Decoding aging clocks: New insights from metabolomics

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

doi:10.1016/j.cmet.2024.11.007

Decoding aging clocks: New insights from metabolomics

Cell Metab. 2024 Dec 9:S1550-4131(24)00453-4. doi: 10.1016/j.cmet.2024.11.007. Online ahead of print.

Authors

Honghao Huang¹, Yifan Chen¹, Wei Xu¹, Linlin Cao¹, Kun Qian², Evelyne Bischof³, Brian K Kennedy⁴, Jun Pu⁵

Affiliations

¹ 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.
² 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; School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
³ University Hospital of Basel, Division of Internal Medicine, University of Basel, Basel, Switzerland; Shanghai University of Medicine and Health Sciences, College of Clinical Medicine, Shanghai, China.
⁴ Health Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Centre for Healthy Longevity, National University Health System, Singapore, Singapore; Departments of Biochemistry and Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. Electronic address: [email protected].
⁵ 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; Aging Biomarker Consortium, China. Electronic address: [email protected].

PMID: 39657675
DOI: 10.1016/j.cmet.2024.11.007

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.

Keywords: aging clocks; biological age; chronological age; metabolomics.

Publication types

Review