Wagner Jonas, Wischnewsky Manfred, Kroge Patricia von, Thies Helge Wilhelm, Roser Pia, Wolter Stefan, Hackert Thilo, Izbicki Jakob, Mann Oliver, Duprée Anna
Department of General-, Visceral- and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany.
Mathematics and Informatics, University of Bremen, Universitätsallee, 28359 Bremen, Germany.
J Clin Transl Endocrinol. 2025 Jul 22;41:100410. doi: 10.1016/j.jcte.2025.100410. eCollection 2025 Sep.
Subcutaneous adipose tissue (SAT) is a metabolic organ, which is involved in the pathogenesis of type 2 diabetes (T2D). Methods to predict diabetes remission after metabolic surgery exist, however their prediction accuracy still needs improvement. We hypothesized, that gene expression profiles in the SAT could predict diabetes remission after metabolic surgery more accurately than any current methods.
In this retrospective cohort study, we identified individuals who underwent metabolic surgery. We collected SAT biopsies during the surgery and analyzed the expression of , , and, . The American Diabetes Association criteria were used to define partial and complete remission. Univariate generalized linear models, tree decision algorithms (Exhausted Chaid, CART and Quinlan's C5 with adaptive boosting) and, multilayer perceptron networks were used to develop classifiers for patients with no, partial or complete remission (DiaBar).
In this study 106 patients were included, 66 (62.3%) patients had T2D the remaining 40 (37.7%) were patients with prediabetes. Complete and partial remission were achieved by 69 (65.1%) and 20 (18.9%) patients respectively. Using a multilayer perceptron, we achieved an overall accuracy of 98.0% (remission: no 100%; partial 90.0%; complete 100%). The validated DiaRem Score was used as the comparative score, which had an overall accuracy for classifying patients with complete, partial or no remission of 74.7%.
Using gene expression profiles from the SAT, we developed the DiaBar test, which accurately predicts diabetes remission after metabolic surgery and seems to be superior to the DiaRem score.
皮下脂肪组织（SAT）是一个代谢器官，参与2型糖尿病（T2D）的发病机制。目前存在预测代谢手术后糖尿病缓解的方法，但其预测准确性仍需提高。我们假设，SAT中的基因表达谱比任何现有方法都能更准确地预测代谢手术后的糖尿病缓解情况。
在这项回顾性队列研究中，我们确定了接受代谢手术的个体。我们在手术期间收集了SAT活检样本，并分析了[具体基因名称1]、[具体基因名称2]、[具体基因名称3]和[具体基因名称4]的表达。采用美国糖尿病协会标准来定义部分缓解和完全缓解。使用单变量广义线性模型、树决策算法（穷尽式Chaid、CART和带自适应增强的Quinlan's C5）以及多层感知器网络来为无缓解、部分缓解或完全缓解的患者开发分类器（DiaBar）。
本研究纳入了106例患者，其中66例（62.3%）患有T2D，其余40例（37.7%）为糖尿病前期患者。分别有69例（65.1%）和20例（18.9%）患者实现了完全缓解和部分缓解。使用多层感知器，我们实现了98.0%的总体准确率（缓解情况：无缓解100%；部分缓解90.0%；完全缓解100%）。经过验证的DiaRem评分用作比较评分，其对完全缓解、部分缓解或无缓解患者进行分类的总体准确率为74.7%。
利用SAT的基因表达谱，我们开发了DiaBar测试，该测试能准确预测代谢手术后的糖尿病缓解情况，且似乎优于DiaRem评分。
