The effect of ApoE ε4 on longitudinal brain region-specific glucose metabolism in patients with mild cognitive impairment: a FDG-PET study

Abstract

While the ApoE ε4 allele is a known risk factor for mild cognitive impairment (MCI) and Alzheimer's disease, brain region specific effects remain elusive. In this study, we investigate whether the ApoE ε4 allele exhibits brain region specific effects in longitudinal glucose uptake among patients with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed FDG PET images, MRIs, and demographic information were downloaded from the ADNI database. An iterative reblurred Van Cittertiteration method was used for partial volume correction (PVC) on all PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. Longitudinal changes in ROI FDG standardized uptake value ratio (SUVR) relative to cerebellum in 24 ApoE ε4 carriers and 24 age-matched ApoE ε4 non-carriers were measured for up to 84-months (median 72 months, SD = 11.2 months) and compared using a generalized linear mixed effects model controlling for gender, education, baseline age, and follow-up period. Additionally, voxelwise analysis was performed by implementing a paired t-test comparing matched baseline and 72 month FDG SUVR images in ApoE carriers and non-carriers separately. Results with PVC were compared with ones from non-PVC based analysis. After applying PVC, the superior fontal, parietal, lateral temporal, medial temporal, caudate, thalamus, and post-cingulate, and amygdala regions had greater longitudinal decreases in FDG uptake in ApoE ε4 carriers with MCI compared to non-carriers with MCI. Similar forebrain and limbic clusters were found through voxelwise analysis. Compared to the PVC based analysis, fewer significant ApoE-associated regions and clusters were found in the non-PVC based PET analysis. Our findings suggest that the ApoE ε4 genotype is associated with a longitudinal decline in glucose uptake in 8 forebrain and limbic brain regions in the context of MCI. In conclusion, this 84-months longitudinal FDG PET study demonstrates a novel ApoE ε4-associated brain-region specific glucose metabolism pattern in patients with MCI. Partial volume correction improved FDG PET quantification.

Keywords: Alzheimer's disease; ApoE ε4; FDG PET; Longitudinal; Mild cognitive impairment; Partial volume correction.

Fig. 1

ROI-based analysis: Superior frontal cortex FDG SUVR dynamics in ApoE ε4 Carriers and Noncarriers with Mild Cognitive Impairment. Graphical representation of longitudinal FDG SUVR in ApoE ε4 carriers and non-carriers for PVC and non-PVC PET. FDG SUVR in the superior frontal cortex is plotted over time to show differences between ApoE ε4 carriers and non-carriers over the follow-up period. Differences in rates of FDG decline between ApoE ε4 carriers and non-carriers were assessed by fitting a linear mixed effects model to the ROI SUVRs, controlling for baseline age, education, follow-up interval and sex. (Table 2). The graph visually demonstrates an interaction effect between ApoE ε4 carrier status and follow-up time; ApoE ε4 carriers exhibit greater longitudinal decreases in superior frontal FDG compared to non-carriers. Due to partial volume effects, the contrast of longitudinal SUVRs between ApoE ε4 carriers and noncarriers was reduced in non-PVC PET data. Note, 96 months data is not shown because there were no ApoE ε4 carriers scans at this timepoint.

Fig. 2

Representative single-subject SUVR images with PVC at baseline and 72 months. PVC-based FDG SUVR images at baseline and 72-month scans from two ApoE ε4 carriers and two non-carriers are displayed in MNI standard-space. The scans demonstrate typical longitudinal changes in FDG signal over the study period and confirm the results pooled voxel-wise results (Fig. 3).

Fig. 3

Voxelwise statistical analysis of PVC based FDG SUVR longitudinal changes in ApoE ε4 carriers and non-carriers. MNI152 standard-space T1-weighted average structural template overlay showing voxels with significant decrease in FDG at 72 months compared to baseline (cluster-level correction: FWER <0.05; voxel uncorrected p < .001). Overlay figures were generating by performing a paired t-test comparing 72 month PVC scans with matched baseline PVC scans in SPM separately for ApoE ε4 carriers (left) and non-carriers (right).

Fig. 4

Typical single-subject FDG-PET Image with PVC and non-PVC. Representative cross-sectional slices from selected ApoE ε4 carriers (subject ID: 041_S_1418) and non-carriers (subject ID: 033_S_0906) with and without PVC are displayed to demonstrate the effect of PVC at the single-subject level.

Fig. 5

Mean SUVR Images with PVC and non-PVC. Mean images were generated by computing the mean of images from ApoE ε4 carriers and non-carriers separately at baseline and 72 months. These images have been corrected for partial volume effects. Note that the mean images are averaged over all participants in each group from non-PVC (left) and PVC (right) data.

Supplementary Fig. 1

Survival plots of AD Progression in APOE Genotype Groups. Survival plots representing the percent of individuals with MCI were generated using a Kaplan Meier estimate. The survival plots represent 1- probability of converting to AD. The pluses indicate that at least one subject was censored during this interval. The differences in the survival of APOE ε4 carriers and non-carriers was not significant (p = .17; see Results) yet trended towards the APOE ε4 carriers having a reduced survival over time compared to non-carriers. We hypothesize that in light of existing research showing biomarkers, such as FDG, responding years before cognitive symptoms associated with AD, the participants in our study may have been observed at earlier stages in which changes in diagnosis status from MCI to AD were not significantly different between carriers and non-carriers (Jack et al., 2013).

Supplementary Fig. 2

ROIs drawn superimposed on standard MNI152 MRI template. Representative ROIs are shown drawn on the MNI152 template using PMOD. ROI SUVRs were obtained by applying the above ROIs to SUVR images in the MNI space.