目的:肥胖和2型糖尿病(T2DM)发病率的上升可以通过各种策略来缓解,2型糖尿病缓解通常需要10%的总体重减轻(TBWL)阈值。减肥手术如Roux-en-Y胃旁路术(RYGB)后的T2DM缓解率已经确立;内窥镜套管胃成形术(ESG)是一种侵入性较小的选择,平均TBWL为15%,可缓解T2DM。这项研究探讨了DiaRem(糖尿病缓解后RYGB)评分预测ESG后1年T2DM缓解的能力。
方法:我们对39例接受ESG治疗的T2DM患者进行了一项回顾性队列研究。年龄,糖尿病药物的使用,胰岛素给药,和血红蛋白A1c水平用于计算DiaRem评分。受试者工作特征曲线下面积(AUC)用于评估DiaRem在区分糖尿病缓解方面的辨别能力。
结果:在39例血红蛋白中位数为6.7的患者中,12.8%需要胰岛素,43.6%使用糖尿病药物。在ESG后1年,69.2%的患者经历了糖尿病缓解,TWBL的中位数百分比为12.7。DiaRem评分检测ESG患者糖尿病消退的能力具有100%的灵敏度和58.3%的特异性,在最佳截止值10处。AUC为0.779(95%CI0.546-0.959)。
结论:我们的研究表明DiaRem评分对ESG后T2DM缓解的预测价值,强调其在ESG相关结果的临床决策中的效用。需要进一步调查以确定可能提高预测准确性的替代指标,从而完善该患者群体的个性化决策。
OBJECTIVE: Rising obesity and type 2 diabetes mellitus (T2DM) rates can be mitigated by various strategies, with a 10% total body weight loss (TBWL) threshold often required for T2DM remission. T2DM remission rates after bariatric surgery like Roux-en-Y gastric bypass (RYGB) are well established; endoscopic sleeve
gastroplasty (ESG) is a less invasive option that averages 15% TBWL and allows for T2DM remission. This study explores the DiaRem (Diabetes Remission post-RYGB) score\'s ability to predict T2DM remission 1-year post-ESG.
METHODS: We conducted a retrospective cohort study on 39 individuals with T2DM who underwent ESG. Age, utilization of diabetes medications, insulin administration, and hemoglobin A1c levels were used to calculate the DiaRem score. The area under the receiver operating characteristic curve (AUC) was employed to evaluate the discriminative ability of DiaRem in distinguishing diabetes remission.
RESULTS: Among the 39 patients with a median hemoglobin A1c of 6.7, 12.8% required insulin, and 43.6% used diabetes medication. At 1-year post-ESG, 69.2% of patients experienced diabetes remission with a median %TWBL of 12.7. The DiaRem score\'s ability to detect diabetes resolution for ESG patients had a sensitivity of 100% and a specificity of 58.3%, at the optimal cutoff value of 10. The AUC was 0.779 (95% CI 0.546-0.959).
CONCLUSIONS: Our study demonstrated the DiaRem score\'s predictive value for T2DM remission post-ESG, highlighting its utility in clinical decision-making for ESG-related outcomes. Further investigation is needed to identify alternative indicators that may enhance predictive accuracy, thus refining personalized decision-making for this patient group.