Metabolomic profiles predict individual multidisease outcomes

Thore Buergel, Jakob Steinfeldt, Greg Ruyoga, Maik Pietzner, Daniele Bizzarri, Dina Vojinovic, Julius Upmeier zu Belzen, Lukas Loock, Paul Kittner, Lara Christmann, Noah Hollmann, Henrik Strangalies, Jana M. Braunger, Benjamin Wild, Scott T. Chiesa, Joachim Spranger, Fabian Klostermann, Erik B. van den Akker, Stella Trompet, Simon P. MooijaartNaveed Sattar, J. Wouter Jukema, Birgit Lavrijssen, Maryam Kavousi, Mohsen Ghanbari, Mohammad A. Ikram, Eline Slagboom, Mika Kivimaki, Claudia Langenberg, John Deanfield, Roland Eils*, Ulf Landmesser

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with -1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.

Original languageEnglish
Pages (from-to)2309-2320
Number of pages32
JournalNature Medicine
Volume28
Issue number11
DOIs
Publication statusE-pub ahead of print - 22 Sep 2022

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© The Author(s) 2022

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