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Interaction of APOE, cerebral blood flow, and cortical thickness in the entorhinal cortex predicts memory decline

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Abstract

The ε4 allele of the apolipoprotein E (APOE) gene, a risk factor for cognitive decline, is associated with alterations in medial temporal lobe (MTL) structure and function, yet little research has been dedicated to understanding how these alterations might interact to negatively impact cognition. To bridge this gap, the present study employed linear regression models to determine the extent to which APOE genotype (ε4+, ε4-) modifies interactive effects of baseline arterial spin labeling MRI-measured cerebral blood flow (CBF) and FreeSurfer-derived cortical thickness/volume (CT/Vo) in two MTL regions of interest (entorhinal cortex, hippocampus) on memory change in 98 older adults who were cognitively normal at baseline. Baseline entorhinal CBF was positively associated with memory change, but only among ε4 carriers with lower entorhinal CT. Similarly, baseline entorhinal CT was positively associated with memory change, but only among ε4 carriers with lower entorhinal CBF. Findings suggest that APOE ε4 carriers may experience concomitant alterations in neurovascular function and morphology in the MTL that interact to negatively affect cognition prior to the onset of overt clinical symptoms. Results also suggest the presence of distinct multimodal neural signatures in the entorhinal cortex that may signal relative risk for cognitive decline among this group, perhaps reflecting different stages of cerebrovascular compensation (early effective vs. later ineffective).

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Data availability

The data that support the findings of this study may be available on request from the corresponding author [CEW]. The data are not publicly available due to them containing information that could compromise research participant privacy/consent.

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Acknowledgements

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by VA CSR&D Merit Award [5I01CX000565 C.E.W.], the National Science Foundation Graduate Research Fellowship Program [2015207525 C.C.H.], and the National Institute on Aging of the National Institutes of Health [K23AG049906 Z.Z.Z.], [K24AG026431 M.W.B], [R01AG054049 M.W.B]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the VA, National Science Foundation, or National Institutes of Health. We would also like to acknowledge Dr. Robert Rissman, Associate Professor of Neurosciences at the University of California San Diego for his contributions to this project.

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Authors and Affiliations

  1. VA San Diego Healthcare System, 3350 La Jolla Village Dr., MC 151B, San Diego, CA, 9216, USA

    Chelsea C. Hays, M.J. Meloy, Mark W. Bondi & Christina E. Wierenga

  2. SDSU/UCSD Joint Doctoral Program in Clinical Psychology, 6363 Alvarado Ct, Suite 103, San Diego, CA, 92120, USA

    Chelsea C. Hays, Mark W. Bondi, Paul E. Gilbert, Jonathan L. Helm & Christina E. Wierenga

  3. Department of Psychiatry, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA

    Zvinka Z. Zlatar, M.J. Meloy, Mark W. Bondi & Christina E. Wierenga

  4. Department of Psychology, San Diego State University, 5500 Campanile Drive, San Diego, CA, 92182, USA

    Paul E. Gilbert & Jonathan L. Helm

  5. Department of Radiology, UC San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA

    Thomas Liu

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  1. Chelsea C. Hays
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  2. Zvinka Z. Zlatar
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Correspondence to Christina E. Wierenga.

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Appendix

Appendix

Table 3 Cognitive tests, domains, and normative data
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Regression-based cognitive change score

As an alternative approach to account for practice effect and regression toward the mean, standardized change scores were also calculate to estimate neuropsychological change in individuals (Cysique et al., 2009; Heaton et al., 2001). To develop the mean scaled score regression change score (MSR-CS), the first step consisted of defining a reference group for which no neuropsychological change is expected beyond practice effect: we used a sample of 77 cognitively normal older adults who were also assessed serially at one-year intervals. The demographic characteristics of the reference group were as follows: mean age of 73.3 years (SD = 8.0); with a mean level of education of 16.3 years (SD = 2.1); 36% were men. These demographic characteristics were not statistically different from the target sample. All subjects in the reference group were cognitively normal (using the same criteria described above for the target sample) at baseline and remained cognitively normal at all follow-up timepoints. The second step of the MSR-CS development was as follows: the reference group yielded a set of regression equations (see Table 4 in the Appendix) from baseline to first follow-up (mean time between baseline to first follow-up in reference sample = 1.3 years, SD = 0.77), statistically adjusting for age, sex, education, and the follow-up interval, which was then used to derive predicted follow-up scores in the target sample. Subtracting the predicted follow-up score from individual’s observed follow-up score and dividing that result by the standard deviation of the residuals from the reference group regression models provided a standard score of cognitive change. This score was then used as a continuous variable; the MSR-CS (i.e., significant neuropsychological decline as: MSR-CS ≤ -1.04 based on a two-tailed 70% confidence interval, MSR-CS ≤ -1.28 based on a two-tailed 80% confidence interval, MSR-CS ≤ -1.645 based on a two-tailed 90% confidence interval). The methods above were applied to the following cognitive test scores: trials 1–5, short delay free-recall, and long delay free-recall raw scores from the California Verbal Learning Test – Second Edition (CVLT-II), measuring word list recall, and the Logical Memory immediate and delayed recall subtests of the Wechsler Memory Scale-Revised (WMS-R), measuring story recall. These tests were selected based on results from a principal component analysis previously reported by our group on a similar sample of older adults (Wierenga et al., 2012). A verbal memory change score composite (Memory-CS-Composite) was calculated by averaging the MSR-CS’s for the five verbal memory tests.

Table 4 Regression equations for predicting follow-up scores on CVLT-II and WMS-R Logical Memory variables
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Table 5 Effects of CBF, CT/Vo, and APOE, on regression-based memory change
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Table 6 Post hoc analysis of EC CBF, CT/Vo, and APOE, on baseline memory
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Hays, C.C., Zlatar, Z.Z., Meloy, M. et al. Interaction of APOE, cerebral blood flow, and cortical thickness in the entorhinal cortex predicts memory decline. Brain Imaging and Behavior 14, 369–382 (2020). https://doi.org/10.1007/s11682-019-00245-x

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