Metabolomic profiles predict individual multidisease outcomes

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 on...

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Vydané v:Nature medicine Ročník 28; číslo 11; s. 2309 - 2320
Hlavní autori: Buergel, Thore, Steinfeldt, Jakob, Ruyoga, Greg, Pietzner, Maik, Bizzarri, Daniele, Vojinovic, Dina, Upmeier zu Belzen, Julius, Loock, Lukas, Kittner, Paul, Christmann, Lara, Hollmann, Noah, Strangalies, Henrik, Braunger, Jana M., Wild, Benjamin, Chiesa, Scott T., Spranger, Joachim, Klostermann, Fabian, van den Akker, Erik B., Trompet, Stella, Mooijaart, Simon P., Sattar, Naveed, Jukema, J. Wouter, Lavrijssen, Birgit, Kavousi, Maryam, Ghanbari, Mohsen, Ikram, Mohammad A., Slagboom, Eline, Kivimaki, Mika, Langenberg, Claudia, Deanfield, John, Eils, Roland, Landmesser, Ulf
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Nature Publishing Group US 01.11.2022
Nature Publishing Group
Predmet:
631/114/1305
631/45/320
692/308/174
692/53/2423
Biobanks
Biomedical and Life Sciences
Biomedicine
Breast cancer
Breast Neoplasms
Cancer Research
Congestive heart failure
Decision analysis
Dementia disorders
Diabetes mellitus (non-insulin dependent)
Diabetes Mellitus, Type 2
Disease
Failure analysis
Female
Health risks
Heart Failure - metabolism
Humans
Infectious Diseases
Magnetic Resonance Spectroscopy
Metabolic Diseases
Metabolism
Metabolomics
Molecular Medicine
Neural networks
Neurological diseases
Neurosciences
NMR
NMR spectroscopy
Nuclear magnetic resonance
Risk
Spectroscopy
United Kingdom > UK
ISSN:1078-8956, 1546-170X, 1546-170X
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Shrnutí: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. In a study involving more than 100,000 individuals in the UK Biobank, a neural network model trained on metabolomic data can predict disease risk for over 20 conditions and adds predictive information over clinical variables for eight common diseases.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1078-8956
1546-170X
1546-170X
DOI:10.1038/s41591-022-01980-3