Aging CellNeurosciencePeer-reviewed journal article
Epigenetic age acceleration predicts subject-specific white matter degeneration in the human brain.
Benjamin T. NewmaniD, Joshua S. DanoffiD, Morgan E. LynchiD, Stephanie N. GiamberardinoiD, Simon G. GregoryiD, Jessica J. ConnellyiD, T. Jason DruzgaliD, James P. MorrisiD
Abstract
Epigenetic clocks provide powerful tools for estimating health and lifespan but their ability to predict brain degeneration and neuronal damage during the aging process is unknown. In this study, we use GrimAge, an epigenetic clock correlated to several blood plasma proteins, to longitudinally investigate brain cellular microstructure in axonal white matter from a cohort of healthy aging individuals. Given the blood plasma correlations used to develop GrimAge, a specific focus was made on white matter hyperintensities, a visible neurological manifestation of small vessel disease, and the axonal pathways throughout each individuals brain affected by their unique white matter hyperintensity location and volume. 98 subjects over 55 years of age were scanned at baseline with 41 returning for a follow-up scan 2 years later. Using diffusion MRI lesionometry, we reconstructed subject-specific networks of affected axonal tracts and examined the diffusion cellular microstructure composition of these areas, both at baseline and longitudinally, for evidence of cellular degeneration. A chronological age-adjusted version of GrimAge was significantly correlated with baseline WMH volume and markers of neuronal decline, indicated by increased extracellular free water, increased intracellular signal, and decreased axonal signal within WMH. By isolating subject-specific axonal regions lesioned by crossing through a WMH, age-adjusted GrimAge was also able to predict longitudinal development of similar patterns of neuronal decline throughout the brain. This study is the first to establish a relationship between accelerated epigenetic GrimAge and brain cellular microstructure in humans.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 10
- FWCI
- 2.66
- Field percentile
- top 9%
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