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

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  • 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
  • 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 (2019)
Number of pages8
JournalNature Communications
DateAccepted/In press - 8 Jul 2019
DatePublished (current) - 20 Aug 2019


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 5,512 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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