Metabolomic profiles predict individual multidisease outcomes

Author:

Buergel ThoreORCID,Steinfeldt Jakob,Ruyoga Greg,Pietzner Maik,Bizzarri DanieleORCID,Vojinovic Dina,Upmeier zu Belzen JuliusORCID,Loock Lukas,Kittner Paul,Christmann Lara,Hollmann NoahORCID,Strangalies Henrik,Braunger Jana M.,Wild BenjaminORCID,Chiesa Scott T.ORCID,Spranger JoachimORCID,Klostermann Fabian,van den Akker Erik B.ORCID,Trompet StellaORCID,Mooijaart Simon P.,Sattar NaveedORCID,Jukema J. WouterORCID,Lavrijssen Birgit,Kavousi Maryam,Ghanbari MohsenORCID,Ikram Mohammad A.ORCID,Slagboom Eline,Kivimaki MikaORCID,Langenberg Claudia,Deanfield John,Eils RolandORCID,Landmesser UlfORCID

Abstract

AbstractRisk 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.

Funder

Bundesministerium für Bildung und Forschung

Wellcome Trust

RCUK | Medical Research Council

Publisher

Springer Science and Business Media LLC

Subject

General Biochemistry, Genetics and Molecular Biology,General Medicine

Reference78 articles.

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