Aging | White Matter Hyperintensity Load Associated With Premature Brain Aging
12-22-2022“Our results support previous work suggesting there is a relationship between chronological age and WMH [white matter hyperintensities] volume, and chronological age and estimated brain age.”
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BUFFALO, NY- December 22, 2022 – A new research paper was published in Aging (listed as "Aging (Albany NY)" by MEDLINE/PubMed and "Aging-US" by Web of Science) Volume 14, Issue 23, entitled, “White matter hyperintensity load is associated with premature brain aging.”
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.
In this new study, researchers Natalie Busby, Sarah Newman-Norlund, Sara Sayers, Roger Newman-Norlund, Sarah Wilson, Samaneh Nemati, Chris Rorden, Janina Wilmskoetter, Nicholas Riccardi, Rebecca Roth, Julius Fridriksson, and Leonardo Bonilha from University of South Carolina, Medical University of South Carolina and Emory University tested the hypothesis that WMHs are independently associated with premature brain age in an original aging cohort.
“We hypothesized that a higher WMH load is linearly associated with premature brain aging controlling for chronological age.”
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).
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.
“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.”
DOI: https://doi.org/10.18632/aging.204397
Corresponding Author: Natalie Busby - hethern@mailbox.sc.edu
Keywords: brain age, white matter hyperintensity, brain health, aging, health
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About Aging-US:
Aging publishes research papers in all fields of aging research including but not limited, aging from yeast to mammals, cellular senescence, age-related diseases such as cancer and Alzheimer’s diseases and their prevention and treatment, anti-aging strategies and drug development and especially the role of signal transduction pathways such as mTOR in aging and potential approaches to modulate these signaling pathways to extend lifespan. The journal aims to promote treatment of age-related diseases by slowing down aging, validation of anti-aging drugs by treating age-related diseases, prevention of cancer by inhibiting aging. Cancer and COVID-19 are age-related diseases.
Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).
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