Abstract
Objective
Cardiovascular disease remains the leading cause of mortality worldwide, with significant gender disparities in risk factor profiles and outcomes. Understanding gender-specific biomarker interactions is crucial for precision prevention strategies. This cross-sectional study investigated gender differences in cardiometabolic biomarkers and their interrelationships, with particular focus on uric acid as a potential integrative marker of metabolic dysregulation.
Material and Methods
Clinical and laboratory data from 169 patients (82 males, 87 females) were analyzed. Parameters included demographic characteristics, lipid profile [low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride, total cholesterol], hematological markers [hemoglobin (Hb), platelet (PLT), mean platelet volume], uric acid, fasting glucose, creatinine, and modification of diet in renal disease -estimated glomerular filtration rate. Gender comparisons utilized independent t-tests and Mann-Whitney U tests. Correlation analyses and principal component analysis (PCA) identified biomarker clustering patterns.
Results
Males demonstrated significantly higher uric acid (5.72±1.48 vs. 4.38±1.20 mg/dL, p<0.001), Hb (15.0±1.7 vs. 13.1±1.6 g/dL, p<0.001), triglycerides (172.3±90.1 vs. 148.5±85.3 mg/dL, p<0.05), and smoking rates (58.5% vs. 20.7%, p<0.001). Females exhibited higher HDL (49.2±12.0 vs. 39.3±8.8 mg/dL, p<0.001) and PLT counts (276.8±71.5 vs. 233.2±64.8 ×103/µL, p<0.001). Uric acid positively correlated with LDL (r=+0.32, p<0.001), Hb (r=+0.29, p<0.001), and creatinine (r=+0.23, p<0.01), while inversely correlating with HDL (r=-0.25, p<0.01). PCA identified three distinct biomarker clusters: (1) lipid-uric acid- Hb axis, (2) renal-hematologic axis, and (3) inflammatory-thrombotic markers.
Conclusion
Uric acid emerges as an integrative marker linking dyslipidemia, hematologic parameters, and renal function, with significant gender-specific patterns. These findings support sex-tailored approaches to cardiometabolic risk assessment and intervention.
Keywords: Gender differences, cardiometabolic risk, uric acid, lipid profile, biomarker clustering, metabolic syndrome, precision prevention
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