Hao Meng, Zhang Hui, Wu Jingyi, Huang Yaqi, Li Xiangnan, Wang Meijia, Wang Shuming, Wang Jiaofeng, Chen Jie, Jun Bao Zhi, Jin Li, Wang Xiaofeng, Hu Zixin, Jiang Shuai, Li Yi
Department of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China.
Adv Sci (Weinh). 2025 Aug;12(32):e01765. doi: 10.1002/advs.202501765. Epub 2025 Jul 2.
Biological age reflects actual ageing and overall health, but current ageing clocks are often complex and difficult to interpret, which limits their clinical application. This study introduces a Gompertz law-based biological age (GOLD BioAge) model designed to simplify the assessment of ageing. We calculated GOLD BioAge using clinical biomarkers and found significant associations between the difference from chronological age (BioAgeDiff) and the risks of morbidity and mortality in the NHANES and UK Biobank. Using proteomics and metabolomics data, we developed GOLD ProtAge and MetAge, which outperformed the clinical biomarker models in predicting mortality and chronic disease risk in UK Biobank. Benchmark analyses demonstrated that the models outperformed common ageing clocks in predicting mortality across diverse age groups in both the NHANES and UK Biobank cohorts. Additionally, a simplified version called Light BioAge is created, which uses three biomarkers to assess ageing. The Light model reliably captured the mortality risk across three validation cohorts (CHARLS, RuLAS, and CLHLS). It significantly predicted the onset of frailty, stratified frail individuals, and collectively identified individuals at high risk of mortality. In summary, the GOLD BioAge algorithm provides a valuable framework for the assessment of ageing in public health and clinical practice.
生物学年龄反映了实际衰老情况和整体健康状况，但目前的衰老时钟往往复杂且难以解读，这限制了它们在临床中的应用。本研究引入了一种基于冈珀茨定律的生物学年龄（GOLD生物年龄）模型，旨在简化衰老评估。我们使用临床生物标志物计算了GOLD生物年龄，并在国家健康与营养检查调查（NHANES）和英国生物银行中发现，与实足年龄的差异（生物年龄差值）与发病和死亡风险之间存在显著关联。利用蛋白质组学和代谢组学数据，我们开发了GOLD蛋白质组年龄和代谢组年龄，在预测英国生物银行中的死亡率和慢性病风险方面，它们优于临床生物标志物模型。基准分析表明，在预测NHANES和英国生物银行队列中不同年龄组的死亡率方面，这些模型优于常见的衰老时钟。此外，还创建了一个名为简易生物年龄的简化版本，它使用三种生物标志物来评估衰老。简易模型在三个验证队列（中国健康与养老追踪调查、俄罗斯纵向衰老研究和中国老年健康影响因素跟踪调查）中可靠地捕捉到了死亡风险。它显著预测了衰弱的发生，对衰弱个体进行了分层，并共同识别出了高死亡风险个体。总之，GOLD生物年龄算法为公共卫生和临床实践中的衰老评估提供了一个有价值的框架。
