Decoding aging clocks: New insights from metabolomics.

Honghao Huang, Yifan Chen, Wei Xu, Linlin Cao, Kun Qian, Evelyne Bischof, Brian K Kennedy, Jun Pu

Division of Cardiology, State Key Laboratory for Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. · pubmed

Paper

Abstract

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.

The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.

Research summary

The paper discusses the use of metabolomics to establish aging clocks, which is directly related to understanding biological aging and its implications for longevity research. While it provides a solid overview of the topic and highlights potential applications, the contributions appear to be more incremental rather than groundbreaking, limiting its overall impact on the field.

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