SEMASEARCH Study Design: Real‐World Evaluation of Semaglutide 2.4 mg in Adults With Severe Obesity Underrepresented in Clinical Trials

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ABSTRACT Background Although semaglutide 2.4 mg has demonstrated significant weight loss efficacy in clinical trials, real‐world data, particularly with regard to clinically complex and underrepresented populations, remain limited. Objectives The study aims to assess the real‐world effectiveness and patient‐reported outcomes associated with the use of semaglutide 2.4 mg in individuals with severe and complex obesity. The study also intends to characterize weight loss response in pre‐defined subgroups of patients and to identify predictors of weight loss using machine learning. Methods SEMASEARCH is a retrospective multicentric observational cohort embedded within the French early‐access program for semaglutide 2.4 mg. A total of 1100 patients with severe obesity (BMI ≥ 40 kg/m 2 with at least one treated obesity‐related complication) were retrospectively included from 11 expert obesity centers, based on prospectively collected data at baseline, 6 months, and 12 months. Subgroups include patients with a history of bariatric surgery, binge eating disorder, hypothalamic obesity, age ≥ 60 years or altered body composition, BMI ≥ 60 kg/m 2 , and those receiving psychotropic medications. Assessments include clinical, biological, and body composition data, as well as standardized questionnaires evaluating eating behaviour, physical activity, sleep, quality of life, digestive symptoms, and mental health. Primary Outcome Body weight change since treatment initiation, with assessment at 6 and 12 months. Secondary Outcomes Clinically meaningful weight loss (≥ 10%) at 12 months, metabolic improvements, patient‐reported outcomes, body composition changes, and tolerance. Expected Impact SEMASEARCH will provide real‐world evidence on Semaglutide 2.4 mg use in patients living with severe and complex obesity. It will also address major knowledge gaps in specific populations underrepresented in clinical trials and generate predictive models of weight loss response.

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