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. Author manuscript; available in PMC: 2022 May 19.
Published in final edited form as: Hippocampus. 2021 Feb 15;31(5):469–480. doi: 10.1002/hipo.23308

Influence of regional white matter hyperintensity volume and APOE ε4 status on hippocampal volume in healthy older adults

Emily J Van Etten a,b, Pradyumna K Bharadwaj a,b, Georg A Hishaw c, Matthew J Huentelman b,d,i, Theodore P Trouard b,e,i, Matthew D Grilli a,b,c, Gene E Alexander a,b,f,g,h,i
PMCID: PMC9119498  NIHMSID: NIHMS1745272  PMID: 33586848

Abstract

While total white matter hyperintensity (WMH) volume on magnetic resonance imaging (MRI) has been associated with hippocampal atrophy, less is known about how the regional distribution of WMH volume may differentially affect the hippocampus in healthy aging. Additionally, apolipoprotein E (APOE) ε4 carriers may be at an increased risk for greater WMH volumes and hippocampal atrophy in aging. The present study sought to investigate whether regional WMH volume mediates the relationship between age and hippocampal volume and if this association is moderated by APOE ε4 status in a group of 190 cognitively healthy adults (APOE ε4 status [carrier/non-carrier] = 59/131), ages 50–89. Analyses revealed that temporal lobe WMH volume significantly mediated the relationship between age and average bilateral hippocampal volume, and this effect was moderated by APOE ε4 status (−.020 (SE=.009), 95% CI, [−.039, −.003]). APOE ε4 carriers, but not non-carriers, showed negative indirect effects of age on hippocampal volume through temporal lobe WMH volume (APOE ε4 carriers: −.016 (SE=.007), 95% CI, [−.030, −.003]; APOE ε4 non-carriers: .005 (SE=.006), 95% CI, [−.006, .017]). These findings remained significant after additionally adjusting for sex, years of education, hypertension status and duration, cholesterol status, diabetes status, Body Mass Index, history of smoking and the Wechsler Adult Intelligence Scale-IV Full Scale IQ. There were no significant moderated mediation effects for frontal, parietal, and occipital lobe WMH volumes, with or without covariates. Our findings indicate that in cognitively healthy older adults, elevated WMH volume regionally localized to the temporal lobes in APOE ε4 carriers is associated with reduced hippocampal volume, suggesting greater vulnerability to brain aging and the risk for Alzheimer’s disease.

Keywords: Regional white matter hyperintensities, Hippocampal volume, APOE ε4, Brain aging, Preclinical Alzheimer’s disease risk

Introduction

The older adult population is rapidly increasing and is expected to nearly double by 2050 (Ortman, Velkoff, & Hogan, 2014). While many cognitive functions differ with age, aspects of memory, executive functions, and processing speed are particularly sensitive to the effects of aging (Alexander et al., 2012b; Glisky, 2007; Park & Reuter-Lorenz 2009; Salthouse, 1992). These cognitive changes may reflect, in part, neuroanatomical differences in brain structure associated with healthy aging, including reductions in frontal and temporal gray matter that are disproportionally affected with increasing age (Alexander et al., 2006; Bergfield et al., 2010; Brickman, Habeck, Zarahn, Flynn, & Stern, 2007; Fjell & Walvhold, 2010; Raz & Rodrigue, 2006). The hippocampus, an important neuroanatomical structure for memory, is especially vulnerable to aging (Du et al., 2006; Jack et al., 2000; Jernigan et al., 2001; Raz, Rodrigue, Head, Kennedy, & Acker, 2004), and hippocampal atrophy is an important biomarker of preclinical Alzheimer’s disease (AD; Chételat, 2018; De Flores, La Joie, & Chételat, 2015; Henneman et al., 2009; Tepest et al., 2008). AD is the most common neurodegenerative disease in older adults and many factors, including vascular health and genetic risk factors, may exacerbate the development of AD (Brickman, 2013; Bertram, Lill & Tanzi, 2010; Corder et al., 1993; Liu, Kanekiyo, Xu, & Bu, 2013; Luchsinger et al., 2005). How these risk factors impact brain aging and increase the risk for AD, however, is not fully understood.

Vascular health factors often investigated in aging research include hypertension status and white matter hyperintensity (WMH) volume on magnetic resonance imaging (MRI). Hypertension reflects sustained elevated systolic and diastolic blood pressure and is prevalent in more than 60% of adults over 60 years of age (Centers for Disease Control and Prevention [CDC], 2012). WMH volume provides a neuroimaging marker of white matter lesion load and is thought to reflect chronic ischemia related to cerebral small vessel disease (Biesbroek, Weaver, & Biessels, 2017; Prins & Scheltens, 2015). While WMH volume is typically related to vascular health, the presence of white matter hyperintensities are also often observed in healthy older adults without significant conditions associated with vascular risk, suggesting other factors may contribute to their aggregation in aging (Alber et al., 2019; Gouw et al., 2010). In addition, apolipoprotein ε4 (APOE ε4) is a strong genetic risk factor for late-onset AD (Bertram et al., 2010; Corder et al., 1993; Liu et al., 2013), and the ε4 allele has been associated with vascular disease mechanisms, including increased cardiovascular risk and the breakdown of the blood brain barrier (Montagne et al., 2020; Raichlen & Alexander, 2014; Zipser et al., 2007). APOE ε4 genotype may, in part, influence the development of Alzheimer’s-related pathology through its impact on vascular health (Brickman et al., 2014; Liu et al., 2013).

Reductions in total and regional cortical gray matter, primarily in frontal and temporoparietal brain regions, have been associated with total WMH volume in healthy aging (Habes et al., 2016; Kern et al., 2017; Peng et al., 2016). Notably, previous studies have found total WMH volume has an inverse relationship with hippocampal volume (Crane et al., 2015; McNeely et al., 2015) and the rate of hippocampal atrophy (Fiford et al., 2017). Fiford and colleagues (2017) observed that increased WMH volume was associated with greater hippocampal atrophy in cognitively healthy older adults, even when controlling for concurrent whole-brain atrophy rate and cerebrospinal fluid (CSF) AD biomarkers of β-amyloid and tau. This suggests the hippocampus may be especially vulnerable to increases in WMH volume, potentially related to cortical disconnection and/or axonal loss and subsequent atrophy via Wallerian degeneration (Schmidt et al., 2011).

While many studies have investigated the impacts of total WMH volume, fewer studies have considered how the regional lobar distribution of WMH volume may affect healthy aging and age-related brain atrophy. Recent studies have observed selective vulnerability, with greater regional WMH volumes in some, but not all, cerebral lobes suggesting detrimental effects on cognitive performance or gray matter in aging (Brugulat-Serrat et al., 2019; Lampe et al., 2019; Meier et al., 2012; Rizvi et al., 2018). For instance, Rizvi and colleagues (2018) found that greater WMH volumes in frontal, parietal, and occipital, but not temporal, lobes were associated with reduced thickness and volume of medial temporal lobe regions, which in turn led to poorer memory performance in a sample of healthy older adults. This highlights the importance of examining the differential effects of the regional distribution of WMH volume in brain and cognitive aging. More research is needed, however, to investigate how differences in other risk factors, such as APOE ε4 status, may modify the relationship between regional WMH volume and gray matter changes associated with healthy aging.

Structural differences in gray matter, including in the hippocampus, between APOE ε4 carriers and non-carriers have not been consistently observed in cognitively healthy middle-aged to older adults. While some studies have found hippocampal volume differences in APOE ε4 carriers relative to non-carriers (Alexander et al., 2012a; Cacciaglia et al., 2018; Honea, Vidoni, Harsha, & Burns, 2009; Jak, Houston, Nagel, Corey-Bloom, & Bondi, 2007; Lind et al., 2006; Tohgi et al., 1997; Wishart et al., 2006), others have not observed significant volume differences in relation to APOE ε4 status (Schmidt et al., 1996; Reiman et al., 1998; Trivedi et al., 2006). Studies examining WMH volume in healthy aging have found APOE ε4 group differences in total and regional WMH load (Brickman et al., 2014; de Leeuw et al., 2004; Godin et al., 2009; Rojas et al., 2018; Schilling et al., 2013). In a recent study, Brickman and colleagues (2014) observed greater temporal, parietal, and occipital WMH volumes in APOE ε4 carriers than non-carriers. Moreover, older adults who were APOE ε4 carriers and had increased parietal WMH volume were more likely to subsequently develop dementia than non-carriers and ε4 carriers that had lower parietal WMH volume. These findings suggest that both the regional distribution of WMH volume and the effects of regional WMH volumes on dementia risk may, in part, depend on APOE ε4 status. Yet, how regional WMH volumes and APOE ε4 status interact to affect the hippocampus, a brain region thought to be most vulnerable to early AD, remains unclear.

