutation-specific Functional Impairments in Distinct Tau Isoforms of Hereditary Frontotemporal Dementia and Parkinsonism Linked to Chromosome-17: Genotype Predicts Phenotype, Microtubule Stabilizing Therapies for Neurodegenerative Disorders, and Treatment of Alzheimer's and Related Diseases with an Antibody; and receives research support from the NIH National Institute on Aging (NIA) and National Institute of Neurological Disorders and Stroke (grants P01 AG 09215-20 [PI], P30 AG 10124-18 [PI], PO1 AG 17586-10 [project 4 leader], 1PO1 AG-19724-07 [core C leader], U01 AG 024904-05 [co-PI, Biomarker Core Laboratory], P50 NS053488-02 [PI], UO1 AG029213-01 [coinvestigator], RC2NS069368 [PI], RC1AG035427 [PI], and P30AG036468 [PI]) and from the Marian S. Ware Alzheimer Program. Dr Petersen serves on scientific advisory boards for Pfizer, Janssen Alzheimer Immunotherapy, Elan, and GE Healthcare; receives royalties from the publication of Mild Cognitive Impairment (Oxford University Press, 2003); and receives research support from the NIH/NIA (grants U01 AG 06786 [PI], P50 AG 16574 [PI], U01 AG 024904 [subcontract PI], and R01 AG11378 [coinvestigator]). Dr Aisen serves on a scientific advisory board for NeuroPhage; serves as a consultant to Elan, Wyeth, Eisai, Neurochem, Schering-Plough, Bristol-Myers Squibb, Eli Lilly, NeuroPhage, Merck, Roche, Amgen, Genentech, Abbott, Pfizer, Novartis, and Medivation; receives research support from Pfizer, Baxter, Neuro-Hitech, Abbott, Martek, and the NIH/NIA (grants U01-AG10483 [PI], U01-AG024904 [coordinating center director], R01-AG030048 [PI], and R01-AG16381 [coinvestigator]); and has received stock options from Medivation and NeuroPhage. Dr Weiner serves on scientific advisory boards for Bayer Schering Pharma, Eli Lilly, CoMentis, Neurochem, Eisai, Avid, Aegis Therapies, Genentech, Allergan, Lippincott Williams & Wilkins, Bristol-Myers Squibb, Forest Laboratories, Pfizer, McKinsey & Company, Mitsubishi Tanabe Pharma Corporation, and Novartis; has received funding for travel from Nestlé and Kenes International to attend conferences not funded by industry; serves on the editorial board of the journal Alzheimer's & Dementia ; has received honoraria from the Rotman Research Institute and BOLT International; serves as a consultant for Elan; receives research support from Merck, Radiopharmaceuticals, the NIH (grants U01AG024904 [PI], P41 RR023953 [PI], R01 AG10897 [PI], P01AG19724 [coinvestigator], P50AG23501 [coinvestigator], R24 RR021992 [coinvestigator], R01 NS031966 [coinvestigator], and P01AG012435 [coinvestigator]), the US Department of Defense (DAMD17-01-1-0764 [PI]), the Veterans Administration (MIRECC VISN 21 [core PI]), and the State of California; and holds stock in Synarc and Elan. Dr Jagust has served on a scientific advisory board for Genentech; has served as a consultant for Bayer Healthcare, GE Healthcare, Synarc, Janssen Alzheimer Immunotherapy, Genentech, TauRx, and Merck; and receives research support from the NIH (grants AG027859 [PI], AG027984 [PI], and AG 024904 [coinvestigator]) and the Alzheimer's Association.

Funding/Support: Data collection and sharing for this project was funded by ADNI (NIH grant U01 AG024904), which is funded by the NIA and the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca, Bayer Schering Pharma, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly, Medpace, Merck, Novartis, Pfizer, F. Hoffman-La Roche, Schering-Plough, Synarc, as well as nonprofit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the US Food and Drug Administration. Private sector contributions to ADNI 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 our study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. The 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 and the Dana Foundation.

Additional Information: Data used in the preparation of this article were obtained from the ADNI database (http://adni.loni.ucla.edu). As such, the investigators in the ADNI contributed to the design and implementation of the ADNI or provided data but did not participate in the analysis or writing of this article.

