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Effects of ApoE4 and ApoE2 genotypes on subcortical magnetic susceptibility and microstructure in 27,535 participants from the UK Biobank †‡

Disrupted iron homeostasis is associated with several neurodegenerative diseases, including Alzheimer’s disease (AD), and may be partially modulated by genetic risk factors. Here we evaluated whether subcortical iron deposition is associated with ApoE genotype, which substantially affects risk for late-onset AD. We evaluated differences in subcortical quantitative susceptibility mapping (QSM), a type of MRI sensitive to cerebral iron deposition, between either ApoE4 (E3E4+E4E4) or ApoE2 (E2E3+E2E2) carriers and E3 homozygotes (E3E3) in 27,535 participants from the UK Biobank (age: 45-82 years). We found that ApoE4 carriers had higher hippocampal (d=0.036; p=0.012) and amygdalar (d=0.035; p=0.013) magnetic susceptibility, particularly individuals aged 65 years or older, while those carrying ApoE2 (which protects against AD) had higher QSM only in the hippocampus (d=0.05; p=0.006), particularly those under age 65. Secondary diffusion MRI microstructural associations in these regions revealed greater diffusivity and less diffusion restriction in E4 carriers, however no differences were detected in E2 carriers. Disease risk conferred by ApoE4 may be linked with higher subcortical iron burden in conjunction with inflammation or neuronal loss in aging individuals, while ApoE2 associations may not necessarily reflect unhealthy iron deposits earlier in life.


Introduction
Iron is essential for normal brain function; however, disrupted iron homeostasis can lead to brain iron accumulation, which is associated with neurodegenerative diseases including Parkinson's and Alzheimer's disease (AD) 1 . Excess iron induces oxidative stress, and accompanies neuritic plaques and neurofibrillary tangles, the pathological hallmarks of AD 2 . Plasma levels of iron-regulating proteins, including ferritin and transferrin, are highly heritable, with a large portion of their heritability explained by variants in the HFE and TF genes 3 ; variants in these genes have been associated specifically with iron overload 4 . Brain iron concentration and other microstructural properties may be detected in vivo using metrics derived from advanced MRI techniques, including susceptibility-and diffusion-weighted MRI sequences (SWI and dMRI, respectively). These metrics are also sensitive to genetic variants associated with iron homeostasis 5,6 .
Quantitative susceptibility mapping (QSM) derived from SWI is sensitive to paramagnetic iron concentrations, a predominant source of magnetic susceptibility variation in brain tissue. QSM has been used to study susceptibility profiles of iron-rich subcortical structures in both normal aging 7 and neurodegenerative diseases 8 . Tissue magnetic susceptibility is also influenced by factors such as calcium and myelin concentrations. While there is less myelin in subcortical gray matter structures than in white matter, myelin is diamagnetic and counteracts iron effects on QSM, thus confounding QSM interpretations.
Brain microstructure can also be evaluated with diffusion MRI (dMRI), which may help disentangle the opposing effects of iron and myelin on QSM. Single-shell diffusion tensor imaging (DTI) is the most widely used method to analyze dMRI datasets, but cannot differentiate crossing fibers, and captures partial volumes from different tissue compartments. This may reduce the sensitivity of DTI measures, in particular for evaluating complex gray matter micro-architecture. Multi-shell neurite orientation dispersion and density imaging (NODDI) is a three-compartment biophysical model that attempts to resolve signal contributions from various tissue compartments, including the restricted intracellular and 'freewater', or isotropic, extracellular compartments 9 . Such models may offer insight into subcortical microstructural properties and associations between QSM and genetic risk factors.
The most well-recognized, and greatest common genetic risk factor for sporadic AD is a haplotype in the APOE gene. ApoE plays a key role in cerebral cholesterol metabolism and βamyloid clearance. Growing evidence suggests that AD risk conferred by the ApoE4 genotype (carried by around 25% of individuals, depending on ancestry) is partially linked with cerebral iron burden. For example, both higher magnetic susceptibility and its association with higher amyloid plaque load have been reported in ApoE4 carriers with mild cognitive impairment 10 . ApoE2, in contrast, has a proposed neurotrophic or neuroprotective effect 11 . However, as ApoE2 is less prevalent in the population (found in around 10% of people), few studies have been sufficiently well-powered to evaluate whether ApoE2 has an effect on brain structure or function 12 ; none, to our knowledge, have investigated associations with QSM.
In this study, we evaluated associations between both ApoE4 and ApoE2 genotypes and in vivo QSM and dMRI measures of subcortical iron concentrations and microstructure in a large, population-based sample of older adults (age range: 45-82 years) from the UK Biobank 13 .

