Measuring Biological Age via Metabonomics: The Metabolic Age Score
Abstract
Chronological age is one of the most important risk factors for adverse clinical outcome. Still, two individuals at the same chronological age could have different biological aging states, leading to different individual risk profiles. Capturing this individual variance could constitute an even more powerful predictor enhancing prediction in age-related morbidity. Applying a nonlinear regression technique, we constructed a metabonomic measurement for biological age, the metabolic age score, based on urine data measured via (1)H NMR spectroscopy. We validated the score in two large independent population-based samples by revealing its significant associations with chronological age and age-related clinical phenotypes as well as its independent predictive value for survival over approximately 13 years of follow-up. Furthermore, the metabolic age score was prognostic for weight loss in a sample of individuals who underwent bariatric surgery. We conclude that the metabolic age score is an informative measurement of biological age with possible applications in personalized medicine.
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Metabolomic age acceleration correlated with triglyceride levels, obesity, heavy drinking, diabetes mellitus, depressive symptoms, depression, anxiety and post-traumatic stress disorder.
“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 [121]
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 modest.
“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 [121]
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Self-citation
- Authors
- Hertel, J., Friedrich, N., Wittfeld, K., Pietzner, M., Budde, K., Van der Auwera, S., Lohmann, T., Teumer, A., Völzke, H., Nauck, M., & Grabe, H. J.
- Affiliations
- Universitätsmedizin Greifswald (DE)
- German Center for Neurodegenerative Diseases (DE)
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, 17475 Greifswald, Germany
- Department of Medicine, Municipal Hospital of Dresden-Neustadt, 01129 Dresden, Germany
- Corresponding author
- Hertel, Johannes
- ORCID
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-12-23
- Volume
- 15
- Issue
- 2
- Pages
- 400-410
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- OpenAlex
- W2318710952
- Cited by
- 181
- References
- 41
- Citations by year
- 2026: 28 · 2025: 22 · 2024: 34 · 2023: 14 · 2022: 18 · 2021: 22 · 2020: 15 · 2019: 13 · 2018: 3 · 2017: 7 · 2016: 3 · 2015: 2
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Proteome Research (publishedVersion)
- PubMed (publishedVersion)
- DZNE Pub (submittedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (01ZZ0103, 01ZZ0403, 01ZZ9603, 03IS2061A)
- Sozialministerium des Landes Mecklenburg-Vorpommern
- Bundesministerium für kulturelle Angelegenheiten
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- biological age, metabolomics, 1H NMR, urinary metabolites, chronological age, age-related morbidity, survival prediction, weight loss, bariatric surgery, personalized medicine, nonlinear regression
- MeSH terms
- Adult, Age Factors, Aged, Aging, Analysis of Variance, Female, Humans, Male, Middle Aged, Magnetic Resonance Spectroscopy, Regression Analysis, Urine, Kaplan-Meier Estimate, Metabolomics, Metabolome, Young Adult
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