Correlation Between Abdominal Fat Distribution and Serum Uric Acid in Patients Recently Diagnosed with Type 2 Diabetes

Objective: To investigated the link between the distribution of abdominal fat and the concentration of serum uric acid (SUA) in individuals recently diagnosed with type 2 diabetes.Methods: Studied 364 individuals had been diagnosed with type 2 diabetes within one month, and evaluated factors such as the distribution of fat in the abdomen, indicators related to glucose and lipid metabolism. The participants' SUA concentrations were divided into a normal control group (CG) and a hyperuricemia group (HG).Results: The HG group had elevated abdominal subcutaneous fat area (SFA), visceral fat content (VFA), body mass index (BMI), fasting blood glucose (FBG), 2-hour postprandial blood glucose (PBG), glycosylated albumin (GA), serum creatinine (SCr), triacylglycerol (TG), and lower values in glomerular filtration rate (eGFR), high-density lipoprotein cholesterol (HDL-C) when compared to the CG group (P < 0.05). Among the obese individuals, the hyperuricemia subgroup exhibited higher measurements in waistline, hipline, VFA, SFA, BMI, PBG, SCr, TG, and lower HDL-C (P < 0.05) compared to the subgroup with normal uric acid levels. In the non-obese group, the hyperuricemia subgroup showed higher VFA, SCr, and FBG levels, and lower HDL-C (P < 0.05). There was a positive correlation between VFA and serum uric acid (SUA) levels (r = 0.329, P < 0.0001). Logistic regression analysis indicated a 24% increased risk of hyperuricemia with every 10cm2 increase in abdominal VFA. Generate the Receiver Operating Characteristic (ROC) curve analysis revealed that VFA was the most effective predictor of hyperuricemia and insulin resistance (P < 0.05).Conclusion: Newly diagnosed type 2 diabetes patients exhibit a strong correlation between abdominal visceral fat and SUA concentration, the former is identified as an autonomous risk factor for hyperuricemia and an effective indicator for assessing the presence of hyperuricemia and predicting insulin resistance.