Journal Article Ppublish 3 citations
PubMed: 39331845 DOI: 10.1002/advs.202406670
A Novel Metabolomic Aging Clock Predicting Health Outcomes and Its Genetic and Modifiable Factors.
Adv Sci (Weinh) 11(43):e2406670
Authors: Xueqing Jia, Jiayao Fan, Xucheng Wu, Xingqi Cao, Lina Ma, Zeinab Abdelrahman, Fei Zhao, Haitao Zhu, Daniele Bizzarri, Erik B van den Akker, P Eline Slagboom, Joris Deelen, Dan Zhou, Zuyun Liu
Affiliations
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.
- Department of General Practice, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
- Department of Geriatrics, National Clinical Research Center for Geriatric Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK.
- Hangzhou Meilian Medical Co. Ltd. Hangzhou, 311200, China.
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, 2333 ZC, The Netherlands.
- The Delft Bioinformatics Lab, Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, 2628 CC, The Netherlands.
- Max Planck Institute for Biology of Ageing, 50931, Cologne, Germany.
- Cologne Excellence Cluster on Cellular Stress Responses in Ageing-Associated Diseases (CECAD), University of Cologne, 50931, Cologne, Germany.
Abstract
Existing metabolomic clocks exhibit deficiencies in capturing the heterogeneous aging rates among individuals with the same chronological age. Yet, the modifiable and non-modifiable factors in metabolomic aging have not been systematically studied. Here, a new aging measure-MetaboAgeMort-is developed using metabolomic profiles from 239,291 UK Biobank participants for 10-year all-cause mortality prediction. The MetaboAgeMort showed significant associations with all-cause mortality, cause-specific mortality, and diverse incident diseases. Adding MetaboAgeMort to a conventional risk factors model improved the predictive ability of 10-year mortality. A total of 99 modifiable factors across seven categories are identified for MetaboAgeMort. Among these, 16 factors representing pulmonary function, body composition, socioeconomic status, dietary quality, smoking status, alcohol intake, and disease status showed quantitatively stronger associations. The genetic analyses revealed 99 genomic risk loci and 271 genes associated with MetaboAgeMort. The tissue-enrichment analysis showed significant enrichment in liver. While the external validation of the MetaboAgeMort is required, this study illuminates heterogeneous metabolomic aging across the same age, providing avenues for identifying high-risk individuals, developing anti-aging therapies, and personalizing interventions, thus promoting healthy aging and longevity.
MeSH
Mortality, Biomedical Research, Aging, Metabolomics