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Efficacy and safety of retatrutide for the treatment of obesity: a systematic review of clinical trials

  • Saurav Misra EMAIL logo , Ravi Kant Narayan and Manmeet Kaur
Published/Copyright: July 21, 2025

Abstract

Introduction

Obesity is a major public health issue linked to various health complications. Retatrutide, a triple agonist peptide targeting the glucagon receptor, GIP receptor, and GLP-1 receptor, shows promise in addressing this need.

Content

This systematic review assessed the safety and efficacy of retatrutide for obesity treatment using available clinical trial data. We conducted a comprehensive search of databases, including PubMed, Cochrane and ClinicalTrials.gov, from their inception until March 15, 2025 following PRISMA guidelines.

Summary

Three articles were included in this systematic review, screening a total of 1,082 patients, with 691 randomly assigned to groups. The average age of participants was 54.26 ± 9.9 years, consisting of 335 men (48 %) and 356 women (52 %). Retatrutide was administered to 510 participants, while 130 received a placebo. The 12 mg dosage of retatrutide showed the most significant reductions in body weight, body mass index, and waist circumference. It also led to a higher percentage of patients achieving weight losses of ≥5 , 10, 15, and 20 %. Gastrointestinal adverse effects were the most commonly reported.

Outlook

Weekly subcutaneous injections of retatrutide in obese patients resulted in significant weight loss and metabolic improvements compared to a placebo.


Corresponding author: Dr. Saurav Misra, MBBS, MD, Assistant Professor, Department of Pharmacology, Kalpana Chawla Government Medical College, Karnal, India, E-mail:

  1. Research ethics: The local Institutional Review Board deemed the study exempt from review.

  2. Informed consent: Not applicable.

  3. Author contributions: SM – Concept, Idea, Data collection, data analysis, Writing the article, final editing, Submission; RKN – Concept, Idea, Data collection, data analysis, Writing the article, final editing; MK – Data collection, data analysis, Writing the article, final editing.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interests: None of the authors have competing interest.

  6. Research funding: None declared.

  7. Data availability: Yes.

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Received: 2025-06-19
Accepted: 2025-06-28
Published Online: 2025-07-21

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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