Box Reference
Investigators of Alzheimer's Disease Neuroimaging Initiative

Paul Aisen, MD (University of California, San Diego, Alzheimer's Disease Cooperative Study PI [principal investigator] and director of coordinating center clinical core, executive committee); Clifford R. Jack Jr, MD (Mayo Clinic, Rochester, Minnesota, PI of MRI core, executive committee); Arthur W. Toga, PhD (University of California, Los Angeles, PI of informatics core, executive committee); Laurel Beckett, PhD (University of California, Davis, PI of biostatistics core, executive committee); Anthony Gamst, PhD (University of California, San Diego, executive committee); Holly Soares, PhD (Pfizer, New York, Chair, Industry Scientific Advisory Board); Robert C. Green, MD, MPH (Boston University, Massachusetts, Chair, Data and Publication Committee); Tom Montine, MD, PhD (University of Washington, Seattle, Chair, Resource Allocation Review Committee); Ronald G. Thomas, PhD (University of California, San Diego, Clinical Informatics and Operations); Michael Donohue, PhD (University of California, San Diego, Clinical Informatics and Operations); Sarah Walter, MSc (University of California, San Diego, Clinical Informatics and Operations); Anders Dale, PhD (University of California, San Diego, MRI core); Matthew Bernstein, PhD (Mayo Clinic, Rochester, MRI core); Joel Felmlee, PhD (Mayo Clinic, Rochester, MRI core); Nick Fox, MD (University of London, England, MRI core); Paul Thompson, PhD (University of California, Los Angeles, David Geffen School of Medicine, MRI core); Norbert Schuff, PhD (University of California, San Francisco, MRI core); Gene Alexander, PhD (Banner Alzheimer Institute, Phoenix, Arizona, MRI core); and Charles DeCarli, MD (University of California, Davis, MRI core). Dan Bandy, MS, CNMT (Banner Alzheimer Institute, PET core); Kewei Chen, PhD (Banner Alzheimer Institute, PET core); John Morris, MD (Washington University, St Louis, Missouri, neuropathology core); Virginia M.-Y. Lee, PhD, MBA (University of Pennsylvania School of Medicine, Philadelphia, biomarkers core); Magdalena Korecka, PhD (University of Pennsylvania School of Medicine, biomarkers core); Karen Crawford (University of California, Los Angeles, informatics core); Scott Neu, PhD (University of California, Los Angeles, informatics core); Danielle Harvey, PhD (University of California, Davis, biostatistics core); John Kornak, PhD (University of California, Davis, biostatistics core); Andrew J. Saykin, PsyD (Indiana University, Indianapolis, genetics core); Tatiana M. Foroud, PhD (Indiana University, genetics core); Steven Potkin, MD (University of California, Irvine, genetics core); Li Shen, PhD (Indiana University, genetics core); Neil Buckholtz, PhD (NIH/NIA, Bethesda, Maryland); Jeffrey Kaye, MD (Oregon Health and Science University, Portland, site investigator); Sara Dolen, BS (Oregon Health and Science University, site investigator); Joseph Quinn, MD (Oregon Health and Science University, site investigator); Lon Schneider, MD (University of Southern California, Los Angeles, site investigator); Sonia Pawluczyk, MD (University of Southern California, site investigator); Bryan M. Spann, DO, PhD (University of Southern California, site investigator); James Brewer, MD, PhD (University of California, San Diego, site investigator); Helen Vanderswag, RN (University of California, San Diego, site investigator); Judith L. Heidebrink, MD, MS (University of Michigan, Ann Arbor, site investigator); Joanne L. Lord, LPN, BA, CCRC (University of Michigan, site investigator); Ronald Petersen, MD, PhD (Mayo Clinic, Rochester, site investigator); Kris Johnson, RN (Mayo Clinic, Rochester, site investigator); Rachelle S. Doody, MD, PhD (Baylor College of Medicine, Houston, Texas, site investigator); Javier Villanueva-Meyer, MD (Baylor College of Medicine, site investigator); Munir Chowdhury, MS (Baylor College of Medicine, site investigator); Yaakov