Abstract 15461: Proteomic Profiling Identifies Metabolic and Extracellular Matrix Markers That Predict MACE in Individuals With Type 2 Diabetes: An EXSCEL Substudy

Background: Patients (pts) with type 2 diabetes mellitus (T2DM) are at greater risk for major adverse cardiovascular events (MACE; e.g. stroke, myocardial infarction, and cardiovascular death). Given marked heterogeneity of risk, better multi-marker models are required to improve risk prediction.

Methods: We evaluated 5473 pts with T2DM in the EXSCEL trial of exenatide, a GLP1-RA, vs. placebo by profiling ~5000 proteins at baseline and 12 months. Multivariable Cox proportional hazards models (adjusted for age, race, sex, study treatment, BMI, eGFR, BP, HbA1c, CHF and CV disease) were used to test for association between individual proteins with MACE. Pathway analysis using gene set enrichment (GSEA) was performed and LASSO, elastic net, random forest, and gradient boosted decision tree models were used to predict MACE on a held-out test set (N=1094). Protein importance scores and coefficients were analyzed to determine proteins predicting MACE. Linear mixed models (LMM) were performed to assess treatment effect on protein levels.

Results: Over a median of 3.2 (IQR 2.2-4.4) years, 813 (6.72%) pts experienced MACE. A total of 1278 individual proteins were associated with MACE (FDR adjusted p <0.05). GSEA identified 8 pathways enriched in these proteins including fatty acid metabolism (p=0.01) and cardiac muscle contraction (p=0.04). LASSO outperformed other models in discrimination for MACE (AUC = 0.75) and prioritized tetranectin (an adipocyte secretory protein associated with insulin resistance and fibrinolysis) and fibulin-1 (a regulator of extracellular matrix) as highly predictive of MACE. Both proteins were highly associated with MACE in Cox proportional hazards models (tetranectin: FDR adjusted p=8.86e-26, HR = 0.11 (95%CI 0.07-0.16), fibulin-1: FDR adjusted p=6.72e-14, HR = 6.49 (95%CI 4.13- 10.19). LMM showed treatment modification in tetranectin levels (p=0.0018) with exenatide.

Conclusions: Leveraging a large trial of a GLP1-RA in pts with T2DM, we identified proteomic models highly discriminative of MACE. Models using proteins involved in molecular pathways of fatty acid metabolism and cardiac contraction may have clinical utility in identifying higher risk pts and for understanding beneficial molecular mechanisms of GLP1-RA.