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Original Investigation |

Age, Sex, and APOE ε4 Effects on Memory, Brain Structure, and β-Amyloid Across the Adult Life Span FREE

Clifford R. Jack Jr, MD1; Heather J. Wiste, BA2; Stephen D. Weigand, MS2; David S. Knopman, MD3; Prashanthi Vemuri, PhD1; Michelle M. Mielke, PhD2; Val Lowe, MD1; Matthew L. Senjem, MS1; Jeffrey L. Gunter, PhD1; Mary M. Machulda, PhD4; Brian E. Gregg, BS1; V. Shane Pankratz, PhD2; Walter A. Rocca, MD, MPH2,3; Ronald C. Petersen, MD, PhD3
[+] Author Affiliations
1Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota
2Department of Health Sciences Research, Mayo Clinic and Foundation, Rochester, Minnesota
3Department of Neurology, Mayo Clinic and Foundation, Rochester, Minnesota
4Department of Psychiatry and Psychology, Mayo Clinic and Foundation, Rochester, Minnesota
JAMA Neurol. 2015;72(5):511-519. doi:10.1001/jamaneurol.2014.4821.
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Published online

Importance  Typical cognitive aging may be defined as age-associated changes in cognitive performance in individuals who remain free of dementia. Ideally, the full adult age spectrum should be included to assess brain imaging findings associated with typical aging.

Objective  To compare age, sex, and APOE ε4 effects on memory, brain structure (adjusted hippocampal volume [HVa]), and amyloid positron emission tomography (PET) in cognitively normal individuals aged 30 to 95 years old.

Design, Setting, and Participants  Cross-sectional observational study (March 2006 to October 2014) at an academic medical center. We studied 1246 cognitively normal individuals, including 1209 participants aged 50 to 95 years old enrolled in a population-based study of cognitive aging and 37 self-selected volunteers aged 30 to 49 years old.

Main Outcomes and Measures  Memory, HVa, and amyloid PET.

Results  Overall, memory worsened from age 30 years through the 90s. The HVa worsened gradually from age 30 years to the mid-60s and more steeply beyond that age. The median amyloid PET was low until age 70 years and increased thereafter. Memory was worse in men than in women overall (P < .001) and more specifically beyond age 40 years. The HVa was lower in men than in women overall (P < .001) and more specifically beyond age 60 years. There was no sex difference in amyloid PET at any age. Within each sex, memory performance and HVa were not different by APOE ε4 status at any age. From age 70 years onward, APOE ε4 carriers had significantly greater median amyloid PET than noncarriers. However, the ages at which 10% of the population were amyloid PET positive were 57 years for APOE ε4 carriers and 64 years for noncarriers.

Conclusions and Relevance  Male sex is associated with worse memory and HVa among cognitively normal individuals, while APOE ε4 is not. In contrast, APOE ε4 is associated with greater amyloid PET (from age 70 years onward), while sex is not. Worsening memory and HVa occur at earlier ages than abnormal amyloid PET. Therefore, neuropathological processes other than β-amyloidosis must underlie declines in brain structure and memory function in middle age. Our findings are consistent with a model of late-onset Alzheimer disease in which β-amyloidosis arises in later life on a background of preexisting structural and cognitive decline that is associated with aging and not with β-amyloid deposits.

Figures in this Article

Typical cognitive aging may be defined as age-associated changes in cognitive performance in individuals who remain free of dementia. Interrelationships among biomarkers of β-amyloid, neurodegeneration, and cognitive performance have been the focus of much recent literature. However, studies13 that include all of these variables have focused predominantly on elderly individuals, typically included few (if any) individuals younger than 60 years, and tended to be composed of selected volunteers rather than population-based samples. We measured memory performance, hippocampal volume, and β-amyloidosis as a function of age using cross-sectional data from a large sample of cognitively normal individuals aged 30 to 95 years old. Individuals were grouped by sex and APOE ε4 status. The present study differs from a recent publication4 in which our group examined neither memory performance nor individuals younger than 50 years and in which our independent variables were not continuous measures. Differentiating features of the present study compared with other multimodality imaging studies in aging are (1) inclusion of the full adult age spectrum (30-90 years), (2) the population-based nature of 97.0% of our participants, (3), our transformation of the imaging and cognitive measures to a common scale to facilitate comparison across different modalities, and (4) the large sample size. Our objectives were to compare age, sex, and APOE ε4 effects on memory performance, hippocampal volume, and amyloid positron emission tomography (PET) across the adult life span.

Standard Protocol Approvals, Registrations, and Patient Consents

This study was approved by the Mayo Clinic and Olmsted Medical Center (Rochester, Minnesota) institutional review boards. Written informed consent was obtained from all participants.

Study Participants

We studied 1246 cognitively normal individuals from 2 different cohorts. The largest group (n = 1209) was 50 to 95 years old and comprised participants enrolled in the Mayo Clinic Study of Aging (MCSA). The MCSA5 is a population-based study of cognitive aging among Olmsted County, Minnesota, residents. The Olmsted County population is enumerated in the eligible age strata. From this enumeration, we selected individuals for recruitment using an age-stratified and sex-stratified random sampling strategy. These individuals were then invited to participate. The second group (n = 37) was 30 to 49 years old, equally stratified by 5-year age groups and sex (referred to as young normal). These individuals were self-selected volunteers and were recruited via word of mouth and were not population based. The study dates were March 2006 to October 2014.

All participants in this study were judged to have no cognitive impairment according to published criteria.5 All 1246 individuals (MCSA and young normal) underwent identical PET, magnetic resonance imaging, and memory testing protocols, which included the Auditory Verbal Learning Test. The sum of trials 1 through 5 plus the immediate and delayed recall trials (possible total score, 105) was the learning and memory performance measure (referred to as memory) used in our analyses.

