Sun Yilan, Wang Liang, Zhu Guangyi, Chen Xiyuan, Lian Dongbo, Zhang Nengwei, Xu Guangzhong
Surgery Centre of Diabetes Mellitus, Capital Medical University Affiliated Beijing Shijitan Hospital, Tieyi Road, Haidian District, Beijing, 100038, China.
General Surgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring Road West, Fengtai District, 100070, Beijing, China.
BMC Surg. 2025 Aug 6;25(1):343. doi: 10.1186/s12893-025-03069-3.
Excessive 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.
This 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.
This 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.
In 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.
The 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.
内脏脂肪组织（VAT）过度堆积与多种代谢紊乱密切相关。腹腔镜袖状胃切除术（LSG）可减少VAT，从而改善代谢状况。然而，个体差异较大，导致部分接受LSG手术的患者代谢改善效果欠佳。目前，尚无术后VAT含量的预测模型，仅依靠宏观人体测量或基本代谢参数无法准确预测术后代谢结果。
本研究旨在评估LSG对减少VAT的长期影响，确定影响VAT减少的因素，并建立一个临床适用的风险评估模型。
本研究纳入177例患者，随机分为建模组（132例）和验证组（45例）。收集人口统计学、代谢和影像学数据，并根据LSG术后12个月VAT变化中位数对患者进行分类。通过单因素和多因素逻辑回归确定独立预测因素，建立列线图模型，随后进行外部验证。
在建模组中，高变化组和低变化组在性别、腰臀比（WHR）、VAT、高密度脂蛋白胆固醇（HDL-c）和高血压方面存在显著差异。多因素逻辑回归确定术前VAT和HDL-c为体重减轻结果的独立预测因素。列线图模型显示出良好的区分能力，训练集的AUC为0.7，验证组为0.88。校准曲线证实了高预测准确性，决策曲线分析（DCA）和临床影响曲线（CIC）分析强调了该模型强大的临床适用性。
术前HDL-c和VAT的联合可有效预测LSG术后VAT的减少，为改善术前评估和促进个体化患者管理提供了理论依据。
