Association of hyperuricemia with risk of cardiovascular disease according to the number of risk factors within target range

Background and aims: Risk factor modification may decrease the risk of cardiovascular disease (CVD). Whether risk factor modification can mitigate the effect of hyperuricemia on CVD is unclear. This study aimed to investigate the risk of CVD among individuals with hyperuricemia, according to risk factors on target, compared with controls without hyperuricemia. Methods and results: This prospective study included 91,722 participants free of CVD at baseline (2006-2007) of the Kailuan study. Individuals with hyperuricemia were categorized according to the number of seven selected risk factors within the guideline-recommended target range (nonsmoking, physical activity, healthy diet, guideline-recommended levels of body mass index, blood pressure, fasting blood glucose, and total cholesterol). During a median follow-up of 13.00 years, 671 out of 6740 individuals (9.96%) with hyperuricemia and 6301 out of 84,982 control subjects (7.41%) had incident CVD. Compared with control subjects without hyperuricemia, individuals with hyperuricemia who had 4 or 5 to 7 risk factors on target had no significant excess CVD risk, the hazard ratio (HR) (95% confidence internal [CI]) was 0.93 (0.79-1.10) and 0.88 (0.71-1.10), respectively. Among individuals with hyperuricemia, excess CVD risk decreased stepwise for a higher number of risk factors on target, the HR of CVD associated with per additional risk factor within target range was 0.82 (95% CI, 0.77-0.87). Similar results were yielded for CVD subtypes. Conclusions: Among individuals with hyperuricemia, excess CVD risk decreased stepwise for a higher number of risk factors within target. (c) 2024 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.