In sum, age-related increases in total WMH volume have been associated with gray matter volume reductions, notably in the hippocampus (Fiford et al., 2017). Less is known, however, about how the regional distribution of WMH volumes may differentially affect hippocampal volume in healthy aging. In addition, the APOE ε4 allele, a genetic risk factor for both AD and vascular disease, may exacerbate total and regional WMH burden (Brickman et al., 2014; de Leeuw et al., 2004; Godin et al., 2009; Rojas et al., 2018; Schilling et al., 2013), as well as hippocampal atrophy (Honea et al., 2009; Jak et al., 2007; Lind et al., 2006; Tohgi et al., 1997). The present study aims to investigate how the distribution of regional WMH volumes may influence hippocampal volume in healthy aging and if the relation differs by APOE ε4 status. We used moderated mediation analyses (Hayes, 2017) that test how the relationships between age, regional WMH volume, and hippocampal volume differ between APOE ε4 carriers and non-carriers. Given that the APOE ε4 allele is associated with greater vulnerability to brain aging and the development of AD and vascular disease, we hypothesized that increasing age would be associated with greater regional WMH volumes, which in turn would be related to decreased hippocampal volume, and that these relationships would be greater in APOE ε4 carriers than non-carriers among healthy older adults.

Method

Participants

One hundred and ninety participants who were 50–89 years of age were drawn from a cohort of 210 community-dwelling healthy older adults, as part of a study on healthy cognitive aging. Six participants were excluded due to unavailable data. In addition, given that the WMH volumes remained skewed after a log transformation and adjustment for total intracranial volume (TIV), outliers who were ±2.5 or more standard deviations away from the mean in one or more lobes (n =14; three from frontal, four from temporal, one from parietal, three from occipital, and three who had outlier values in more than one lobe) were removed from the primary analyses to enhance normality. The resulting sample (n =190) was predominately Caucasian (94.8%), with an average age of 70.40 years (SD = 10.09) and consisted of 94 female participants (48.47%). The average education was 15.95 years (SD = 2.57), and the average Mini Mental Status Exam (MMSE; Folstein, Folstein, & McHugh, 1975) score was 28.95 (SD = 1.24).

Participants underwent a comprehensive medical screen, and a physical and neurological examination performed by a neurologist (GAH), who specializes in aging. Individuals were excluded from the study if they had an MMSE score less than 26, a Hamilton Depression Rating Scale (HAM-D; Hamilton, 1960) greater than 9, or a history of major neurological, medical, or psychiatric disorders. All participants provided written consent and all procedures were approved by the Institutional Review Board at the University of Arizona.

APOE Genotyping

Extracted DNA was assayed via restriction fragment length polymorphism (RFLP) to determine the APOE genotype. DNA was amplified using AmpliTaq Gold Fast PCR Master Mix (Applied Biosystems: Thermo Fisher Scientific, Waltham, MA) with the following primer sequences (FWD 5’-ACA-GAA-TTG-GCC-CCG-GCC-TGG-TAC-3’ and REV 5’-TAA-GCT-TGG-CAC-GGC-TGT-CCA-AGG-A-3’). Following PCR amplification of the APOE fragment each sample was incubated with 10 units of HhaI (New England BioLabs, Ipswich, MA) restriction enzyme for 16h at 37 °C. Samples were then assessed on a 4% agarose gel for the characteristic banding patterns that indicate one of the six common APOE genotypes according to previously published methods (Addya, Wang, & Leonard, 1997). In the cohort, there were 59 APOE ε4 carriers (homozygous: n = 7, heterozygous: n = 52) and 131 ε4 non-carriers.

Cognitive Measures

All participants were administered a battery of neuropsychological measures to evaluate cognitive functioning in multiple domains, including general cognitive abilities, intellectual functioning, memory, executive functions, language, and visuospatial abilities. The tests included the Mini Mental Status Exam (Folstein, Folstein, & McHugh, 1975), the Full Scale IQ (FSIQ) from the Wechsler Adult Intelligence Scale-IV (WAIS-IV; Wechsler, 2008), total sum recall, consistent long-term retrieval, and delayed recall from the Buschke Selective Reminding Test (SRT; Buschke, 1973), copy, immediate, and delayed recall trials from the Rey Complex Figure Test (Meyers & Meyers, 1995), the Trail Making Test (Reitan, 1956), the Stroop Color and Word Test (Golden, 1978), the Boston Naming Test (Kaplan, Goodglass, & Weintraub, 2000), and the Controlled Oral Word Association Test (Benton and Hamsher, 1976).

Magnetic Resonance Imaging

Volumetric T1-weighted 3D Spoiled Gradient Echo (SPGR) MRI scans (slice thickness = 1.0mm, TR = 5.3ms, TE = 2.0ms, TI = 500ms, FA = 15°, matrix = 256×256, FOV = 25.6cm) and T2 Fluid-Attenuation Inversion Recovery (FLAIR) scans (slice thickness = 2.6mm, TR = 11000ms, TE = 120ms, TI = 2250ms, flip angle = 90°, matrix = 256×256, FOV = 25.0cm) were acquired on a 3T GE Signa Excite scanner (General Electric, Milwaukee, WI).

Image Processing

Hippocampal Volumes

T1-weighted 3D volumetric MRIs were processed using FreeSurfer v5.3. The technical details of the procedures involved have been described in prior publications (Dale, Fischl, & Sereno, 1999; Fischl, Liu, & Dale, 2001; Fischl et al., 2002; Fischl et al., 2004a; Fischl et al., 2004b; Reuter, Rosas, & Fischl, 2010). Briefly, this process generates labeled subcortical segmentations (Fischl et al., 2002; Fischl et al., 2004a), as well as cortical parcellations (Desikan et al., 2006; Fischl et al., 2004b). Cortical surfaces and subcortical segmentations were visually inspected for accuracy and were re-processed as needed.

The volumes for the right and left hippocampal regions were obtained from the subcortical segmentation stream of FreeSurfer v5.3. TIV was computed for each participant in native brain space using T1 scans with Statistical Parametric Mapping (SPM12; Wellcome Trust Centre for Neuroimaging, London, UK; Alexander et al., 2012a). Hippocampal volume was averaged across hemispheres and residualized after linear regression to adjust for differences in TIV.

Total and Regional WMH volumes

Total WMH volume was computed using T1 and T2 FLAIR scans with the lesion segmentation toolbox (LST; Schmidt et al., 2012) for SPM12. We utilized the multispectral lesion growth algorithm approach (LGA) with both T1 and T2 FLAIR scans, as previous findings have suggested it provides better quantification of WMH volume than the lesion prediction algorithm (LPA) method (Waymont, Petsa, McNeil, Murray, & Waiter, 2019).

For the MRI scans in our healthy aging cohort, the accuracy of LGA across a range of kappa thresholds (0.05 – 1.00) was initially assessed in a subset of 35 participants using manually segmented reference WMH maps that were produced using ITK-SNAP (www.itksnap.org; Yushkevich et al., 2006), and reviewed to consensus on a slice-by-slice basis by expert raters (PKB and GEA) and a neurologist who specializes in aging (GAH). The LGA generated lesion maps were binarized at a probability value of 1 for each kappa threshold assessed to generate conservative global spatial WMH maps. The spatial overlap with the reference WMH maps, assessed using the dice coefficient, was highest (0.70 ± 0.13) at the kappa threshold of 0.35, with the corresponding total lesion volumes being closely aligned with the reference WMH volumes (r2 = 0.93, p = 1.22E-20), as well. Following this threshold optimization procedure, LGA lesion probability maps were generated for all study participants at a kappa of 0.35, and visually inspected for segmentation quality before computing total lesion volumes (Franchetti et al., 2020).

