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 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.

Automated summary

Risk stratification is crucial for early identification and prevention of high-risk individuals and diseases. This study explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles in predicting the risk of 24 common diseases. The results showed that metabolomic profiles were associated with incident rates in all investigated diseases except breast cancer, and the combination of age, sex, and metabolomic profiles outperformed established predictors for 10-year outcome prediction in 15 endpoints. In addition, metabolomic profiles provided predictive information for common diseases even when comprehensive clinical variables were considered. Decision curve analysis demonstrated the clinical utility of predictive improvements at various decision thresholds.