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Original Investigation |

Cross-Sectional and Longitudinal Relationships Between Cerebrospinal Fluid Biomarkers and Cognitive Function in People Without Cognitive Impairment From Across the Adult Life Span FREE

Ge Li, MD, PhD1; Steven P. Millard, PhD2; Elaine R. Peskind, MD1,2; Jing Zhang, MD, PhD3; Chang-En Yu, PhD4,5; James B. Leverenz, MD1,6; Cynthia Mayer, DO2; Jane S. Shofer, MS1; Murray A. Raskind, MD1,2; Joseph F. Quinn, MD7,8; Douglas R. Galasko, MD9; Thomas J. Montine, MD, PhD3
[+] Author Affiliations
1School of Medicine, Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle
2Veterans Affairs (VA) Northwest Network Mental Illness Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, Washington
3Department of Pathology, School of Medicine, University of Washington, Seattle
4Department of Medicine, School of Medicine, University of Washington, Seattle
5Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, Washington
6Department of Neurology, School of Medicine, University of Washington, Seattle
7Department of Neurology, School of Medicine, Oregon Health and Science University, Portland
8VA Parkinson’s Disease Research, Education, and Clinical Centers, Portland, Oregon
9School of Medicine, Department of Neurosciences, University of California, San Diego, La Jolla
JAMA Neurol. 2014;71(6):742-751. doi:10.1001/jamaneurol.2014.445.
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Published online

Importance  Age-related cognitive decline among older individuals with normal cognition is a complex trait that potentially derives from processes of aging, inherited vulnerabilities, environmental factors, and common latent diseases that can progress to cause dementia, such as Alzheimer disease and vascular brain injury.

Objective  To use cerebrospinal fluid (CSF) biomarkers to gain insight into this complex trait.

Design, Setting, and Participants  Secondary analyses of an academic multicenter cross-sectional (n = 315) and longitudinal (n = 158) study of 5 neuropsychological tests (Immediate Recall, Delayed Recall, Trail Making Test Parts A and B, and Category Fluency) in cognitively normal individuals aged 21 to 100 years.

Main Outcomes and Measures  To investigate the association of these cognitive function test results with age, sex, educational level, inheritance of the ε4 allele of the apolipoprotein E gene, and CSF concentrations of β-amyloid 42 (Aβ42) and tau (biomarkers of Alzheimer disease) as well as F2-isoprostanes (measures of free radical injury).

Results  Age and educational level were broadly predictive of cross-sectional cognitive performance; of the genetic and CSF measures, only greater CSF F2-isoprostane concentration was significantly associated with poorer executive function (adjusted R2 ≤0.31). Longitudinal measures of cognitive abilities, except Category Fluency, also were associated broadly with age; of the genetic and CSF measures, only lower baseline CSF Aβ42 concentration was associated with longitudinal measures of immediate and delayed recall (marginal R2 ≤0.31).

Conclusions and Relevance  Our results suggest that age and educational level accounted for a substantial minority of variance in cross-sectional or longitudinal cognitive test performance in this large group of cognitively normal adults. Latent Alzheimer disease and other diseases that produce free radical injury, such as vascular brain injury, accounted for a small amount of variation in cognitive test performance across the adult human life span. Additional genetic and environmental factors likely contribute substantially to age-related cognitive decline.

Figures in this Article

Cognitive function, especially declarative memory and executive function, decreases with age in nonhuman primates1 and in humans, even in individuals who have not crossed the clinical thresholds for mild cognitive impairment (MCI) or dementia.2 Age-related cognitive decline appears to be associated with several factors, such as events that occurred earlier in life, which include the genetic complement inherited from parents, environment, processes of aging, and latent disease. However, the extent to which these different factors drive age-related cognitive decline remains unclear.

Observational studies35 using neuropathologic examination of adults who were cognitively normal proximate to death suggest that latent Alzheimer disease (AD) and vascular brain injury (VBI) are prevalent in those aged 65 years or older. Accumulation of cerebral amyloid, determined by molecular neuroimaging, and changes in cerebrospinal fluid (CSF) concentrations of β-amyloid 42 (Aβ42) and tau that are characteristic of individuals with amnestic MCI or AD dementia also occur in a proportion of cognitively normal adults, raising the possibility that some amount of age-related cognitive decline may be the result of the earliest expression of AD.68 Similarly, if one assumes that white matter hyperintensities as revealed by T2-weighted magnetic resonance imaging are at least in part a consequence of microvascular VBI,9 then structural neuroimaging further supports a possible role for microvascular VBI in age-related cognitive decline.10 Finally, studies of CSF F2-isoprostanes (IsoPs), biomarkers of oxidative injury to the brain, have shown1114 that levels of IsoPs are characteristically elevated in MCI, AD dementia, and/or VBI; these associations have been validated mechanistically in animal models.11 In combination, the findings of these laboratory-based studies raise the possibility that some, or perhaps even most, age-related cognitive decline is the earliest expression of latent diseases of the brain. If this hypothesis is true, it would suggest that existing therapies to control risk factors for microvascular VBI and disease-modifying therapies for AD that may be developed soon could greatly affect age-related cognitive decline.

A focus of our AD centers is developing CSF biomarker approaches to aid in the diagnosis and management of MCI and dementia. As part of these efforts, we have obtained CSF samples from many research participants across the adult life span who were carefully evaluated to establish that they are cognitively healthy control individuals. In the present study we used this valuable resource to examine the association between CSF biomarkers of AD or oxidative injury and cognitive function in relation to aging using cross-sectional and longitudinal data.

Recruitment of Participants

Participants in the present study were recruited between October 26, 2001, and September 24, 2009, from the University of Washington Alzheimer’s Disease Research Center and collaborating AD centers, including the University of California, San Diego; Oregon Health and Science University; Indiana University; the University of Pennsylvania; and the University of California, Davis. All procedures were approved by the institutional review boards of each study site. All participants provided written informed consent and received financial compensation. Participants underwent detailed neuropsychological testing and clinical and laboratory evaluations and then were classified as having no cognitive impairment, MCI, or dementia based on standard research criteria as previously described.15 Clinical diagnosis was made at a consensus conference based on history provided by informants, neurologic examination, detailed neuropsychological test results, and laboratory studies (including neuroimaging with magnetic resonance imaging in the case of MCI or AD). Exclusion criteria were major neurologic diagnoses that may affect cognitive function, such as stroke, Parkinson disease, multiple sclerosis, and history of moderate to severe head injury; major psychiatric disorders, such as schizophrenia, major affective disorder, and posttraumatic stress disorder; unstable major medical conditions, such as insulin-dependent diabetes mellitus; and use of illegal drugs. Individuals aged 45 years or older were asked to participate in a longitudinal study with annual follow-up visits. Additionally, 5 younger individuals (aged 35, 38, 40, 41, and 43 years) had follow-up visits and were included in the longitudinal analyses.