Our approach for processing regional WMH volume has been described previously (Franchetti et al., 2020). For regional WMH volumes (see Figure 1), the MNI152 template was initially processed using FreeSurfer v5.3 to generate the cortical gray matter labels of the four major lobes by combining labels from the Desikan-Killiany atlas (Desikan et al., 2006) using FreeSurfer’s standard schema (https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation). Subcortical structures in each hemisphere were initially re-labeled as WM, and the labels for the standard cortical lobar annotation were extended to all WM. This WM labeling procedure employed FreeSurfer’s native WM parcellation approach and involved constructing a Voronoi diagram in WM, based on distance to the nearest cortical parcellation label (Salat et al., 2009), which in this case was one of the four cortical lobes. The default search space for this approach was expanded to include deep WM as well as the relabeled subcortical structures up to the inter-hemispheric midline. The cortical GM lobar regions and corresponding subcortical WM within each lobe were then combined to generate volumetric regions of interest (ROI) of the four major lobes in each hemisphere in MNI152 template space. Next, the Advanced Normalization Tools’ Greedy SyN algorithm (ANTs; Avants et al., 2011) was used to non-linearly register the FreeSurfer conformed and skull stripped MNI152 template to each participant’s skull stripped T1 scan. The resultant warping parameters were applied to the MNI152 template’s four lobes in each hemisphere to generate T1 native space lobar ROIs for each participant. Finally, these T1 native space lobar ROIs were employed as search regions for the binarized lesion probability maps from the LST LGA to extract regional WMH volumes for each participant. Regional WMH volumes were summed across hemispheres, log transformed and residualized using linear regressions to adjust for differences in TIV.

Figure 1.

Figure 1.

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An illustration of regional white matter hyperintensity lobar volume segmentation. Frontal lobe white matter hyperintensity (WMH) volume is shown in red, parietal lobe WMH volume is in yellow, temporal WMH volume is shown in light blue, and occipital lobe WMH volume is in pink. R= right hemisphere; L = left hemisphere.

Statistical analysis

Differences in cognitive performance, demographic characteristics, and imaging variables between APOE ε4 carriers and non-carriers were evaluated using independent sample t-tests. Sex distribution and hypertension status differences were compared with chi-square tests.

All moderated mediation analyses were performed using the PROCESS macro for SPSS (v3.1; Hayes, 2017), using non-parametric bootstrap resampling with 10,000 iterations to produce 95% percentile confidence intervals, which indicate significance when they do not include zero. Four separate moderated mediation models were performed for the primary analyses to test the mediation of the relationship between age (independent variable [x]) and average bilateral hippocampal volume (dependent variable [y]) by the sum of the regional WMH volumes from each lobe, across hemispheres (frontal, temporal, parietal, and occipital; mediator’s [m’s]), with APOE ε4 carrier status as the moderator (w). Each analysis tested the indirect and direct effects of the relationships between age, regional WMH volume, and hippocampal volume and how these associations differ between APOE ε4 carriers and non-carriers within one model (model 59; Hayes, 2017). Sex and years of education were subsequently included as covariates, as these demographic characteristics have been shown to influence hippocampal volume differences in aging (Nobis et al., 2019; Noble et al., 2012). In addition, to adjust for common vascular health factors that may impact hippocampal volume and memory performance in aging (Bobb, Schwartz, Davatzikos, & Caffo, 2014; Hawkins et al., 2018; Milne et al., 2018; Van Etten et al, 2020; Wolf et al., 2004), hypertension status and duration, cholesterol status, diabetes status, Body Mass Index (BMI), and history of smoking were added as covariates. Finally, WAIS-FSIQ was entered as a covariate to control for potential differences in intellectual function and associated cognitive reserve (Alexander et al., 1997; Stern, 2012).

In follow-up analyses, we additionally tested whether significant moderated mediation effects of the relation between age and average hippocampal volume for the lobar regional WMH volumes showed similar relationships when testing the moderated mediation models with left and right hippocampal volumes separately. These models similarly tested the mediation of the relationship between age (independent variable [x]) and left and right hippocampal volume (dependent variables [y’s]) by regional WMH volume (mediator [m]), with APOE ε4 status as the moderator (w) and sex, years of education, hypertension status and duration, cholesterol status, diabetes status, BMI, history of smoking, and the WAIS-FSIQ as covariates.

Significant moderated mediation effects of the relation between age and average hippocampal volume for the lobar regional WMH volumes were also followed with moderated serial mediation models to examine if the moderated mediation effects lead to differences in objective memory. These models tested the mediation of the relationship between age (independent variable [x]) and objective memory performance (dependent variables [y’s]) by regional WMH volume and average hippocampal volume (serial mediator’s [m’s]), with APOE ε4 status as the moderator (w). Sex, years of education, hypertension status and duration, cholesterol status, diabetes status, BMI, history of smoking, and the WAIS-FSIQ were also included as covariates. The objective memory measures used in these analyses were the sum recall, consistent long-term retrieval, and delayed recall measures from the SRT (Buschke, 1973). These memory measures were selected, as such verbal list-learning tests tend to be highly sensitive to cognitive aging effects (Alexander et al., 2012b), and the SRT has been shown to provide utility in predicting cognitive decline and the development of dementia in older adults (Fuld, Masur, Blau, Crystal, & Aronson, 1990).

Results

The APOE ε4 carrier and non-carrier groups significantly differed in the distribution of cholesterol status, but did not differ in any other demographic, cognitive, and neuroimaging characteristics, as shown in Table 1. A moderated mediation model revealed that the mediation of the relationship between age and average hippocampal volume by temporal WMH volume was moderated by APOE ε4 status (−.020 (SE= .009), 95% CI, [−.039, −.003]). APOE ε4 carriers showed negative indirect effects of age on average hippocampal volume through temporal WMH volume (−.016 (SE= .007), 95% CI, [−.030, −.003]), but in non-carriers, temporal WMH volume did not significantly mediate the relationship between age and average hippocampal volume (.005 (SE= .006), 95% CI, [−.006, .017]). These findings remained significant after including sex, years of education, hypertension status, hypertension duration, cholesterol status, diabetes status, BMI, history of smoking, and the WAIS-IV FSIQ as covariates (−.022 (SE= .010), 95% CI, [−.042, −.003]; see Table 2).

Table 1.

Table of demographic variables, cognitive performance, and brain imaging values.

Variable APOE ε4 carriers APOE ε4
non-carriers		 p
N 59 131 -
Age [years; M (SD)] 70.59 (9.75) 70.31 (10.27) .858
Education [years; M (SD)] 16.15 (2.55) 15.86 (2.58) .473
Sex (F/M) 31/28 63/68 .570
Hypertension Status (Y/N) 20/39 43/88 .884
Cholesterol Status (Y/N) 34/25 53/78 .028
Diabetes Status (Y/N) 56/3 121/10 .631
History of Smoking (Y/N) 21/38 54/77 .463
Body Mass Index [M (SD)] 25.34 (3.91) 25.57 (3.72) .703
MMSE [M (SD)] 29.05 (1.21) 28.91 (1.25) .464
WAIS-IV FSIQ [M (SD)] 112.44 (12.80) 112.76 (12.02) .870
SRT Total Sum Recall [M (SD)] 102.25 (22.87) 103.72 (19.53) .651
SRT Consistent Long-Term Retrieval [M (SD)] 61.88 (40.35) 61.42 (36.55) .938
SRT Delayed Recall [M (SD)] 7.76 (3.00) 8.07 (2.75) .491
CFT Immediate [M (SD)] 16.07 (6.66) 15.08 (6.91) .360
CFT Delayed [M (SD)] 15.51 (6.51) 14.81 (7.00) .516
Trail Making Test A [M (SD)] 34.35 (13.61)§ 33.07 (11.82) .512
Trail Making Test B [M (SD)] 80.88 (40.67)§ 77.64 (32.14) .557
Stroop Color-Word Interference [M (SD)] 35.32 (9.18) 36.52 (9.40) .413
CFT Copy [M (SD)] 31.52 (3.64) 32.00 (3.49) .385
Boston Naming Test [M (SD)] 56.88 (2.98) 56.51 (3.62) .493
COWAT [M (SD)] 44.68 (13.42) 45.25 (13.22) .783
Frontal WMH Volume† [M (SD)] .23 (.80) .06 (.91) .212
Temporal WMH Volume† [M (SD)] .22 (.88) .02 (.93) .172
Parietal WMH Volume† [M (SD)] .19 (.81) .10 (.95) .528
Occipital WMH Volume† [M (SD)] .16 (.91) .05 (.95) .420
Average Hippocampal Volume‡ [M (SD)] .00 (.92) −.02 (1.05) .878
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Note:

†

Log Transformed and TIV-adjusted standardized values.

