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Metabolomic aging clock predicts risk of different cardiovascular diseases in the UK Biobank
Guochang You, K. L. Wang, Runnan Shen, Chen Xiong, Jinjin Jiang, Yunhao Sun, Dan Wu, Jiatang Xu et al.. Metabolism, 2025-12-08
Topic: Risk, prevention and care in population studies
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.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
You, G., Wang, K. L., Shen, R., Xiong, C., Jiang, J., Sun, Y., et al. (2025). Metabolomic aging clock predicts risk of different cardiovascular diseases in the UK Biobank. Metabolism. https://doi.org/10.1016/j.metabol.2025.156467
Vancouver
You G, Wang KL, Shen R, Xiong C, Jiang J, Sun Y, et al. Metabolomic aging clock predicts risk of different cardiovascular diseases in the UK Biobank. Metabolism. 2025. doi:10.1016/j.metabol.2025.156467. PMID: 41371334.
BibTeX
@article{you2025-41371334,
author = {You, Guochang and Wang, K. L. and Shen, Runnan and Xiong, Chen and Jiang, Jinjin and Sun, Yunhao and Wu, Dan and Xu, Jiatang and others},
title = {{Metabolomic aging clock predicts risk of different cardiovascular diseases in the UK Biobank}},
journal = {Metabolism},
year = {2025},
doi = {10.1016/j.metabol.2025.156467},
pmid = {41371334},
url = {https://doi.org/10.1016/j.metabol.2025.156467}
}
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Key findings
- 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.
- 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.
Why it matters
This UK Biobank study built a metabolomic aging clock that was linked to seven cardiovascular diseases but not to dementia. The clock may help predict cardiovascular risk, but it showed no association with dementia in this analysis.
Study details
- Participants196,790
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Metabolism) |
| Journal's citation record | 2 / 4 | Metabolism: journal h-index 239 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 4 / 4 | 196,790 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 9 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 2.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 9 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Chen Yao: h-index 19 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Sun Yat-sen University: institution h-index 851 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41371334) |
| Free to read | 2 / 2 | Free to read |
| Total | 45 / 100 | — |
What each of these 20 components measures, and what this score is not →
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