Imaging

Amyloid PET was performed with Pittsburgh Compound B C 11 tracer.6 Standardized uptake value ratios were formed from the prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, posterior cingulate, and precuneus regions of interest normalized to the whole cerebellum.7,8 Magnetic resonance imaging was performed at 3 T, and hippocampal volume was measured with publicly available software (FreeSurfer, version 5.3.0; https://surfer.nmr.mgh.harvard.edu/). Total intracranial volume was measured using an in-house method.4

Statistical Analysis

Some individuals were enrolled in the MCSA before availability of amyloid PET and received prior cognitive testing. To eliminate confounding due to the well-established learning effect on serial Auditory Verbal Learning Test performance in cognitively normal individuals, we created a partial residual that adjusted for educational level and the number of times an individual had taken the test before baseline, which for this study was the date of the imaging studies. This adjusted Auditory Verbal Learning Test measure can be interpreted as the difference (in the number of words correctly recalled) from the expected number for a person given his or her educational level and the number of previous exposures to the test. To adjust hippocampal volume for total intracranial volume, we fit a regression model among 133 individuals aged 30 to 59 years old of hippocampal volume vs total intracranial volume. The adjusted hippocampal volume (HVa) was defined as the residual from this model8 and can be interpreted as the difference (in cubic centimeters) compared with the expected hippocampal volume given a person’s head size.

Memory performance, HVa, and amyloid PET are reported in modality-specific native units and in centiloid-like units (scale, 0-100).9 This process is similar to scaling biomarkers from normal to maximum abnormal levels (as described by Jack et al10). To create reference points for scaling, we defined 0 (normal) for the scaled units as the 95th percentile for memory and HVa and the 5th percentile for amyloid PET among the young normal study participants 30 to 49 years old. We defined 100 (abnormal) for the scaled units as the 5th percentile for HVa and the 95th percentile for amyloid PET among a group of 42 individuals with moderately demented Alzheimer disease (AD) (Clinical Dementia Rating, 1-3). We defined 100 (abnormal) for memory based on the 5th percentile among a larger group of 382 individuals with moderately demented AD (Clinical Dementia Rating, 1-3) who underwent memory testing but not necessarily magnetic resonance imaging and PET. These individuals with AD were participants in the MCSA or the Mayo Clinic Alzheimer’s Disease Research Center and had undergone the same battery of evaluations as our study participants. An individual’s memory, HVa, or amyloid PET in native units was scaled linearly to centiloid-like units (eFigure 1 in the Supplement).

We used quantile regression to estimate the median (rather than the mean) memory, HVa, and amyloid PET vs age by sex and APOE ε4 status. Quantile regression is particularly appropriate for modeling amyloid PET because its distribution is highly skewed and not conditionally normal even after log or other parametric transformations. For each response variable, we fit a single model that included age, sex, and APOE ε4 status along with all 2-way interactions. To allow for nonlinear associations with age, we modeled age with restricted cubic splines using knots at ages 50, 75, and 80 years.11 As recommended by Harrell,11 we prespecified the knot locations based on the distribution of ages in our data set, as well as to serve as reference points to support a broad class of flexible nonlinear curves.

We used the percentile bootstrap based on 5000 replicates to report 95% CIs for the median memory, HVa, or amyloid PET as a function of age and to report 95% CIs for differences in the medians between 2 measures or between 2 groups. We based inferences on whether 95% CIs for differences included the null value of zero.

We also reported the P values for a general sex effect for each outcome from a 4-df Wald test, which tests the additive and interaction terms involving sex. Similarly, we report the P values for a general APOE ε4 effect for each outcome.

We assessed the influence of individuals younger than 50 years on model fit and our conclusions. This assessment was performed with a sensitivity analysis limited to individuals aged 50 years or older.

In a secondary analysis, we fit a logistic regression model with age and APOE ε4 genotype to predict the probability of abnormal amyloid PET and used the estimates from this model to identify the age at which the probability reached 10% for both APOE ɛ4 carriers and noncarriers. To be consistent with our group’s recent publications,4,12,13 we defined abnormal as a standardized uptake value ratio of 1.4 or greater. Sex was not included in the model because it was not significantly associated with the probability of abnormal amyloid PET.

Demographic features and imaging and memory performance data by age group are listed in the Table. There was no significant difference in age by sex, but APOE ε4 carriers were on average 1 year younger than noncarriers (median, 71 vs 72 years; P = .04). The proportion of APOE ε4 carriers did not differ by sex. Educational level was not different by APOE ε4 status, but men were slightly more educated than women (median, 16 vs 14 years of education; P < .001). We show the young normal volunteers separately from the MCSA participants, who are grouped into 15-year age strata to illustrate the effects of advancing age (Table and eFigure 1 in the Supplement).

Table Graphic Jump LocationTable.  Characteristics of All Participantsa

Associations of memory, HVa, and amyloid PET vs age by sex and APOE ε4 status are shown in Figure 1, Figure 2, and eFigure 2 in the Supplement and are interpreted descriptively. Plots comparing differences in the outcomes among APOE ε4 carriers vs noncarriers within men and within women isolate the effect of APOE ε4 within sex (Figure 3). Plots comparing differences in the outcomes among men vs women within carriers and within noncarriers isolate the effect of sex within APOE ε4 genotype. These difference plots illustrate approximate ages at which significant differences were present in the outcomes by sex and APOE ε4 status. That is, when the 95% bootstrap CI around the median is above 0, we interpret this result as a significant difference in the outcomes by the group of interest at those ages. Therefore, plots in Figures 1 and 2 and eFigure 2 in the Supplement are interpreted qualitatively, while plots in Figure 3 are interpreted statistically.

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Figure 1.
Memory, Adjusted Hippocampal Volume (HVa), and Amyloid Positron Emission Tomography (PET) in Modality-Specific Units by Age, With Participants Categorized Into 4 Groups by Sex and APOE ε4 Genotype (Carriers vs Noncarriers)

Solid lines represent estimated median regression lines, while dotted lines represent 95% bootstrap CIs. Knots were placed at ages 50, 75, and 80 years.

Graphic Jump Location
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Figure 2.
Estimated Median Regression Lines in Scaled Units vs Age for All 4 Demographic Groups, With Separate Panels for Memory, Adjusted Hippocampal Volume (HVa), and Amyloid Positron Emission Tomography (PET)

Knots were placed at ages 50, 75, and 80 years. Blue lines represent relationships in men, and orange lines represent relationships in women. Solid lines represent APOE ε4 carriers, and dashed lines represent APOE ε4 noncarriers.