‡

TIV-adjusted standardized values.

§

n = 58.

n = 130. M (SD) = Mean (standard deviation), F/M = female/male, Y/N = yes/no, MMSE = Mini Mental Status Exam, WAIS-IV = Wechsler Adult Intelligence Scale-Fourth Edition, FSIQ = Full-Scale IQ, SRT = Selective Reminding Test, CFT = Complex Figure Test, COWAT = Controlled Oral Word Association Test, WMH = white matter hyperintensity.

Table 2.

Moderated mediation model conditional indirect effects of age predicting average hippocampal volume through regional lobar WMH volumes in relation to APOE ε4 status.

Regional WMH volume APOE ε4 status Effect SE LLCI ULCI
Frontal WMH volume APOE ε4 carrier −.012 .008 −.030 .003
APOE ε4 non-carrier −.004 .005 −.015 .006
Temporal WMH volume APOE ε4 carrier −.017 .008 −.033 −.003
APOE ε4 non-carrier .005 .006 −.007 .017
Parietal WMH volume APOE ε4 carrier −.013 .010 −.035 .005
APOE ε4 non-carrier −.002 .005 −.012 .008
Occipital WMH volume APOE ε4 carrier .001 .004 −.008 .010
APOE ε4 non-carrier .001 .003 −.004 .006
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Note: Conditional indirect effects of the moderated mediation models for APOE ε4 carriers and APOE ε4 non-carriers, with sex, years of education, hypertension status, hypertension duration, cholesterol status, diabetes status, BMI, history of smoking and WAIS-FSIQ as covariates. Independent variable (x) = age, mediator (m) = regional white matter hyperintensity volumes (i.e., frontal, temporal, parietal, and occipital white matter hyperintensity volumes), dependent variable (y) = average hippocampal volume, moderator (w) = APOE ε4 status. WMH = white matter hyperintensity; SE = standard error; LLCI = lower limit confidence interval; ULCI = upper limit confidence interval. Percentile bootstrap resampling with 10,000 iterations produced 95% confidence intervals that indicate significance when they do not include 0. Bolded indirect effects are significant.

Examination of the individual associations of this moderated mediation model with temporal WMH volume (see Figure 2) showed that there was a significant overall negative direct association between age and hippocampal volume (−.060 (SE= .008), p < .0001, 95% CI, [−.075, −.045]), and this effect was significantly moderated by APOE ε4 status (.018 (SE= .008), p = .016, 95% CI, [.004, .033]). In APOE ε4 carriers, the relation between age and hippocampal volume was significantly negative (−.042 (SE= .013), p = .001, 95% CI, [−.067, −.017]) and this negative association was stronger in APOE ε4 non-carriers (−.078 (SE= .008), p < .0001, 95% CI, [−.095, −.062]). For indirect associations, there was a significant overall positive relation between age and temporal WMH volume (.053 (SE= .006), p < .0001, 95% CI, [.041, .066]), but this path was not significantly moderated by APOE ε4 status (−.002 (SE= .006), p = .700, 95% CI, [−.014, .010]). In addition, there was no significant overall relation between temporal WMH volume and hippocampal volume (−.121 (SE= .083), p = .145, 95% CI, [−.284, .042]), but this path was significantly moderated by APOE ε4 status (−.211 (SE= .082), p = .011, 95% CI, [−.373, −.048]). In APOE ε4 carriers, the association between temporal WMH volume and hippocampal volume was significantly negative (−.331 (SE= .136), p = .016, 95% CI, [−.600, −.063]), but in APOE ε4 non-carriers, this path was not significant (.090 (SE= .093), p = .338, 95% CI, [−.095, .274]).

Figure 2.

Figure 2.

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Coefficients of the moderated mediation model with sex, years of education, hypertension status, hypertension duration, cholesterol status, diabetes status, BMI, history of smoking, and WAIS-FSIQ included as covariates. A: The relationship between age and average hippocampal volume mediated by temporal WMH volume and moderated by APOE ε4 status. B: The relationship between age and average hippocampal volume mediated by temporal WMH volume in APOE ε4 carriers. C: The relationship between age and average hippocampal volume mediated by temporal WMH volume in APOE ε4 non-carriers. Percentile bootstrap resampling was performed with 10,000 iterations to produce 95% confidence intervals that indicate significance when they do not include 0. #95% confidence interval (CI) and regression coefficients significant; *p < .05, **p <.01, ***p <.001; WMH = white matter hyperintensity.

There were no significant moderated mediations between age and average hippocampal volume by frontal (−.008 (SE= .009), 95% CI, [−.026, .009]), parietal (−.009 (SE= .010), 95% CI, [−.032, .009]), or occipital WMH volumes (.004 (SE = .004), 95% CI, [−.005, .013]), by APOE ε4 status without covariates. In addition, these results remained non-significant after including sex, years of education, hypertension status, hypertension duration, cholesterol status, diabetes status, BMI, history of smoking, and the WAIS-IV FSIQ as covariates for frontal (−.008 (SE= .010), 95% CI, [−.028, .010]), parietal (−.011 (SE= .011), 95% CI, [−.035, .009]), and occipital (.0003 (SE = .005), 95% CI, [−.010, .010]) WMH volumes (see Table 2).

Furthermore, we repeated the four moderated mediation models after including the 14 participants initially removed from the primary analyses with outlier regional WMH volume values and the significance of the results remained unchanged (see Supplement Table 1).

Right and Left Hippocampal Volume

We conducted follow-up analyses testing the mediation of the relation between age and right or left hippocampal volume by temporal WMH volume in the primary analysis cohort with APOE ε4 status as the moderator and sex, years of education, hypertension status, hypertension duration, cholesterol status, diabetes status, BMI, history of smoking, and WAIS-FSIQ as covariates. We found that temporal WMH volume significantly mediated the relation between age and left (−.021 (SE= .010), 95% CI, [−.041, −.003]) and right (−.020 (SE= .010), 95% CI, [−.041, −.001]) hippocampal volume in APOE ε4 carriers, but not non-carriers. Similarly to average hippocampal volume, APOE ε4 carriers showed negative indirect effects of age on left (−.015 (SE= .008), 95% CI, [−.031, −.001]) and right (−.017 (SE= .008), 95% CI, [−.034, −.002]) hippocampal volume through temporal WMH volume, but in non-carriers, temporal WMH volume did not significantly mediate the relationship between age and left (.006 (SE= .006), 95% CI, [−.005, .018]) and right (.003 (SE= .006), 95% CI, [−.009, .016]) hippocampal volume. The values for the individual associations and figures of these moderated mediation models are provided in the Supplement (see Supplement Figures 1 & 2).

Serial Moderated Mediation Models with Memory Performance

In order to examine whether the moderated mediation analyses with temporal WMH volume and average hippocampal volume led to differences in objective memory performance, we conducted follow-up analyses with moderated serial mediation models. These models tested the relationship between age and objective memory performance by temporal WMH volume and average hippocampal volume and if these relationships differ by APOE ε4 status with sex, years of education, hypertension status, hypertension duration, cholesterol status, diabetes status, BMI, history of smoking, and WAIS-FSIQ as covariates. There were no significant moderated serial mediations of the relationships between age and SRT sum recall (.035 (SE= .053), 95% CI, [−.074, .144]), consistent long-term recall (.002 (SE= .100), 95% CI, [−.221, .190]), and delayed recall (.002 (SE= .009), 95% CI, [−.018, .019]) by temporal WMH volume and average hippocampal volume in relation to APOE ε4 status.

