Estimated small dense low-density lipoprotein cholesterol and hyperuricemia in diabetic patients

TL;DR

This study shows that higher estimated small dense LDL cholesterol is linked to increased risk of high uric acid levels in people with diabetes.

Contribution

The study identifies a non-linear relationship between estimated sdLDL-C and hyperuricemia in diabetic patients.

Findings

Diabetic patients with hyperuricemia had significantly higher E-sdLDL-C levels.

Each standard deviation increase in E-sdLDL-C was associated with 39% higher odds of hyperuricemia.

A non-linear relationship was confirmed with an inflection point at 25.83 μmol/L.

Abstract

Small dense low-density lipoprotein cholesterol (sdLDL-C) is a key driver of atherosclerotic cardiovascular disease risk. This study aims to investigate the relationship between estimated sdLDL-C (E-sdLDL-C) and hyperuricemia in diabetic populations.

This study analyzed 3572 diabetic participants from the NHANES dataset and an independent validation cohort of 248 Chinese subjects from the Affiliated Wujin Hospital of Jiangsu University. E-sdLDL-C was derived from basic lipid profile parameters. Hyperuricemia was determined by serum uric acid ≥420 μmol/L for men and ≥360 μmol/L for women. The relationship between E-sdLDL-C and hyperuricemia was examined using logistic regression, with restricted cubic splines applied to explore non-linear associations.

Diabetic patients with hyperuricemia had significantly higher E-sdLDL-C levels (P<0.001). Each standard deviation increase in E-sdLDL-C was associated with 39% higher odds of hyperuricemia (OR = 1.39, 95% CI: 1.28-1.51, P<0.001). Quartile analysis showed a dose-response relationship, with progressively higher odds ratios across increasing quartiles of E-sdLDL-C. Restricted cubic spline modeling identified a non-linear relationship, with an inflection point at 25.83 μmol/L. The robustness of these associations was confirmed through external validation in an independent Chinese diabetic cohort.

E-sdLDL-C might serve as a practical biomarker for identifying diabetic patients at increased hyperuricemia risk.

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