Graphic Jump Location
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Figure 3.
Plots of Groupwise Differences in Scaled Units for Memory, Adjusted Hippocampal Volume (HVa), and Amyloid Positron Emission Tomography (PET)

Comparisons are shown for differences among APOE ε4 carriers vs noncarriers within sex and for male vs female within APOE ε4 genotype. The solid line in each plot represents the estimated difference in medians, while the dotted lines represent 95% bootstrap CIs for this difference. A horizontal line at 0 (ie, no difference) is shown for reference. Plots in which significant groupwise differences were found are outlined in red. This red outlining illustrates a pattern showing that differences in memory and HVa were due to sex and not APOE ε4, while differences in amyloid PET were due to APOE ɛ4 and not sex.

Graphic Jump Location
Age, Sex, and APOE ε4 Group Effects on Memory

In all 4 groups, the median memory performance worsened from age 30 years through the 90s (Figures 1 and 2 and eFigure 2 in the Supplement), with a steeper decline after age 70 years in male APOE ε4 carriers and in women. Memory was worse in men than in women overall (P < .001) and more specifically beyond age 40 years (Figure 3). There was no difference in memory by APOE ε4 status (P = .24); however, carriers trended toward worse memory beyond age 80 years. Individual values within each group followed a gaussian distribution around the median for age (Figure 1 and eFigure 2 in the Supplement).

Age, Sex, and APOE ε4 Group Effects on HVa

In all 4 groups, HVa worsened gradually from age 30 years to the mid-60s and more steeply beyond that age (Figures 1 and 2 and eFigure 2 in the Supplement). The HVa was lower in men than in women overall (P < .001) and more specifically beyond age 60 years (Figure 3). Within each sex, HVa was not different by APOE ε4 status (P = .15). Individual values within each group followed a gaussian distribution around the median for age (Figure 1 and eFigure 2 in the Supplement).

Age, Sex, and APOE ε4 Group Effects on Amyloid PET

Unlike memory or HVa, the distribution of amyloid PET standardized uptake value ratios by age is highly skewed above age 65 years (Figure 1 and eFigure 2 in the Supplement). Amyloid PET was different by APOE ε4 status (P < .001). The median amyloid PET has a slight upward trend from age 30 years through the 90s among APOE ε4 noncarriers. In APOE ε4 carriers, there was a slight upward trend until age 70 years and then a steeper increase in the median after that (Figures 1 and 2 and eFigure 2 in the Supplement). While the median amyloid PET was greater in APOE ɛ4 carriers compared with noncarriers older than age 70 years (Figure 3), the ages at which 10% of the population were amyloid PET positive were 57 years (95% CI, 53-59 years) for APOE ε4 carriers and 64 years (95% CI, 62-66 years) for noncarriers (Figure 4). Sex differences in amyloid PET were not significant (P = .25); however, women trended toward greater β-amyloid beyond age 70 years (Figure 3).

Place holder to copy figure label and caption
Figure 4.
Probability of Amyloid Positron Emission Tomography (PET) Positivity vs Age by APOE ε4 Genotype

The data are estimated from a logistic model. Amyloid PET positivity was defined as a standardized uptake value ratio of 1.4 or greater. The estimated age at which 10% (the dashed horizontal line) of the population is positive is 57 years (95% CI, 53-59 years) for APOE ε4 carriers and 64 years (95% CI, 62-66 years) for APOE ε4 noncarriers.

Graphic Jump Location
Comparisons Between Memory, HVa, and Amyloid PET vs Age Within Group

Both memory and HVa were more abnormal than amyloid PET beyond age 30 to 40 years in all 4 groups. These results are shown in eFigure 3 and eFigure 4 in the Supplement.

Our major findings are that the median amyloid PET is greater in cognitively normal APOE ε4 carriers compared with noncarriers older than age 70 years and that the age at which 10% of the carriers are classified as amyloid PET positive is 7 years younger compared with noncarriers. Male sex is associated with worse memory and HVa among cognitively normal individuals, while APOE ε4 is not. Declining memory performance and HVa occur at earlier ages than abnormal amyloid PET.

The estimated age at which 10% of our APOE ɛ4 carriers were amyloid PET positive was 57 years compared with 64 years for noncarriers. These ages depend on the cut point used for amyloid PET positivity as well as the threshold chosen for the proportion who are positive. However, we wanted to make a concrete statement about the age at which abnormal amyloid PET first appears in the population and operationalized this finding as the age when the frequency of abnormality in the population reaches 10%. This is more robust than reporting the age when an abnormal scan first occurs in a single individual, which is very sensitive to outliers. In addition, our data show that, from age 70 years onward, APOE ε4 carriers had significantly greater median amyloid PET than noncarriers. These results are consistent with the well-established link between APOE ε4 and increased risk of β-amyloidosis.2,3,1417 In turn, β-amyloidosis increases the risk of cognitive impairment and dementia.1,1824

