H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort
Authors' conclusion
This study demonstrated the benefits of using precision techniques like H-NMR to better assess the risk of obesity-derived cardiometabolic disease.
Study context
- Population
- Individuals with obesity, subset of the Di@bet.es cohort
- Participants
- 1,387 individuals
- Exposure
- H-NMR metabolomics-based clusters in obesity
- Outcome
- Future development of cardiometabolic disease by H-NMR metabolomics-based cluster
- Follow-up
- 8 years
Methodological detailsH-NMR metabolomics, K-means clustering, machine learning-based prediction models
- H-NMR metabolomics
- K-means clustering
- Machine learning-based prediction models
Main finding
As reported in the abstract
Future cardiometabolic disease by cluster
- Three clusters with no differences in BMI nor waist-to-hip ratio but very different metabolomics profiles
- Atherogenic dyslipidemia cluster: higher risk of developing T2D over 8 years follow-up
- Hypercholesterolemia cluster: higher risk of a cardiovascular event in follow-up
- Metabolically healthy cluster: lower association with future cardiometabolic disease, though some association with future T2D still observed
Observational data: associations, not effects.
Further findings
Prediction of future cardiometabolic disease
- H-NMR metabolomics improved prediction vs models relying on anthropometric measures only
Original publication
H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort. Cardiovasc Diabetol. Published 7 November 2024. doi:10.
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