代谢组
生物
衰老
索引(排版)
计算生物学
代谢组学
生物信息学
遗传学
万维网
计算机科学
作者
Shruthi Hamsanathan,Tamil S. Anthonymuthu,Denise Prosser,Anna Lokshin,Susan L. Greenspan,Neil M. Resnick,Subashan Perera,Shinpei Okawa,Giri Narasimhan,Aditi U. Gurkar
摘要
Abstract Unlike chronological age, biological age is a strong indicator of health of an individual. However, the molecular fingerprint associated with biological age is ill‐defined. To define a high‐resolution signature of biological age, we analyzed metabolome, circulating senescence‐associated secretome (SASP)/inflammation markers and the interaction between them, from a cohort of healthy and rapid agers. The balance between two fatty acid oxidation mechanisms, β‐oxidation and ω‐oxidation, associated with the extent of functional aging. Furthermore, a panel of 25 metabolites, Healthy Aging Metabolic (HAM) index, predicted healthy agers regardless of gender and race. HAM index was also validated in an independent cohort. Causal inference with machine learning implied three metabolites, β‐cryptoxanthin, prolylhydroxyproline, and eicosenoylcarnitine as putative drivers of biological aging. Multiple SASP markers were also elevated in rapid agers. Together, our findings reveal that a network of metabolic pathways underlie biological aging, and the HAM index could serve as a predictor of phenotypic aging in humans.
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