Overall age-dependent trends in our data are largely consistent with prior studies that show progressive declines in memory25,26 and brain volumes27,28 with age. Recognition that AD pathology, particularly amyloid plaques, can exist in situ for a decade or longer without producing overt cognitive symptoms1,2931 has raised the idea that subclinical declines in brain structure and cognitive function in middle age are often due to underlying β-amyloid deposition. However, we found that memory and HVa worsen continuously from age 30 years onward and that these trends are established before obviously abnormal amyloid PET appears in the population (Figures 1 and 2 and eFigure 2 in the Supplement). Memory and HVa values are symmetrically distributed around the population age median, which implies that declines in brain structure and memory are a fundamental characteristic of typical aging. In contrast, amyloid PET is skewed above age 65 years (Figure 1 and eFigure 2 in the Supplement) such that some individuals accumulate high amyloid loads, while many survive to old age without developing significant β-amyloidosis. The differing distributions of memory and HVa vs amyloid PET around the population medians with age imply that declining memory and HVa must have some mechanistic independence from β-amyloid accumulation. Also, direct comparisons of memory, HVa, and amyloid PET within each group (eFigures 3 and 4 in the Supplement) show that memory and HVa were consistently more abnormal than amyloid PET beyond age 30 to 40 years. We acknowledge that amyloid PET measures only fibrillar amyloid deposits and that potential effects of soluble β-amyloid cannot be assessed. Given this caveat, our data are nonetheless consistent with the concept that age-related degenerative processes affecting brain structure and cognitive function that are unrelated to fibrillar β-amyloid deposition8,13,3237 exist from at least age 30 years onward and are characteristic of typical aging. Reasonable candidates for non-AD processes associated with structural and functional decline in middle age are cerebrovascular disease and its risk factors, including primary age-related tauopathy,38,39 brain aging in the absence of any specific pathophysiological process,36,40 or combinations of these. Our data are consistent with models of late-onset AD in which β-amyloidosis, which defines preclinical AD,41 typically arises in later life on a background of preexisting age-related cognitive and structural decline.12,13,34,38,4246

With regard to sex effects, we found that men perform worse than women on memory beginning in their 40s, as has been shown previously.47 We also found that HVa was smaller in men than in women beyond age 60 years. This sex effect on memory and HVa was likely not due to sex differences in age or APOE ε4 because differences in age by sex or by APOE ε4 were small (median, approximately 1 year different) and because there were no differences in APOE ε4 prevalence by sex. Men were slightly more educated than women (median, 16 vs 14 years of education). However, if anything, this factor would tend to enhance memory performance in men compared with women, which is opposite from what we found. Moreover, we adjusted memory for educational level and practice effects. This detrimental effect of male sex on memory and HVa must also be independent of β-amyloid deposition because (1) we found no sex differences in amyloid PET at any age (Figure 3) and (2) sex differences were present in memory (beginning at age 40 years) well before abnormal amyloid PET first appeared in the population (Figures 1, 3, and 4 and eFigure 2 in the Supplement). These sex differences in memory and HVa could be developmental,48 a hormonal protective effect,49 or attributable to a greater prevalence of adverse lifestyle-related exposures (eg, vascular risk factors) in men.27

Perhaps the most controversial findings from this study come from comparing associations between sex vs APOE ε4 on age-dependent trends in memory and HVa. Some prior studies50,51 are consistent with our finding of no association between APOE ε4 and hippocampal volume in cognitively normal individuals. However, other studies have indicated that, among cognitively normal individuals without β-amyloid deposition, APOE ε4 carriers have hypometabolism in AD-like regions,52 abnormal functional connectivity,53,54 worse cognitive performance,55 and smaller regional brain volumes.56,57 Such findings have been taken as evidence that APOE ε4 exerts harmful effects throughout life on brain structure and function that are independent of its role in promoting β-amyloid deposition.58,59 In contrast, we found that, while male sex was associated with smaller hippocampal volume and worse memory, APOE ε4 carriers within each sex did not have worse memory or HVa than noncarriers at any age.60 Had we examined other imaging measures (eg, fluorodeoxyglucose F 18 PET or functional magnetic resonance imaging) or perhaps other cognitive indexes, the findings might have been different. Nonetheless, our results paint a different picture than is presented in much of the recent imaging literature, which has focused great attention on the effect of APOE ε4 but little on the effect of sex on brain structure and function.

Our study has limitations. While all individuals 50 years or older in our sample were derived from an epidemiologically defined cohort, the non–population-based nature of those aged 30 to 49 years old and the small sample size in this age range are acknowledged limitations. However, when obvious inflection points exist in plots of memory, HVa, and amyloid PET vs age, they occur well within the age range of 50 years to the 90s of the MCSA cohort (Figures 1 and 2 and eFigures 2 and 3 in the Supplement) and not at the age junction of the young normal group and the MCSA cohort. In addition, a sensitivity analysis indicated that plots (among those 50 years or older) of memory, HVa, and amyloid PET vs age did not change when individuals younger than 50 years were excluded. This finding indicates that the imbalance in the numbers of individuals aged 50 years or older vs younger than 50 years did not unduly influence our conclusions. Another limitation is that cross-sectional studies tend to confound age effects with birth cohort effects.61 However, cohort effects are unavoidable when examining age-dependent trends covering a range of 60 years. Within-individual longitudinal data typically found in studies with intensive multimodality imaging (1-5 years) will not ameliorate birth cohort effects when the research questions of interest are age trends spanning 60 years. A final methodological point concerns interpretation of the results given that all participants were cognitively normal. Individuals who remain cognitively normal into old age represent a subset of those who were members of their birth cohort at younger ages.

Despite these limitations, we believe that this study of typical aging reveals interesting sex and APOE ε4 effects on age-related trends in brain structure, function, and β-amyloidosis. To date, these effects have not been widely appreciated. Our findings are consistent with a model of late-onset AD in which β-amyloidosis arises in later life on a background of preexisting structural and cognitive decline that is associated with aging and not with β-amyloid deposits.

Accepted for Publication: December 30, 2014.

Corresponding Author: Clifford R. Jack Jr, MD, Department of Radiology, Mayo Clinic and Foundation, 200 First St SW, Rochester, MN 55905 (jack.clifford@mayo.edu).

Published Online: March 16, 2015. doi:10.1001/jamaneurol.2014.4821.

Author Contributions: Dr Jack 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: Jack, Wiste, Weigand.

Acquisition, analysis, or interpretation of data: Jack, Wiste, Weigand.

Drafting of the manuscript: Jack, Wiste, Weigand, Knopman, Vemuri, Mielke, Lowe, Senjem, Gunter, Machulda, Pankratz, Rocca, Petersen.

Critical revision of the manuscript for important intellectual content: Jack, Wiste, Weigand, Knopman, Vemuri, Mielke, Lowe, Senjem, Gunter, Machulda, Pankratz, Rocca, Petersen.

Statistical analysis: Wiste, Weigand, Pankratz.

Administrative, technical, or material support: Senjem, Gunter, Gregg.

