Clinical nephrology · 2026
Epigenetic age enhances eGFR-based prediction of all-cause mortality: A cross-cohort validation of a novel risk model.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Wang Xinyu, Song Lu, Lv Qin, Li Li, Wang Qiao, Liu Chunli, Chen Meiyu, Liu Feifei, Yu Yaling, Shen Ying, Cui Jianchen, Zhao Yaxi, Deng Qinyuan
Аннотация
AIM: Chronic kidney disease and biological aging share overlapping mechanisms, but conventional risk models may not adequately capture their combined impact on mortality. This study aimed to determine whether integrating DNA methylation-based biological age (epigenetic age) with renal function estimates could enhance mortality risk prediction.
MATERIALS AND METHODS: A mortality risk model incorporating estimated glomerular filtration rate was developed in the UK Biobank and externally validated in the National Health and Nutrition Examination Survey (1999 - 2002). Four epigenetic age measures (Horvath, Hannum, PhenoAge, and GrimAge) were evaluated for their added value in predicting mortality. Model performance was assessed using discrimination metrics and survival analyses.
RESULTS: Among the epigenetic age measures, GrimAge showed the most substantial improvement in model performance when combined with creatinine-based kidney function estimates. Adding GrimAge improved discrimination for mortality prediction (change in C-statistic: 0.16; p < 0.001). In the external cohort, individuals classified as low risk by chronological age but as high risk by GrimAge had significantly higher mortality. Chronological age-adjusted kidney function showed no predictive value, while GrimAge-based models identified at-risk individuals with preserved kidney function. Models using creatinine outperformed those using cystatin C across all measures of biological age.
CONCLUSION: Integrating epigenetic age measures with renal function indicators improves mortality prediction. GrimAge is particularly effective in identifying individuals at high risk who may not be detected using standard approaches. Incorporating biological aging markers into kidney health assessments may allow more precise and personalized risk stratification.
MATERIALS AND METHODS: A mortality risk model incorporating estimated glomerular filtration rate was developed in the UK Biobank and externally validated in the National Health and Nutrition Examination Survey (1999 - 2002). Four epigenetic age measures (Horvath, Hannum, PhenoAge, and GrimAge) were evaluated for their added value in predicting mortality. Model performance was assessed using discrimination metrics and survival analyses.
RESULTS: Among the epigenetic age measures, GrimAge showed the most substantial improvement in model performance when combined with creatinine-based kidney function estimates. Adding GrimAge improved discrimination for mortality prediction (change in C-statistic: 0.16; p < 0.001). In the external cohort, individuals classified as low risk by chronological age but as high risk by GrimAge had significantly higher mortality. Chronological age-adjusted kidney function showed no predictive value, while GrimAge-based models identified at-risk individuals with preserved kidney function. Models using creatinine outperformed those using cystatin C across all measures of biological age.
CONCLUSION: Integrating epigenetic age measures with renal function indicators improves mortality prediction. GrimAge is particularly effective in identifying individuals at high risk who may not be detected using standard approaches. Incorporating biological aging markers into kidney health assessments may allow more precise and personalized risk stratification.