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Exogenous sex hormone effects on brain microstructure in women: a diffusion MRI study in the UK Biobank

By Leila Nabulsi, Katherine E Lawrence, Vigneshwaran Santhalingam, Zvart Abaryan, Christina P Boyle, Julio E. Villalon-Reina, Talia M. Nir, Iyad Ba Gari, Alyssa H Zhu, Elizabeth Haddad, Alexandra M. Muir, Neda Jahanshad, Paul M. Thompson

Posted 20 Sep 2020
bioRxiv DOI: 10.1101/2020.09.18.304154

This study used advanced diffusion-weighted MRI (dMRI) to examine the association between exogenous sex-hormone exposure and the brain’s white matter aging trajectories in a large population-based sample of women. To investigate the effect of pre- and post-menopausal sex hormones on brain aging, cross-sectional brain dMRI data from the UK Biobank was analyzed using 3 diffusion models: conventional diffusion tensor imaging (DTI), the tensor distribution function (TDF), and neurite orientation dispersion and density imaging (NODDI). Mean skeletonized diffusivity measures were extracted and averaged across the whole brain, including fractional anisotropy, isotropic volume fraction, intracellular volume fraction and orientation dispersion index. We used general linear models and fractional polynomial regressions to characterize age-related trajectories in white matter measures following hormone therapy (HT) and oral contraceptive (OC) use in women (HT analysis: N=8,301; OC analysis: N=8,913). Sex hormone treatment (HT and OC) was statistically associated with the aging trends in white matter measures. Estrogen therapy alone appeared to exert a neuroprotective effect on age-related white matter processes, compared to HT containing both estrogen and progestin therapy – which was associated with accelerated aging-related processes in women. These results support the hypothesis that exogenous sex hormone exposure may impact white matter aging; white matter metrics may also be sensitive to sex hormone levels in women. Furthermore, we discuss the necessity to test alternative models for lifespan trajectories beyond popular linear and quadratic models, especially when dealing with large samples. Fractional polynomial models may provide a more adaptive alternative to linear or quadratic models. ### Competing Interest Statement The authors have declared no competing interest.

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