Abstract 13914: Additional ASCVD Risk Factors for Patients With Diabetes: FIB-4 Score and NLR Are Higher in Adults With Diabetes and Current Risk Enhancers vs No Risk Enhancers in Real-World Evidence

Introduction:Obesity, chronic inflammation, and elevated levels of atherogenic lipids have been associated with increased risk of ASCVD events. Current AHA/ACC guidelines identify existing risk enhancers for ASCVD including persistently elevated LDL-C or triglycerides, chronic kidney disease, chronic inflammatory conditions, and elevated inflammatory markers.Hypothesis:We hypothesized that in a population-level study of adults with diabetes mellitus (DM) without a history of prior ASCVD events, the average body mass index (BMI), hepatic fibrosis score (FIB-4), and neutrophil to lymphocyte ratio (NLR) are higher in the cohort of patients with other, AHA-identified ASCVD risk enhancers.Methods:We performed a hypothesis-driven secondary analysis using a cross-sectional dataset of EHR data from 7 health systems participating in PaTH, a Partner Network in PCORnet. Adults over 40 with DM and without ASCVD were included if they had LDL-c assessed in the most recent year of available data and had >6 months of pre-index data available. We describe cohort-level averages and standard deviations of the most recent lab values and clinical measurements for cohorts with and without AHA/ACC defined ASCVD risk enhancers.Results:We identified 50,749 patients in our baseline risk cohort and 67,593 patients in our enhanced risk cohort across the 7 sites. The enhanced risk cohort had a higher mean (SE) BMI 34.1 (0.28) vs 33.7 (0.29) kg/m2, FIB-4 score 1.5 (0.01) vs 1.4 (0.01) and NLR 62.5 (0.24) vs 61.2 (0.23) compared to the baseline risk cohort. NLR and FIB-4 demonstrated consistently higher averages in the enhanced-risk cohorts across sites.Conclusions:FIB-4 score and NLR both have higher population averages in cohorts with other markers of enhanced ASCVD risk in an adult diabetic patient population. The role of these markers in independently predicting ASCVD risk should be further explored with adjusted analyses in longitudinal datasets.