Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomic aging clocks from nuclear magnetic resonance spectroscopy data. The UK Biobank data, including 168 plasma metabolites from up to N = 225,212 middle-aged and older adults (mean age, 56.97 years), were used to train and internally validate 17 algorithms. Metabolomic age (MileAge) delta, the difference between metabolite-predicted and chronological age, from a Cubist rule–based regression model showed the strongest associations with health and aging markers. Individuals with an older MileAge were frailer, had shorter telomeres, were more likely to suffer from chronic illness, rated their health worse, and had a higher all-cause mortality hazard (HR = 1.51; 95% CI, 1.43 to 1.59; P < 0.001). This metabolomic aging clock (MileAge) can be applied in research and may find use in health assessments, risk stratification, and proactive health tracking.

中文翻译：

---

代谢组学年龄 （MileAge） 预测健康和寿命：多种机器学习算法的比较


生物衰老时钟产生的年龄估计值可以跟踪与年龄相关的健康结果。本研究旨在对机器学习算法进行基准测试，包括正则化回归、基于核的方法和集成，用于从核磁共振波谱数据开发代谢组衰老时钟。英国生物样本库数据，包括来自 N = 225,212 名中老年人（平均年龄 56.97 岁）的 168 种血浆代谢物，用于训练和内部验证 17 种算法。代谢组学年龄 （MileAge） delta，代谢物预测年龄与实际年龄之间的差异，来自基于 Cubist 规则的回归模型，显示出与健康和衰老标志物的最强关联。里程年龄较大的个体更虚弱，端粒更短，更容易患慢性病，健康状况更差，全因死亡风险更高（HR = 1.51;95% CI，1.43 至 1.59;P < 0.001）。这种代谢组学衰老时钟 （MileAge） 可以应用于研究，并可能用于健康评估、风险分层和主动健康跟踪。