Li Zhehong, Wang Liang, Wang Zheng, Wuyun Qiqige, Amin Buhe, Lian Dongbo, Xu Guangzhong, Zhang Nengwei, Wang Dezhong
Beijing Shijitan Hospital, Surgery Centre of Diabetes Mellitus, Capital Medical University, Beijing, 100038, China.
Department of General Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
BMC Surg. 2025 Jul 28;25(1):319. doi: 10.1186/s12893-025-03073-7.
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.
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.
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).
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.
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.
肥胖是一种慢性低度炎症状态。腹腔镜袖状胃切除术（LSG）是一种广泛认可的体重管理干预措施；然而，患者实现的总体重减轻百分比（%TWL）差异很大。
本研究旨在基于术中内脏脂肪组织（VAT）中与炎症（INF）相关的甲基化位点开发一种列线图，以预测LSG术后三年的%TWL。
接受LSG的患者根据LSG术后三年的%TWL分为两组：满意组（%TWL≥25）和不满意组（%TWL<25）。对VAT样本的850K甲基化微阵列进行比较分析，以鉴定与INF相关基因相关的甲基化位点。使用最小绝对收缩和选择算子、随机森林以及带有递归特征消除分析的支持向量机对差异甲基化位点进行分析，以识别关键的预测性甲基化位点。随后使用这些核心甲基化位点开发列线图。通过采用自抽样重采样的受试者操作特征（ROC）曲线分析、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）来评估模型的性能。
在25例患者（11例满意，14例不满意）中，鉴定出151个差异INF相关甲基化位点。选择两个核心甲基化位点cg14027957和cg20666492作为列线图的预测因子。内部验证显示出优异的预测性能，曲线下面积（AUC）为96.8%。该模型还显示出强大的校准和临床实用性。
基于两个核心甲基化位点的列线图可有效预测LSG术后三年的%TWL结果。其高预测准确性和临床相关性表明，在指导接受LSG的患者制定个性化治疗策略方面具有巨大潜力。
