Judith Aron-Wisnewsky1,2, Nataliya Sokolovska1,2, Yuejun Liu1,2, Doron S Comaneshter3, Shlomo Vinker3,4, Tal Pecht5, Christine Poitou1,2, Jean-Michel Oppert1, Jean-Luc Bouillot6, Laurent Genser7, Dror Dicker4,8, Jean-Daniel Zucker1,2,9, Assaf Rudich5, Karine Clément10,11. 1. Institute of Cardiometabolism and Nutrition (ICAN), Nutrition Department, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, 46-83 Boulevard de l'Hôpital, F-75013, Paris, France. 2. Team 6 Nutriomics, UPMC Université Paris 06 and Inserm, UMR_S 1166, Sorbonne Universités, Paris, France. 3. Central Headquarters, Clalit Health Services, Tel Aviv, Israel. 4. Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. 5. Department of Clinical Biochemistry and Pharmacology at the Faculty of Health Sciences and the National Institute of Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel. 6. Surgery Department, Assistance Publique - Hôpitaux de Paris, Ambroise Paré Hospital, Boulogne-Billancourt, France. 7. Hepato-biliary and Digestive Surgery Department, Assistance Publique - Hôpitaux de Paris, Pitié-Salpétrière Hospital, Paris, France. 8. Internal Medicine D and Obesity Clinic, Hasharon Hospital - Rabin Medical Center, Petach-Tikva, Israel. 9. Unité Mixte Internationale Modélisation Math. et Info. des Systèmes Complexes, UMMISCO UMI 209 IRD/UPMC, Paris, France. 10. Institute of Cardiometabolism and Nutrition (ICAN), Nutrition Department, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, 46-83 Boulevard de l'Hôpital, F-75013, Paris, France. karine.clement@psl.aphp.fr. 11. Team 6 Nutriomics, UPMC Université Paris 06 and Inserm, UMR_S 1166, Sorbonne Universités, Paris, France. karine.clement@psl.aphp.fr.
Abstract
AIMS/HYPOTHESIS: Not all people with type 2 diabetes who undergo bariatric surgery achieve diabetes remission. Thus it is critical to develop methods for predicting outcomes that are applicable for clinical practice. The DiaRem score is relevant for predicting diabetes remission post-Roux-en-Y gastric bypass (RYGB), but it is not accurate for all individuals across the entire spectrum of scores. We aimed to develop an improved scoring system for predicting diabetes remission following RYGB (the Advanced-DiaRem [Ad-DiaRem]). METHODS: We used a retrospective French cohort (n = 1866) that included 352 individuals with type 2 diabetes followed for 1 year post-RYGB. We developed the Ad-DiaRem in a test cohort (n = 213) and examined its accuracy in independent cohorts from France (n = 134) and Israel (n = 99). RESULTS: Adding two clinical variables (diabetes duration and number of glucose-lowering agents) to the original DiaRem and modifying the penalties for each category led to improved predictive performance for Ad-DiaRem. Ad-DiaRem displayed improved area under the receiver operating characteristic curve and predictive accuracy compared with DiaRem (0.911 vs 0.856 and 0.841 vs 0.789, respectively; p = 0.03); thus correcting classification for 8% of those initially misclassified with DiaRem. With Ad-DiaRem, there were also fewer misclassifications of individuals with mid-range scores. This improved predictive performance was confirmed in independent cohorts. CONCLUSIONS/ INTERPRETATION: We propose the Ad-DiaRem, which includes two additional clinical variables, as an optimised tool with improved accuracy to predict diabetes remission 1 year post-RYGB. This tool might be helpful for personalised management of individuals with diabetes when considering bariatric surgery in routine care, ultimately contributing to precision medicine.
AIMS/HYPOTHESIS: Not all people with type 2 diabetes who undergo bariatric surgery achieve diabetes remission. Thus it is critical to develop methods for predicting outcomes that are applicable for clinical practice. The DiaRem score is relevant for predicting diabetes remission post-Roux-en-Y gastric bypass (RYGB), but it is not accurate for all individuals across the entire spectrum of scores. We aimed to develop an improved scoring system for predicting diabetes remission following RYGB (the Advanced-DiaRem [Ad-DiaRem]). METHODS: We used a retrospective French cohort (n = 1866) that included 352 individuals with type 2 diabetes followed for 1 year post-RYGB. We developed the Ad-DiaRem in a test cohort (n = 213) and examined its accuracy in independent cohorts from France (n = 134) and Israel (n = 99). RESULTS: Adding two clinical variables (diabetes duration and number of glucose-lowering agents) to the original DiaRem and modifying the penalties for each category led to improved predictive performance for Ad-DiaRem. Ad-DiaRem displayed improved area under the receiver operating characteristic curve and predictive accuracy compared with DiaRem (0.911 vs 0.856 and 0.841 vs 0.789, respectively; p = 0.03); thus correcting classification for 8% of those initially misclassified with DiaRem. With Ad-DiaRem, there were also fewer misclassifications of individuals with mid-range scores. This improved predictive performance was confirmed in independent cohorts. CONCLUSIONS/ INTERPRETATION: We propose the Ad-DiaRem, which includes two additional clinical variables, as an optimised tool with improved accuracy to predict diabetes remission 1 year post-RYGB. This tool might be helpful for personalised management of individuals with diabetes when considering bariatric surgery in routine care, ultimately contributing to precision medicine.
Entities:
Keywords:
Bariatric surgery; Diabetes remission; Obese; Type 2 diabetes mellitus
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