Difference between ZhangeAge clock and chronological age predicts all-cause mortality

Tieshi Zhu^1,2Runzhui Lin³Yong He⁴Yixi Wang⁵Le Zhao^1*

• ¹Department of Medical Affairs, Agricultural Reclamation Central Hospital of Guangdong, Zhanjiang, Guangdong Province, China
• ²Department of Neurology, Zhanjiang Central Hospital, Guangdong Medical University, Zhanjiang, Guangdong Province, China
• ³Department of Hepatobiliary, pancreatic and splenic surgery，Second Affiliated Hospital of Shantou University Medical College, Shantou, China
• ⁴Department of Neurology, Liuyang Jili Hospital, Changsha, Anhui Province, China
• ⁵Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China

Introduction: Epigenetic age clocks are DNA methylation–based biomarkers widely used to quantify biological aging. ZhangAge, a recently developed clock built from over 13,000 samples using elastic net regression and principal component analysis to identify informative CpG sites, provides precise estimates of biological age. Two commonly derived indicators are age acceleration, defined as the residual from regressing ZhangAge on chronological age, and ΔAge, calculated as the direct difference between ZhangAge and chronological age. While ZhangAge AA has been shown to predict mortality, the role of ΔAge as a simpler, individually applicable metric remains less studied. Aim: To evaluate the association between ZhangAge ΔAge and all-cause, cancer, and cardiac mortality. Methods: Data were obtained from the National Health and Nutrition Examination Survey 1999–2002. After exclusions for missing ZhangAge, mortality status, and covariates, 1,942 participants were included. Restricted cubic spline (RCS) analysis was used to examine nonlinear relationships. Cox regression assessed the association between ΔAge and all-cause mortality, while competing risk regression models evaluated cancer-and cardiac-specific mortality. Sensitivity analyses included inverse probability weighting and exclusion of participants with stroke or coronary heart disease. Results: During a median follow-up of 208 months, 997 deaths occurred. Higher ΔAge was associated with an increased risk of all-cause mortality, with hazard ratios rising across quartiles (Q2: HR = 1.40; Q3: HR = 1.65; Q4: HR = 2.21; all P < 0.05). RCS analysis suggested a progressive increase in all-cause mortality with higher ΔAge. In contrast, ΔAge was not significantly associated with cancer or cardiac mortality in competing risk analyses. These findings were robust across sensitivity analyses. Conclusion: Δ Age, a straightforward measure derived from ZhangAge and chronological age, is positively associated with all-cause mortality but not with cancer or cardiac mortality. As an easily calculated biomarker, ΔAge may broaden the practical application of epigenetic clocks in aging research and individual risk assessment.