Published: Jan. 1, 2024
Language: Английский
Published: Jan. 1, 2024
Language: Английский
International Immunopharmacology, Journal Year: 2025, Volume and Issue: 152, P. 114405 - 114405
Published: March 13, 2025
Language: Английский
Citations
0medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 15, 2024
Abstract Biological age reflects actual aging and overall health, but current clocks are often complex difficult to interpret, limiting their clinical application. In this study, we introduced a Gompertz law-based biological (GOLD BioAge) model that simplified assessment. We estimated GOLD BioAge using biomarkers found significant associations of the difference from chronological (BioAgeDiff) with risks morbidity mortality in NHANES. Moreover, developed ProtAge MetAge proteomics metabolomics data, which outperformed clinical-only predicting chronic disease UK Biobank. Benchmark analysis illustrated our models exceeded common across diverse groups both NHANES The results demonstrated algorithm effectively applied omics showing excellent performance age-related outcomes. Additionally, created version called Light BioAge, used three for reliably captured validation cohorts (CHARLS, RuLAS, CLHLS). It significantly predicted onset frailty, stratified frail individuals, collectively identified individuals at high risk mortality. summary, could provide valuable framework assessment public health practice. Highlights law based was proposed construct convenient interpretable calculations, had better risks. Our approach applicable metabolomics, yielding great prospect improve accuracy prevent diseases. version, biomarkers, it independently cohorts. BioAgeDiff
Language: Английский
Citations
0Published: Nov. 27, 2024
Biological age reflects actual aging and overall health, but current clocks are often complex difficult to interpret, limiting their clinical application. In this study, we introduced a Gompertz law-based biological (GOLD BioAge) model that simplified assessment. We estimated GOLD BioAge using biomarkers found significant associations of the difference from chronological (BioAgeDiff) with risks morbidity mortality in NHANES. Moreover, developed ProtAge MetAge proteomics metabolomics data, which outperformed clinical-only predicting chronic disease UK Biobank. Benchmark analysis illustrated our models exceeded common across diverse groups both NHANES The results demonstrated algorithm effectively applied omics showing excellent performance age-related outcomes. Additionally, created version called Light BioAge, used three for reliably captured validation cohorts (CHARLS, RuLAS, CLHLS). It significantly predicted onset frailty, stratified frail individuals, collectively identified individuals at high risk mortality. summary, could provide valuable framework assessment public health practice.
Language: Английский
Citations
0Published: Jan. 1, 2024
Language: Английский
Citations
0