Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

中文翻译：

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解码衰老时钟：代谢组学的新见解


实际年龄是老年人患病和残疾的关键风险因素。然而，相同实际年龄的个体通常表现出不同的生物衰老状态，从而导致不同的个体风险状况。基于组学数据和机器学习技术的实际年龄估计器（称为衰老时钟）为解释分子水平的生物衰老提供了一个有价值的框架。代谢组学是一个有趣且快速发展的研究领域，涉及体内小分子的全面分析，并提供最终的基因组-环境相互作用读数。因此，利用代谢组学来表征生物衰老具有巨大的潜力。本综述的目的是概述代谢组学方法，强调代谢组学衰老时钟的建立和解释，同时强调它们的优势、局限性和应用，并讨论它们的潜在生物学意义，这有可能推动长寿研究和开发的创新。