1799-LB: Identification of Proteomics Signature for Vascular Complications in Type 2 Diabetes

Introduction: Robust predictive measures for Type 2 diabetes (T2D) vascular complications to facilitate early intervention strategies are needed. Methods: Fasting plasma from 363 Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial participants (mean±SD) age: 61.3±6.4 yrs, 5.6±5.4 yrs T2D, HbA1c: 6.7±1.2% (49.7±13.4 mmol/mol), were collected at baseline and stored (-80°C) until quantitative proteomics analysis of tryptic peptides. Protein signatures associated with future micro- or macrovascular complications (100 and 54 subjects, respectively) were identified using a random forest approach, adjusted for treatment allocation. Exhaustive search using logistic regression was then carried out to optimise models. Pathway analysis was carried out using the NIH functional annotation tool, DAVID Bioinformatics. Resources. Results: Fifty targets were selected: 14 were associated with microvascular, 13 with macrovascular complications and 23 were common for both. Pathway analysis revealed six main pathways: cholesterol metabolism, platelet activation, complement and coagulation, focal cell adhesion, actin cytoskeleton and hypertrophic cardiomyopathy. In logistic regression, the optimal model for prediction of microvascular complications with overall p