Stern, PhD (Columbia University Medical Center, New York, site investigator); Lawrence S. Honig, MD, PhD (Columbia University Medical Center, site investigator); Karen L. Bell, MD (Columbia University Medical Center, site investigator); John C. Morris, MD (Washington University, St Louis, site investigator); Mark A. Mintun, MD (Washington University, St Louis, site investigator); Stacy Schneider, APRN, BC, GNP (Washington University, St Louis, site investigator); Daniel Marson, JD, PhD (University of Alabama, Birmingham, site investigator); Randall Griffith, PhD, ABPP (University of Alabama, Birmingham, site investigator); David Clark, MD (University of Alabama, Birmingham, site investigator); Hillel Grossman, MD (Mount Sinai School of Medicine, New York, site investigator); Cheuk Tang, PhD (Mount Sinai School of Medicine, site investigator); George Marzloff, BS (Mount Sinai School of Medicine, site investigator); Leyla deToledo-Morrell, PhD (Rush University Medical Center, Chicago, Illinois, site investigator); Raj C. Shah, MD (Rush University Medical Center, site investigator); Ranjan Duara, MD (Wein Center, Miami, Florida, site investigator); Daniel Varon, MD (Wein Center, site investigator); Peggy Roberts, CNA (Wein Center, site investigator); Marilyn S. Albert, PhD (Johns Hopkins University, Baltimore, Maryland, site investigator); Julia Pedroso, MA (Johns Hopkins University, site investigator); Jaimie Toroney, BA (Johns Hopkins University, site investigator); Henry Rusinek, PhD (New York University, site investigator); Mony J. de Leon, EdD (New York University, site investigator); Susan M. De Santi, PhD (New York University, site investigator); P. Murali Doraiswamy, MD (Duke University Medical Center, Durham, North Carolina, site investigator); Jeffrey R. Petrella, MD (Duke University Medical Center, site investigator); Marilyn Aiello, BS (Duke University Medical Center, site investigator); Christopher M. Clark, MD (University of Pennsylvania, Philadelphia, site investigator); Cassie Pham, BS (University of Pennsylvania, site investigator); Jessica Nunez (University of Pennsylvania, site investigator); Charles D. Smith, MD (University of Kentucky, Lexington, site investigator); Curtis A. Given II, MD (University of Kentucky, site investigator); Peter Hardy, PhD (University of Kentucky, site investigator); Oscar L. Lopez, MD (University of Pittsburgh, site investigator); MaryAnn Oakley, MA (University of Pittsburgh, site investigator); Donna M. Simpson, CRNP, MPH (University of Pittsburgh, site investigator); M. Saleem Ismail, MD (University of Rochester Medical Center, New York, site investigator); Connie Brand, RN (University of Rochester Medical Center, site investigator); Jennifer Richard, BA (University of Rochester Medical Center, site investigator); Ruth A. Mulnard, DNSc, RN, FAAN (University of California, Irvine, site investigator); Gaby Thai, MD (University of California, Irvine, site investigator); Catherine McAdams-Ortiz, MSN, RN, A/GNP (University of California, Irvine, site investigator); Ramon Diaz-Arrastia, MD, PhD (University of Texas Southwestern Medical School, Dallas, site investigator); Kristen Martin-Cook, MA (University of Texas Southwestern Medical School, site investigator); Michael DeVous, PhD (University of Texas Southwestern Medical School, site investigator); Allan I. Levey, MD, PhD (Emory University, Atlanta, Georgia, site investigator); James J. Lah, MD, PhD (Emory University, site investigator); Janet S. Cellar, RN, MSN (Emory University, site investigator); Jeffrey M. Burns, MD (University of Kansas Medical Center, Kansas City, site investigator); Heather S. Anderson, MD (University of Kansas Medical Center, site investigator); Mary M. Laubinger, MPA, BSN (University of Kansas Medical Center, site investigator); George Bartzokis, MD (University of California, Los Angeles, site investigator); Daniel H. S. Silverman, MD, PhD (University of California, Los