| Title: | Metabolomic aging clock predicts risk of different cardiovascular diseases in the UK Biobank |
| Journal: | Metabolism |
| Published: | 8 Dec 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41371334/ |
| DOI: | https://doi.org/10.1016/j.metabol.2025.156467 |
Publication 17264
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Abstract
Current metabolomic aging clocks inadequately capture individual heterogeneity in biological aging trajectories, constraining their clinical utility. Here, we developed a metabolomic age clock in the UK Biobank (n = 196,790) using a comprehensive panel of 249 plasma metabolites. This framework was trained to predict phenotypic age (PhenoAge), a validated composite biomarker that integrates clinical chemistry across multiple systems, and was evaluated for its utility to predict incident cardiovascular diseases (CVDs) and dementia. We found that this new measure accurately predicted actual PhenoAge (Pearson's r = 0.90) and was significantly associated with the incidence of seven CVDs, including major adverse cardiovascular events, atherosclerotic cardiovascular disease, myocardial infarction, stroke, aortic stenosis, heart failure, and abdominal aortic aneurysm, but not dementia. Furthermore, metabolomic aging was associated with biological, physical, and cognitive age-related phenotypes, comprising telomere length, frailty index, and reaction time. Incorporating the metabolomic age clock with PREVENT (Predicting Risk of CVD Events) risk score modestly improved the performance, as measured by C-statistic and net reclassification index. Genetic analyses revealed 91 genomic loci and 168 genes (e.g., SERPINA1, FADS cluster), with tissue-enrichment analysis highlighting the liver's significant role in metabolic aging. By bridging metabolomic profiles with multisystem aging information, this framework provides a measure of biological aging that is associated with age-related functional status and cardiovascular risk.</p>
14 Keywords
- Aged
- Aged, 80 and over
- Aging
- Biological Specimen Banks
- Biomarkers
- Cardiovascular Diseases
- Female
- Humans
- Male
- Metabolomics
- Middle Aged
- Risk Factors
- UK Biobank
- United Kingdom
10 Authors
- Guochang You
- Kangjie Wang
- Runnan Shen
- Xiong Chen
- Jinjin Jiang
- Yunhao Sun
- Dan Wu
- Jiatang Xu
- Kai Huang
- Chen Yao
1 Application
| Application ID | Title |
|---|---|
| 100739 | Artificial intelligence for using multi-modal data to improve the identification and prediction of diseases in the UK Biobank |
Enabling scientific discoveries that improve human health