A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals
Open accessAbstract
Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18–109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex ( C -statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality ( C -statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.
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A large-scale biobank study used 1H-NMR of blood plasma to identify metabolites predictive of mortality risk in an Estonian and Finnish cohort.
“A large-scale biobank study used 1H-NMR of blood plasma to identify metabolites predictive of mortality risk in an Estonian and Finnish cohort117,118.”
From Measuring biological age using omics data (2022) · cites this paper as [118]
The authors identified well-studied metabolites, such as albumin, very low-density lipoprotein particles and amino acids, that were associated with mortality due to multiple causes.
“The authors identified well-studied metabolites, such as albumin, very low-density lipoprotein particles and amino acids, that were associated with mortality due to multiple causes.”
From Measuring biological age using omics data (2022) · cites this paper as [118]
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 [118]
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- Authors
- Deelen, J., Kettunen, J., Fischer, K., van der Spek, A., Trompet, S., Kastenmüller, G., Boyd, A., Zierer, J., van den Akker, E. B., Ala-Korpela, M., Amin, N., Demirkan, A., Ghanbari, M., van Heemst, D., Ikram, M. A., van Klinken, J. B., Mooijaart, S. P., Peters, A., Salomaa, V., Sattar, N., Spector, T. D., Tiemeier, H., Verhoeven, A., Waldenberger, M., Würtz, P., Davey Smith, G., Metspalu, A., Perola, M., Menni, C., Geleijnse, J. M., Drenos, F., Beekman, M., Jukema, J. W., van Duijn, C. M., & Slagboom, P. E.
- Affiliations
- Leiden University Medical Center (NL)
- Finnish Institute for Health and Welfare (FI)
- University of Tartu (EE)
- St Thomas' Hospital (GB)
- University of Bristol (GB)
- Novartis (Switzerland) (CH)
- Erasmus MC (NL)
- Mashhad University of Medical Sciences (IR)
- Helmholtz Munich (DE)
- University of Glasgow (GB)
- University of Helsinki (FI)
- Wageningen University & Research (NL)
- Corresponding author
- Deelen, Joris
- Slagboom, P. Eline
- ORCID
- Deelen, Joris
- Kettunen, Johannes
- Fischer, Krista
- Trompet, Stella
- Kastenmüller, Gabi
- Boyd, Andy
- Zierer, Jonas
- van den Akker, Erik B.
- Ala-Korpela, Mika
- Amin, Najaf
- Ghanbari, Mohsen
- Ikram, M. Arfan
- van Klinken, Jan Bert
- Mooijaart, Simon P.
- Peters, Annette
- Salomaa, Veikko
- Sattar, Naveed
- Spector, Tim D.
- Tiemeier, Henning
- Verhoeven, Aswin
- Würtz, Peter
- Davey Smith, George
- Metspalu, Andres
- Perola, Markus
- Menni, Cristina
- Geleijnse, Johanna M.
- Drenos, Fotios
- Beekman, Marian
- Jukema, J. Wouter
- Slagboom, P. Eline
- Venue
- Nature Communications
- Published
- 2019-08-20
- Volume
- 10
- Issue
- 1
- Pages
- 3346-3346
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6702196
- OpenAlex
- W2957854144
- Cited by
- 362
- References
- 32
- Citations by year
- 2026: 47 · 2025: 45 · 2024: 68 · 2023: 50 · 2022: 36 · 2021: 77 · 2020: 31 · 2019: 8
- FWCI
- 13.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
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- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Leiden Repository (Leiden University) (submittedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by)
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- Research Portal (King's College London) (submittedVersion, cc-by)
- Site cant be reached (submittedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- RePub (Erasmus University Rotterdam) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
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- WOS
- Funders
- Alexander von Humboldt-Stiftung
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- all-cause mortality, circulating metabolites, metabolic biomarkers, mortality prediction, metabolomics, surrogate endpoints, risk prediction model, sex differences
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Female, Follow-Up Studies, Humans, Male, Middle Aged, Mortality, Prognosis, Risk Factors, Biomarkers, Survival Analysis, Risk Assessment, Metabolomics, Metabolome, Young Adult
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