Angeles, site investigator); Po H. Lu, PsyD (University of California, Los Angeles, site investigator); Neill R. Graff-Radford, MBBCH, FRCP (Mayo Clinic, Jacksonville, Florida, site investigator); Francine Parfitt, MSH, CCRC (Mayo Clinic, Jacksonville, site investigator); Heather Johnson, MLS, CCRP (Mayo Clinic, Jacksonville, site investigator); Martin Farlow, MD (Indiana University, site investigator); Scott Herring, RN (Indiana University, site investigator); Ann M. Hake, MD (Indiana University, site investigator); Christopher H. van Dyck, MD (Yale University School of Medicine, New Haven, Connecticut, site investigator); Martha G. MacAvoy, PhD (Yale University School of Medicine, site investigator); Amanda L. Benincasa, BA (Yale University School of Medicine, site investigator); Howard Chertkow, MD (McGill University, Montreal-Jewish General Hospital, Montreal, Quebec, Canada, site investigator); Howard Bergman, MD (McGill University, Montreal-Jewish General Hospital, site investigator); Chris Hosein, MEd (McGill University, Montreal-Jewish General Hospital, site investigator); Sandra Black, MD, FRCPC (Sunnybrook Health Sciences, Toronto, Ontario, Canada, site investigator); Simon Graham, PhD (Sunnybrook Health Sciences, Ontario, site investigator); Curtis Caldwell, PhD (Sunnybrook Health Sciences, Ontario, site investigator); Ging-Yuek Robin Hsiung, MD, MHSc, FRCPC (University of British Columbia Clinic for AD and related diseases, Vancouver, British Columbia, Canada, site investigator); Howard Feldman, MD, FRCPC (University of British Columbia Clinic for AD and related diseases, site investigator); Michele Assaly, MA (University of British Columbia Clinic for AD and related diseases, site investigator); Andrew Kertesz, MD (Cognitive Neurology–St. Joseph's, London, Ontario, Canada, site investigator); John Rogers, MD (Cognitive Neurology–St. Joseph's, Ontario, site investigator); Dick Trost, PhD (Cognitive Neurology–St. Joseph's, Ontario, site investigator); Charles Bernick, MD (Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland, Ohio, site investigator); Donna Munic, PhD (Cleveland Clinic Lou Ruvo Center for Brain Health, site investigator); Chuang-Kuo Wu, MD, PhD (Northwestern University, Chicago, Illinois, site investigator); Nancy Johnson, PhD (Northwestern University, site investigator); Marsel Mesulam, MD (Northwestern University, site investigator); Carl Sadowsky, MD (Premiere Research Institute [Palm Beach Neurology], Palm Beach, Florida, site investigator); Walter Martinez, MD (Premiere Research Institute [Palm Beach Neurology], site investigator); Teresa Villena, MD (Premiere Research Institute [Palm Beach Neurology], site investigator); Scott Turner, MD (Georgetown University Medical Center, Washington, DC, site investigator); Kathleen B. Johnson, ANP (Georgetown University Medical Center, site investigator); Kelly E. Behan, BA (Georgetown University Medical Center, site investigator); Reisa A. Sperling, MD (Brigham and Women's Hospital, Boston, Massachusetts, site investigator); Dorene M. Rentz, PsyD (Brigham and Women's Hospital, site investigator); Keith A. Johnson, MD (Brigham and Women's Hospital, site investigator); Allyson Rosen, PhD (Stanford University, Palo Alto, California, site investigator); Jared Tinklenberg, MD (Stanford University, site investigator); Wes Ashford, MD, PhD (Stanford University, site investigator); Marwan Sabbagh, MD, FAAN, CCRI (Sun Health Research Institute, Sun City, Arizona, site investigator); Donald Connor, PhD (Sun Health Research Institute, site investigator); Sandra Jacobson, MD (Sun Health Research Institute, site investigator); Ronald Killiany, PhD (Boston University, site investigator); Alexander Norbash, MD (Boston University, site investigator); Anil Nair, MD (Boston University, site investigator); Thomas O. Obisesan, MD, MPH (Howard University, Washington, DC, site investigator); Annapurni Jayam-Trouth, MD (Howard