scholarly journals Hepatic and Peripheral Insulin Sensitivity and Diabetes Remission at 1 Month After Roux-en-Y Gastric Bypass Surgery in Patients Randomized to Omentectomy

Diabetes Care ◽  
2011 ◽  
Vol 35 (1) ◽  
pp. 137-142 ◽  
Author(s):  
J. P. Dunn ◽  
N. N. Abumrad ◽  
I. Breitman ◽  
P. A. Marks-Shulman ◽  
C. R. Flynn ◽  
...  
Keyword(s):  
Gastric Bypass ◽  
Insulin Sensitivity ◽  
Bypass Surgery ◽  
Gastric Bypass Surgery ◽  
Diabetes Remission ◽  
Peripheral Insulin Sensitivity

A126 Presence of liver steatosis is associated with greater diabetes remission after gastric bypass surgery

2019 ◽  
Vol 15 (10) ◽  
pp. S18
Author(s):  
Ali Aminian ◽  
Sangeeta Kashyap ◽  
David E. Cummings ◽  
Deepa Cherla ◽  
Philip Schauer ◽  
...  
Keyword(s):  
Gastric Bypass ◽  
Bypass Surgery ◽  
Gastric Bypass Surgery ◽  
Liver Steatosis ◽  
Diabetes Remission

scholarly journals Striatal dopamine receptor binding in morbidly obese women before and after gastric bypass surgery and its relationship with insulin sensitivity

Diabetologia ◽  
2014 ◽  
Vol 57 (5) ◽  
pp. 1078-1080 ◽  
Author(s):  
Barbara A. de Weijer ◽  
Elsmarieke van de Giessen ◽  
Ignace Janssen ◽  
Frits J. Berends ◽  
Arnold van de Laar ◽  
...  
Keyword(s):  
Gastric Bypass ◽  
Insulin Sensitivity ◽  
Receptor Binding ◽  
Bypass Surgery ◽  
Gastric Bypass Surgery ◽  
Morbidly Obese ◽  
Obese Women ◽  
Before And After ◽  
Striatal Dopamine Receptor ◽  
Dopamine Receptor Binding

scholarly journals Mechanism for Improved Insulin Sensitivity after Gastric Bypass Surgery

2008 ◽  
Vol 93 (12) ◽  
pp. 4656-4663 ◽  
Author(s):  
Benjamin T. Bikman ◽  
Donghai Zheng ◽  
Walter J. Pories ◽  
William Chapman ◽  
John R. Pender ◽  
...  
Keyword(s):  
Gastric Bypass ◽  
Insulin Sensitivity ◽  
Bypass Surgery ◽  
Gastric Bypass Surgery

scholarly journals Circulating Apolipoprotein A-IV presurgical levels are associated with improvement in insulin sensitivity after Roux-en-Y gastric bypass surgery

2017 ◽  
Vol 13 (3) ◽  
pp. 468-473 ◽  
Author(s):  
Raghavendra Rao ◽  
Alexander Roche ◽  
Gerardo Febres ◽  
Marc Bessler ◽  
Patrick Tso ◽  
...  
Keyword(s):  
Gastric Bypass ◽  
Insulin Sensitivity ◽  
Bypass Surgery ◽  
Gastric Bypass Surgery ◽  
Apolipoprotein A

scholarly journals Using convolutional neural network to predict remission of diabetes after gastric bypass surgery: a machine learning study from the Scandinavian Obesity Surgery Register (Preprint)

2020 ◽  
Author(s):  
Yang Cao ◽  
Ingmar Näslund ◽  
Erik Näslund ◽  
Johan Ottosson ◽  
Scott Montgomery ◽  
...  
Keyword(s):  
Neural Network ◽  
Gastric Bypass ◽  
Complete Remission ◽  
Obesity Surgery ◽  
Bypass Surgery ◽  
Gastric Bypass Surgery ◽  
Diabetes Remission ◽  
Predictive Capability ◽  
Final Test ◽  
Better Than

BACKGROUND Prediction of diabetes remission is an important topic in the evaluation of patients with type-2 diabetes (T2D) before bariatric surgery. While several high-quality predictive indices are available, artificial intelligence (AI) algorithms offer the potential for higher predictive capability. OBJECTIVE The objective was to construct and validate an AI prediction model for diabetes remission after Roux-en-Y gastric bypass surgery. METHODS Patients who underwent surgery from 2007 until 2017 were included in the study, with collection of individual data from the Scandinavian Obesity Surgery Registry (SOReg), the Swedish National Patients Register, the Swedish Prescribed Drugs Register, and Statistics Sweden. A 7-layer convolution neural network (CNN) model was developed using 80% of patients randomly selected from SOReg and 20% of patients for external testing. The predictive capability of the CNN model and currently used scores (DiaRem, Ad-DiaRem, DiaBetter and IMS) were compared. RESULTS In total, 8057 patients with T2D were included in the study. At 2 years after surgery 77.1% achieved pharmacological remission, while 62.2% achieved complete remission. The area under the receiver operating curve (AUC) for the CNN-model for pharmacological remission was 0.85 [95% confidence interval (CI): 0.83-0.86] during validation, and 0.83 for the final test, which was 9-12% better than the traditional predictive indices. AUC for complete remission was 0.83 (95% CI: 0.81-0.85) during validation, and 0.82 for the final test, which was 9-11% better than the traditional predictive indices. CONCLUSIONS The CNN method had better predictive capability compared to traditional indices for diabetes remission. However, further validation is needed in other countries to evaluate its external generalizability.

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