CSF Biomarkers

Cerebrospinal fluid was obtained by lumbar puncture as previously described,16 using 24-gauge atraumatic spinal needles (Sprotte). All CSF samples were analyzed in a single laboratory17 using 0.5-mL aliquots that had been stored in polypropylene cryotubes, frozen, and maintained at −80°C and never previously thawed. The CSF was analyzed for Aβ42 and total tau using multiplexed reagents (Luminex; InnoGenetics) according to the manufacturer’s instructions and as previously described.18 Levels of CSF F2-IsoP were quantified using a stable isotope dilution assay with gas chromatography/mass spectrometry and selective ion monitoring as described previously.19 Apolipoprotein E (APOE; GenBank, M12529) genotype was determined by a restriction digest method.20 Assays were performed with personnel blinded to clinical diagnosis.

Statistical Analysis

We selected 5 neuropsychological tests to examine multiple domains of cognition. The Wechsler Memory Scale–Revised Logical Memory Immediate Recall and Delayed Recall paragraph tasks measure verbal episodic memory21; we used total scores for the immediate and delayed recall functions (each with a possible range of 0-25). Category Fluency is a test of semantic memory22; we used a total number of unique animals generated in 1 minute. Trail Making Test Parts A and B (Trails A and B) are timed tests of a person’s ability to adapt to shifting task demands. Time taken to complete Part A (upper boundary of 150 seconds) is a measure of processing speed, and time taken to complete Part B (upper boundary of 300 seconds) is a measure of executive function.23

Inclusion criteria for the cross-sectional investigation were (1) all participants classified as having no cognitive impairment at baseline evaluation, (2) CSF at baseline that had assay results for all 3 CSF biomarkers, and (3) a full set of neuropsychological test scores at baseline. The longitudinal investigation included individuals from the cross-sectional study who had at least 1 follow-up visit at approximately 12 months with results for at least 1 of the cognitive tests. The number of follow-up visits and time span they encompassed varied depending on the time of recruitment to the study and the person’s age. The longitudinal study sample was a subset of the cross-sectional study participants; characteristics of each study sample are reported in Table 1. At each follow-up visit, history obtained from the informants, clinical examination, and neuropsychological test data were reviewed to determine whether the cognitive status of the participant remained the same or changed to MCI or dementia.

Table Graphic Jump LocationTable 1.  Demographics and Baseline Biomarkers and Cognitive Test Scores for Control Participants in Cross-Sectional and Longitudinal Analyses

Linear regression models were used to assess cross-sectional relationships between CSF biomarker concentrations and coincident cognitive test performance. Raw scores were used for each test except log10-transformed times for Trails A and B to remove skewness. In addition, we created a composite test score constructed by computing z scores for each of the 5 cognitive tests based on the baseline mean (SD) (z scores for log10-transformed Trails A and B were multiplied by −1) and then determining the mean. Regression models consisted of cognitive test performance as the dependent variable and baseline CSF biomarker concentrations as predictors, along with the covariates baseline age, sex, educational level, and APOE ε4 status (no ε4 alleles vs at least 1 ε4 allele).

To assess the association of baseline CSF biomarker concentrations with subsequent longitudinal changes of cognitive test performance, we used linear mixed-effects models,24 with cognitive test performance as the dependent variable and time since baseline and baseline CSF biomarker concentrations as predictors, along with the covariates baseline age, sex, educational level, and APOE ε4 status. The associations of baseline age and CSF biomarker concentrations with change in cognitive performance were tested by including time × baseline age and time × biomarker concentration interaction terms in the models. Marginal R2 values for the linear mixed-effects models were computed according to the method of Nakagawa and Schielzeth.25

We performed several kinds of sensitivity analyses. For both the cross-sectional and longitudinal analyses, we included the ratio of tau to Aβ42 as a predictor (per Kronmal,26 both tau and Aβ42 were kept in the models as main effects as well). We also looked at models in which Aβ42 was dichotomized as 192 pg/mL or less vs greater than 192 pg/mL based on the cutoff suggested by Shaw et al.27 Because the relationship between CSF biomarkers and cognitive function may differ between older and younger people, we restricted all analyses to those 60 years or older. To understand the relationship between cognition and CSF biomarkers that is related to normal aging, we looked at models in which we excluded participants whose clinical diagnosis converted to MCI, AD, or other dementias. For the longitudinal analyses, we also used 2-stage regression (least-squares slope for each test in each individual over time, followed by a weighted regression model with slope as a response variable and baseline test score included as a predictor variable),28 with weights based on participants having different numbers of follow-up visits at different times after baseline. Finally, to understand the role of APOE genotype in cognitive decline, we examined APOE ε4 gene dose-effect relationship in the cross-sectional analyses by coding APOE ε4 genotype as follows: ε2/ε2 = −2, ε2/ε3 = −1, ε2/ε4 = 0, ε3/ε3 = 0, ε3/ε4 = 1, and ε4/ε4 = 2. In the longitudinal analysis, we expanded the linear mixed-effects model to allow for interaction effects between APOE ε4 status and biomarkers.

