A machine-learning algorithm integrating baseline serum proteomic signatures predicts exercise responsiveness in overweight males with prediabetes.
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ABSTRACT:
SUBMITTER: Diaz-Canestro C
PROVIDER: S-EPMC9975321 | biostudies-literature | 2023 Feb
REPOSITORIES: biostudies-literature
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A machine-learning algorithm integrating baseline serum proteomic signatures predicts exercise responsiveness in overweight males with prediabetes.
Diaz-Canestro Candela C Chen Jiarui J Liu Yan Y Han Hao H Wang Yao Y Honoré Eric E Lee Chi-Ho CH Lam Karen S L KSL Tse Michael Andrew MA Xu Aimin A
Cell reports. Medicine 20230213 2
The molecular transducers conferring the benefits of chronic exercise in diabetes prevention remain to be comprehensively investigated. Herein, serum proteomic profiling of 688 inflammatory and metabolic biomarkers in 36 medication-naive overweight and obese men with prediabetes reveals hundreds of exercise-responsive proteins modulated by 12-week high-intensity interval exercise training, including regulators of metabolism, cardiovascular system, inflammation, and apoptosis. Strong associations ...[more]
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