UK Biobank Participants
The UK Biobank (UKBB) is a publicly available dataset of over 500,000 community-based middleaged and older adults residing in the United Kingdom 13 . At the time of this analysis, nearly 43,000 of these individuals had undergone brain MRI on a 3T Siemens Skyra. SWI, dMRI, T2-weighted FLAIR, and T1-weighted (T1w) MRI were downloaded from the UKBB database through application 11559; available MRI acquisition protocols and preprocessing are detailed in Miller et al. (2016). In total, 40,069 participants had SWI and T1w images, 35,600 of whom also had dMRI. MRI scans that failed preprocessing or did not pass quality control (QC), as described for each modality below, were removed from analyses. Participants whose genetic ethnic group was not of European ancestry, who were missing demographic or genetic data, or whose available ApoE genotype was not E2E2, E2E3, E3E3, E3E4, or E4E4, were also excluded -leaving 27,535 participants with QSM and 27,313 participants with dMRI ( Figure 1; Table 1).

T1-Weighted MRI
T1w 3D volumetric MPRAGE DICOMs were downloaded for processing (1x1x1 mm 3 voxels). FreeSurfer version 7.1 was used to obtain segmentations for seven subcortical regions of interest (ROIs): the thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and nucleus accumbens. Participants with a subcortical volume laterality index (LI=(L-R)/(L+R)) greater than five standard deviations from the mean were flagged for visual inspection and possible removal from the analysis.

Quantitative Magnetic Susceptibility
QSM maps were derived from the SWI DICOMs (0.8x0.8x3 mm 3 voxels; TE=9.42, 20 ms) and constructed using the Morphology Enabled Dipole Inversion (MEDI) toolbox (http://pre.weill.cornell.edu/mri/pages/qsm.html). Magnitude images were isolated for each head coil channel (N=32) and then combined by taking the square root of the sum of squares. The resulting magnitude image was skull-stripped using HD-BET 14 . Phase images were combined across two echo times by using a complex fitting approach to estimate the frequency offset in each voxel 15 . The combined phase was unwrapped using the Laplacian operation method, and the background field was removed using the Laplacian Boundary Value 16 . Finally, the MEDI solver was implemented with model error reduction through iterative tuning (MERIT) and a spherical mean value (SMV) operator for background field removal (lambda=1000, SMV kernel=3) 15 .
All QSM images were visually inspected for quality. Images were rated for overall image quality (pass/fail) and Gibbs ringing (on a scale of 0-2); images with poor image quality and severe Gibbs ringing (i.e., 2s) were removed. Each participant's SWI magnitude image was linearly registered to their respective T1w image using FSL's flirt with 12 degrees-of-freedom (dof) and normalized mutual information; the resulting transform was then applied to the QSM image.

Diffusion-Weighted MRI
Multi-shell dMRI scans were preprocessed and available for download as previously described 17 . All dMRI were acquired with 2x2x2 mm 3 voxels and included 50 b=1000 s/mm 2 and 50 b=2000 s/mm 2 diffusion-weighted images (δ=21.4 ms, ∆=45.5 ms), and 5 b0 volumes. Two dMRI models were evaluated: 1) DTI, fit to the subset of b=0 and 1000 s/mm 2 volumes using FSL, was made available for download by the UKBB; 2) we further fit NODDI 9 to all shells using DmiPy 18 . Resulting DTI fractional anisotropy (FA) and mean diffusivity (MD) and NODDI intracellular (ICVF), isotropic volume fractions (ISOVF), and orientation dispersion index (ODI) maps were transformed to respective T1w images through a multi-step process.
The UKBB processing includes proprietary gradient distortion correction (GDC) of all modalities; UKBB processed dMRI, therefore, had to be warped to achieve correspondence with the non-GDC T1w and SWI images that we independently processed for this study. For each participant, the GDC mean b0 image was linearly registered to the respective GDC T2-FLAIR, using FSL's flirt with 12 dof and a boundary-based registration (BBR) using the FreeSurfer-derived white matter mask, which in turn was non-linearly warped to the original T2-FLAIR using ANTs symmetric normalization. The T2-FLAIR was then linearly registered to the respective T1w image using FSL's flirt with 6 dof and BBR. The transformations from GDC b0 to T1w were concatenated and applied to all dMRI scalar maps.

