Cardiometabolic & kidneyCohort study
Combining serum metabolomic profiles with traditional risk factors improves 10-year cardiovascular risk prediction in people with type 2 diabetes
- Prospective cohort study
Authors' conclusion
Metabolomics data might improve predictive performance of current CVD risk scores based on traditional risk factors in people with type 2 diabetes.
Study context
- Population
- People with T2DM (Edinburgh Type 2 Diabetes Study)1,021 analysed
- Participants
- 1,066 people
- Exposure
- Reference model plus MRS vs reference model alone
- Outcome
- Incident CVD; predictive performance of a metabolites-based risk score (MRS) over QRISK3
- Follow-up
- 10 years
Main finding
As reported in the abstract
Prediction of 10-year CV risk · median follow-up 10.6 years
Reference model + MRS
0.728 (95%CI 0.700, 0.757)
c-statistic
Reference model
0.709 (95%CI 0.679, 0.739)
c-statistic
Each group vs reference modelHazard ratio (95% CI)
MRSassociation of MRS with 10-year CV risk, adjusted for traditional risk factors
2.67(1.96–3.64)
- Reference model + MRS
- net reclassification index 0.362 (95% CI 0.179–0.506)
- Reference model + MRS
- integrated discrimination index 0.041 (95% CI 0.020–0.071)
- Incident cardiovascular disease (CVD)
- 255 (25.0%)
- Metabolites selected for MRS
- 12
Observational data: associations, not effects.
Original publication
Combining serum metabolomic profiles with traditional risk factors improves 10-year cardiovascular risk prediction in people with type 2 diabetes. Eur J Prev Cardiol. Published 12 May 2023. doi:10.1093/eurjpc/zwad160
Summarised from the published abstract; every number is checked against it