Research Paper|Volume 14, Issue 23|pp 9458—9465

White matter hyperintensity load is associated with premature brain aging

Natalie Busby¹, Sarah Newman-Norlund¹, Sara Sayers¹, Roger Newman-Norlund², Sarah Wilson¹, Samaneh Nemati¹, Chris Rorden², Janina Wilmskoetter³, Nicholas Riccardi², Rebecca Roth⁴, Julius Fridriksson¹, Leonardo Bonilha⁴

¹Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29201, USA
²Department of Psychology, University of South Carolina, Columbia, SC 29201, USA
³Department of Health and Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
⁴Department of Neurology, Emory University, Atlanta, GA 30322, USA

Received: July 12, 2022Accepted: November 14, 2022Published: November 30, 2022

https://doi.org/10.18632/aging.204397

Copyright: © 2022 Busby et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Brain age is an MRI-derived estimate of brain tissue loss that has a similar pattern to aging-related atrophy. White matter hyperintensities (WMHs) are neuroimaging markers of small vessel disease and may represent subtle signs of brain compromise. We tested the hypothesis that WMHs are independently associated with premature brain age in an original aging cohort.

Methods: Brain age was calculated using machine-learning on whole-brain tissue estimates from T1-weighted images using the BrainAgeR analysis pipeline in 166 healthy adult participants. WMHs were manually delineated on FLAIR images. WMH load was defined as the cumulative volume of WMHs. A positive difference between estimated brain age and chronological age (BrainGAP) was used as a measure of premature brain aging. Then, partial Pearson correlations between BrainGAP and volume of WMHs were calculated (accounting for chronological age).

Results: Brain and chronological age were strongly correlated (r(163)=0.932, p<0.001). There was significant negative correlation between BrainGAP scores and chronological age (r(163)=-0.244, p<0.001) indicating that younger participants had higher BrainGAP (premature brain aging). Chronological age also showed a positive correlation with WMH load (r(163)=0.506, p<0.001) indicating older participants had increased WMH load. Controlling for chronological age, there was a statistically significant relationship between premature brain aging and WMHs load (r(163)=0.216, p=0.003). Each additional year in brain age beyond chronological age corresponded to an additional 1.1mm³ in WMH load.

Conclusions: WMHs are an independent factor associated with premature brain aging. This finding underscores the impact of white matter disease on global brain integrity and progressive age-like brain atrophy.