Conflict of Interest Disclosures: Dr Jack reported providing consulting services for Eli Lilly and reported receiving research funding from the National Institutes of Health (grants R01-AG011378, U01-AG024904, R01-AG041851, R01-AG37551, R01-AG043392, and U01-AG06786) and the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Foundation. Dr Knopman reported serving as deputy editor for Neurology and on data safety monitoring boards for Lundbeck Pharmaceuticals and for the Dominantly Inherited Alzheimer Network (DIAN) study; reported being an investigator in clinical trials sponsored by TauRX Pharmaceuticals, Lilly Pharmaceuticals, and the Alzheimer’s Disease Cooperative Study; and reported receiving research support from the National Institutes of Health. Dr Lowe reported being a consultant for Bayer Schering Pharma and reported receiving research support from GE Healthcare, Siemens Molecular Imaging, Avid Radiopharmaceuticals, the National Institutes of Health (National Institute on Aging and National Cancer Institute), the Elsie and Marvin Dekelboum Family Foundation, the Minnesota Partnership for Biotechnology and Medical Genomics, and The Leukemia & Lymphoma Society. Dr Pankratz reported being funded by the National Institutes of Health (grants R01-AG040042, U01-AG06786, Mayo Clinic Alzheimer’s Disease Research Center/Core C P50-AG16574/Core C, and R01-AG32990). Dr Rocca reported serving on the editorial boards for Revista de Neurologia, Clinical Epidemiology & Risk Management, Maturitas, Menopause, and Journal of Chinese Clinical Medicine and reported receiving research support from the National Institutes of Health (grants R01-AG034676, U01-AG006786, P50-AG04417, and K12-HD065987). Dr Petersen reported serving on scientific advisory boards for Elan Pharmaceuticals, Wyeth Pharmaceuticals, and GE Healthcare; reported receiving royalties from publishing Mild Cognitive Impairment (Oxford University Press, 2003); and reported receiving research support from the National Institutes of Health (grants P50-AG16574, U01-AG06786, R01-AG11378, and U01-24904). No other disclosures were reported.

Funding/Support: This study was supported by grants R01-AG011378, R01-AG041851, and U01-AG06786 from the National Institute on Aging and by the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Foundation.

Role of the Funder/Sponsor: The funding sources 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.

