Precision Obesity Medicine: Genetic Prediction of Response to GLP-1/GIP Agonists.

Obesity is a chronic disease resulting from complex interactions between genetic, environmental, and behavioral factors. Genetic influences account for a substantial proportion of obesity susceptibility, with heritability estimates ranging from 40% to 75%. Genome-wide association studies have identified numerous genetic variants associated with body mass index, appetite regulation, energy homeostasis, and metabolic function. Glucagon-like peptide-1 (GLP-1) receptor agonists and dual GIP/GLP-1 receptor agonists have significantly improved the pharmacological management of obesity. Semaglutide and tirzepatide produce clinically meaningful weight reduction and improvement in metabolic outcomes. However, treatment response varies considerably among individuals, and the factors underlying this variability are not fully understood. Genetic Risk Scores (GRS), which combine the effects of multiple obesity-related genetic variants, have emerged as potential tools for predicting disease risk and therapeutic response. Their role in predicting response to anti-obesity pharmacotherapy remains largely unexplored. This prospective observational cohort study aims to evaluate whether a GRS and selected obesity-related single nucleotide polymorphisms (SNPs) can predict weight loss response to semaglutide or tirzepatide in adults with obesity. Eligible participants initiating treatment with semaglutide or tirzepatide will be consecutively recruited and followed for six months. At baseline, demographic, clinical, anthropometric, biochemical, and genetic data will be collected. Approximately 18 obesity-related SNPs, including variants in genes involved in appetite regulation, energy balance, and glucose metabolism, will be analyzed. An additive Genetic Risk Score will be calculated based on the cumulative number of risk alleles. The primary outcome will be percentage weight loss at six months. Participants will subsequently be classified according to treatment response. Associations between genetic markers, GRS categories, and treatment outcomes will be evaluated using multivariable regression models. Receiver operating characteristic (ROC) curve analyses will be performed to assess the predictive performance of genetic and combined genetic-clinical models. The study is expected to identify genetic predictors of response to GLP-1/GIP receptor agonist therapy and contribute to the development of precision medicine approaches in obesity management. Improved prediction of treatment response may facilitate individualized therapeutic selection, optimize clinical outcomes, and reduce the trial-and-error approach currently used in obesity pharmacotherapy.