A nomogram for predicting 3-year total weight loss percentage following LSG: insights from visceral adipose tissue inflammatory methylation sites - Zhehong Li; Liang Wang; Zheng Wang; Qiqige Wuyun; Buhe Amin; Dongbo Lian; Guangzhong Xu; Nengwei Zhang; Dezhong Wang (BMC Surgery)
Abstract Background Obesity is a chronic low-grade inflammatory condition. Laparoscopic sleeve gastrectomy (LSG) is a widely recognized intervention for weight management; however, the percentage of total weight loss (%TWL) achieved varies significantly among patients. Objective This study aims to develop a nomogram based on methylation sites associated with the inflammatory (INF) in intraoperative visceral adipose tissue (VAT) to predict %TWL at three years post-LSG. Methods Patients undergoing LSG were categorized into two groups based on their%TWL three years post-LSG: satisfactory (%TWL ≥25) and unsatisfactory (%TWL<25). Comparative analyses of 850K methylation microarrays from VAT samples were performed to identify methylation sites associated with INF-related genes. Differentially methylated sites were analyzed using least absolute shrinkage and selection operator, random forest, and support vector machine with recursive feature elimination analyses to identify key predictive methylation sites. A nomogram was subsequently developed using these hub methylation sites. The model's performance was assessed through receiver operating characteristic (ROC) curve analysis with bootstrap resampling, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results Among 25 patients (11 satisfactory and 14 unsatisfactory), 151 differential INF-related methylation sites were identified. Two hub methylation sites, cg14027957 and cg20666492, were selected as predictors for the nomogram. Internal validation demonstrated excellent predictive performance, with an area under the curve (AUC) of 96.8%. The model also showed strong calibration and clinical utility. Conclusion The nomogram, based on two hub methylation sites, effectively predicts%TWL outcomes three years post-LSG. Its high predictive accuracy and clinical relevance suggest significant potential for guiding personalized treatment strategies in patients undergoing LSG