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PubMed   |  Link to Article
Vemuri  P, Wiste  HJ, Weigand  SD,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Effect of apolipoprotein E on biomarkers of amyloid load and neuronal pathology in Alzheimer disease. Ann Neurol. 2010;67(3):308-316.
PubMed
Hedden  T, Oh  H, Younger  AP, Patel  TA.  Meta-analysis of amyloid-cognition relations in cognitively normal older adults. Neurology. 2013;80(14):1341-1348.
PubMed   |  Link to Article
Doré  V, Villemagne  VL, Bourgeat  P,  et al.  Cross-sectional and longitudinal analysis of the relationship between Aβ deposition, cortical thickness, and memory in cognitively unimpaired individuals and in Alzheimer disease. JAMA Neurol. 2013;70(7):903-911.
PubMed   |  Link to Article
Landau  SM, Mintun  MA, Joshi  AD,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol. 2012;72(4):578-586.
PubMed   |  Link to Article
Rowe  CC, Bourgeat  P, Ellis  KA,  et al.  Predicting Alzheimer disease with β-amyloid imaging: results from the Australian Imaging, Biomarkers, and Lifestyle Study of Ageing. Ann Neurol. 2013;74(6):905-913.
PubMed   |  Link to Article
Lim  YY, Pietrzak  RH, Bourgeat  P,  et al.  Relationships between performance on the Cogstate Brief Battery, neurodegeneration, and Aβ accumulation in cognitively normal older adults and adults with MCI. Arch Clin Neuropsychol.2015;301(1):49-58.
PubMed   |  Link to Article
Chételat  G, Villemagne  VL, Pike  KE,  et al; Australian Imaging, Biomarkers, and Lifestyle Study of Ageing Research Group.  Relationship between memory performance and β-amyloid deposition at different stages of Alzheimer’s disease. Neurodegener Dis. 2012;10(1-4):141-144.
PubMed   |  Link to Article
Donohue  MC, Sperling  RA, Salmon  DP,  et al; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Cooperative Study.  The Preclinical Alzheimer Cognitive Composite: measuring amyloid-related decline. JAMA Neurol. 2014;71(8):961-970.
PubMed   |  Link to Article
Van der Elst  W, van Boxtel  MP, van Breukelen  GJ, Jolles  J.  Rey’s Verbal Learning Test: normative data for 1855 healthy participants aged 24-81 years and the influence of age, sex, education, and mode of presentation. J Int Neuropsychol Soc. 2005;11(3):290-302.
PubMed   |  Link to Article
Hedden  T, Gabrieli  JD.  Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci. 2004;5(2):87-96.
PubMed   |  Link to Article
DeCarli  C, Massaro  J, Harvey  D,  et al.  Measures of brain morphology and infarction in the Framingham Heart Study: establishing what is normal. Neurobiol Aging. 2005;26(4):491-510.
PubMed   |  Link to Article
Raz  N, Lindenberger  U, Rodrigue  KM,  et al.  Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex. 2005;15(11):1676-1689.
PubMed   |  Link to Article
Benzinger  TL, Blazey  T, Jack  CR  Jr,  et al.  Regional variability of imaging biomarkers in autosomal dominant Alzheimer’s disease. Proc Natl Acad Sci U S A. 2013;110(47):E4502-E4509. doi:10.1073/pnas.1317918110.
PubMed   |  Link to Article
Jack  CR  Jr, Wiste  HJ, Lesnick  TG,  et al.  Brain β-amyloid load approaches a plateau. Neurology. 2013;80(10):890-896.
PubMed   |  Link to Article
Jack  CR  Jr, Vemuri  P, Wiste  HJ,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Evidence for ordering of Alzheimer disease biomarkers. Arch Neurol. 2011;68(12):1526-1535.
PubMed   |  Link to Article
Jagust  W.  Apolipoprotein E, neurodegeneration, and Alzheimer disease. JAMA Neurol. 2013;70(3):299-300.
PubMed   |  Link to Article
Chételat  G.  Alzheimer disease: Aβ-independent processes: rethinking preclinical AD. Nat Rev Neurol. 2013;9(3):123-124.
PubMed   |  Link to Article
Jack  CR  Jr, Knopman  DS, Jagust  WJ,  et al.  Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12(2):207-216.
PubMed   |  Link to Article
Wirth  M, Madison  CM, Rabinovici  GD, Oh  H, Landau  SM, Jagust  WJ.  Alzheimer’s disease neurodegenerative biomarkers are associated with decreased cognitive function but not β-amyloid in cognitively normal older individuals. J Neurosci. 2013;33(13):5553-5563.
PubMed   |  Link to Article
Fjell  AM, McEvoy  L, Holland  D, Dale  AM, Walhovd  KB; Alzheimer’s Disease Neuroimaging Initiative.  Brain changes in older adults at very low risk for Alzheimer’s disease. J Neurosci. 2013;33(19):8237-8242.
PubMed   |  Link to Article
Nebes  RD, Snitz  BE, Cohen  AD,  et al.  Cognitive aging in persons with minimal amyloid-β and white matter hyperintensities. Neuropsychologia. 2013;51(11):2202-2209.
PubMed   |  Link to Article
Price  JL, Morris  JC.  Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol. 1999;45(3):358-368.
PubMed   |  Link to Article
Crary  JF, Trojanowski  JQ, Schneider  JA,  et al.  Primary age-related tauopathy (PART): a common pathology associated with human aging. Acta Neuropathol. 2014;128(6):755-766.
PubMed   |  Link to Article
Jagust  W.  Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron. 2013;77(2):219-234.
PubMed   |  Link to Article
Sperling  RA, Aisen  PS, Beckett  LA,  et al.  Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging–Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):280-292.
PubMed   |  Link to Article
Duyckaerts  C, Hauw  JJ.  Prevalence, incidence and duration of Braak’s stages in the general population: can we know? Neurobiol Aging. 1997;18(4):362-369.
PubMed   |  Link to Article
Delacourte  A, Sergeant  N, Wattez  A,  et al.  Tau aggregation in the hippocampal formation: an ageing or a pathological process? Exp Gerontol. 2002;37(10-11):1291-1296.
PubMed   |  Link to Article
Jack  CR  Jr, Holtzman  DM.  Biomarker modeling of Alzheimer’s disease. Neuron. 2013;80(6):1347-1358.
PubMed   |  Link to Article
Musiek  ES, Holtzman  DM.  Origins of Alzheimer’s disease: reconciling cerebrospinal fluid biomarker and neuropathology data regarding the temporal sequence of Aβ and tau involvement. Curr Opin Neurol. 2012;25(6):715-720.
PubMed   |  Link to Article
Storandt  M, Head  D, Fagan  AM, Holtzman  DM, Morris  JC.  Toward a multifactorial model of Alzheimer disease. Neurobiol Aging. 2012;33(10):2262-2271.
PubMed   |  Link to Article
Cerhan  JR, Folsom  AR, Mortimer  JA,  et al; Atherosclerosis Risk in Communities (ARIC) Study Investigators.  Correlates of cognitive function in middle-aged adults. Gerontology. 1998;44(2):95-105.
PubMed   |  Link to Article
Baron-Cohen  S, Knickmeyer  RC, Belmonte  MK.  Sex differences in the brain: implications for explaining autism. Science. 2005;310(5749):819-823.
PubMed   |  Link to Article
Rocca  WA, Bower  JH, Maraganore  DM,  et al.  Increased risk of cognitive impairment or dementia in women who underwent oophorectomy before menopause. Neurology. 2007;69(11):1074-1083.
PubMed   |  Link to Article
Jack  CR  Jr, Petersen  RC, Xu  YC,  et al.  Hippocampal atrophy and apolipoprotein E genotype are independently associated with Alzheimer’s disease. Ann Neurol. 1998;43(3):303-310.
PubMed   |  Link to Article
Protas  HD, Chen  K, Langbaum  JB,  et al.  Posterior cingulate glucose metabolism, hippocampal glucose metabolism, and hippocampal volume in cognitively normal, late–middle-aged persons at 3 levels of genetic risk for Alzheimer disease. JAMA Neurol. 2013;70(3):320-325.
PubMed   |  Link to Article
Reiman  EM, Chen  K, Alexander  GE,  et al.  Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer’s dementia. Proc Natl Acad Sci U S A. 2004;101(1):284-289.
PubMed   |  Link to Article
Sheline  YI, Morris  JC, Snyder  AZ,  et al.  APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Aβ42. J Neurosci. 2010;30(50):17035-17040.
PubMed   |  Link to Article
Filippini  N, MacIntosh  BJ, Hough  MG,  et al.  Distinct patterns of brain activity in young carriers of the APOE-ε4 allele. Proc Natl Acad Sci U S A. 2009;106(17):7209-7214.
PubMed   |  Link to Article
Caselli  RJ, Dueck  AC, Osborne  D,  et al.  Longitudinal modeling of age-related memory decline and the APOE ε4 effect. N Engl J Med. 2009;361(3):255-263.
PubMed   |  Link to Article
Shaw  P, Lerch  JP, Pruessner  JC,  et al.  Cortical morphology in children and adolescents with different apolipoprotein E gene polymorphisms: an observational study. Lancet Neurol. 2007;6(6):494-500.
PubMed   |  Link to Article
Alexander  GE, Bergfield  KL, Chen  K,  et al.  Gray matter network associated with risk for Alzheimer’s disease in young to middle-aged adults. Neurobiol Aging. 2012;33(12):2723-2732.
PubMed   |  Link to Article
Jagust  WJ, Landau  SM; Alzheimer’s Disease Neuroimaging Initiative.  Apolipoprotein E, not fibrillar β-amyloid, reduces cerebral glucose metabolism in normal aging. J Neurosci. 2012;32(50):18227-18233.
PubMed   |  Link to Article
Wolf  AB, Valla  J, Bu  G,  et al.  Apolipoprotein E as a β-amyloid–independent factor in Alzheimer’s disease. Alzheimers Res Ther. 2013;5(5):38.
PubMed   |  Link to Article
Lim  YY, Ellis  KA, Pietrzak  RH,  et al; AIBL Research Group.  Stronger effect of amyloid load than APOE genotype on cognitive decline in healthy older adults. Neurology. 2012;79(16):1645-1652.
PubMed   |  Link to Article
Arking  RA. Measuring age-related change in individuals. In: Biology of Aging: Observations and Principles. 2nd ed. Sunderland, MA: Sinauer Associates; 1998:61-105.

