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Genetic and environmental influences on structural- and diffusion-based Alzheimer’s disease neuroimaging signatures across midlife and early old age

      Abstract

      Background

      Composite scores of MRI-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 to 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 an MRI-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 three waves of the Vietnam Era Twin Study of Aging (VETSA; baseline age=56.1, SD=2.6, range=51.1-60.2). Subsequent waves were 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

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