Correction for multiple comparisons, taking into account 6 separate cognitive outcomes (the 5 test scores plus the composite score) was based on the method of Holm.29 Statistical analyses were performed using R, version 3.0.130; linear mixed-effects models were fit using the R packages nlme31 or lme4.32

Baseline Demographics

Table 1 presents demographic characteristics, baseline CSF biomarker levels, and cognitive test scores for the 315 eligible cognitively normal participants in our cross-sectional analysis. Of these, 157 did not have a follow-up visit and 158 had follow-up visits and were included in our longitudinal analysis. Individuals in the longitudinal analysis had a mean length of follow-up of 4.4 years and were approximately 10 years older than those in the cross-sectional analysis. Four participants completed only 1 test session but had 1 or more clinical follow-up visits. Of the 162 participants with clinical follow-up, cognitive function in 14 persons (8.6%) had converted to MCI, 7 (4.3%) to AD, 2 (1.2%) to dementia with Lewy bodies or Parkinson disease dementia, and 4 (2.5%) to another type of cognitive impairment. The Supplement (eTable 1) presents baseline information stratified by final clinical diagnosis and baseline test score vs age stratified by final clinical diagnosis (Supplement [eFigure 1]). Participants whose clinical diagnosis converted to MCI, AD, or other cognitive impairments were older at baseline and had slightly longer durations of follow-up than did those who remained cognitively normal. As expected, people whose clinical diagnosis converted to MCI or AD were more likely to be APOE ε4 carriers.

Cross-Sectional Analysis

Figure 1 presents results for the 3 CSF biomarkers from our 315 cognitively normal participants compared with age at baseline evaluation and stratified by final clinical diagnosis. We initially focused on associations between these baseline CSF biomarker concentrations and baseline cognitive abilities, including memory, in cognitively normal participants. Table 2 reports the regression coefficients and P values associated with demographic characteristics and baseline CSF biomarker levels for each cognitive test in multivariable regression models. Model 1 included age, sex, educational level, and presence of the APOE ε4 allele, and model 2 included the previous variables as well as concentrations of CSF Aβ42, tau, and F2-IsoPs. Age was significantly (P < .05) associated with lower cognitive function for all cognitive tests and models, and educational level was associated with higher cognitive function except for Trails A. For Trails A and B, lower scores reflect better cognitive function. The CSF F2-IsoP concentration was associated with lower cognitive function for Trails A (P = .04), Trails B (P = .007), and the composite score (P = .02), and low Aβ42 concentrations were associated with lower cognitive function for Trails B (P = .05); however, after adjusting for multiple comparisons based on 6 different cognitive measures, only the association between CSF F2-IsoP concentration and Trails B remained significant (Holm-corrected P = .04). Figure 2 shows unadjusted Trails B scores vs F2-IsoP concentration, along with a fitted line and 95% CIs for the line. Adding biomarkers to model 1 did not noticeably improve the adjusted R2 for any of the cognitive tests. Phosphorylated tau was highly correlated with total tau but did not provide additional predictability in any model.

Place holder to copy figure label and caption
Figure 1.
Cross-Sectional Relationships Between Concentration of Cerebrospinal Fluid (CSF) β-Amyloid 42 (Aβ42), Tau, and F2-Isoprostanes (F2-IsoPs) vs Age at Baseline for 315 Cognitively Normal Participants

A, Aβ42 slope (95% CI), −0.3 (−1.2 to 0.6); r2 = 0.001; P = .54. B, Tau slope, 0.2 (0.1 to 0.3); R2 = 0.06; P < .001. C, F2-IsoPs slope, 0.1 (0.04 to 0.15); R2 = 0.04; P < .001. Solid line indicates the fitted least-squares line unadjusted for any covariates; dashed lines, 95% CIs. AD indicates Alzheimer disease; MCI, mild cognitive impairment.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Cross-Sectional Relationship Between Trail Making Test Part B Scores and Concentration of Cerebrospinal Fluid (CSF) F2-Isoprostanes at Baseline for 315 Cognitively Normal Participants

The solid line indicates the fitted least-squares line for the log10 score unadjusted for any covariates; dashed lines are 95% CIs for the line. Slope, 0.005; 95% CI, 0.003-0.007; R2 = 0.06; P < .001. AD indicates Alzheimer disease; MCI, mild cognitive impairment.

Graphic Jump Location
Longitudinal Analysis

Our next analysis focused on the association between baseline CSF biomarker concentrations and longitudinal change in cognitive performance. Table 3 presents the regression coefficients and P values associated with baseline age and CSF biomarker levels for a 10-year change (ie, slope coefficients multiplied by 10) in each cognitive test based on linear mixed-effects regression models. Model 1 included baseline age, sex, educational level, presence of the APOE ε4 allele, time (decades since baseline), and a time × baseline age interaction term; model 2 included the previous variables as well as concentrations of CSF Aβ42, tau, and F2-IsoP at baseline, as well as time × biomarker interaction terms. Baseline age was associated with declining cognitive function for all tests except model 2 of Trails A, model 2 of Trails B, and both models of Category Fluency. Low baseline CSF Aβ42 concentration was associated with declining cognitive function for Immediate Recall (P = .004), Delayed Recall (P = .001), and the composite score (P = .007), and all of these associations remained significant after adjustment for multiple comparisons (Holm-corrected P = .02, P = .006, and P = .04, respectively). Also, baseline CSF tau concentration was associated with declining cognitive function for Immediate Recall and Delayed Recall (P = .03 and P = .04, respectively), but these associations did not remain significant after adjustment for multiple comparisons. The Supplement (eFigure 2) shows spaghetti plots of test score vs time stratified by final clinical diagnosis for all 5 tests and spaghetti plots of test score vs time stratified by baseline Aβ42 quartiles for Immediate Recall and Delayed Recall (Supplement [eFigure 3]).

Table Graphic Jump LocationTable 3.  Longitudinal Analyses Based on Linear Mixed-Effects Modelsa
Sensitivity Analysis

Results were similar when the tau to Aβ42 ratio was added to the cross-sectional analysis models (ie, the ratio was not significant and did not affect the other associations) except that the Trails B association with low Aβ42 became nominally nonsignificant (P = .34). When the tau to Aβ42 ratio was added to the longitudinal analysis models, it was not significant for any of the cognitive tests. When we modeled CSF Aβ42 level as a dichotomous variable (low vs high) instead of as a continuous variable (model 2 compared with model 3 in the Supplement [eTable 2 and eTable 3]), we observed a significant cross-sectional association of low Aβ42 with low composite score and with poor performance in Trails A (Supplement [eTable 2]). For the longitudinal analyses, the association between low baseline CSF Aβ42 concentration and declining cognitive function for Immediate Recall became nonsignificant (Supplement [eTable 3]). When we restricted analyses to individuals aged 60 years or older, the cross-sectional associations of CSF F2-IsoP concentration with Trails A, Trails B, and the composite score were all attenuated to nonsignificance (Supplement [eTable 2]). The relationship between baseline biomarkers and cognitive trajectories essentially remained unchanged in this restricted group of older participants (Supplement [eTable 3]). When we omitted individuals whose clinical diagnosis converted to MCI, AD, or other dementias or cognitive impairments, our findings did not change significantly in either the cross-sectional or longitudinal analysis (Supplement [eTables 2 and 3], models 2 and 4, respectively). Results were similar for the longitudinal analysis based on using 2-stage regression with weights that account for different numbers of follow-up visits at different times after baseline compared with the results based on linear mixed-effect models; however, the associations between CSF tau concentration and Immediate Recall and Delayed Recall scores were no longer nominally significant.

