Resumo

Objectives: To integrate comprehensive genetic association data across obesity, insulin resistance, glycaemic traits, lipid metabolism, and cardiovascular disease to identify variants, genes, and biological pathways that may predict weight loss responsiveness to GLP-1RAs and inform personalised metabolic intervention strategies. Introduction: Interindividual variability in weight loss outcomes and cardiometabolic improvements with glucagon-like peptide-1 receptor agonists (GLP-1RAs) poses a significant clinical challenge. Common therapeutic agents such as semaglutide have demonstrated mean weight reductions approaching 15 percent at extended follow-up, yet genetic modifiers of response remain poorly defined. A mechanistically informed genomic framework may enhance prediction of therapeutic efficacy and side effect profiles.  Materials / method: GWAS summary statistics from large cardiometabolic consortia were harmonised to rsIDs with allele alignment, MAF filtering, and LD control. Additive models were applied using inverse-variance weighted regression with heterogeneity assessment (Cochran Q, I²). Genome-wide significance was set at p < 5 × 10^−8 with FDR correction for pathway analyses. Gene mapping and enrichment integrated obesity, glycaemic, lipid, and CVD traits to identify convergent biological mechanisms relevant to GLP-1 response. Results: Multiple loci reached genome-wide significance (p < 5 × 10^−8), including FTO rs9939609 (β ≈ 0.30 kg/m² per allele), MC4R, LEPR, PPARG, and TCF7L2 rs7903146 (OR ≈ 1.35 for T2D). GLP1R variants rs2268641 and rs6923761 showed significant associations with BMI and fasting glucose (p < 1 × 10^−5). Cross-trait enrichment (FDR < 0.05) identified convergence of hypothalamic appetite regulation, incretin signalling, beta-cell function, adipogenesis, lipid metabolism (APOA5), and inflammatory pathways relevant to GLP-1 response variability. Conclusion: This bioinformatics framework integrates genome-wide significant loci and pathway-level signals across obesity, glycaemic, lipid, and cardiovascular traits to define mechanistic predictors of weight regulation. Convergent evidence supports genetic modulation of incretin signalling, insulin sensitivity, adipogenesis, and vascular biology. These data provide a biologically grounded basis for stratifying GLP-1 receptor agonist efficacy, anticipating metabolic variability, and informing personalised weight management strategies in clinical and aesthetic practice.