Genetic and Environmental Influences on Structural and Diffusion-Based Alzheimer's Disease Neuroimaging Signatures Across Midlife and Early Old Age

McKenna E Williams; Nathan A Gillespie; Tyler R Bell; Anders M Dale; Jeremy A Elman; Lisa T Eyler; Christine Fennema-Notestine; Carol E Franz; Donald J Hagler Jr; Michael J Lyons; Linda K McEvoy; Michael C Neale; Matthew S Panizzon; Chandra A Reynolds; Mark Sanderson-Cimino; William S Kremen

doi:10.1016/j.bpsc.2022.06.007

Genetic and Environmental Influences on Structural and Diffusion-Based Alzheimer's Disease Neuroimaging Signatures Across Midlife and Early Old Age

Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 Sep;8(9):918-927. doi: 10.1016/j.bpsc.2022.06.007. Epub 2022 Jun 20.

Authors

McKenna E Williams¹, Nathan A Gillespie², Tyler R Bell³, Anders M Dale⁴, Jeremy A Elman³, Lisa T Eyler⁵, Christine Fennema-Notestine⁶, Carol E Franz³, Donald J Hagler Jr⁷, Michael J Lyons⁸, Linda K McEvoy⁷, Michael C Neale², Matthew S Panizzon³, Chandra A Reynolds⁹, Mark Sanderson-Cimino¹⁰, William S Kremen³

Affiliations

¹ Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California. Electronic address: mewillia@health.ucsd.edu.
² Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia.
³ Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California.
⁴ Department of Radiology, University of California San Diego, San Diego, California; Department of Neuroscience, University of California San Diego, San Diego, California.
⁵ Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, California.
⁶ Department of Psychiatry, University of California San Diego, San Diego, California; Department of Radiology, University of California San Diego, San Diego, California.
⁷ Department of Radiology, University of California San Diego, San Diego, California.
⁸ Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts.
⁹ Department of Psychology, University of California Riverside, Riverside, California.
¹⁰ Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California.

Abstract

Background: Composite scores of magnetic resonance imaging-derived metrics in brain regions associated with Alzheimer's disease (AD), commonly termed AD signatures, have been developed to distinguish early AD-related atrophy from normal age-associated changes. Diffusion-based gray matter signatures may be more sensitive to early AD-related changes compared with thickness/volume-based signatures, demonstrating their potential clinical utility. The timing of early (i.e., midlife) changes in AD signatures from different modalities and whether diffusion- and thickness/volume-based signatures each capture unique AD-related phenotypic or genetic information remains unknown.

Methods: Our validated thickness/volume signature, our novel mean diffusivity (MD) signature, and a magnetic resonance imaging-derived measure of brain age were used in biometrical analyses to examine genetic and environmental influences on the measures as well as phenotypic and genetic relationships between measures over 12 years. Participants were 736 men from 3 waves of the Vietnam Era Twin Study of Aging (VETSA) (baseline/wave 1: mean age [years] = 56.1, SD = 2.6, range = 51.1-60.2). Subsequent waves occurred at approximately 5.7-year intervals.

Results: MD and thickness/volume signatures were highly heritable (56%-72%). Baseline MD signatures predicted thickness/volume signatures over a decade later, but baseline thickness/volume signatures showed a significantly weaker relationship with future MD signatures. AD signatures and brain age were correlated, but each measure captured unique phenotypic and genetic variance.

Conclusions: Cortical MD and thickness/volume AD signatures are heritable, and each signature captures unique variance that is also not explained by brain age. Moreover, results are in line with changes in MD emerging before changes in cortical thickness, underscoring the utility of MD as a very early predictor of AD risk.

Keywords: Alzheimer’s disease; Brain age; Cortical thickness; Early prediction; Genetics; Mean diffusivity.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Alzheimer Disease* / diagnostic imaging
Alzheimer Disease* / genetics
Alzheimer Disease* / pathology
Brain / diagnostic imaging
Brain / pathology
Child
Diffusion Tensor Imaging / methods
Humans
Magnetic Resonance Imaging / methods
Male
Neuroimaging

Abstract

Publication types

MeSH terms

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