Wang Kaiyue, Gao Jingli, Liu Ying, Liu Zuyun, Li Yaqi, Chen Shuohua, Sun Liang, Wu Shouling, Gao Xiang
Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, 200032, China.
Department of Intensive Care Unit, Kailuan General Hospital, Tangshan, 063000, China.
Geroscience. 2025 Mar 28. doi: 10.1007/s11357-025-01612-x.
Efforts to increase health span bring to light the necessity of constructing biological age (BA) for measuring aging. However, universally adaptive BA needs further investigation, especially among the Chinese population. Therefore, this study aimed to construct BA using routine clinical markers for the Chinese population. Included were two Chinese prospective cohorts, the Kailuan Study I (n = 83,571) for developing BA and the Kailuan Study II (n = 21,229) for validation. Leveraging baseline age-related clinical markers, we developed phenotypic BA (Pheno-Age) using Levine's methods and Klemera-Doubal BA (KDM-Age) using KDM methods and calculated the residuals of regressions of the two BA measured at baseline and during follow-up on chronological age, namely BA acceleration. The predictive performance of baseline, cumulative average, and updated BAs on mortality was evaluated using the area under the curve (AUC) and calibration plots. COX regressions were used to estimate hazard rations (HRs) and 95% confidence intervals (CIs) for the BA acceleration and risk of mortality. During 1,443,857 person-years of follow-up, 12,679 deaths were recorded in the two cohorts. Baseline Pheno-Age and KDM-Age produced desirable predictions for mortality in both the Kailuan Study I (AUC, 0.810 and 0.806, respectively) and the Kailuan Study II (AUC, 0.867 and 0.819, respectively). Calibration plots showed reasonable agreement between predicted and observed probabilities. The pooled multivariable-adjusted HRs (95% CIs) for per standard deviation increment of baseline Pheno-Age acceleration and mortality was 1.24 (1.18, 1.30), and for KDM-Age acceleration was 1.16 (1.10, 1.21). Similar predictive performance and association were observed when using cumulative average or updated BA. The associations were stronger in the adults aged ≤60 years, smokers, and drinkers, relative to their counterparts (P for interaction <0.05 for all). Pheno-Age and KDM-Age, developed and validated in the two large prospective cohorts, could predict mortality, independent of chronological age and other potential confounders, in Chinese populations.
延长健康寿命的努力凸显了构建用于衡量衰老的生物学年龄（BA）的必要性。然而，普遍适用的BA仍需进一步研究，尤其是在中国人群中。因此，本研究旨在利用常规临床指标为中国人群构建BA。研究纳入了两个中国前瞻性队列，即用于开发BA的开滦研究I（n = 83,571）和用于验证的开滦研究II（n = 21,229）。利用基线年龄相关的临床指标，我们采用莱文方法开发了表型BA（Pheno-Age），采用KDM方法开发了Klemera-Doubal BA（KDM-Age），并计算了在基线和随访期间测量的两种BA相对于实际年龄的回归残差，即BA加速。使用曲线下面积（AUC）和校准图评估基线、累积平均值和更新后的BA对死亡率的预测性能。采用COX回归估计BA加速与死亡风险的风险比（HRs）和95%置信区间（CIs）。在1,443,857人年的随访期间，两个队列中共记录了12,679例死亡。基线Pheno-Age和KDM-Age在开滦研究I（AUC分别为0.810和0.806）和开滦研究II（AUC分别为0.867和0.819）中对死亡率均产生了理想的预测。校准图显示预测概率与观察概率之间具有合理的一致性。基线Pheno-Age加速每增加一个标准差与死亡率的合并多变量调整HR（95%CI）为1.24（1.18，1.30），KDM-Age加速的为1.16（1.10，1.21）。使用累积平均值或更新后的BA时观察到类似的预测性能和关联。相对于同龄人，在年龄≤60岁的成年人、吸烟者和饮酒者中，这种关联更强（所有交互作用的P<0.05）。在两个大型前瞻性队列中开发并验证的Pheno-Age和KDM-Age可以独立于实际年龄和其他潜在混杂因素预测中国人群的死亡率。