University, site investigator); Paul Wang, PhD (Howard University, site investigator); Alan Lerner, MD (Case Western Reserve University, Cleveland, Ohio, site investigator); Leon Hudson, MPH (Case Western Reserve University, site investigator); Paula Ogrocki, PhD (Case Western Reserve University, site investigator); Charles DeCarli, MD (University of California, Davis-Sacramento, site investigator); Evan Fletcher, PhD (University of California, Davis-Sacramento, site investigator); Owen Carmichael, PhD (University of California, Davis-Sacramento, site investigator); Smita Kittur, MD (Neurological Care of Central New York, site investigator); Seema Mirje, MBBS (Neurological Care of Central New York, site investigator); Michael Borrie, MD (Parkwood Hospital, London, Ontario, Canada, site investigator); T.-Y. Lee, PhD (Parkwood Hospital, site investigator); Rob Bartha, PhD (Parkwood Hospital, site investigator); Sterling Johnson, PhD (University of Wisconsin, Madison, site investigator); Sanjay Asthana, MD (University of Wisconsin, site investigator); Cynthia M. Carlsson, MD (University of Wisconsin, site investigator); Steven G. Potkin, MD (University of California, Irvine [Brain Imaging Center], site investigator); Adrian Preda, MD (University of California, Irvine [Brain Imaging Center], site investigator); Dana Nguyen, PhD (University of California, Irvine [Brain Imaging Center], site investigator); Pierre Tariot, MD (Banner Alzheimer Institute, site investigator); Adam Fleisher, MD (Banner Alzheimer Institute, site investigator); Stephanie Reeder, BA (Banner Alzheimer Institute, site investigator); Vernice Bates, MD (Dent Neurologic Institute, Buffalo, New York, site investigator); Horacio Capote, MD (Dent Neurologic Institute, site investigator); Michelle Rainka, PhD (Dent Neurologic Institute, site investigator); Barry A. Hendin, MD (Dent Neurologic Institute, site investigator); Douglas W. Scharre, MD (Ohio State University, Columbus, site investigator); Maria Kataki, MD, PhD (Ohio State University, site investigator); Earl A. Zimmerman, MD (Albany Medical College, New York, site investigator); Dzintra Celmins, MD (Albany Medical College, site investigator); Alice D. Brown, FNP (Albany Medical College, site investigator); Sam Gandy, MD, PhD (Thomas Jefferson University, Philadelphia, Pennsylvania, site investigator); Marjorie E. Marenberg, MD (Thomas Jefferson University, site investigator); Barry W. Rovner, MD (Thomas Jefferson University, site investigator); Godfrey Pearlson, MD (Hartford Hospital, Olin Neuropsychiatry Research Center, Hartford, Connecticut) Karen Blank, MD (Hartford Hospital, Olin Neuropsychiatry Research Center); Karen Anderson, RN (Hartford Hospital, Olin Neuropsychiatry Research Center); Andrew J. Saykin, PsyD (Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, site investigator); Robert B. Santulli, MD (Dartmouth-Hitchcock Medical Center, site investigator); Jessica Englert, PhD (Dartmouth-Hitchcock Medical Center, site investigator); Jeff D. Williamson, MD, MHS (Wake Forest University Health Sciences, Greensboro, North Carolina, site investigator); Kaycee M. Sink, MD, MS (Wake Forest University Health Sciences, site investigator); Franklin Watkins, MD (Wake Forest University Health Sciences, site investigator); Brian R. Ott, MD (Rhode Island Hospital, Providence, site investigator); Chuang-Kuo Wu, MD, PhD (Rhode Island Hospital, site investigator); Ronald Cohen, PhD (Rhode Island Hospital, site investigator); Stephen Salloway, MD, MS (Butler Hospital, Providence, Rhode Island, site investigator); Paul Malloy, PhD (Butler Hospital, site investigator); Stephen Correia, PhD (Butler Hospital, site investigator); Howard J. Rosen, MD (University of California, San Francisco, site investigator); Bruce L. Miller, MD (University of California, San Francisco, site investigator); and Jacobo Mintzer, MD (Medical University South Carolina, Charleston, site investigator).