Statistical Analyses
The median QSM and diffusion values across voxels in each of the left and right subcortical ROIs were extracted. The average of the left and right medians was then calculated, and the bilateral averages used for analysis. Multiple linear regressions were performed to evaluate differences in each of the seven regional QSM indices between ApoE3 homozygotes (E3E3) and either 1) ApoE4 carriers (E4E3 and E4E4) or 2) ApoE2 carriers (E2E3 and E2E2). Secondary analyses evaluated the additive effect of carrying one or two E4 or E2 alleles. Sixteen covariates were included in all statistical models: age, sex, ageby-sex interaction, age 2 , age 2 -by-sex interaction, educational attainment ("college" or "no college"), and population structure (measured using the first 10 principal components of the UKBB ancestry analysis). To correct for multiple comparisons across seven ROIs, we used the false discovery rate (FDR) procedure (q=0.05). All reported p-values are uncorrected; only associations for which pvalues survived FDR correction were considered significant.
In regions that showed significant associations in primary analyses, we further evaluated 1) QSM effects beyond structural volume by also covarying for regional volume and total intracranial volume; and 2) associations between ApoE genotype and subcortical microstructure indexed with dMRI measures, using the same statistical models. We also tested for ApoE-by-age interactions in these regions, as ApoE genotypes may have an age-dependent effect on the brain. Interaction analyses were conducted using both linear and generalized additive models (GAM), including ApoE genotype and age as covariates. The R `mgcv` package was used for GAM analyses; age, age-bysex and ApoE-by-age interaction terms were modeled using spline smoothing functions (cubic regression splines, k = 10) in place of the linear and non-linear age-related covariate. Finally, we stratified linear associations by age, testing for ApoE associations within the subsets of participants either < 65 or ≥ 65 years of age.

ApoE2 Microstructural Associations
Compared to the E3E3 group (N=16,590), E2 carriers (N=3,710 E3E2 and E2E2 combined) had higher magnetic susceptibility values in the hippocampus using both the dominant (d=0.05; p ≤

ApoE Associations Stratified by Age
When stratified by age, ApoE4 was significantly associated with QSM values in both the hippocampus and amygdala only in the subset of participants age 65 years and older (hippocampus d=0.049; p=0.015; amygdala d=0.062; p=0.002).
In contrast, E2 carriers younger than 65 years of age had, on average, higher hippocampal magnetic susceptibility (d=0.073; p=0.005) than those with the E3E3 genotype, but differences were not significant in the older subset.
As with the full sample, ApoE2 associations with hippocampal diffusion measures were not significant in either age group.  the (A, B) hippocampus and (C, D) amygdala plotted against age using either (A, C) a linear approach (adjusted for sex, age-by-sex, age 2 , education, and population structure), or (B, D) using GAM (linearly adjusted for sex, age-by-sex, age 2 -by-sex, education, and population structure).

