Combining serum metabolomic profiles with traditional risk factors improves 10-year cardiovascular risk prediction in people with type 2 diabetes

AIMS: To identify a group of metabolites associated with incident CVD in people with type 2 diabetes and assess its predictive performance over-and-above a current CVD risk score (QRISK3).

METHODS: A panel of 228 serum metabolites was measured at baseline in 1,066 individuals with type 2 diabetes (Edinburgh Type 2 Diabetes Study) who were then followed up for CVD over the subsequent 10 years. We applied 100 repeats of Cox LASSO (least absolute shrinkage and selection operator) to select metabolites with frequency >90% as components for a metabolites-based risk score (MRS). The predictive performance of the MRS was assessed in relation to a reference model which was based on QRISK3 plus prevalent CVD and statin use at baseline.

RESULTS: Of 1,021 available individuals, 255 (25.0%) developed CVD (median follow-up: 10.6 years). Twelve metabolites relating to fluid balance, ketone bodies, amino acids, fatty acids, glycolysis and lipoproteins were selected to construct the MRS which showed positive association with 10-year cardiovascular risk following adjustment for traditional risk factors [HR 2.67 (95%CI 1.96, 3.64)]. C-statistic was 0.709 (95%CI 0.679, 0.739) for the reference model alone, increasing slightly to 0.728 (95%CI 0.700, 0.757) following addition of the MRS. Compared with the reference model, the net reclassification index and integrated discrimination index for the reference model plus the MRS was 0.362 (95%CI 0.179, 0.506) and 0.041 (95%CI 0.020, 0.071), respectively.

CONCLUSIONS: Metabolomics data might improve predictive performance of current CVD risk scores based on traditional risk factors in people with type 2 diabetes. External validation is warranted to assess the generalizability of improved CVD risk prediction using the MRS.