Finally, coding APOE genotype as a dose instead of APOE ε4 status did not change the results for the cross-sectional analyses (Supplement [eTable 4]). For the longitudinal analyses, when we expanded model 2 to allow for interaction effects between APOE ε4 status and biomarkers, the only significant result was for Trails A, for which there was a significant interaction between APOE ε4 status and Aβ42 concentration. Low Aβ42 concentration was associated with worse 10-year change in Trails A for APOE ε4+ individuals compared with APOE ε4− individuals (difference in slopes for 10-year change in log10-transformed time [seconds] for every 100-ng/mL decrease in Aβ42 = 0.1, SD of difference in slopes, 0.05; P = .01). However, this interaction effect did not remain significant after controlling for multiple comparisons.

Cognitive decline occurs in older adults, even in those whose cognitive function does not cross clinical thresholds to MCI or dementia or the Clinical Dementia Rating score does not change.33 Several processes may contribute to age-related cognitive decline, including genetics, environment, and latent disease. Using the resources of the large research CSF repository built among our collaborating AD centers, in the present study we tested the hypothesis that age-related cognitive decline could be accounted for in part by latent AD or oxidative injury to the brain as detected by CSF biomarkers. We tested our hypothesis in a cross-sectional analysis of 315 adults who underwent baseline lumbar puncture and neuropsychological testing and in a longitudinal analysis of 158 persons from that cohort who had follow-up neuropsychological testing.

Our study focused on decline in cognitive abilities among cognitively normal adults, not on progression to cognitive impairment or dementia, which has been investigated in previous studies15,3436 using biomarkers for AD. Our cross-sectional analysis of 315 carefully characterized individuals from across the human adult life span showed that age, sex, educational level, and APOE ε4 status accounted for a small percentage of the variability in cognitive performance on tests of immediate and delayed recall, executive function, and verbal fluency. Indeed, these 4 variables were strongest in accounting for variability in our measure of executive function (adjusted R2 = 0.31) and weakest for our measure of immediate recall (adjusted R2 = 0.08). The associations that existed were driven largely by age and educational level, without a significant contribution by sex or APOE. Subsequent addition of the 3 CSF biomarkers to the model did not alter the adjusted R2 for any of the neuropsychological test results; however, after controlling for multiple end points, we observed a novel finding that CSF F2-IsoP concentration was significantly associated with tests of executive function in middle-aged and elderly adults.

We speculate that this association may derive from CSF F2-IsoP concentration being a more-sensitive but less-specific marker of age-related brain injury and baseline executive function similarly being a more-sensitive but less-specific index of age-related cognitive decline. This association was diminished when analysis was restricted to participants aged 60 years or older, perhaps indicating the importance of mid-life free radical injury to the brain similar, and perhaps even mechanistically related, to the contribution of mid-life hypertension. The influence of age may also explain in part why no significant association was observed between CSF F2-IsoPs and longitudinal executive function because the mean age of the longitudinal group was greater than that of the cross-sectional group.

We did not observe an association between CSF F2-IsoP concentration and APOE ε4 in cognitively normal adults as suggested by others.37 Neither baseline CSF Aβ42 nor tau concentration was significantly associated with any of the 5 cognitive test results in cognitively normal adults, similar to other smaller cross-sectional studies38,39 of cognitive function in older individuals.

Our analysis of longitudinal change in neuropsychological test performance in a subset of 158 individuals using linear mixed-effects models showed that little change in cognitive test performance was explained by models that used age, sex, educational level, APOE ε4 status, and a time × baseline age interaction term (marginal R2 ranged from 0.21 to 0.31). The addition of CSF biomarkers to these models increased the marginal R2 very little; however, after controlling for multiple end points, low CSF Aβ42 concentration was significantly associated with cognitive decline in measures of immediate and delayed recall (R2 ≤ 0.31). These findings are similar to the results of another study40 of 165 older adults with normal cognition with or without subjective cognitive impairment.

Overall, these results indicate that age, sex, educational level, and inheritance of APOE ε4 accounted for little variability in the 5 neuropsychological tests used. These results highlight the need for deeper understanding of the genetic factors that influence age-related cognitive decline and the likely important contributions of systemic disease and environmental factors, such as nutrition, drug and alcohol abuse, and traumatic brain injury, which were not captured in our study. Cognitive performance in aging may also be influenced by lifestyle factors such as cognitive stimulation, physical exercise, and socialization, which may contribute to cognitive reserve. To the extent that the variables included in our study were significantly related to cognitive performance, age and educational level were the dominant variables in cross section, and baseline test performance and age were the dominant variables longitudinally. One interpretation of these results is that they reinforce the cognitive reserve hypothesis, similar to findings of recent neuroimaging studies.41

The CSF biomarkers contributed little to the R2 measures of goodness-of-fit when included with age, sex, educational level, and APOE ε4 status. Nevertheless, 3 significant associations withstood correction for multiple comparisons. These were an association between CSF F2-IsoP concentration and poorer performance on executive function in cross-sectional analysis and an association between low CSF Aβ42 concentration and longitudinal decline in immediate and delayed recall. The direction of these associations deserves some comment. Our measure of executive function, Trails B, is measured in seconds (maximum, 300 seconds) needed to complete the task; thus, a higher value indicates poorer performance. In our cross-sectional analysis, higher levels of oxidative injury to the brain, as measured by CSF F2-IsoP concentration, were associated with poorer executive function, as measured by longer time needed for Trails B (Figure 2). Interpretation of the association between low CSF Aβ42 concentration and decline of immediate and delayed recall is more complicated. Outside of autosomal dominant forms of AD,42 recent findings8 indicate that CSF Aβ42 concentration does not change much with aging until parenchymal Aβ42 deposition begins and ultimately progresses to clinical expression as MCI and dementia. Our results showed a negative correlation between decline in immediate and delayed recall and CSF Aβ42 level, suggesting that this association reflects the phase of declining CSF Aβ42 concentration and parenchymal deposition with increased risk for near-term conversion to MCI or AD dementia.15,43

