Sex dimorphism in the effect and predictors of weight loss after sleeve gastrectomy.

Abstract

No sex-specific guidelines for surgical anti-obesity strategies have been proposed, partially due to the controversy regarding sex-related differences in weight loss after bariatric metabolic surgery. To explore sex dimorphism in the effect and predictors of weight loss after sleeve gastrectomy (SG), thereby providing clinical evidence for the sex-specific surgical treatment strategy. In a prospective cohort design, participants scheduled for SG at an affiliated hospital between November 2020 and January 2022 were assessed for eligibility and allocated to the Male or Female group with a 1-year follow-up after surgery. The primary outcome was the sex difference in the weight-loss effect after SG indicated by both percentage of total weight loss (TWL%) and excess weight loss (EWL%). The secondary outcome was the analysis of sex-specific preoperative predictors of weight loss after SG based on univariate and multivariate analyses. Independent predictors were obtained to construct a nomogram model. The discrimination, calibration, and clinical utility of the nomogram were based on receiver operating characteristic curve, concordance index, calibration curve, and decision curve analysis, respectively. Ninety-five male and 226 female patients were initially included. After propensity score matching by baseline body mass index (BMI), 85 male and 143 female patients achieved comparable TWL% and EWL% for 1 year after SG. For male patients, baseline BMI, area under the curve for insulin during oral glucose tolerance test, and progesterone were independent predictors of weight loss after SG. Baseline BMI, age, thyroid stimulating hormone, and Self-Rating Anxiety Scale score were independent predictors for female patients. No obvious sex difference is detected in the weight-loss effect after SG. Sex dimorphism exists in the predictors of weight loss after SG. Further research with long-term and a multicenter design is needed to confirm the predictive model.