Genotype Combinations and Genetic Risk Score Analyses of MTHFR, MTRR, and MTR Polymorphisms in Hypothyroidism Susceptibility: A Case–Control Study

Hypothyroidism is a multifactorial endocrine disorder where genetic predisposition plays a significant role. The MTHFR, MTRR, and MTR genes influence thyroid hormone regulation via homocysteine remethylation and DNA methylation. This study examined associations between hypothyroidism and polymorphisms in MTHFR (C677T–rs1801133, A1298C–rs1801131), MTRR (A66G–rs1801394), and MTR (A2756G–rs1805087) genes. Eighty-six patients with hypothyroidism and 87 healthy controls were included. Genotyping was performed using PCR-RFLP. Post hoc analysis confirmed adequate statistical power (95% for MTRR A66G, 84.6% for MTR A2756G). The study adhered to STROBE guidelines. MTHFR polymorphisms showed no significant association when considered individually. However, the MTRR A66G AA genotype was significantly more frequent in patients and conferred a markedly increased disease risk (OR: 4.373; 95% CI: 2.174–8.797; p < 0.001), while the MTR A2756G AG genotype was also more prevalent among patients and associated with higher susceptibility (OR: 2.178; 95% CI: 1.156–4.104; p = 0.008). Genotype combination analysis revealed that CT–AA (OR = 6.898; 95% CI: 1.941–24.516; p = 0.001) and AG–AA (OR = 6.892; 95% CI: 1.494–31.797; p = 0.007) conferred high risk. Certain genotypes correlated with clinical features, including hypercholesterolemia, diabetes, and cardiovascular disease. MTRR A66G and MTR A2756G polymorphisms are associated with hypothyroidism and metabolic comorbidities, both individually and in genotype combinations. These findings underscore the value of multilocus genetic models for understanding thyroid disorders and support the potential role of genetic biomarkers in personalized risk assessment and early diagnosis. GRS analysis demonstrated that each additional risk allele increased hypothyroidism risk (OR = 1.58; 95% CI: 1.18–2.10; p = 0.0018), and the total score showed moderate predictive power (AUC = 0.665; p < 0.001).