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Related Experiment Video

Updated: Aug 28, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

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Metabolomic profiles predict individual multidisease outcomes.

Thore Buergel1, Jakob Steinfeldt2, Greg Ruyoga1

  • 1Center for Digital Health, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.

Nature Medicine
|September 22, 2022
PubMed
Summary

Nuclear magnetic resonance (NMR) metabolomic profiles can predict the risk of numerous diseases. These profiles, combined with age and sex, equal or surpass traditional predictors for many common conditions.

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Area of Science:

  • Biochemistry
  • Genomics
  • Preventive Medicine

Background:

  • Risk stratification is crucial for early disease identification and prevention.
  • Conventional clinical predictors have limitations in assessing multidisease risk.

Purpose of the Study:

  • To evaluate nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles for multidisease risk prediction.
  • To assess the utility of metabolomic states beyond established clinical predictors for 24 common conditions.

Main Methods:

  • Trained a neural network on metabolomic data from 117,981 UK Biobank participants.
  • Utilized 168 circulating metabolic markers and ~1.4 million person-years of follow-up.
  • Validated the model in four independent cohorts.

Main Results:

  • Metabolomic states associated with incident event rates for most investigated conditions (except breast cancer).
  • Combined metabolomic state with age and sex equaled or outperformed established predictors for 10-year outcome prediction.
  • Metabolomic state provided additional predictive value over clinical variables for type 2 diabetes, dementia, and heart failure.

Conclusions:

  • NMR-derived metabolomic profiles show potential as a multidisease risk assessment tool.
  • Metabolomic profiling offers clinical utility for risk stratification across a spectrum of common diseases.
  • The study highlights both the strengths and limitations of metabolomic data in disease risk prediction.