The serum uric acid to serum creatinine ratio is an independent risk factor for type 2 diabetes in community-dwelling elderly: A longitudinal analysis

Background and aim: Elevated serum uric acid (SUA) levels are an important marker of metabolic disorders. However, SUA levels largely depend on renal clearance function. This study aims to investigate the relationship between renal function-normalized SUA [SUA to serum creatinine (SCr) ratio] and the risk of developing type 2 diabetes in a community-dwelling elderly population. Methods and results: A retrospective cohort study was conducted on elderly, non-diabetic individuals from the Kunshan community in China, who participated in annual health check-ups between January 2018 and December 2023. The relationship between the baseline SUA/SCr ratio and the risk of type 2 diabetes was examined using Cox regression models, restricted cubic splines (RCS), and subgroup analyses. After a median follow-up of 3.88 years, 778 cases of type 2 diabetes were identified among 7671 elderly non-diabetic individuals. Adjusting for confounding variables, the baseline SUA/SCr ratio was significantly linked to type 2 diabetes risk (P < 0.001). Individuals in the highest SUA/SCr ratio quartile had a 1.323 times higher risk of developing type 2 diabetes compared to those in the lowest quartile (HR = 1.323, 95% CI 1.053-1.661, P = 0.016). RCS analysis further confirmed this positive association. Additionally, subgroup analyses suggested that this relationship was particularly pronounced in female individuals. Conclusions: In Chinese elderly community residents, the baseline SUA/SCr ratio is linked to the risk of type 2 diabetes. Monitoring this ratio could aid in predicting and assessing the risk of type 2 diabetes. (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.