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

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 d...

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Published in:Nature communications Vol. 10; no. 1; pp. 3346 - 8
Main Authors: Deelen, Joris, Kettunen, Johannes, Fischer, Krista, van der Spek, Ashley, Trompet, Stella, Kastenmüller, Gabi, Boyd, Andy, Zierer, Jonas, van den Akker, Erik B., Ala-Korpela, Mika, Amin, Najaf, Demirkan, Ayse, Ghanbari, Mohsen, van Heemst, Diana, Ikram, M. Arfan, van Klinken, Jan Bert, Mooijaart, Simon P., Peters, Annette, Salomaa, Veikko, Sattar, Naveed, Spector, Tim D., Tiemeier, Henning, Verhoeven, Aswin, Waldenberger, Melanie, Würtz, Peter, Davey Smith, George, Metspalu, Andres, Perola, Markus, Menni, Cristina, Geleijnse, Johanna M., Drenos, Fotios, Beekman, Marian, Jukema, J. Wouter, van Duijn, Cornelia M., Slagboom, P. Eline
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 20.08.2019
Nature Publishing Group
Nature Portfolio
Subjects:
101/6
631/1647/320
692/53/2423
Adolescent
Adult
Aged
Aged, 80 and over
Biomarkers
Biomarkers - blood
Female
Follow-Up Studies
Health risk assessment
Health risks
Humanities and Social Sciences
Humans
Male
Men
Metabolism
Metabolome
Metabolomics
Metabolomics - methods
Middle Aged
Mortality
Mortality risk
multidisciplinary
Observational studies
Predictions
Prognosis
Risk analysis
Risk Assessment
Risk Factors
Science
Science (multidisciplinary)
Survival Analysis
Young Adult
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary: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. Biomarkers that predict mortality are of interest for clinical as well as research applications. Here, the authors analyze metabolomics data from 44,168 individuals and identify key metabolites independently associated with all-cause mortality risk.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-019-11311-9