Associations of genetic variants in ABCB1, ABCG2, CYP3A4, CYP3A5, and SLCO1B1 with statin-associated muscle symptom (SAMS) in South African populations - Lusiki, Zizo (Department of Pathology, 2025)
Background: Dyslipidaemia, particularly elevated plasma LDL-cholesterol levels, is a key modifiable risk factor for cardiovascular disease (CVD). Statins recognized as LDL-c lowering drugs, have traditionally been the cornerstone in dyslipidaemia treatment and are reported to reduce CVD mortality by 20-30%. However, over the years, the efficacy of statins has been compromised by the emergence of adverse effects, especially statinassociated muscle symptoms (SAMS). Strong evidence links SAMS susceptibility to pharmacogenetic variability of ABCB1, ABCG2, CYP3A4, CYP3A5, and SLCO1B1, particularly in Asian and European populations. However, limited knowledge exists regarding the impact of pharmacogenetics on statin tolerance among South African patients. This study aimed to examine the relationships between genetic variants in the five pharmacogenes mentioned and SAMS in a cohort of dyslipidaemia patients from South Africa undergoing statin treatment. Methods: Employing a retrospective matched case-control (1:2) study design, we assessed a cohort of 332 dyslipidaemia patients from Groote Schuur Hospital, South Africa. Most patients were of Mixed Ancestry. A lipid expert determined SAMS status through adjudication. Genetic variants in five key genes—ABCB1 (rs1045642), ABCG2 (rs2231142, rs2199939), CYP3A4 (rs2740574), CYP3A5 (rs776746, rs10264272, and rs41303343), and SLCO1B1 (rs4149056, rs2306283, and rs4363657)—were chosen for genotyping, using PCR-RFLP, TaqMan genotyping assays, and validated through Sanger sequencing. Statistical analysis, encompassing Chi-Square, Mann-Whitney U (Wilcoxon rank sum), and logistic regression tests, was conducted using STATA v15 and R, while linkage disequilibrium and haplotype analysis were performed using SHEsis online software. Non-genetic and genetic variables were correlated with SAMS status. Results: The median age of patients was 58 years, with 50% being female. Univariate analysis revealed significant associations of SAMS with BMI (p=0.026), waist circumference (p=0.03), and triglyceride levels (p=0.01). The ABCB1 rs1045642 C/T genotype (56.36%, OR=1.86, 95%CI=1.10-3.14, p=0.02) was more prevalent among patients with SAMS compared to those without. Conversely, ABCG2 rs2231142 A/A (6.31%, OR=0.13, 95%CI=0-0.81, p=0.02) and SLCO1B1 rs4149056 C/C (6.76%, OR=0.14, 95%CI=0-0.84, p= 0.03) genotypes were significantly less prevalent in cases than controls. The ABCG2 rs2231142A variant allele occurred more frequently in controls (p=0.03). Multivariate analysis, incorporating genetic and clinical variables through stepwise logistic regression, identified ABCB1 rs1045642 C/T as the sole significant predictor for SAMS in this cohort. Moreover, the SLCO1B1 rs4149056T>C and SLCO1B1 rs4363657T>C polymorphisms exhibited strong linkage disequilibrium—further analysis identified six inferred haplotypes for SLCO1B1. Haplotype 6 (G-T-T) was significantly more prevalent in cases (38.9%, OR=1.44, 95%CI=1.03-2.03, p=0.03) than in controls. Conversely, haplotype 2 (G-C-C) exhibited a significantly higher occurrence in controls (6.8%, OR=0.23, 95%CI=0.08-0.69, p=0.04) than in cases. Conclusion: This study underscores the role of genetic variations in ABCB1, ABCG2, and SLCO1B1 in predisposing South African Mixed Ancestry patients to SAMS. Specifically, ABCB1 rs1045642 C/T genotype emerged as a significant predictor for SAMS in this cohort, indicating its potential role in statin toxicity. Significant differences were observed in the distribution of variant alleles for most SNPs in our cohort compared to global populations, suggesting that universal guidelines may not be universally applicable. This observation was further supported by disparities in inferred haplotypes, underscoring the necessity for comprehensive pharmacogenetic studies within the South African population. These findings expand our knowledge of SAMS in a population with limited pharmacogenomic data, potentially informing personalized statin therapy. Key Words: dyslipidaemia, SAMS, pharmacogenetics, ABCB1, ABCG2, SLCO1B1, South African