Sunderland T, Linker G, Mirza N,  et al.  Decreased beta-amyloid1-42 and increased tau levels in cerebrospinal fluid of patients with Alzheimer disease.  JAMA. 2003;289(16):2094-2103
PubMed   |  Link to Article
Silverman DH, Small GW, Chang CY,  et al.  Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome.  JAMA. 2001;286(17):2120-2127
PubMed   |  Link to Article
Jack CR Jr, Petersen RC, O’Brien PC, Tangalos EG. MR-based hippocampal volumetry in the diagnosis of Alzheimer's disease.  Neurology. 1992;42(1):183-188
PubMed   |  Link to Article
Strozyk D, Blennow K, White LR, Launer LJ. CSF Abeta 42 levels correlate with amyloid-neuropathology in a population-based autopsy study.  Neurology. 2003;60(4):652-656
PubMed   |  Link to Article
Mosconi L, Mistur R, Switalski R,  et al.  FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified Alzheimer's disease.  Eur J Nucl Med Mol Imaging. 2009;36(5):811-822
PubMed   |  Link to Article
Whitwell JL, Josephs KA, Murray ME,  et al.  MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study.  Neurology. 2008;71(10):743-749
PubMed   |  Link to Article
Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L. Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study.  Lancet Neurol. 2006;5(3):228-234
PubMed   |  Link to Article
Blennow K, Zetterberg H, Minthon L,  et al.  Longitudinal stability of CSF biomarkers in Alzheimer's disease.  Neurosci Lett. 2007;419(1):18-22
PubMed   |  Link to Article
Zetterberg H, Pedersen M, Lind K,  et al.  Intra-individual stability of CSF biomarkers for Alzheimer's disease over two years.  J Alzheimers Dis. 2007;12(3):255-260
PubMed
Jack CR Jr, Shiung MM, Gunter JL,  et al.  Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD.  Neurology. 2004;62(4):591-600
PubMed   |  Link to Article
Chételat G, Landeau B, Eustache F,  et al.  Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study.  Neuroimage. 2005;27(4):934-946
PubMed   |  Link to Article
Mungas D, Harvey D, Reed BR,  et al.  Longitudinal volumetric MRI change and rate of cognitive decline.  Neurology. 2005;65(4):565-571
PubMed   |  Link to Article
Jack CR Jr, Weigand SD, Shiung MM,  et al.  Atrophy rates accelerate in amnestic mild cognitive impairment.  Neurology. 2008;70(19, pt 2):1740-1752
PubMed   |  Link to Article
Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer's disease treatment studies.  Am J Psychiatry. 2002;159(5):738-745
PubMed   |  Link to Article
Drzezga A, Lautenschlager N, Siebner H,  et al.  Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer's disease: a PET follow-up study.  Eur J Nucl Med Mol Imaging. 2003;30(8):1104-1113
PubMed   |  Link to Article
Mosconi L, De Santi S, Li J,  et al.  Hippocampal hypometabolism predicts cognitive decline from normal aging.  Neurobiol Aging. 2008;29(5):676-692
PubMed   |  Link to Article
Fouquet M, Desgranges B, Landeau B,  et al.  Longitudinal brain metabolic changes from amnestic mild cognitive impairment to Alzheimer's disease.  Brain. 2009;132(pt 8):2058-2067
PubMed   |  Link to Article
Schuff N, Woerner N, Boreta L,  et al; Alzheimer's Disease Neuroimaging Initiative.  MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers.  Brain. 2009;132(pt 4):1067-1077
PubMed
Fagan AM, Head D, Shah AR,  et al.  Decreased cerebrospinal fluid Abeta(42) correlates with brain atrophy in cognitively normal elderly.  Ann Neurol. 2009;65(2):176-183
PubMed   |  Link to Article
Tosun D, Schuff N, Truran-Sacrey D,  et al; Alzheimer's Disease Neuroimaging Initiative.  Relations between brain tissue loss, CSF biomarkers, and the ApoE genetic profile: a longitudinal MRI study.  Neurobiol Aging. 2010;31(8):1340-1354
PubMed   |  Link to Article
Petrie EC, Cross DJ, Galasko D,  et al.  Preclinical evidence of Alzheimer changes: convergent cerebrospinal fluid biomarker and fluorodeoxyglucose positron emission tomography findings.  Arch Neurol. 2009;66(5):632-637
PubMed   |  Link to Article
de Leon MJ, DeSanti S, Zinkowski R,  et al.  Longitudinal CSF and MRI biomarkers improve the diagnosis of mild cognitive impairment.  Neurobiol Aging. 2006;27(3):394-401