Age-related cognitive decline is a complex trait that potentially derives from the confluence of multiple processes, including aging, environmental factors, inherited vulnerabilities, and latent disease. The strengths of our work are that it is relatively large for a CSF biomarker–based study, it used standardized data collection and central laboratory analysis, and participants were relatively healthy and selected to exclude factors that obviously may have a major effect on cognition. Shortcomings of our work are that our neuropsychological test battery was relatively limited and that we may have limited variance in cognitive function because of the general healthiness of our group. Lack of statistically significant associations between CSF biomarkers and baseline test scores and decline in test scores for most of the cognitive tests could be the result of issues related to statistical power, such as limited sample size (especially in the longitudinal samples), relatively short duration of follow-up, and relatively large within-participant variability in the neuropsychological tests used. Future studies should consider addressing these limitations. Regardless of these concerns, the fact that the CSF biomarkers contributed little to the R2 measures of goodness-of-fit even when they showed statistical significance suggests the importance of other underlying factors for cognitive health not captured by these CSF biomarkers.

With these strengths and weaknesses in mind, our analysis showed that CSF biomarkers of free radical injury and early changes of AD accounted for little variability in specific cognitive domains and that age, educational level, and previous cognitive performance were the predominant predictors of cognitive function in cross section or over time. Our results also suggest that factors not accounted for in the present study contribute to most of the variance in cognitive function in older, cognitively normal individuals. These factors may include other genetic factors, systemic disease, environmental factors, or white matter dysfunction as suggested by some neuroimaging studies.4446

Accepted for Publication: February 19, 2014.

Corresponding Author: Thomas J. Montine, MD, PhD, Department of Pathology, School of Medicine, University of Washington, PO Box 357470, Seattle, WA 98195 (tmontine@uw.edu).

Published Online: April 21, 2014. doi:10.1001/jamaneurol.2014.445.

Author Contributions: Dr Montine had full access to all 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: Li, Millard, Peskind, Raskind, Galasko, Montine.

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

Drafting of the manuscript: Li, Millard, Zhang, Montine.

Critical revision of the manuscript for important intellectual content: Li, Millard, Peskind, Yu, Leverenz, Mayer, Shofer, Raskind, Quinn, Galasko, Montine.

Statistical analysis: Millard, Shofer, Montine.

Obtained funding: Peskind, Leverenz, Raskind, Galasko, Montine.

Administrative, technical, or material support: Peskind, Zhang, Yu, Mayer, Raskind, Galasko, Montine.

Study supervision: Li, Peskind, Raskind, Montine.

Conflict of Interest Disclosures: Dr Leverenz serves as a consultant for Boehringer Ingelheim, Navidea Biopharmaceuticals, and Piramal Health Care. Dr Galasko serves as editor of Alzheimerʼs Disease Research and Treatment; serves on data safety monitoring boards for Elan, Janssen, and Balance Pharmaceuticals; and is a consultant for Elan Pharmaceuticals, Inc, and Genentech, Inc. He receives research support from the National Institutes of Health, the Michael J. Fox Foundation, and the Alzheimerʼs Drug Discovery Foundation. Dr Montine receives grants from the National Institutes of Health and personal compensation in the form of honoraria from invited scientific presentations to universities and professional societies not exceeding $5000 per year. No other disclosures were reported.

Funding/Support: This work was supported by National Institutes of Health grants AG023185, AG031892, AG05131, AG05136, AG08017, AG033693, and U01-NS082137; the Department of Veterans Affairs; the Oregon Clinical and Translational Research Institute grant UL1TR000128; the Jane and Lee Seidman Fund; and the Nancy and Buster Alvord Endowment.

Role of the Sponsor: The sponsors 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.

Additional Contributions: Kathleen Montine, PhD, Department of Pathology, School of Medicine, University of Washington, provided editorial assistance. No financial compensation was provided.

