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Determinants of accelerated metabolomic and epigenetic aging in a UK cohort
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Abstract
Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry in urine and serum, within a large sample ( N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration ( N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.
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“Robinson et al.120 additionally assessed mass spectrometry metabolomic ageing clocks for the enrichment of biological pathways and identified enrichments for the metabolism of several vitamins, amino acids and xenobiotics.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“Metabolomic age acceleration correlated with triglyceride levels, obesity, heavy drinking, diabetes mellitus, depressive symptoms, depression, anxiety and post-traumatic stress disorder.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“Furthermore, metabolomic clocks have demonstrated lower age prediction accuracy than other omics data types despite training on extremely large sample sizes, and effect sizes for ageing traits have been modest113,116–121.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“and Robinson et al.’s metabolomic age gap had no correlation with the Horvath, Hannum and PhenoAge clocks in the same cohort, yet there was some overlap in the correlation between age gap and phenotypes tested120.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“This study compared multiple untargeted metabolomic methods for developing ageing clocks”
From Measuring biological age using omics data (2022) · cites this paper as [120]
In the citing paper
“and additionally assessed their relationship to health traits and to the Horvath, Hannum and PhenoAge epigenetic ageing clocks.”
From Measuring biological age using omics data (2022) · cites this paper as [120]
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Biosynthesis and Biotransformation of Plant Triterpenoids: Current Advances and Future Perspectives
Xiang, L., Chen, L., Lv, B., & Li, C. (2026) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2026_355
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Distribution of metabolic acceleration of age in premenopausal (Pre) and postmenopausal (Post) women
Muhametshina, R. D., & Muhametshina, A. D. (2026) · Cifra LLC Journals · doi:10.60797/jbg.2026.33.2.10
Predicted and chronological age
Muhametshina, R. D., & Muhametshina, A. D. (2026) · Cifra LLC Journals · doi:10.60797/jbg.2026.33.2.1
Distribution of age acceleration
Muhametshina, R. D., & Muhametshina, A. D. (2026) · Cifra LLC Journals · doi:10.60797/jbg.2026.33.2.8
Muhametshina, R. D., & Muhametshina, A. D. (2026) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.60797/jbg.2026.33.2
Muhametshina, R. D., & Muhametshina, A. D. (2026) · Cifra LLC Journals · doi:10.60797/jbg.2026.33.2.6
Mean absolute error stratified by age quartiles
Muhametshina, R. D., & Muhametshina, A. D. (2026) · Cifra LLC Journals · doi:10.60797/jbg.2026.33.2.4
Sex-specific distribution of age acceleration
Muhametshina, R. D., & Muhametshina, A. D. (2026) · Cifra LLC Journals · doi:10.60797/jbg.2026.33.2.9
Metabolites associated with prediction accuracy
Muhametshina, R. D., & Muhametshina, A. D. (2026) · Cifra LLC Journals · doi:10.60797/jbg.2026.33.2.7
Comparison with baseline models
Muhametshina, R. D., & Muhametshina, A. D. (2026) · Cifra LLC Journals · doi:10.60797/jbg.2026.33.2.5
Prediction error depending on age
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Overall performance of the age prediction model
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The mechanism of dietary intervention in delaying brain aging and neurodegenerative diseases
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Metadata
- Authors
- Robinson, O., Chadeau Hyam, M., Karaman, I., Climaco Pinto, R., Ala-Korpela, M., Handakas, E., Fiorito, G., Gao, H., Heard, A., Jarvelin, M., Lewis, M., Pazoki, R., Polidoro, S., Tzoulaki, I., Wielscher, M., Elliott, P., & Vineis, P.
- Affiliations
- MRC Centre for Environment and Health (GB)
- MRC Centre for Environment and Health Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK
- Computational Medicine, Faculty of Medicine University of Oulu and Biocenter Oulu Oulu Finland
- University of Sassari (IT)
- Imperial College London (GB)
- Italian institute for Genomic Medicine (IT)
- Corresponding author
- Robinson, Oliver
- ORCID
- Venue
- Aging Cell
- Publisher
- Wiley
- Published
- 2020-05-03
- Volume
- 19
- Issue
- 6
- Pages
- e13149-e13149
- ISSN
- 1474-9718 (ISSN-L)
- 1474-9726
- PMC
- PMC7294785
- OpenAlex
- W3021249782
- Cited by
- 205
- References
- 87
- Citations by year
- 2026: 42 · 2025: 30 · 2024: 49 · 2023: 33 · 2022: 23 · 2021: 18 · 2020: 9
- FWCI
- 6.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Aging Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by)
- UnissResearch (Università degli Studi di Sassari) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Horizon 2020 Framework Programme (633666, 633595, 733206)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Keywords
- biological aging, DNA methylation age, accelerated aging, untargeted metabolomics, lifestyle risk factors, alcohol consumption, depression, obesity, diabetes mellitus, hypertension, socioeconomic status, epigenetic clocks, cross-cohort validation
- MeSH terms
- Adult, Aged, Aging, Female, United Kingdom, Humans, Male, Middle Aged, Cohort Studies, DNA Methylation, Metabolomics, Young Adult, Epigenomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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