Serum Orotidine: A Novel Biomarker of Increased CVD Risk in Type 2 Diabetes Discovered Through Metabolomics Studies

Abstract

Objective: To identify novel biomarkers of cardiovascular disease (CVD) risk in type 2 diabetes (T2D) via a hypothesis-free global metabolomics study, while taking into account renal function, an important confounder often overlooked in previous metabolomics studies of CVD.

Research design and methods: We conducted a global serum metabolomics analysis using the Metabolon platform in a discovery set from the Joslin Kidney Study having a nested case-control design comprising 409 individuals with T2D. Logistic regression was applied to evaluate the association between incident CVD events and each of the 671 metabolites detected by the Metabolon platform, before and after adjustment for renal function and other CVD risk factors. Significant metabolites were followed up with absolute quantification assays in a validation set from the Joslin Heart Study including 599 individuals with T2D with and without clinical evidence of significant coronary heart disease (CHD).

Results: In the discovery set, serum orotidine and 2-piperidinone were significantly associated with increased odds of incident CVD after adjustment for glomerular filtration rate (GFR) (odds ratio [OR] per SD increment 1.94 [95% CI 1.39-2.72], P = 0.0001, and 1.62 [1.26-2.08], P = 0.0001, respectively). Orotidine was also associated with increased odds of CHD in the validation set (OR 1.39 [1.11-1.75]), while 2-piperidinone did not replicate. Furthermore, orotidine, being inversely associated with GFR, mediated 60% of the effects of declining renal function on CVD risk. Addition of orotidine to established clinical predictors improved (P < 0.05) C statistics and discrimination indices for CVD risk (ΔAUC 0.053, rIDI 0.48, NRI 0.42) compared with the clinical predictors alone.

Conclusions: Through a robust metabolomics approach, with independent validation, we have discovered serum orotidine as a novel biomarker of increased odds of CVD in T2D, independent of renal function. Additionally, orotidine may be a biological mediator of the increased CVD risk associated with poor kidney function and may help improve CVD risk prediction in T2D.

Figure 1

Global metabolomics screen for CVD risk in T2D in the discovery set. Volcano plots showing results of global metabolomics study using conditional logistic regression models to test effects of 1-SD increases in each metabolite on incident CVD risk. A: Model conditioned on the matching strata (sex, baseline SCre-eGFR, sample storage time, and HbA_1c) (model 1). B: Model 1 plus SCre-eGFR adjustments (model 2). C: Model 1 plus SCys-eGFR adjustments (model 3). D: Forest plot showing effects of top four metabolites on CVD. ORs and 95% CIs presented for effects of each of the four metabolites on CVD risk in various models. In model 4, the metabolic markers include baseline BMI, HbA_1c, ACR, HDL-c, and triglycerides. Medications include diuretics, nitrates, β-blockers, calcium channel antagonists, statins, ACE inhibitors, and angiotensin receptor blockers. Other covariates include baseline CVD, race, and smoking status. A Bonferroni cutoff of P < 0.01 for four tests was used to determine significance for model 4. meds, medications.

Figure 2

A: Correlation between orotidine and SCys-eGFR in the discovery set. Scatter plots and R² coefficient of variation presented for orotidine and SCys-eGFR correlations among CVD case (red) and control (blue) subjects. B: Mediation analysis for estimating how much of the effect of SCys-eGFR on CVD is mediated by orotidine in the discovery set.

Figure 3

ROC curves for prediction of incident CVD risk in discovery set. Models tested include orotidine only, clinical predictors only, and both combined (red). Clinical predictors include the American College of Cardiology/American Heart Association atherosclerotic CVD risk score, BMI, SCys-eGFR, and prior CVD history. The AUC (C statistics) are presented.