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Lo  RY, Jagust  WJ; Alzheimer’s Disease Neuroimaging Initiative.  Vascular burden and Alzheimer disease pathologic progression. Neurology. 2012;79(13):1349-1355.
PubMed   |  Link to Article
Longstreth  WT  Jr, Sonnen  JA, Koepsell  TD, Kukull  WA, Larson  EB, Montine  TJ.  Associations between microinfarcts and other macroscopic vascular findings on neuropathologic examination in 2 databases. Alzheimer Dis Assoc Disord. 2009;23(3):291-294.
PubMed   |  Link to Article
Sonnen  JA, Breitner  JC, Lovell  MA, Markesbery  WR, Quinn  JF, Montine  TJ.  Free radical–mediated damage to brain in Alzheimer’s disease and its transgenic mouse models. Free Radic Biol Med. 2008;45(3):219-230.
PubMed   |  Link to Article
Bayer-Carter  JL, Green  PS, Montine  TJ,  et al.  Diet intervention and cerebrospinal fluid biomarkers in amnestic mild cognitive impairment. Arch Neurol. 2011;68(6):743-752.
PubMed   |  Link to Article
Brys  M, Pirraglia  E, Rich  K,  et al.  Prediction and longitudinal study of CSF biomarkers in mild cognitive impairment. Neurobiol Aging. 2009;30(5):682-690.
PubMed   |  Link to Article
Seet  RC, Lee  CY, Chan  BP,  et al.  Oxidative damage in ischemic stroke revealed using multiple biomarkers. Stroke. 2011;42(8):2326-2329.
PubMed   |  Link to Article
Li  G, Sokal  I, Quinn  JF,  et al.  CSF tau/Aβ42 ratio for increased risk of mild cognitive impairment: a follow-up study. Neurology. 2007;69(7):631-639.
PubMed   |  Link to Article
Peskind  ER, Riekse  R, Quinn  JF,  et al.  Safety and acceptability of the research lumbar puncture. Alzheimer Dis Assoc Disord. 2005;19(4):220-225.
PubMed   |  Link to Article
Dumurgier  J, Vercruysse  O, Paquet  C,  et al.  Intersite variability of CSF Alzheimer's disease biomarkers in clinical setting. Alzheimers Dement. 2013;9(4):406-413.
PubMed   |  Link to Article
Mattsson  N, Andreasson  U, Persson  S,  et al.  The Alzheimer's Association external quality control program for cerebrospinal fluid biomarkers. Alzheimers Dement. 2011;7(4):386-395.e386. doi:10.1016/j.jalz.2011.05.2243.
PubMed   |  Link to Article
Milatovic  D, VanRollins  M, Li  K, Montine  KS, Montine  TJ.  Suppression of murine cerebral F2-isoprostanes and F4-neuroprostanes from excitotoxicity and innate immune response in vivo by α- or γ-tocopherol. J Chromatogr B Analyt Technol Biomed Life Sci. 2005;827(1):88-93.
PubMed   |  Link to Article
Hixson  JE, Vernier  DT.  Restriction isotyping of human apolipoprotein E by gene amplification and cleavage with HhaI. J Lipid Res. 1990;31(3):545-548.
PubMed
Wechsler  D. Wechsler Memory Scale–Revised. New York, NY: Harcourt Brace Jovanovich; 1987.
Gomez  RG, White  DA.  Using verbal fluency to detect very mild dementia of the Alzheimer type. Arch Clin Neuropsychol. 2006;21(8):771-775.
PubMed   |  Link to Article
Reitan  RWD. The Halstead-Reitan Neuropsychological Test Battery. Tucson, AZ: Neuropsychology Press; 1985.
Pinheiro  J, Bates  D. Mixed-Effects and Models in S and S-PLUS. New York, NY: Springer; 2000.
Nakagawa  S, Schielzeth  H.  A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol. 2013;4(2):133-142.
Link to Article
Kronmal  RA.  Spurious correlation and the fallacy of the ratio standard revisited. J R Stat Soc Ser A Stat Soc. 1993;156(3):379-392.
Link to Article
Shaw  LM, Vanderstichele  H, Knapik-Czajka  M,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Cerebrospinal fluid biomarker signature in Alzheimer’s Disease Neuroimaging Initiative subjects. Ann Neurol. 2009;65(4):403-413.
PubMed   |  Link to Article
Milliken  JK, Edland  SD.  Mixed effect models of longitudinal Alzheimer’s disease data: a cautionary note. Stat Med. 2000;19(11-12):1617-1629.
PubMed   |  Link to Article
Holm  S.  A simple sequential rejective multiple test procedure. Scand J Stat. 1979;6:65-70.
R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2012.
Pinheiro  J, DebRoy  S, Sarkar  D; R Core Team. nlme: Linear and Nonlinear Mixed Effects Models, R package, Version 3.1-104. Vienna, Austria: R Foundation for Statistical Computing; 2012.
Bates  D, Maechler  M, Bolker  B, Walker  S. Lme4: linear mixed-effects models using Eigen and S4. R package, version 1.0-5. http://CRAN.Rproject.org/package=lme4. 2013. Accessed February 14, 2014.
Vos  SJ, Xiong  C, Visser  PJ,  et al.  Preclinical Alzheimer’s disease and its outcome: a longitudinal cohort study. Lancet Neurol. 2013;12(10):957-965.
PubMed   |  Link to Article
Rolstad  S, Berg  AI, Bjerke  M, Johansson  B, Zetterberg  H, Wallin  A.  Cerebrospinal fluid biomarkers mirror rate of cognitive decline. J Alzheimers Dis. 2013;34(4):949-956.
PubMed
Roe  CM, Fagan  AM, Grant  EA,  et al.  Cerebrospinal fluid biomarkers, education, brain volume, and future cognition. Arch Neurol. 2011;68(9):1145-1151.
PubMed   |  Link to Article
Fagan  AM, Roe  CM, Xiong  C, Mintun  MA, Morris  JC, Holtzman  DM.  Cerebrospinal fluid tau/β-amyloid42 ratio as a prediction of cognitive decline in nondemented older adults. Arch Neurol. 2007;64(3):343-349.
PubMed   |  Link to Article
Duits  FH, Kester  MI, Scheffer  PG,  et al.  Increase in cerebrospinal fluid F2-isoprostanes is related to cognitive decline in APOE ε4 carriers. J Alzheimers Dis. 2013;36(3):563-570.
PubMed
Ewers  M, Insel  P, Jagust  WJ,  et al; Alzheimer’s Disease Neuroimaging Initiative (ADNI).  CSF biomarker and PIB-PET–derived β-amyloid signature predicts metabolic, gray matter, and cognitive changes in nondemented subjects. Cereb Cortex. 2012;22(9):1993-2004.
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
Rolstad  S, Berg  AI, Bjerke  M,  et al.  Amyloid-β42 is associated with cognitive impairment in healthy elderly and subjective cognitive impairment. J Alzheimers Dis. 2011;26(1):135-142.
PubMed
Ewers  M, Insel  PS, Stern  Y, Weiner  MW; Alzheimer’s Disease Neuroimaging Initiative (ADNI).  Cognitive reserve associated with FDG-PET in preclinical Alzheimer disease. Neurology. 2013;80(13):1194-1201.
PubMed   |  Link to Article
Reiman  EM, Quiroz  YT, Fleisher  AS,  et al.  Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: a case-control study. Lancet Neurol. 2012;11(12):1048-1056.
PubMed   |  Link to Article
Vos  SJ, van Rossum  IA, Verhey  F,  et al.  Prediction of Alzheimer disease in subjects with amnestic and nonamnestic MCI. Neurology. 2013;80(12):1124-1132.
PubMed   |  Link to Article
Ryberg  C, Rostrup  E, Paulson  OB,  et al; LADIS study group.  Corpus callosum atrophy as a predictor of age-related cognitive and motor impairment: a 3-year follow-up of the LADIS study cohort. J Neurol Sci. 2011;307(1-2):100-105.
PubMed   |  Link to Article
Verdelho  A, Madureira  S, Moleiro  C,  et al; LADIS Study.  White matter changes and diabetes predict cognitive decline in the elderly: the LADIS Study. Neurology. 2010;75(2):160-167.
PubMed   |  Link to Article
Haight  TJ, Landau  SM, Carmichael  O, Schwarz  C, DeCarli  C, Jagust  WJ; Alzheimer’s Disease Neuroimaging Initiative.  Dissociable effects of Alzheimer disease and white matter hyperintensities on brain metabolism. JAMA Neurol. 2013;70(8):1039-1045.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Cross-Sectional Relationships Between Concentration of Cerebrospinal Fluid (CSF) β-Amyloid 42 (Aβ42), Tau, and F2-Isoprostanes (F2-IsoPs) vs Age at Baseline for 315 Cognitively Normal Participants

