MRI-based multi-organ clocks for healthy aging and disease assessment
Huizi Cao, Zhiyuan Song, Michael R. Duggan, Güray Erus, Dhivya Srinivasan, Ye Tian, Wenjia Bai, Michael S. Rafii 等 15 位
Columbia University National Institutes of Health National Institute on Aging University of Pennsylvania
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摘要与影响
Biological aging clocks across organ systems and tissues have advanced understanding of human aging and disease. In this study, we expand this framework to develop seven magnetic resonance imaging-based multi-organ biological age gaps (MRIBAGs), including the brain, heart, liver, adipose tissue, spleen, kidney and pancreas. Using data from 313,645 individuals curated by the MULTI Consortium, we link the seven MRIBAGs to 2,923 plasma proteins, 327 metabolites and 6,477,810 common genetic variants. Genome-wide associations identify 53 MRIBAG−locus pairs (P < 5 × 10−8). Genetic correlation and Mendelian randomization analyses support organ-specific and cross-organ interconnection, including 24 non-MRI biological aging clocks and 525 disease endpoints. Through functional gene mapping and Bayesian co-localization multi-omics evidence, we prioritize nine druggable genes as targets for future anti-aging treatments. Furthermore, the seven MRIBAGs are linked to future risk of systemic disease endpoints (for example, diabetes mellitus) and all-cause mortality. Finally, participants with more youthful versus more aged brain profiles exhibited distinct cognitive decline trajectories over 240 weeks of treatment with the Alzheimer’s disease drug solanezumab, although this heterogeneity cannot be fully attributed to the drug. In summary, we developed seven MRIBAGs that enhance the existing multi-organ biological aging framework, and we demonstrate their clinical potential to advance aging research. A new study combines MRI data with proteomic, metabolomic and RNA data to develop and examine seven organ-specific MRI-based aging clocks, uncovering links to overall mortality and organ-specific diseases.
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生物医学Genetics, Aging, and Longevity in Model Organisms
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