Discussion

In a cohort of cognitively unimpaired older adults, we found age-related reductions in hippocampal volume are mediated by temporal WMH volume and this effect depends on APOE ε4 status. Thus, hippocampal volume in generally healthy cognitive aging may be sensitive to increases in temporal WMH volume in APOE ε4 carriers, but not in non-carriers. These effects were significant even while controlling for health conditions that typically contribute to vascular risk, including hypertension status and duration, cholesterol status, diabetes status, BMI, and history of smoking, suggesting that greater temporal WMH volume in APOE ε4 carriers may negatively impact hippocampal volume distinctly from the adverse effects of common vascular health factors on brain aging. In contrast, such moderated mediations were not observed with WMH volume in the other cerebral lobes (frontal, parietal, and occipital), indicating the regional proximity of temporal WMH volume may have important effects on hippocampal volume in APOE ε4 carriers. Together, these findings suggest that the combination of elevated temporal WMH volume and at least one APOE ε4 allele may lead to greater vulnerability for brain aging and preclinical risk for AD.

Few studies have investigated how the regional distribution of WMH volumes may affect healthy brain aging. One previous study observed that greater frontal, parietal, and occipital, but not temporal, WMH volumes were associated with reduced thickness and volume of medial temporal lobe regions, which in turn led to poorer memory performance (Rizvi et al., 2018). We found the effect of temporal lobe WMH volume on hippocampal volume was, however, modified by APOE ε4 status, suggesting the effects of regional WMH volume on brain aging may differ by APOE ε4 status. Notably, the association between temporal WMH volume and hippocampal volume was significantly moderated by APOE ε4 status, but the relation between age and temporal WMH volume was not. This indicates that, in our sample of generally healthy older adults, APOE ε4 carriers did not have increased temporal lobe WMH volumes compared to non-carriers, but that the interaction between temporal WMH volume and APOE ε4 status may preferentially affect key brain regions in healthy aging. In the present study, the moderated serial mediation models did not lead to significant differences in memory performance, which may indicate that the mediation of age-related hippocampal volume reductions by temporal white matter hyperintensities may precede observable cognitive effects in generally healthy older APOE ε4 carriers. Moreover, this could suggest reductions in age-related hippocampal volume by temporal WMH volume in APOE ε4 carriers may be a harbinger of future cognitive decline. Further research, particularly with longitudinal data, would be important to test this hypothesis and to investigate how the regional distribution of WMH volume and APOE ε4 status interact to impact other neuroanatomical regions to better understand how the combination of these vascular and genetic risk factors may influence the course of healthy aging and preclinical risk for AD.

When examining the individual associations of the significant moderated mediation model with temporal WMH volume, we found that the direct effect between age and hippocampal volume was significantly moderated by APOE ε4 status. Although age was significantly associated with decreased hippocampal volume in both APOE ε4 carriers and non-carriers, this relation was greater in non-carriers, suggesting the influence of age on hippocampal volume may differ as a function of APOE ε4 status. As a study of healthy aging, however, our sample was screened to only include individuals who were neurologically and cognitively healthy. APOE ε4 carriers in the population who are in their seventh or eighth decade of life may be more likely than APOE ε4 non-carriers to have notable cognitive difficulties, which could have made them ineligible for inclusion in our study. As this study is cross-sectional, longitudinal follow-up studies would be important to examine how differences in APOE ε4 status may influence the impact of age on hippocampal volume over time.

A previous study found that increased total WMH volume was associated with greater hippocampal atrophy in older adults (Fiford et al., 2017). Our results expand upon these findings to suggest that hippocampal volume may be preferentially vulnerable to the accumulation of WMH lesions that are localized in temporal lobe regions in cognitively healthy older adults who are APOE ε4 carriers, but not non-carriers. It has been shown that APOE ε4 carriers are at an increased risk for cerebrovascular disease (Schilling et al., 2013), which may lead to greater vulnerability of hippocampal atrophy through vascular mechanisms. Indeed, reduced hippocampal vascular reserve has also been associated with hippocampal atrophy (Perosa et al., 2020). In APOE ε4 carriers, it is possible that decreased hippocampal vascular supply with elevated temporal WMH lesion volumes leads to an increase in age-related hippocampal atrophy that, in turn, sets the stage for greater vulnerability to developing AD pathology. Given there were no significant moderated mediations between age and hippocampal volume by WMH volumes in frontal, parietal, or occipital lobes by APOE ε4 status, the effect of temporal WMH volume on hippocampal volume in our sample of healthy older APOE ε4 carriers appears to be regionally specific. Although there tends to be numerically less volume of white matter hyperintensities in the temporal lobe compared to the other lobes, we only observed the moderated mediation effects with temporal WMH volume. This further suggests that the regional location of white matter hyperintensities, rather than overall burden of WMH volume, may be particularly important when investigating the impacts of WMH volume on brain aging and the risk for AD. This potentially indicates cortical disconnection and/or axonal loss and subsequent atrophy via Wallerian degeneration (Schmidt et al., 2011), leading to greater hippocampal atrophy. Elevated temporal WMH volume may disrupt connections between the hippocampus and other cortical regions, resulting in accelerated brain aging in APOE ε4 carriers. We have previously suggested that APOE ε4 carriers may be more reliant on the hippocampus to maintain normal cognitive performance during middle age (Alexander et al., 2012a). Thus, this cortical disconnection of the hippocampus by temporal WMH volume in APOE ε4 carriers may also contribute to greater vulnerability to subsequent cognitive aging and preclinical AD. In the present study, there were no significant differences between APOE ε4 carriers and non-carriers on any cognitive or neuroimaging variables, suggesting these interactive effects between temporal WMH volume and APOE ε4 status may pre-date detectable individual structural brain and cognitive differences associated with the APOE ε4 allele.

Reports on the effects of APOE ε4 status on hippocampal volume in cognitively healthy middle-aged to older adults have been variable, with some studies finding significant differences between ε4 carriers and non-carriers (Alexander et al., 2012a; Cacciaglia et al., 2018; Honea et al., 2009; Jak et al., 2007; Lind et al., 2006; Tohgi et al., 1997; Wishart et al., 2006), whereas others do not (Schmidt et al., 1996; Reiman et al., 1998; Trivedi et al., 2006). In our current study, we did not observe significant group differences in hippocampal volume simply between APOE ε4 carriers and non-carriers, but only through the meditational role of temporal WMH volume. This highlights the importance of the moderated mediation approach for detecting these very early effects of APOE ε4 status in healthy cognitive aging. Moreover, these findings may help elucidate some of the discrepancies in the literature by indicating that the effects of APOE ε4 status on hippocampal volume in aging may, in part, depend on temporal WMH volume. APOE ε4 may promote regional vulnerability in brain structure (Alexander et al., 2012a), making the hippocampus more susceptible to the impact of temporal WMH volume in aging individuals. Given hippocampal atrophy is an early marker of AD (Chételat, 2018; De Flores et al., 2015; Henneman et al., 2009; Tepest et al., 2008), the findings from the current study indicate that temporal lobe vascular pathology, in combination with APOE ε4, may exacerbate older adults’ preclinical risk for AD through their synergistic effects on hippocampal volume. As some hippocampal subfield volumes are more sensitive to aging, vascular health, or genetic effects (de Flores et al., 2015), it would be important for future studies to examine how regional WMH volume and APOE ε4 may differentially influence hippocampal subfields in aging.