Figures

Place holder to copy figure label and caption
Figure 1.
Memory, Adjusted Hippocampal Volume (HVa), and Amyloid Positron Emission Tomography (PET) in Modality-Specific Units by Age, With Participants Categorized Into 4 Groups by Sex and APOE ε4 Genotype (Carriers vs Noncarriers)

Solid lines represent estimated median regression lines, while dotted lines represent 95% bootstrap CIs. Knots were placed at ages 50, 75, and 80 years.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Estimated Median Regression Lines in Scaled Units vs Age for All 4 Demographic Groups, With Separate Panels for Memory, Adjusted Hippocampal Volume (HVa), and Amyloid Positron Emission Tomography (PET)

Knots were placed at ages 50, 75, and 80 years. Blue lines represent relationships in men, and orange lines represent relationships in women. Solid lines represent APOE ε4 carriers, and dashed lines represent APOE ε4 noncarriers.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.
Plots of Groupwise Differences in Scaled Units for Memory, Adjusted Hippocampal Volume (HVa), and Amyloid Positron Emission Tomography (PET)

Comparisons are shown for differences among APOE ε4 carriers vs noncarriers within sex and for male vs female within APOE ε4 genotype. The solid line in each plot represents the estimated difference in medians, while the dotted lines represent 95% bootstrap CIs for this difference. A horizontal line at 0 (ie, no difference) is shown for reference. Plots in which significant groupwise differences were found are outlined in red. This red outlining illustrates a pattern showing that differences in memory and HVa were due to sex and not APOE ε4, while differences in amyloid PET were due to APOE ɛ4 and not sex.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 4.
Probability of Amyloid Positron Emission Tomography (PET) Positivity vs Age by APOE ε4 Genotype

The data are estimated from a logistic model. Amyloid PET positivity was defined as a standardized uptake value ratio of 1.4 or greater. The estimated age at which 10% (the dashed horizontal line) of the population is positive is 57 years (95% CI, 53-59 years) for APOE ε4 carriers and 64 years (95% CI, 62-66 years) for APOE ε4 noncarriers.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable.  Characteristics of All Participantsa