Discussion
With aging, iron dysregulation and accumulation can induce oxidative stress, trigger the aggregation of proteins involved in pathogenesis of neurodegenerative disorders, and cause cell death 2 . Here, we found higher QSM susceptibility in the hippocampi and amygdala of E4 carriers, temporal lobe structures known to be affected in AD and dementia; this likely reflects higher iron deposition, but may also be driven, in part, by less myelin. Several studies have found higher magnetic susceptibility in the hippocampi and amygdala of individuals with mild cognitive impairment and AD 8,19 . To date, only a few studies have linked ApoE4 genotype with greater magnetic susceptibility and iron. Significantly higher cerebrospinal fluid ferritin levels have been found across cognitively normal and AD ApoE4 carriers (N=302) 20 . In a study by van Bergen et al. (2016), ApoE4 carriers had both higher cortical QSM iron deposition and cortical PiB-PET amyloid plaque load (N=37) 10 . Findings from yet another QSM study suggested that ApoE4 may moderate iron effects on default mode network connectivity in cognitively healthy older adults (N=69) 21 . Still, most published studies are relatively small and largely evaluate older individuals with cognitive impairment. Large-scale studies of the normal aging population are needed to better understand genetic risk for disease.
Higher QSM susceptibility in subcortical structures of E4 carriers was consistently accompanied by higher dMRI MD and ISOVF and lower FA and ICVF. These dMRI differences indicate greater diffusivity and less restriction, and could reflect, for example, lower neuronal density or greater inflammation and edema. Other studies have similarly reported lower FA and higher MD in white matter of older ApoE4 carriers 22 . Transgenic mice expressing ApoE4 in neurons have been shown to develop axonal degeneration and gliosis in the brain 23 . Moreover, iron deposits are also found in activated microglia 24 , the inflammatory cells of the nervous system, which, post mortem, have been found to colocalize with tau and amyloid in the hippocampi of individuals with AD 25 .
Growing evidence suggests that ApoE4 effects on brain structure and function may become more pronounced after age 60; two relatively large studies suggest that ApoE4 does not affect cognitive function before age 65 (N=6,560) 26 , and that in older individuals, ApoE4 carriers have a faster rate of cognitive decline than non-carriers (N=501) 27 . While smaller hippocampal volumes in ApoE4 carriers have been reported in young healthy carriers (N=44) 28 , other studies have failed to find this association 29 . Smaller hippocampal and amygdalar volumes have more consistently been found in older ApoE4 carriers with cognitive impairment 29,30 . Accordingly, in addition to significant ApoE4-by-age interactions indicating greater age-related increases in iron and diffusivity in E4 carriers, we found that QSM and dMRI E4 effects only survived in individuals over age 65.
ApoE2 has been associated with lower risk for AD 11 . While greater hippocampal volumes have been detected in E2 carriers in ADNI 30 , to date, large scale population neuroimaging studies have found limited evidence to suggest it confers protection through improved brain integrity. Here, we found higher magnetic susceptibility in the hippocampi of E2 carriers in, to our knowledge, the first study to evaluate QSM differences. While this could reflect higher iron concentration, this does not necessarily reflect unhealthy deposits; it could, for example, be driven by hemoglobin in the surrounding vasculature and reflect greater blood circulation around the hippocampus. In contrast to ApoE4 carriers, who showed both higher QSM and compromised dMRI microstructural integrity, we did not detect any associations between the ApoE2 genotype and dMRI measures; this may suggest that subcortical iron depositions play a different role in E2 carriers. Furthermore, while no ApoE2-by-age interaction was found, ApoE2 effects were only detected in younger participants. This could reflect the important role iron plays in the central nervous system; cerebral iron increases quickly early in development and is necessary for myelin production, neurotransmitter synthesis and breakdown, and microglial activation, among other essential functions 2 . There is also evidence, however, that ApoE2 is not entirely benign, and is associated with an increased risk of cerebral amyloid angiopathy 11 and type 2 diabetes mellitus 31 . Further studies are necessary to disentangle the relationship between ApoE2 and cerebral iron.
As with all quantitative MRI methods, QSM values depend on acquisition and processing protocols 32,33 . In the processing approach used here, we used the QSM values directly output from our pipeline without inter-individual harmonization through the use of a reference region. Ventricular CSF or specific white matter bundles may be used for this purpose, however choice of reference region is not standardized, and QSM variation within reference regions may further confound measurements 8 . It will be important to determine whether the choice of QSM processing affects the overall conclusions of this work.
As with DTI, since the inception of NODDI a number of limitations have also been identified. To avoid overparameterization, many model assumptions and fixed parameters are imposed that have not been widely validated and are highly dependent on acquisition parameters 34 ; this complicates the biological specificity and interpretability of resulting microstructural measures.
In the largest QSM study to evaluate ApoE genotypes to date, we found that higher subcortical iron burden is associated with ApoE4. Our findings also offer some initial insights into ApoE2 mechanisms. Both AD risk and protective genotypes showed higher magnetic susceptibility compared to the most common E3E3 genotype; however, these effects were driven by different age groups, and had inconsistent effects on diffusion metrics. Independent replication and further histopathological investigation of the effects of ApoE genotype on hippocampal iron deposition, and its relationship to age, is needed. QSM provides complementary information to more commonly acquired MRI modalities, and acquisition of these scans in prospective studies of aging may provide further insights into neurodegeneration.