PubMed   |  Link to Article
Sluimer JD, Bouwman FH, Vrenken H,  et al.  Whole-brain atrophy rate and CSF biomarker levels in MCI and AD: a longitudinal study.  Neurobiol Aging. 2010;31(5):758-764
PubMed   |  Link to Article
Petersen RC, Aisen PS, Beckett LA,  et al.  Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization.  Neurology. 2010;74(3):201-209
PubMed   |  Link to Article
Shaw LM. PENN biomarker core of the Alzheimer's disease Neuroimaging Initiative.  Neurosignals. 2008;16(1):19-23
PubMed   |  Link to Article
Landau SM, Harvey D, Madison CM,  et al; the Alzheimer's Disease Neuroimaging Initiative.  Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI [published online ahead of print August 4, 2009].  Neurobiol Aging
PubMed  |  Link to Article
Jack CR Jr, Bernstein MA, Fox NC,  et al.  The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods.  J Magn Reson Imaging. 2008;27(4):685-691
PubMed   |  Link to Article
Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach.  Biometrics. 1988;44(4):1049-1060
PubMed   |  Link to Article
Hubbard AE, Ahern J, Fleischer NL,  et al.  To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health.  Epidemiology. 2010;21(4):467-474
PubMed   |  Link to Article
Beckett LA, Harvey DJ, Gamst A,  et al; Alzheimer's Disease Neuroimaging Initiative.  The Alzheimer's Disease Neuroimaging Initiative: annual change in biomarkers and clinical outcomes.  Alzheimers Dement. 2010;6(3):257-264
PubMed   |  Link to Article
Caselli RJ, Dueck AC, Osborne D,  et al.  Longitudinal modeling of age-related memory decline and the APOE epsilon4 effect.  N Engl J Med. 2009;361(3):255-263
PubMed   |  Link to Article
Moffat SD, Szekely CA, Zonderman AB, Kabani NJ, Resnick SM. Longitudinal change in hippocampal volume as a function of apolipoprotein E genotype.  Neurology. 2000;55(1):134-136
PubMed   |  Link to Article
Reiman EM, Caselli RJ, Chen K, Alexander GE, Bandy D, Frost J. Declining brain activity in cognitively normal apolipoprotein E epsilon 4 heterozygotes: a foundation for using positron emission tomography to efficiently test treatments to prevent Alzheimer's disease.  Proc Natl Acad Sci U S A. 2001;98(6):3334-3339
PubMed   |  Link to Article
Polvikoski T, Sulkava R, Haltia M,  et al.  Apolipoprotein E, dementia, and cortical deposition of beta-amyloid protein.  N Engl J Med. 1995;333(19):1242-1247
PubMed   |  Link to Article
Reiman EM, Chen K, Liu X,  et al.  Fibrillar amyloid-beta burden in cognitively normal people at 3 levels of genetic risk for Alzheimer's disease.  Proc Natl Acad Sci U S A. 2009;106(16):6820-6825
PubMed   |  Link to Article
Jack CR Jr, Knopman DS, Jagust WJ,  et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade.  Lancet Neurol. 2010;9(1):119-128
PubMed   |  Link to Article
Caroli A, Frisoni GB.Alzheimer's Disease Neuroimaging Initiative.  The dynamics of Alzheimer's disease biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort.  Neurobiol Aging. 2010;31(8):1263-1274
PubMed   |  Link to Article
Stomrud E, Hansson O, Zetterberg H, Blennow K, Minthon L, Londos E. Correlation of longitudinal cerebrospinal fluid biomarkers with cognitive decline in healthy older adults.  Arch Neurol. 2010;67(2):217-223
PubMed   |  Link to Article
Tapiola T, Pirttilä T, Mikkonen M,  et al.  Three-year follow-up of cerebrospinal fluid tau, beta-amyloid 42 and 40 concentrations in Alzheimer's disease.  Neurosci Lett. 2000;280(2):119-122
PubMed   |  Link to Article
Buchhave P, Blennow K, Zetterberg H,  et al.  Longitudinal study of CSF biomarkers in patients with Alzheimer's disease.  PLoS One. 2009;4(7):e6294
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure. Hypothetical longitudinal changes in the Aβ42 level in cerebrospinal fluid (CSF), fludeoxyglucose F18 (FDG) uptake using positron emission tomography, hippocampal volume using magnetic resonance imaging, and Alzheimer Disease's Assessment Scale–cognitive subscale (ADAS-cog) score for a 75-year-old person at different cognitive states. AD indicates Alzheimer disease; MCI, mild cognitive impairment; and NC, normal cognition.

