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Genetic polymorphisms contribute to inter-individual variability in cardiometabolic risk and quality-of-life outcomes, yet their clinical relevance often remains unclear due to population heterogeneity and reliance on single-variant analyses. Integrative approaches combining genetic and phenotypic data may improve the characterization of complex disease profiles, particularly in orthopedic populations burdened by cardiometabolic comorbidities. This study included 289 patients scheduled for orthopedic surgery. Polymorphisms in the vitamin D receptor (VDR; ApaI, FokI, BsmI), catechol-O-methyltransferase (COMT rs4680), and opioid receptor mu 1 (OPRM1 rs510769) genes were genotyped. Clinical, anthropometric, hematological, biochemical, and quality-of-life (SF-36) data were collected. Unsupervised k-means clustering was applied to standardized phenotypic variables to identify homogeneous patient subgroups. Inter-cluster differences were assessed using analysis of variance and chi-squared tests. Three distinct patient clusters were identified, characterized by specific combinations of cardiometabolic, inflammatory, and quality-of-life features. VDR polymorphisms were differentially distributed across clusters associated with differences in body mass index, hypertension prevalence, and inflammatory status. COMT and OPRM1 variants were primarily associated with variability in physical and mental quality-of-life dimensions. The cluster-based approach revealed multidimensional clinical heterogeneity not captured by conventional univariate analyses. Integrating genetic polymorphisms with clinical and quality-of-life data may support the identification and interpretation of distinct cardiometabolic profiles among orthopedic patients. Cluster-based stratification represents a valuable framework for capturing complex patient heterogeneity and supports future precision-oriented research in orthopedic and cardiometabolic populations.