A, Aβ42 slope (95% CI), −0.3 (−1.2 to 0.6); r2 = 0.001; P = .54. B, Tau slope, 0.2 (0.1 to 0.3); R2 = 0.06; P < .001. C, F2-IsoPs slope, 0.1 (0.04 to 0.15); R2 = 0.04; P < .001. Solid line indicates the fitted least-squares line unadjusted for any covariates; dashed lines, 95% CIs. AD indicates Alzheimer disease; MCI, mild cognitive impairment.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Cross-Sectional Relationship Between Trail Making Test Part B Scores and Concentration of Cerebrospinal Fluid (CSF) F2-Isoprostanes at Baseline for 315 Cognitively Normal Participants

The solid line indicates the fitted least-squares line for the log10 score unadjusted for any covariates; dashed lines are 95% CIs for the line. Slope, 0.005; 95% CI, 0.003-0.007; R2 = 0.06; P < .001. AD indicates Alzheimer disease; MCI, mild cognitive impairment.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Demographics and Baseline Biomarkers and Cognitive Test Scores for Control Participants in Cross-Sectional and Longitudinal Analyses
Table Graphic Jump LocationTable 3.  Longitudinal Analyses Based on Linear Mixed-Effects Modelsa

References

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PubMed   |  Link to Article
Buckner  RL.  Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron. 2004;44(1):195-208.
PubMed   |  Link to Article
Negash  S, Bennett  DA, Wilson  RS, Schneider  JA, Arnold  SE.  Cognition and neuropathology in aging: multidimensional perspectives from the Rush Religious Orders Study and Rush Memory and Aging Project. Curr Alzheimer Res. 2011;8(4):336-340.
PubMed   |  Link to Article
Gelber  RP, Launer  LJ, White  LR.  The Honolulu-Asia Aging Study: epidemiologic and neuropathologic research on cognitive impairment. Curr Alzheimer Res. 2012;9(6):664-672.
PubMed   |  Link to Article
Montine  TJ, Sonnen  JA, Montine  KS, Crane  PK, Larson  EB.  Adult Changes in Thought study: dementia is an individually varying convergent syndrome with prevalent clinically silent diseases that may be modified by some commonly used therapeutics. Curr Alzheimer Res. 2012;9(6):718-723.
PubMed   |  Link to Article
Rabinovici  GD, Jagust  WJ.  Amyloid imaging in aging and dementia: testing the amyloid hypothesis in vivo. Behav Neurol. 2009;21(1):117-128.
PubMed   |  Link to Article
Roe  CM, Fagan  AM, Grant  EA,  et al.  Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later. Neurology. 2013;80(19):1784-1791.
PubMed   |  Link to Article
Bateman  RJ, Xiong  C, Benzinger  TL,  et al; Dominantly Inherited Alzheimer Network.  Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 2012;367(9):795-804.
PubMed   |  Link to Article
Lo  RY, Jagust  WJ; Alzheimer’s Disease Neuroimaging Initiative.  Vascular burden and Alzheimer disease pathologic progression. Neurology. 2012;79(13):1349-1355.
PubMed   |  Link to Article
Longstreth  WT  Jr, Sonnen  JA, Koepsell  TD, Kukull  WA, Larson  EB, Montine  TJ.  Associations between microinfarcts and other macroscopic vascular findings on neuropathologic examination in 2 databases. Alzheimer Dis Assoc Disord. 2009;23(3):291-294.
PubMed   |  Link to Article
Sonnen  JA, Breitner  JC, Lovell  MA, Markesbery  WR, Quinn  JF, Montine  TJ.  Free radical–mediated damage to brain in Alzheimer’s disease and its transgenic mouse models. Free Radic Biol Med. 2008;45(3):219-230.
PubMed   |  Link to Article
Bayer-Carter  JL, Green  PS, Montine  TJ,  et al.  Diet intervention and cerebrospinal fluid biomarkers in amnestic mild cognitive impairment. Arch Neurol. 2011;68(6):743-752.
PubMed   |  Link to Article
Brys  M, Pirraglia  E, Rich  K,  et al.  Prediction and longitudinal study of CSF biomarkers in mild cognitive impairment. Neurobiol Aging. 2009;30(5):682-690.
PubMed   |  Link to Article
Seet  RC, Lee  CY, Chan  BP,  et al.  Oxidative damage in ischemic stroke revealed using multiple biomarkers. Stroke. 2011;42(8):2326-2329.
PubMed   |  Link to Article
Li  G, Sokal  I, Quinn  JF,  et al.  CSF tau/Aβ42 ratio for increased risk of mild cognitive impairment: a follow-up study. Neurology. 2007;69(7):631-639.
PubMed   |  Link to Article
Peskind  ER, Riekse  R, Quinn  JF,  et al.  Safety and acceptability of the research lumbar puncture. Alzheimer Dis Assoc Disord. 2005;19(4):220-225.
PubMed   |  Link to Article
Dumurgier  J, Vercruysse  O, Paquet  C,  et al.  Intersite variability of CSF Alzheimer's disease biomarkers in clinical setting. Alzheimers Dement. 2013;9(4):406-413.
PubMed   |  Link to Article
Mattsson  N, Andreasson  U, Persson  S,  et al.  The Alzheimer's Association external quality control program for cerebrospinal fluid biomarkers. Alzheimers Dement. 2011;7(4):386-395.e386. doi:10.1016/j.jalz.2011.05.2243.
PubMed   |  Link to Article
Milatovic  D, VanRollins  M, Li  K, Montine  KS, Montine  TJ.  Suppression of murine cerebral F2-isoprostanes and F4-neuroprostanes from excitotoxicity and innate immune response in vivo by α- or γ-tocopherol. J Chromatogr B Analyt Technol Biomed Life Sci. 2005;827(1):88-93.
PubMed   |  Link to Article
Hixson  JE, Vernier  DT.  Restriction isotyping of human apolipoprotein E by gene amplification and cleavage with HhaI. J Lipid Res. 1990;31(3):545-548.
PubMed
Wechsler  D. Wechsler Memory Scale–Revised. New York, NY: Harcourt Brace Jovanovich; 1987.
