Obesity reviews : an official journal of the International Association for the Study of ObesityMay 5, 2026

Low Weight Loss with Incretin-Based Medicines: A Systematic Review

GLP-1 Therapies Weekly Brief ↗PubMed ↗DOI ↗OA ↗

Updated Jun 27, 2026

Abstract

Essence

Low weight-loss response to GLP-1 and GIP analogs appears tied to baseline metabolic status, adherence, lifestyle, demographics, genetics, and possibly the gut microbiome.

Evidence

This systematic review synthesizes evidence on predictors of poor weight-loss response to incretin analogs, including higher baseline body weight, BMI, HbA1c, insulin resistance, GLP1R variants, adherence duration, diet, physical activity, male sex, older age, and microbiome composition.

Caveat

The review identifies candidate predictors, but the abstract notes that they still need validation in diverse populations and translation into practical clinical decision tools.

Simplified

The development of GLP-1 and GIP analogs has been a significant breakthrough in type 2 diabetes and obesity pharmacotherapy. However, individual responses to these medications can vary widely. This systematic review examines factors associated with low or no weight loss response to GLP-1 and GIP analogs. Key predictors of poor response include higher baseline body weight, BMI, HbA1c, and insulin resistance. Genetic factors, such as variants in the GLP1R gene, may also influence treatment outcomes. Metabolic health status, particularly glycemic control and insulin sensitivity, plays a crucial role in determining weight loss efficacy. Adherence to medication regimens is strongly associated with treatment success, with each additional month of treatment linked to greater weight loss. Lifestyle factors, including dietary habits and physical activity, can modulate responses to these medications. Emerging evidence suggests that gut microbiome composition may mediate weight loss outcomes. Demographic factors such as male sex and older age are associated with lower weight loss responses. Understanding these predictors is essential for optimizing treatment strategies, setting realistic expectations, and moving towards a precision medicine approach in obesity care. Further research is needed to validate these findings in diverse populations and develop practical tools for clinical decision-making.