Tables

Table Graphic Jump LocationTable 1. Demographic Features of 819 Participants in the Alzheimer's Disease Neuroimaging Initiative at Enrollment
Table Graphic Jump LocationTable 2. Monthly Change in Biomarkers Among Study Population and Intergroup Rate Comparisona
Table Graphic Jump LocationTable 3. Influence of APOE4 Gene on Biomarkers Among Participants in the Alzheimer's Disease Neuroimaging Initiativea
Table Graphic Jump LocationTable 4. Goodness of Fit of Regression Analyses Modeling Cognitive Change by Biomarkersa

References

Sunderland T, Linker G, Mirza N,  et al.  Decreased beta-amyloid1-42 and increased tau levels in cerebrospinal fluid of patients with Alzheimer disease.  JAMA. 2003;289(16):2094-2103
PubMed   |  Link to Article
Silverman DH, Small GW, Chang CY,  et al.  Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome.  JAMA. 2001;286(17):2120-2127
PubMed   |  Link to Article
Jack CR Jr, Petersen RC, O’Brien PC, Tangalos EG. MR-based hippocampal volumetry in the diagnosis of Alzheimer's disease.  Neurology. 1992;42(1):183-188
PubMed   |  Link to Article
Strozyk D, Blennow K, White LR, Launer LJ. CSF Abeta 42 levels correlate with amyloid-neuropathology in a population-based autopsy study.  Neurology. 2003;60(4):652-656
PubMed   |  Link to Article
Mosconi L, Mistur R, Switalski R,  et al.  FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified Alzheimer's disease.  Eur J Nucl Med Mol Imaging. 2009;36(5):811-822
PubMed   |  Link to Article
Whitwell JL, Josephs KA, Murray ME,  et al.  MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study.  Neurology. 2008;71(10):743-749
PubMed   |  Link to Article
Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L. Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study.  Lancet Neurol. 2006;5(3):228-234
PubMed   |  Link to Article
Blennow K, Zetterberg H, Minthon L,  et al.  Longitudinal stability of CSF biomarkers in Alzheimer's disease.  Neurosci Lett. 2007;419(1):18-22
PubMed   |  Link to Article
Zetterberg H, Pedersen M, Lind K,  et al.  Intra-individual stability of CSF biomarkers for Alzheimer's disease over two years.  J Alzheimers Dis. 2007;12(3):255-260
PubMed
Jack CR Jr, Shiung MM, Gunter JL,  et al.  Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD.  Neurology. 2004;62(4):591-600
PubMed   |  Link to Article
Chételat G, Landeau B, Eustache F,  et al.  Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study.  Neuroimage. 2005;27(4):934-946
PubMed   |  Link to Article
Mungas D, Harvey D, Reed BR,  et al.  Longitudinal volumetric MRI change and rate of cognitive decline.  Neurology. 2005;65(4):565-571
PubMed   |  Link to Article
Jack CR Jr, Weigand SD, Shiung MM,  et al.  Atrophy rates accelerate in amnestic mild cognitive impairment.  Neurology. 2008;70(19, pt 2):1740-1752
PubMed   |  Link to Article
Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer's disease treatment studies.  Am J Psychiatry. 2002;159(5):738-745
PubMed   |  Link to Article
Drzezga A, Lautenschlager N, Siebner H,  et al.  Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer's disease: a PET follow-up study.  Eur J Nucl Med Mol Imaging. 2003;30(8):1104-1113
PubMed   |  Link to Article
Mosconi L, De Santi S, Li J,  et al.  Hippocampal hypometabolism predicts cognitive decline from normal aging.  Neurobiol Aging. 2008;29(5):676-692
PubMed   |  Link to Article
Fouquet M, Desgranges B, Landeau B,  et al.  Longitudinal brain metabolic changes from amnestic mild cognitive impairment to Alzheimer's disease.  Brain. 2009;132(pt 8):2058-2067
PubMed   |  Link to Article
Schuff N, Woerner N, Boreta L,  et al; Alzheimer's Disease Neuroimaging Initiative.  MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers.  Brain. 2009;132(pt 4):1067-1077
PubMed
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Longitudinal Change of Biomarkers in Cognitive Decline
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