References

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PubMed   |  Link to Article
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PubMed   |  Link to Article
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity. Neurology. 2013;81(20):1732-1740.
PubMed   |  Link to Article
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PubMed   |  Link to Article
Morris  JC, Roe  CM, Xiong  C,  et al.  APOE predicts amyloid-β but not tau Alzheimer pathology in cognitively normal aging. Ann Neurol. 2010;67(1):122-131.
PubMed   |  Link to Article
Vemuri  P, Wiste  HJ, Weigand  SD,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Effect of apolipoprotein E on biomarkers of amyloid load and neuronal pathology in Alzheimer disease. Ann Neurol. 2010;67(3):308-316.
PubMed
Hedden  T, Oh  H, Younger  AP, Patel  TA.  Meta-analysis of amyloid-cognition relations in cognitively normal older adults. Neurology. 2013;80(14):1341-1348.
PubMed   |  Link to Article
Doré  V, Villemagne  VL, Bourgeat  P,  et al.  Cross-sectional and longitudinal analysis of the relationship between Aβ deposition, cortical thickness, and memory in cognitively unimpaired individuals and in Alzheimer disease. JAMA Neurol. 2013;70(7):903-911.
PubMed   |  Link to Article
Landau  SM, Mintun  MA, Joshi  AD,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol. 2012;72(4):578-586.
PubMed   |  Link to Article
Rowe  CC, Bourgeat  P, Ellis  KA,  et al.  Predicting Alzheimer disease with β-amyloid imaging: results from the Australian Imaging, Biomarkers, and Lifestyle Study of Ageing. Ann Neurol. 2013;74(6):905-913.
PubMed   |  Link to Article
Lim  YY, Pietrzak  RH, Bourgeat  P,  et al.  Relationships between performance on the Cogstate Brief Battery, neurodegeneration, and Aβ accumulation in cognitively normal older adults and adults with MCI. Arch Clin Neuropsychol.2015;301(1):49-58.
PubMed   |  Link to Article
Chételat  G, Villemagne  VL, Pike  KE,  et al; Australian Imaging, Biomarkers, and Lifestyle Study of Ageing Research Group.  Relationship between memory performance and β-amyloid deposition at different stages of Alzheimer’s disease. Neurodegener Dis. 2012;10(1-4):141-144.
PubMed   |  Link to Article
Donohue  MC, Sperling  RA, Salmon  DP,  et al; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Cooperative Study.  The Preclinical Alzheimer Cognitive Composite: measuring amyloid-related decline. JAMA Neurol. 2014;71(8):961-970.
PubMed   |  Link to Article
Van der Elst  W, van Boxtel  MP, van Breukelen  GJ, Jolles  J.  Rey’s Verbal Learning Test: normative data for 1855 healthy participants aged 24-81 years and the influence of age, sex, education, and mode of presentation. J Int Neuropsychol Soc. 2005;11(3):290-302.
PubMed   |  Link to Article
Hedden  T, Gabrieli  JD.  Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci. 2004;5(2):87-96.
PubMed   |  Link to Article
DeCarli  C, Massaro  J, Harvey  D,  et al.  Measures of brain morphology and infarction in the Framingham Heart Study: establishing what is normal. Neurobiol Aging. 2005;26(4):491-510.
PubMed   |  Link to Article
Raz  N, Lindenberger  U, Rodrigue  KM,  et al.  Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex. 2005;15(11):1676-1689.
PubMed   |  Link to Article
Benzinger  TL, Blazey  T, Jack  CR  Jr,  et al.  Regional variability of imaging biomarkers in autosomal dominant Alzheimer’s disease. Proc Natl Acad Sci U S A. 2013;110(47):E4502-E4509. doi:10.1073/pnas.1317918110.
PubMed   |  Link to Article
Jack  CR  Jr, Wiste  HJ, Lesnick  TG,  et al.  Brain β-amyloid load approaches a plateau. Neurology. 2013;80(10):890-896.
PubMed   |  Link to Article
Jack  CR  Jr, Vemuri  P, Wiste  HJ,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Evidence for ordering of Alzheimer disease biomarkers. Arch Neurol. 2011;68(12):1526-1535.
PubMed   |  Link to Article
Jagust  W.  Apolipoprotein E, neurodegeneration, and Alzheimer disease. JAMA Neurol. 2013;70(3):299-300.
PubMed   |  Link to Article
Chételat  G.  Alzheimer disease: Aβ-independent processes: rethinking preclinical AD. Nat Rev Neurol. 2013;9(3):123-124.
PubMed   |  Link to Article
Jack  CR  Jr, Knopman  DS, Jagust  WJ,  et al.  Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12(2):207-216.
PubMed   |  Link to Article
Wirth  M, Madison  CM, Rabinovici  GD, Oh  H, Landau  SM, Jagust  WJ.  Alzheimer’s disease neurodegenerative biomarkers are associated with decreased cognitive function but not β-amyloid in cognitively normal older individuals. J Neurosci. 2013;33(13):5553-5563.
PubMed   |  Link to Article
Fjell  AM, McEvoy  L, Holland  D, Dale  AM, Walhovd  KB; Alzheimer’s Disease Neuroimaging Initiative.  Brain changes in older adults at very low risk for Alzheimer’s disease. J Neurosci. 2013;33(19):8237-8242.
PubMed   |  Link to Article
Nebes  RD, Snitz  BE, Cohen  AD,  et al.  Cognitive aging in persons with minimal amyloid-β and white matter hyperintensities. Neuropsychologia. 2013;51(11):2202-2209.
PubMed   |  Link to Article
Price  JL, Morris  JC.  Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol. 1999;45(3):358-368.
PubMed   |  Link to Article
Crary  JF, Trojanowski  JQ, Schneider  JA,  et al.  Primary age-related tauopathy (PART): a common pathology associated with human aging. Acta Neuropathol. 2014;128(6):755-766.
PubMed   |  Link to Article
Jagust  W.  Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron. 2013;77(2):219-234.
PubMed   |  Link to Article
Sperling  RA, Aisen  PS, Beckett  LA,  et al.  Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging–Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):280-292.
PubMed   |  Link to Article
Duyckaerts  C, Hauw  JJ.  Prevalence, incidence and duration of Braak’s stages in the general population: can we know? Neurobiol Aging. 1997;18(4):362-369.
PubMed   |  Link to Article
Delacourte  A, Sergeant  N, Wattez  A,  et al.  Tau aggregation in the hippocampal formation: an ageing or a pathological process? Exp Gerontol. 2002;37(10-11):1291-1296.
PubMed   |  Link to Article
Jack  CR  Jr, Holtzman  DM.  Biomarker modeling of Alzheimer’s disease. Neuron. 2013;80(6):1347-1358.
PubMed   |  Link to Article
Musiek  ES, Holtzman  DM.  Origins of Alzheimer’s disease: reconciling cerebrospinal fluid biomarker and neuropathology data regarding the temporal sequence of Aβ and tau involvement. Curr Opin Neurol. 2012;25(6):715-720.
PubMed   |  Link to Article
Storandt  M, Head  D, Fagan  AM, Holtzman  DM, Morris  JC.  Toward a multifactorial model of Alzheimer disease. Neurobiol Aging. 2012;33(10):2262-2271.
PubMed   |  Link to Article
Cerhan  JR, Folsom  AR, Mortimer  JA,  et al; Atherosclerosis Risk in Communities (ARIC) Study Investigators.  Correlates of cognitive function in middle-aged adults. Gerontology. 1998;44(2):95-105.
PubMed   |  Link to Article
Baron-Cohen  S, Knickmeyer  RC, Belmonte  MK.  Sex differences in the brain: implications for explaining autism. Science. 2005;310(5749):819-823.
PubMed   |  Link to Article
Rocca  WA, Bower  JH, Maraganore  DM,  et al.  Increased risk of cognitive impairment or dementia in women who underwent oophorectomy before menopause. Neurology. 2007;69(11):1074-1083.
PubMed   |  Link to Article
Jack  CR  Jr, Petersen  RC, Xu  YC,  et al.  Hippocampal atrophy and apolipoprotein E genotype are independently associated with Alzheimer’s disease. Ann Neurol. 1998;43(3):303-310.
PubMed   |  Link to Article
Protas  HD, Chen  K, Langbaum  JB,  et al.  Posterior cingulate glucose metabolism, hippocampal glucose metabolism, and hippocampal volume in cognitively normal, late–middle-aged persons at 3 levels of genetic risk for Alzheimer disease. JAMA Neurol. 2013;70(3):320-325.
PubMed   |  Link to Article
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Supplement.

eFigure 1. Relationship Between Modality-Specific and Scaled Units for Memory (Panel A), HVa (Panel B), and Amyloid PET (Panel C)

eFigure 2. Memory, HVa, and Amyloid PET in Scaled Units by Age With Subjects Broken Into Four Groups by Sex and APOE Genotype (APOE e4 Carrier vs Noncarrier)

eFigure 3. Estimated Median Regression Line for Memory, HVa, and Amyloid PET in Scaled Units vs Age by Sex and APOE Genotype (Carrier vs Noncarrier)

eFigure 4. Plots of the Difference in Medians for Memory vs HVa (Row 1), Memory vs Amyloid PET (Row 2), and HVa vs Amyloid PET (Row 3) for Ages 30 to 90 by Demographic Group (Columns) in Scaled Units

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