Long-term Relapse of Type 2 Diabetes After Roux-en-Y Gastric Bypass: Prediction and Clinical Relevance

Author:

Debédat Jean1ORCID,Sokolovska Nataliya12,Coupaye Muriel3,Panunzi Simona4,Chakaroun Rima56,Genser Laurent7,de Turenne Garance1,Bouillot Jean-Luc8,Poitou Christine19,Oppert Jean-Michel9,Blüher Matthias56,Stumvoll Michael56,Mingrone Geltrude1011ORCID,Ledoux Séverine3,Zucker Jean-Daniel1212,Clément Karine129,Aron-Wisnewsky Judith129ORCID

Affiliation:

1. NutriOmics team, Sorbonne Université, INSERM, Paris, France

2. Integromics team, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital, Paris, France

3. Obesity Reference Center, Explorations Fonctionnelles Department, Louis Mourier Hospital, Assistance Publique Hôpitaux de Paris, Colombes, France

4. BioMatLab, Institute for Systems Analysis and Computer Science (IASI), National Council for Research (CNR), Rome, Italy

5. Department of Endocrinology and Nephrology, University of Leipzig, Leipzig, Germany

6. Integrated Research and Treatment Center (IFB) Adiposity Diseases, University Medical Center, Leipzig, Germany

7. Visceral Surgery Department, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris, Paris, France

8. Visceral Surgery Department, Ambroise Paré Hospital, Assistance Publique Hôpitaux de Paris, Paris, France

9. Nutrition Department, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris, Sorbonne Université, Paris, France

10. Department of Internal Medicine, Catholic University, Rome, Italy

11. Department of Diabetes and Nutritional Sciences, King’s College London, London, U.K.

12. Unité de Modélisation Mathématique et Informatique des Systèmes Complexes (UMMISCO), L’Institut de Recherche pour le Développement (IRD), Sorbonne Université, Bondy, France

Abstract

OBJECTIVE Roux-en-Y gastric bypass (RYGB) induces type 2 diabetes remission (DR) in 60% of patients at 1 year, yet long-term relapse occurs in half of these patients. Scoring methods to predict DR outcomes 1 year after surgery that include only baseline parameters cannot accurately predict 5-year DR (5y-DR). We aimed to develop a new score to better predict 5y-DR. RESEARCH DESIGN AND METHODS We retrospectively included 175 RYGB patients with type 2 diabetes with 5-year follow-up. Using machine learning algorithms, we developed a scoring method, 5-year Advanced-Diabetes Remission (5y-Ad-DiaRem), predicting longer-term DR postsurgery by integrating medical history, bioclinical data, and antidiabetic treatments. The scoring method was based on odds ratios and variables significantly different between groups. This score was further validated in three independent RYGB cohorts from three European countries. RESULTS Compared with 5y-DR patients, patients who had relapsed after 5 years exhibited more severe type 2 diabetes at baseline, lost significantly less weight during the 1st year after RYGB, and regained more weight afterward. The 5y-Ad-DiaRem includes baseline (diabetes duration, number of antidiabetic treatments, and HbA1c) and 1-year follow-up parameters (glycemia, number of antidiabetic treatments, remission status, 1st-year weight loss). The 5y-Ad-DiaRem was accurate (area under the receiver operating characteristic curve [AUROC], 90%; accuracy, 85%) at predicting 5y-DR, performed better than the Diabetes Remission score (DiaRem) and the Advanced-DiaRem (AUROC, 81% and 84%; accuracy, 79% and 78%, respectively), and correctly reclassified 13 of 39 patients misclassified with the DiaRem. The 5y-Ad-DiaRem robustness was confirmed in the independent cohorts. CONCLUSIONS The 5y-Ad-DiaRem accurately predicts 5y-DR and appears relevant to identify patients at risk for relapse. Using this score could help personalize patient care after the 1st year post-RYGB to maximize weight loss, limit weight regains, and prevent relapse.

Funder

Assistance Publique Hôpitaux de Paris

Centre de Recherche Clinique

Agence Nationale de la Recherche

Fondation de France

Institut Benjamin Delessert

Publisher

American Diabetes Association

Subject

Advanced and Specialized Nursing,Endocrinology, Diabetes and Metabolism,Internal Medicine

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3