The present study has several limitations. First, the sample was comprised of primarily Caucasian participants with relatively high education and low vascular risk, which may limit the generalizability of the findings. Further research in diverse populations, particularly in groups with higher vascular burden and greater WMH volume, is needed. Previous studies have indicated that the presence of an APOE ε4 allele is a stronger determinant of AD risk in some racial/ethnic groups compared to others (Farrer et al., 1997; Raichlen & Alexander, 2014; Sahota et al., 1997) and the influence of WMH volume on cognitive aging may differ in African American relative to non-Hispanic Caucasian populations (Zahodne et al., 2015). Future work testing the models from the present study within diverse cohorts would be important to elucidate how these variables differentially influence brain aging among different racial/ethnic groups. Finally, this study is cross-sectional in design. While our study highlights important differences in healthy aging, further research with longitudinal data is needed to understand whether greater temporal WMH volumes lead to more hippocampal atrophy and memory decline over time and if this differs by APOE ε4 status, helping to explain in part, the increased risk for AD.

Conclusions

In a cohort of cognitively healthy community-dwelling older adults, elevated temporal WMH volume in APOE ε4 carriers was associated with decreased hippocampal volume. Accumulation of temporal WMH volume may result in cortical disconnection and/or axonal loss and subsequent atrophy via Wallerian degeneration of the hippocampus in older adult APOE ε4 carriers. Additionally, APOE ε4 may promote hippocampal vulnerability to the impacts of temporal WMH volume in healthy aging, leading to greater preclinical risk for AD. The findings of the present study suggest increased temporal WMH volume, in the context of APOE ε4, may be indicative of greater brain aging and preclinical risk for AD in healthy older adults, even before the onset of observable cognitive deficits. Research on interventions and prevention therapies that focus on reducing the impacts of vascular health factors in APOE ε4 carriers may be especially warranted to help diminish the risk of brain aging and AD.

Supplementary Material

Supplement Tables

Acknowledgements

The authors would like to acknowledge support from the National Institute on Aging (AG025526, AG019610, AG049464, and AG067200), the state of Arizona and Arizona Department of Health Services, and the McKnight Brain Research Foundation.

Footnotes

Publisher's Disclaimer: This is the accepted version of the following article: Van Etten EJ, Bharadwaj PK, Hishaw GA, Huentelman MJ, Trouard TP, Grilli MD, & Alexander GE. (2021) Influence of regional white matter hyperintensity volume and APOE ε4 status on hippocampal volume in healthy older adults. Hippocampus, 31, 469–80, which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/hipo.23308.

