Cardiovascular diabetology. Endocrinology reports · 2026
Phenotypic age metrics for risk stratification of chronic disease burden and all-cause mortality in adults.
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Wang Zihao, Zhong Xinru, Cai Yuzhou, Wang Jing, Lin Baojia, Liu Longlong, Li Shuting, He Haimei, Yao Dafu, Yan Chengzhi, Zou Yutong, Fu Yi, Sun Jiaduo, Zhang Qingguo
Аннотация
BACKGROUND: Phenotypic age gap (PAG) and phenotypic age acceleration (PhenoAgeAccel) are measures of biological ageing that have been linked to adverse health outcomes. However, their relationships with common chronic diseases and mortality in nationally representative populations remain incompletely understood. We investigated the associations of PhenoAge-based ageing metrics with chronic disease burden and all-cause mortality in U.S. adults.
METHODS: This retrospective study included 7,624 adults from NHANES 2005-2010 and 2015-2018. Phenotypic age was calculated using chronological age and nine clinical biomarkers. PAG was defined as phenotypic age minus chronological age, and PhenoAgeAccel was derived as a binary ageing-status variable. Cross-sectional analyses examined associations between common chronic diseases and ageing metrics, while machine-learning models were used to identify key predictive factors. Longitudinal analyses evaluated associations with all-cause mortality.
RESULTS: Obesity, hypertension, arthritis, diabetes, and malignancy were among the conditions most strongly associated with accelerated biological ageing. Among the evaluated machine-learning algorithms, LightGBM showed the best predictive performance, and SHAP analyses identified obesity, hypertension, arthritis, diabetes, and malignancy as the most influential predictive features. Positive PhenoAgeAccel was associated with increased all-cause mortality(HR 5.52, 95% CI 4.26-7.15). In addition, participants with cardiometabolic multimorbidity (CMM) and positive PAG had the poorest survival outcomes, suggesting that biological-age measures may provide additional risk stratification within multimorbid populations.
CONCLUSIONS: Several common chronic diseases were strongly associated with greater biological-age burden, and accelerated phenotypic ageing was linked to higher mortality risk. These findings support the potential value of PAG and PhenoAgeAccel as practical tools for risk stratification in relation to chronic disease burden and mortality.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: This retrospective study included 7,624 adults from NHANES 2005-2010 and 2015-2018. Phenotypic age was calculated using chronological age and nine clinical biomarkers. PAG was defined as phenotypic age minus chronological age, and PhenoAgeAccel was derived as a binary ageing-status variable. Cross-sectional analyses examined associations between common chronic diseases and ageing metrics, while machine-learning models were used to identify key predictive factors. Longitudinal analyses evaluated associations with all-cause mortality.
RESULTS: Obesity, hypertension, arthritis, diabetes, and malignancy were among the conditions most strongly associated with accelerated biological ageing. Among the evaluated machine-learning algorithms, LightGBM showed the best predictive performance, and SHAP analyses identified obesity, hypertension, arthritis, diabetes, and malignancy as the most influential predictive features. Positive PhenoAgeAccel was associated with increased all-cause mortality(HR 5.52, 95% CI 4.26-7.15). In addition, participants with cardiometabolic multimorbidity (CMM) and positive PAG had the poorest survival outcomes, suggesting that biological-age measures may provide additional risk stratification within multimorbid populations.
CONCLUSIONS: Several common chronic diseases were strongly associated with greater biological-age burden, and accelerated phenotypic ageing was linked to higher mortality risk. These findings support the potential value of PAG and PhenoAgeAccel as practical tools for risk stratification in relation to chronic disease burden and mortality.
CLINICAL TRIAL NUMBER: Not applicable.