Trial reportBMC surgery2025
HDL-C and visceral adipose tissue as combined predictors of visceral fat changes following laparoscopic sleeve gastrectomy.
Trial report in BMC surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
Funding
Abstract
backgroundExcessive visceral adipose tissue (VAT) accumulation is strongly associated with numerous metabolic disorders. Laparoscopic sleeve gastrectomy (LSG) reduces VAT, leading to improved metabolic conditions. However, considerable individual variability results in suboptimal metabolic improvements in certain patients post-LSG. Currently, no predictive model for postoperative VAT content exists, and reliance on macroscopic anthropometric or basic metabolic parameters alone fails to accurately predict postoperative metabolic outcomes.
objectiveThis study aims to evaluate the long-term effects of LSG on VAT reduction, identify factors influencing VAT loss, and develop a clinically applicable risk assessment model.
methodsThis study included 177 patients, randomly divided into a modeling group (132 patients) and a validation group (45 patients). Demographic, metabolic, and imaging data were collected, and patients were categorized based on the median ΔVAT change at 12 months post-LSG. Independent predictors were identified via univariate and multivariate logistic regression, and a nomogram model was developed, followed by external validation.
resultsIn the modeling group, significant differences in gender, waist-to-hip ratio (WHR), VAT, high-density lipoprotein cholesterol (HDL-c), and hypertension were observed between the high-change and low-change groups. Multivariate logistic regression identified preoperative VAT and HDL-c as independent predictors of weight loss outcomes. The nomogram model demonstrated excellent discriminatory power, with an AUC of 0.7 in the training set and 0.88 in the validation group. The calibration curve confirmed high predictive accuracy, and decision curve analysis (DCA) and clinical impact curve (CIC) analyses underscored the model's strong clinical applicability.
conclusionThe combination of preoperative HDL-c and VAT serves as an effective predictor of VAT reduction post-LSG, offering a theoretical basis for improving preoperative assessment and facilitating personalized patient management.
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