Conflict of Interest Statement

The authors have no actual or potential conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Addya K, Wang YL, & Leonard DG (1997). Optimization of apolipoprotein E genotyping. Molecular Diagnosis, 2(4), 271–276. [DOI] [PubMed] [Google Scholar]
  2. Alber J, Alladi S, Bae HJ, Barton DA, Beckett LA, Bell JM, ... & Hainsworth AH (2019). White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): knowledge gaps and opportunities. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 5, 107–117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alexander GE, Bergfield KL, Chen K, Reiman EM, Hanson KD, Lin L, ... & Moeller JR (2012a). Gray matter network associated with risk for Alzheimer’s disease in young to middle-aged adults. Neurobiology of Aging, 33(12), 2723–2732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Alexander GE, Chen K, Merkley TL, Reiman EM, Caselli RJ, Aschenbrenner M, ... & Moeller JR (2006). Regional network of magnetic resonance imaging gray matter volume in healthy aging. NeuroReport, 17(10), 951–956. [DOI] [PubMed] [Google Scholar]
  5. Alexander GE, Furey ML, Grady CL, Pietrini P, Brady DR, Mentis MJ, & Schapiro MB (1997). Association of premorbid intellectual function with cerebral metabolism in Alzheimer’s disease: implications for the cognitive reserve hypothesis. The American Journal of Psychiatry [DOI] [PubMed]
  6. Alexander GE, Ryan L, Bowers D, Foster TC, Bizon JL, Geldmacher DS, & Glisky EL (2012b). Characterizing cognitive aging in humans with links to animal models. Frontiers in Aging Neuroscience, 4, 21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Avants BB, Tustison NJ, Song G, Cook PA, Klein A, and Gee JC (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Benton AL, & Hamsher K (1976). Multilingual Aphasia Examination Iowa City: University of Iowa Press. [Google Scholar]
  9. Bergfield KL, Hanson KD, Chen K, Teipel SJ, Hampel H, Rapoport SI, ... & Alexander GE (2010). Age-related networks of regional covariance in MRI gray matter: reproducible multivariate patterns in healthy aging. NeuroImage, 49(2), 1750–1759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bertram L, Lill CM, & Tanzi RE (2010). The genetics of Alzheimer disease: back to the future. Neuron, 68(2), 270–281. [DOI] [PubMed] [Google Scholar]
  11. Biesbroek JM, Weaver NA, & Biessels GJ (2017). Lesion location and cognitive impact of cerebral small vessel disease. Clinical Science, 131(8), 715–728. [DOI] [PubMed] [Google Scholar]
  12. Bobb JF, Schwartz BS, Davatzikos C, & Caffo B (2014). Cross‐sectional and longitudinal association of body mass index and brain volume. Human Brain Mapping, 35(1), 75–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Brickman AM (2013). Contemplating Alzheimer’s disease and the contribution of white matter hyperintensities. Current Neurology and Neuroscience Reports, 13(12), 415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Brickman AM, Habeck C, Zarahn E, Flynn J, & Stern Y (2007). Structural MRI covariance patterns associated with normal aging and neuropsychological functioning. Neurobiology of Aging, 28(2), 284–295. [DOI] [PubMed] [Google Scholar]
  15. Brickman AM, Schupf N, Manly JJ, Stern Y, Luchsinger JA, Provenzano FA, ... & Portet F (2014). APOE ε4 and risk for Alzheimer’s disease: Do regionally distributed white matter hyperintensities play a role? Alzheimer’s & Dementia, 10(6), 619–629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Brugulat-Serrat A, Salvadó G, Sudre CH, Grau-Rivera O, Suárez-Calvet M, Falcon C, ... & Gispert JD (2019). Patterns of white matter hyperintensities associated with cognition in middle-aged cognitively healthy individuals. Brain Imaging and Behavior, 1–12. [DOI] [PMC free article] [PubMed]
  17. Buschke H (1973). Selective reminding for analysis of memory and learning. Journal Verbal Learning and Verbal Behavior, 12(5), 543–550. [Google Scholar]
  18. Cacciaglia R, Molinuevo JL, Falcón C, Brugulat-Serrat A, Sánchez-Benavides G, Gramunt N, ... & Gispert JD (2018). Effects of APOE-ε4 allele load on brain morphology in a cohort of middle-aged healthy individuals with enriched genetic risk for Alzheimer’s disease. Alzheimer’s & Dementia, 14(7), 902–912. [DOI] [PubMed] [Google Scholar]
  19. Centers for Disease Control and Prevention. (2012). Vital signs: awareness and treatment of uncontrolled hypertension among adults—United States, 2003–2010. Morbidity and Mortality Weekly Report (MMWR), 61(35), 703–709. [PubMed] [Google Scholar]
  20. Chételat G (2018). Multimodal neuroimaging in Alzheimer’s disease: early diagnosis, physiopathological mechanisms, and impact of lifestyle. Journal of Alzheimer’s Disease, 64(s1), S199–S211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, Small G, ... & Pericak-Vance MA (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science, 261(5123), 921–923. [DOI] [PubMed] [Google Scholar]
  22. Crane DE, Black SE, Ganda A, Mikulis DJ, Nestor SM, Donahue MJ, & MacIntosh BJ (2015). Gray matter blood flow and volume are reduced in association with white matter lesion burden: a cross-sectional MRI study. Frontiers in Aging Neuroscience, 7, 131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Dale AM, Fischl B, & Sereno MI (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage, 9, 179–194. [DOI] [PubMed] [Google Scholar]
  24. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, ... & Killiany RJ (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980. [DOI] [PubMed] [Google Scholar]
  25. de Flores R, La Joie R, & Chételat G (2015). Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience, 309, 29–50. [DOI] [PubMed] [Google Scholar]
  26. de Leeuw FE, Richard F, de Groot JC, van Duijn CM, Hofman A, van Gijn J, & Breteler MM (2004). Interaction between hypertension, apoE, and cerebral white matter lesions. Stroke, 35(5), 1057–1060. [DOI] [PubMed] [Google Scholar]
  27. Du AT, Schuff N, Chao LL, Kornak J, Jagust WJ, Kramer JH, ... & Weiner MW (2006). Age effects on atrophy rates of entorhinal cortex and hippocampus. Neurobiology of Aging, 27(5), 733–740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Farrer LA, Cupples LA, Haines JL, Hyman B, Kukull WA, Mayeux R, ... & Van Duijn CM (1997). Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease: a meta-analysis. Jama, 278(16), 1349–1356. [PubMed] [Google Scholar]
  29. Fiford CM, Manning EN, Bartlett JW, Cash DM, Malone IB, Ridgway GR, ... & Barnes J (2017). White matter hyperintensities are associated with disproportionate progressive hippocampal atrophy. Hippocampus, 27(3), 249–262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Fischl B, Liu A, & Dale AM (2001). Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Transactions on Medical Imaging, 20(1), 70–80. [DOI] [PubMed] [Google Scholar]
  31. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, … & Dale AM (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33, 41–355. [DOI] [PubMed] [Google Scholar]
  32. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Ségonne F, Quinn BT, & Dale AM (2004a). Sequence-independent segmentation of magnetic resonance images. NeuroImage, 23, S69–S84. [DOI] [PubMed] [Google Scholar]
  33. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, … & Dale AM (2004b). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14:11–22. [DOI] [PubMed] [Google Scholar]
  34. Fjell AM, & Walhovd KB (2010). Structural brain changes in aging: courses, causes and cognitive consequences. Reviews in the Neurosciences, 21(3), 187–222. [DOI] [PubMed] [Google Scholar]
  35. Folstein MF, Folstein SE, & McHugh PR (1975). “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. [DOI] [PubMed] [Google Scholar]
  36. Franchetti MK, Bharadwaj PK, Nguyen LA, Van Etten EJ, Klimentidis YC, Hishaw GA, ... & Alexander GE (2020). Interaction of Age and Self-reported Physical Sports Activity on White Matter Hyperintensity Volume in Healthy Older Adults. Frontiers in Aging Neuroscience, 12, 346. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Fuld PA, Masur DM, Blau AD, Crystal H, & Aronson MK (1990). Object-memory evaluation for prospective detection of dementia in normal functioning elderly: predictive and normative data. Journal of Clinical and Experimental Neuropsychology, 12(4), 520–528. [DOI] [PubMed] [Google Scholar]
  38. Glisky EL (2007). Changes in cognitive function in human aging. Brain Aging: Models, Methods, and Mechanisms, 3–20.
  39. Godin O, Tzourio C, Maillard P, Alpérovitch A, Mazoyer B, & Dufouil C (2009). Apolipoprotein E genotype is related to progression of white matter lesion load. Stroke, 40(10), 3186–3190. [DOI] [PubMed] [Google Scholar]
  40. Golden CJ Stroop color and word test: A manual for clinical and experimental uses Skoelting; Chicago: 1978. [Google Scholar]
  41. Gouw AA, Seewann A, Van Der Flier WM, Barkhof F, Rozemuller AM, Scheltens P, & Geurts JJ (2011). Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations. Journal of Neurology, Neurosurgery & Psychiatry, 82(2), 126–135. [DOI] [PubMed] [Google Scholar]
  42. Habes M, Erus G, Toledo JB, Zhang T, Bryan N, Launer LJ, ... & Davatzikos C (2016). White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain, 139(4), 1164–1179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Hamilton M (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23(1), 56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Hayes AF (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach Guilford Publications. [Google Scholar]
  45. Hawkins KA, Emadi N, Pearlson GD, Taylor B, Khadka S, King D, & Blank K (2018). The effect of age and smoking on the hippocampus and memory in late middle age. Hippocampus, 28(11), 846–849. [DOI] [PubMed] [Google Scholar]
  46. Henneman WJP, Sluimer JD, Barnes J, Van Der Flier WM, Sluimer IC, Fox NC, ... & Barkhof F (2009). Hippocampal atrophy rates in Alzheimer disease: added value over whole brain volume measures. Neurology, 72(11), 999–1007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Honea RA, Vidoni E, Harsha A, & Burns JM (2009). Impact of APOE on the healthy aging brain: a voxel-based MRI and DTI study. Journal of Alzheimer’s Disease, 18(3), 553–564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Jack CR, Petersen RC, Xu Y, O’brien PC, Smith GE, Ivnik RJ, ... & Kokmen E (2000). Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology, 55(4), 484–490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Jak AJ, Houston WS, Nagel BJ, Corey-Bloom J, & Bondi MW (2007). Differential cross-sectional and longitudinal impact of APOE genotype on hippocampal volumes in nondemented older adults. Dementia and Geriatric Cognitive Disorders, 23(6), 382–389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Jernigan TL, Archibald SL, Fennema-Notestine C, Gamst AC, Stout JC, Bonner J, & Hesselink JR (2001). Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiology of Aging, 22(4), 581–594. [DOI] [PubMed] [Google Scholar]
