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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.

Original publication

DOI

10.1038/s41467-019-11311-9

Type

Journal article

Journal

Nature communications

Publication Date

08/2019

Volume

10

Addresses

Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Joris.Deelen@age.mpg.de.

Keywords

Humans, Prognosis, Mortality, Risk Assessment, Risk Factors, Survival Analysis, Follow-Up Studies, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Metabolomics, Metabolome, Young Adult, Biomarkers