Wang Feng, Yang Zhe, Chen XiuBing, Peng Yiling, Jiang HaiXing, Qin ShanYu
Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
Discov Oncol. 2022 Aug 2;13(1):68. doi: 10.1007/s12672-022-00534-w.
The aim is to describe a simple and feasible model for the diagnosis of insulinoma. This retrospective study enrolled 37 patients with insulinoma and 44 patients with hypoglycemia not due to insulinoma at the First Affiliated Hospital of Guangxi Medical University. General demographic and clinical characteristics; hemoglobin A1c (HbA1c), insulin and C-peptide concentrations; and the results of 2-h oral glucose tolerance tests (OGTT) were recorded, and a logistic regression model predictive of insulinoma was determined. Body mass index (BMI), HbA1c concentration, 0-h C-peptide concentration, and 0-h and 1-h plasma glucose concentrations (P < 0.05 each) were independently associated with insulinoma. A regression prediction model was established through multivariate logistics regression analysis: Logit p = 7.399+(0.310 × BMI) - (1.851 × HbA1c) - (1.467 × 0-h plasma glucose) + (1.963 × 0-h C-peptide) - (0.612 × 1-h plasma glucose). Using this index to draw a receiver operating characteristic (ROC) curve, the area under the curve (AUC) was found to be 0.957. The optimal cut-off value was - 0.17, which had a sensitivity of 89.2% and a specificity of 86.4%. Logit P ≥ - 0.17 can be used as a diagnostic marker for predicting insulinoma in patients with hypoglycemia.
目的是描述一种简单可行的胰岛素瘤诊断模型。这项回顾性研究纳入了广西医科大学第一附属医院的37例胰岛素瘤患者和44例非胰岛素瘤所致低血糖患者。记录了一般人口统计学和临床特征、糖化血红蛋白（HbA1c）、胰岛素和C肽浓度，以及2小时口服葡萄糖耐量试验（OGTT）结果，并确定了预测胰岛素瘤的逻辑回归模型。体重指数（BMI）、HbA1c浓度、0小时C肽浓度以及0小时和1小时血浆葡萄糖浓度（各P<0.05）与胰岛素瘤独立相关。通过多变量逻辑回归分析建立回归预测模型：Logit p = 7.399 +（0.310×BMI）-（1.851×HbA1c）-（1.467×0小时血浆葡萄糖）+（1.963×0小时C肽）-（0.612×1小时血浆葡萄糖）。使用该指标绘制受试者工作特征（ROC）曲线，发现曲线下面积（AUC）为0.957。最佳截断值为-0.17，灵敏度为89.2%，特异性为86.4%。Logit P≥-0.17可作为预测低血糖患者胰岛素瘤的诊断标志物。