  51. Kaplan E, Goodglass H, & Weintraub S. (2000). Boston naming test Austin, TX: Pro-Ed. [Google Scholar]
  52. Kern KC, Wright CB, Bergfield KL, Fitzhugh MC, Chen K, Moeller JR, ... & Alexander GE (2017). Blood pressure control in aging predicts cerebral atrophy related to small-vessel white matter lesions. Frontiers in Aging Neuroscience, 9, 132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Lampe L, Kharabian-Masouleh S, Kynast J, Arelin K, Steele CJ, Löffler M, ... & Bazin PL (2019). Lesion location matters: the relationships between white matter hyperintensities on cognition in the healthy elderly. Journal of Cerebral Blood Flow & Metabolism, 39(1), 36–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Lind J, Larsson A, Persson J, Ingvar M, Nilsson LG, Bäckman L, ... & Nyberg L (2006). Reduced hippocampal volume in non-demented carriers of the apolipoprotein E ɛ4: relation to chronological age and recognition memory. Neuroscience Letters, 396(1), 23–27. [DOI] [PubMed] [Google Scholar]
  55. Liu CC, Kanekiyo T, Xu H, & Bu G (2013). Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nature Reviews Neurology, 9(2), 106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Luchsinger JA, Reitz C, Honig LS, Tang MX, Shea S, & Mayeux R (2005). Aggregation of vascular risk factors and risk of incident Alzheimer disease. Neurology, 65(4), 545–551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. McNeely AA, Ramirez J, Nestor SM, Zhao J, Gao F, Kiss A, ... & Black SE (2015). Cholinergic subcortical hyperintensities in Alzheimer’s disease patients from the Sunnybrook Dementia Study: relationships with cognitive dysfunction and hippocampal atrophy. Journal of Alzheimer’s Disease, 43(3), 785–796. [DOI] [PubMed] [Google Scholar]
  58. Meier IB, Manly JJ, Provenzano FA, Louie KS, Wasserman BT, Griffith EY, ... & Brickman AM (2012). White matter predictors of cognitive functioning in older adults. Journal of the International Neuropsychological Society, 18(3), 414–427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Meyers JE, & Meyers KR (1995). Rey complex figure test and recognition trial: RCFT: professional manual PAR. [Google Scholar]
  60. Milne NT, Bucks RS, Davis WA, Davis TM, Pierson R, Starkstein SE, & Bruce DG (2018). Hippocampal atrophy, asymmetry, and cognition in type 2 diabetes mellitus. Brain and Behavior, 8(1), e00741. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Montagne A, Nation DA, Sagare AP, Barisano G, Sweeney MD, Chakhoyan A, ... & Zlokovic BV (2020). APOE4 leads to blood–brain barrier dysfunction predicting cognitive decline. Nature, 1–6. [DOI] [PMC free article] [PubMed]
  62. Nobis L, Manohar SG, Smith SM, Alfaro-Almagro F, Jenkinson M, Mackay CE, & Husain M (2019). Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank. NeuroImage: Clinical, 23, 101904. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Noble KG, Grieve SM, Korgaonkar MS, Engelhardt LE, Griffith EY, Williams LM, & Brickman AM (2012). Hippocampal volume varies with educational attainment across the life-span. Frontiers in Human Neuroscience, 6, 307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Ortman JM, Velkoff VA, & Hogan H (2014). An aging nation: the older population in the United States (pp. 25–1140). Washington, DC: United States Census Bureau, Economics and Statistics Administration, US Department of Commerce. [Google Scholar]
  65. Park DC, & Reuter-Lorenz P (2009). The adaptive brain: aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Peng Y, Li S, Zhuang Y, Liu X, Wu L, Gong H, ... & Zhou F (2016). Density abnormalities in normal-appearing gray matter in the middle-aged brain with white matter hyperintense lesions: a DARTEL-enhanced voxel-based morphometry study. Clinical Interventions in Aging, 11, 615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Perosa V, Priester A, Ziegler G, Cardenas-Blanco A, Dobisch L, Spallazzi M, ... & Düzel E (2020). Hippocampal vascular reserve associated with cognitive performance and hippocampal volume. Brain, 143(2), 622–634. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Prins ND, & Scheltens P (2015). White matter hyperintensities, cognitive impairment and dementia: an update. Nature Reviews Neurology, 11(3), 157. [DOI] [PubMed] [Google Scholar]
  69. Raichlen DA, & Alexander GE (2014). Exercise, APOE genotype, and the evolution of the human lifespan. Trends in Neurosciences, 37(5), 247–255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Raz N, & Rodrigue KM (2006). Differential aging of the brain: patterns, cognitive correlates and modifiers. Neuroscience & Biobehavioral Reviews, 30(6), 730–748. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Raz N, Rodrigue KM, Head D, Kennedy KM, & Acker JD (2004). Differential aging of the medial temporal lobe: a study of a five-year change. Neurology, 62(3), 433–438. [DOI] [PubMed] [Google Scholar]
  72. Reiman EM, Uecker A, Caselli RJ, Lewis S, Bandy D, De Leon MJ, ... & Thibodeau SN (1998). Hippocampal volumes in cognitively normal persons at genetic risk for Alzheimer’s disease. Annals of Neurology, 44(2), 288–291. [DOI] [PubMed] [Google Scholar]
  73. Reitan RM (1956). Trail Making Test: Manual for administration, scoring and interpretation Bloomington: Indiana University, 134. [Google Scholar]
  74. Reuter M, Rosas HD, & Fischl B (2010). Highly accurate inverse consistent registration: a robust approach. NeuroImage, 53(4), 1181–1196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Rizvi B, Narkhede A, Last BS, Budge M, Tosto G, Manly JJ, ... & Brickman AM (2018). The effect of white matter hyperintensities on cognition is mediated by cortical atrophy. Neurobiology of Aging, 64, 25–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Rojas S, Brugulat-Serrat A, Bargallo N, Minguillon C, Tucholka A, Falcon C, ... & Gispert JD (2018). Higher prevalence of cerebral white matter hyperintensities in homozygous APOE-ɛ4 allele carriers aged 45–75: results from the ALFA study. Journal of Cerebral Blood Flow & Metabolism, 38(2), 250–261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Sahota A, Yang M, Gao S, Hui SL, Baiyewu O, Gureje O, ... & Hendrie HC (1997). Apolipoprotein E—associated risk for Alzheimer’s disease in the African-American population is genotype dependent. Annals of Neurology, 42(4), 659–661. [DOI] [PubMed] [Google Scholar]
  78. Salat DH, Greve DN, Pacheco JL, Quinn BT, Helmer KG, Buckner RL, & Fischl B (2009). Regional white matter volume differences in nondemented aging and Alzheimer’s disease. NeuroImage, 44(4), 1247–1258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Salthouse TA (1992). Influence of processing speed on adult age differences in working memory. Acta Psychologica, 79(2), 155–170. [DOI] [PubMed] [Google Scholar]
  80. Schilling S, DeStefano AL, Sachdev PS, Choi SH, Mather KA, DeCarli CD, ... & Debette S (2013). APOE genotype and MRI markers of cerebrovascular disease: systematic review and meta-analysis. Neurology, 81(3), 292–300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, ... & Mühlau M (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. NeuroImage, 59(4), 3774–3783. [DOI] [PubMed] [Google Scholar]
  82. Schmidt H, Schmidt R, Fazekas F, Semmler J, Kapeller P, Reinhart B, & Kostner GM (1996). Apolipoprotein E4 allele in the normal elderly: neuropsychologic and brain MRI correlates. Clinical Genetics, 50(5), 293–299. [DOI] [PubMed] [Google Scholar]
  83. Schmidt R, Schmidt H, Haybaeck J, Loitfelder M, Weis S, Cavalieri M, ... & Jellinger K (2011). Heterogeneity in age-related white matter changes. Acta Neuropathologica, 122(2), 171–185. [DOI] [PubMed] [Google Scholar]
  84. Stern Y (2012). Cognitive reserve in ageing and Alzheimer’s disease. The Lancet Neurology, 11(11), 1006–1012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Tepest R, Wang L, Csernansky JG, Neubert P, Heun R, Scheef L, & Jessen F (2008). Hippocampal surface analysis in subjective memory impairment, mild cognitive impairment and Alzheimer’s dementia. Dementia and Geriatric Cognitive Disorders, 26(4), 323–329. [DOI] [PubMed] [Google Scholar]
  86. Tohgi H, Takahashi S, Kato E, Homma A, Niina R, Sasaki K, ... & Sasaki M (1997). Reduced size of right hippocampus in 39-to 80-year-old normal subjects carrying the apolipoprotein E ϵ4 allele. Neuroscience Letters, 236(1), 21–24. [DOI] [PubMed] [Google Scholar]
  87. Trivedi MA, Schmitz TW, Ries ML, Torgerson BM, Sager MA, Hermann BP, ... & Johnson SC (2006). Reduced hippocampal activation during episodic encoding in middle-aged individuals at genetic risk of Alzheimer’s disease: a cross-sectional study. BMC Medicine, 4(1), 1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Van Etten EJ, Bharadwaj PK, Nguyen LA, Hishaw GA, Trouard TP, & Alexander GE (2020). Right hippocampal volume mediation of subjective memory complaints differs by hypertension status in healthy aging. Neurobiology of Aging, 94, 271–280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Waymont JM, Petsa C, McNeil CJ, Murray AD., & Waiter GD (2020). Validation and comparison of two automated methods for quantifying brain white matter hyperintensities of presumed vascular origin. Journal of International Medical Research, 48(2), 0300060519880053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Wechsler D (2008). Wechsler adult intelligence scale–Fourth Edition (WAIS–IV) San Antonio, TX: NCS Pearson, 22, 498. [Google Scholar]
  91. Wishart HA, Saykin AJ, McAllister TW, Rabin LA, McDonald BC, Flashman LA, ... & Rhodes CH (2006). Regional brain atrophy in cognitively intact adults with a single APOE ε4 allele. Neurology, 67(7), 1221–1224. [DOI] [PubMed] [Google Scholar]
  92. Wolf H, Hensel A, Arendt T, Kivipelto M, Winblad B, & Gertz HJ (2004). Serum lipids and hippocampal volume: the link to Alzheimer’s disease?. Annals of Neurology 56(5), 745–749. [DOI] [PubMed] [Google Scholar]
  93. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, & Gerig G (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage, 31(3):1116–28. [DOI] [PubMed] [Google Scholar]
  94. Zahodne LB, Manly JJ, Narkhede A, Griffith EY, DeCarli C, Schupf NS, ... & Brickman AM (2015). Structural MRI predictors of late-life cognition differ across African Americans, Hispanics, and Whites. Current Alzheimer Research, 12(7), 632–639. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Zipser BD, Johanson CE, Gonzalez L, Berzin TM, Tavares R, Hulette CM, ... & Stopa EG (2007). Microvascular injury and blood–brain barrier leakage in Alzheimer’s disease. Neurobiology of Aging, 28(7), 977–986. [DOI] [PubMed] [Google Scholar]

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Supplementary Materials

Supplement Tables
NIHMS1745272-supplement-Supplement_Tables.docx (61.1KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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