Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring (Jan 2018)

Regional tract‐specific white matter hyperintensities are associated with patterns of aging‐related brain atrophy via vascular risk factors, but also independently

  • Mohamad Habes,
  • Guray Erus,
  • Jon B. Toledo,
  • Nick Bryan,
  • Deborah Janowitz,
  • Jimit Doshi,
  • Henry Völzke,
  • Ulf Schminke,
  • Wolfgang Hoffmann,
  • Hans J. Grabe,
  • David A. Wolk,
  • Christos Davatzikos

DOI
https://doi.org/10.1016/j.dadm.2018.02.002
Journal volume & issue
Vol. 10, no. 1
pp. 278 – 284

Abstract

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Abstract Introduction We sought to investigate associations of regional white matter hyperintensities (WMHs) within white matter (WM) tracts with cardiovascular risk and brain aging‐related atrophy throughout adulthood in the general population, leveraging state of the art pattern analysis methods. Methods We analyzed a large sample (n = 2367) from the Study of Health in Pomerania, Germany (range 20–90 years). WMHs were automatically segmented on T1‐weighted and fluid‐attenuated inversion recovery magnetic resonance images, and WMH volumes were calculated in WM regions defined using the John Hopkins University WM tractography atlas. Regions with the highest average WMH volume were selected. We calculated a subject‐specific index, Spatial Pattern of Alteration for Recognition of Brain Aging, to measure age‐related atrophy patterns. The Framingham cardiovascular disease risk score summarized the individual cardiovascular risk profile. We used structural equation models, independently for each region, using Spatial Pattern of Alteration for Recognition of Brain Aging as a dependent variable, age as an independent variable, and cardiovascular disease risk score and regional WMH volumes as mediators. Results Selected 12 WM regions included 75% of the total WMH burden in average. Structural equation models showed that the age effect on Spatial Pattern of Alteration for Recognition of Brain Aging was mediated by WMHs to a different extent in the superior frontal WM, anterior corona radiata, inferior frontal WM, superior corona radiata, superior longitudinal fasciculus, middle temporal WM, posterior corona radiata, superior parietal WM, splenium of corpus callosum, posterior thalamic radiation, and middle occipital WM (variance explained between 2.8% and 10.3%, P < .0001 Bonferroni corrected), but not in precentral WM. Conclusions Our results indicate that WMHs, in most WM tracts, might accelerate the brain aging process throughout adulthood in the general population as a result of vascular risk factors, but also independent of them. Preventive strategies against WMHs (such as controlling vascular risk factors or microglia depletion) could delay brain aging.

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