Serum uric acid levels improve atherosclerotic cardiovascular disease pooled cohort equations model

Background: While serum uric acid (SUA) is associated with adverse cardiovascular outcome, it has been excluded from cardiovascular risk models. We evaluated the impact of SUA on the accuracy of atherosclerotic cardiovascular disease (ASCVD) pooled cohort equations (PCE) model. Methods: We evaluated 15,095 asymptomatic self-referred adults aged 40-79 years who participated in a screening program. All subjects were free of cardiovascular disease at baseline, had their baseline SUA documented and their ASCVD risk score calculated. SUA levels were expressed as a continuous and dichotomous variable (categorized into sex-specific tertiles, with the upper tertile defined as high SUA). The primary end point was the composite of death, acute coronary syndrome and stroke, after excluding subjects diagnosed with metastatic cancer during follow up. Results: Mean age of study population was 50 ± 8 years and 72% were men. During median follow up of 7.4 years 1,293 (9%) subjects developed the study end point. ASCVD risk and both continuous and high SUA were independently associated with the study end point in Cox regression model (p2); SUA remained independently associated with the study end-point among normal-weight subjects (HR 1.4, 95% CI 1.1-1.7) but not among overweight individuals (p for interaction = .004). Addition of SUA to the PCE model in normal-weight sub-group (N=6,191) resulted in a significant 20% and 9% improvement in the model performance when SUA was incorporated as a continuous or dichotomous variable, respectively (pConclusions: SUA significantly improves classification accuracy of PCE model. This effect is especially pronounced among normal-weight subjects.