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A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals

Journal Article (2019)
Authors

Joris Deelen (Max Planck Institute for Biology of Ageing, Leiden University Medical Center)

Johannes Kettunen (University of Oulu, National Institute for Health and Welfare)

Krista Fischer (University of Tartu)

Ashley van der Spek (Erasmus MC)

Stella Trompet (Leiden University Medical Center)

Andy Boyd (University of Bristol, Faculty of Medicine and Dentistry)

Jonas Zierer (King’s College London, Helmholtz Zentrum München, Novartis Pharma)

EB Van Den Akker (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)

Mika Ala-Korpela (Baker Heart and Diabetes Institute, Monash University, University of Eastern Finland, University of Oulu, University of Bristol, Faculty of Medicine and Dentistry)

G.B. Cavadini (External organisation)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2019 Joris Deelen, Johannes Kettunen, Krista Fischer, Ashley van der Spek, Stella Trompet, Andy Boyd, Jonas Zierer, E.B. van den Akker, Mika Ala-Korpela, More Authors
To reference this document use:
https://doi.org/10.1038/s41467-019-11311-9
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Joris Deelen, Johannes Kettunen, Krista Fischer, Ashley van der Spek, Stella Trompet, Andy Boyd, Jonas Zierer, E.B. van den Akker, Mika Ala-Korpela, More Authors
Research Group
Pattern Recognition and Bioinformatics
Issue number
1
Volume number
10
Pages (from-to)
1-8
DOI:
https://doi.org/10.1038/s41467-019-11311-9
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Abstract

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.