Gomez  RG, White  DA.  Using verbal fluency to detect very mild dementia of the Alzheimer type. Arch Clin Neuropsychol. 2006;21(8):771-775.
PubMed   |  Link to Article
Reitan  RWD. The Halstead-Reitan Neuropsychological Test Battery. Tucson, AZ: Neuropsychology Press; 1985.
Pinheiro  J, Bates  D. Mixed-Effects and Models in S and S-PLUS. New York, NY: Springer; 2000.
Nakagawa  S, Schielzeth  H.  A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol. 2013;4(2):133-142.
Link to Article
Kronmal  RA.  Spurious correlation and the fallacy of the ratio standard revisited. J R Stat Soc Ser A Stat Soc. 1993;156(3):379-392.
Link to Article
Shaw  LM, Vanderstichele  H, Knapik-Czajka  M,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Cerebrospinal fluid biomarker signature in Alzheimer’s Disease Neuroimaging Initiative subjects. Ann Neurol. 2009;65(4):403-413.
PubMed   |  Link to Article
Milliken  JK, Edland  SD.  Mixed effect models of longitudinal Alzheimer’s disease data: a cautionary note. Stat Med. 2000;19(11-12):1617-1629.
PubMed   |  Link to Article
Holm  S.  A simple sequential rejective multiple test procedure. Scand J Stat. 1979;6:65-70.
R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2012.
Pinheiro  J, DebRoy  S, Sarkar  D; R Core Team. nlme: Linear and Nonlinear Mixed Effects Models, R package, Version 3.1-104. Vienna, Austria: R Foundation for Statistical Computing; 2012.
Bates  D, Maechler  M, Bolker  B, Walker  S. Lme4: linear mixed-effects models using Eigen and S4. R package, version 1.0-5. http://CRAN.Rproject.org/package=lme4. 2013. Accessed February 14, 2014.
Vos  SJ, Xiong  C, Visser  PJ,  et al.  Preclinical Alzheimer’s disease and its outcome: a longitudinal cohort study. Lancet Neurol. 2013;12(10):957-965.
PubMed   |  Link to Article
Rolstad  S, Berg  AI, Bjerke  M, Johansson  B, Zetterberg  H, Wallin  A.  Cerebrospinal fluid biomarkers mirror rate of cognitive decline. J Alzheimers Dis. 2013;34(4):949-956.
PubMed
Roe  CM, Fagan  AM, Grant  EA,  et al.  Cerebrospinal fluid biomarkers, education, brain volume, and future cognition. Arch Neurol. 2011;68(9):1145-1151.
PubMed   |  Link to Article
Fagan  AM, Roe  CM, Xiong  C, Mintun  MA, Morris  JC, Holtzman  DM.  Cerebrospinal fluid tau/β-amyloid42 ratio as a prediction of cognitive decline in nondemented older adults. Arch Neurol. 2007;64(3):343-349.
PubMed   |  Link to Article
Duits  FH, Kester  MI, Scheffer  PG,  et al.  Increase in cerebrospinal fluid F2-isoprostanes is related to cognitive decline in APOE ε4 carriers. J Alzheimers Dis. 2013;36(3):563-570.
PubMed
Ewers  M, Insel  P, Jagust  WJ,  et al; Alzheimer’s Disease Neuroimaging Initiative (ADNI).  CSF biomarker and PIB-PET–derived β-amyloid signature predicts metabolic, gray matter, and cognitive changes in nondemented subjects. Cereb Cortex. 2012;22(9):1993-2004.
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
Rolstad  S, Berg  AI, Bjerke  M,  et al.  Amyloid-β42 is associated with cognitive impairment in healthy elderly and subjective cognitive impairment. J Alzheimers Dis. 2011;26(1):135-142.
PubMed
Ewers  M, Insel  PS, Stern  Y, Weiner  MW; Alzheimer’s Disease Neuroimaging Initiative (ADNI).  Cognitive reserve associated with FDG-PET in preclinical Alzheimer disease. Neurology. 2013;80(13):1194-1201.
PubMed   |  Link to Article
Reiman  EM, Quiroz  YT, Fleisher  AS,  et al.  Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: a case-control study. Lancet Neurol. 2012;11(12):1048-1056.
PubMed   |  Link to Article
Vos  SJ, van Rossum  IA, Verhey  F,  et al.  Prediction of Alzheimer disease in subjects with amnestic and nonamnestic MCI. Neurology. 2013;80(12):1124-1132.
PubMed   |  Link to Article
Ryberg  C, Rostrup  E, Paulson  OB,  et al; LADIS study group.  Corpus callosum atrophy as a predictor of age-related cognitive and motor impairment: a 3-year follow-up of the LADIS study cohort. J Neurol Sci. 2011;307(1-2):100-105.
PubMed   |  Link to Article
Verdelho  A, Madureira  S, Moleiro  C,  et al; LADIS Study.  White matter changes and diabetes predict cognitive decline in the elderly: the LADIS Study. Neurology. 2010;75(2):160-167.
PubMed   |  Link to Article
Haight  TJ, Landau  SM, Carmichael  O, Schwarz  C, DeCarli  C, Jagust  WJ; Alzheimer’s Disease Neuroimaging Initiative.  Dissociable effects of Alzheimer disease and white matter hyperintensities on brain metabolism. JAMA Neurol. 2013;70(8):1039-1045.
PubMed   |  Link to Article

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Multimedia

Supplement.

eTable 1. Demographics and baseline biomarkers and cognitive test scores by last clinical follow-up diagnosis

eTable 2. Cross-sectional analyses, including sensitivity analyses

eTable 3. Longitudinal analyses based on linear mixed effects models, including sensitivity analyses

eTable 4. Cross-sectional analyses, including dose model for APOE e4

eFigure 1. Baseline test score versus age for 315 cognitively normal subjects

eFigure 2. Longitudinal scores on neuropsychological tests for 158 subjects who were cognitively normal at baseline and had at least one follow-up test visit

eFigure 3. Longitudinal Immediate Recall and Delayed Recall scores for 158 subjects who were cognitively normal at baseline and had at least one follow-up test visit

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