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

Research output: Contribution to journalArticle

Joris Deelen, Johannes Kettunen, Krista Fischer, Ashley van der Spek, Stella Trompet, Gabi Kastenmüller, Andy Boyd, Jonas Zierer, Erik B. van den Akker, Mika Ala-Korpela, Najaf Amin, Ayse Demirkan, Mohsen Ghanbari, Diana van Heemst, M. Arfan Ikram, Jan Bert van Klinken, Simon P. Mooijaart, Annette Peters, Veikko Salomaa, Naveed Sattar & 15 others Tim D. Spector, Henning Tiemeier, Aswin Verhoeven, Melanie Waldenberger, Peter Würtz, George Davey Smith, Andres Metspalu, Markus Perola, Cristina Menni, Johanna M. Geleijnse, Fotios Drenos, Marian Beekman, J. Wouter Jukema, Cornelia M. van Duijn, P. Eline Slagboom

Original languageEnglish
Article number3346
JournalNature Communications
Volume10
Issue number1
DOIs
Publication statusPublished - 1 Dec 2019

King's Authors

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.

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