Clustering-based subgroups of type 2 diabetes mellitus and their associations with diabetes remission after bariatric surgery

Sep 29, 2026

Obesity is a key risk factor for type 2 diabetes mellitus (T2DM), yet bariatric surgery produces widely variable remission outcomes. Current predictors rely on univariate variables and may not capture T2DM heterogeneity. This study applies an established four-subtype clustering framework (SIRD, MARD, SIDD, MOD) to classify Chinese patients and evaluate associations between subtype membership and 1-year diabetes remission after sleeve gastrectomy or Roux-en-Y gastric bypass.

LanguageEnglish

CategoryResearch & Report

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Pages3

Reading Time8 min

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Background

Methods

Study cohorts and eligibility

Results and Clinical Utility

Ethics, data handling, and authorship

Publication information

Correspondence Clustering-based subgroups of type 2 diabetes mellitus and their associations with diabetes remission after bariatric surgery Congling Chen1, Guanda Lu2, Zhen Ying1, Yutong Chen1, Wenhuan Feng3, Manna Zhang4, Mengyi Li2, Yang Liu2, Zhongtao Zhang2, Xiaoying Li1, Ying Chen1, Peng Zhang2 1Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai 200032, China; 2Division of Metabolic and Bariatric Surgery, Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing 100050, China; 3Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, Jiangsu 210008, China; 4Department of General Practice, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China. To the Editor: Obesity is a pivotal risk factor for type 2 diabetes mellitus (T2DM), and its frequent co-occurrence has contributed to increased morbidity and mortality worldwide. Bariatric surgery represents an effective intervention for obese patients with diabetes. However, the extent of postoperative diabetes remission varies considerably across individuals. [1] Current predictive scoring models are derived from variables identified through univariate analyses, which may insufficiently capture the heterogeneity of T2DM and are limited in their ability to discriminate among distinct diabetes subgroups. In contrast, clustering-based approaches offer a more integrative, phenotype-driven classification system that may better inform surgical outcomes. A well-established clustering framework classifies T2DM into four subtypes: severe insulin-resistant diabetes (SIRD), mild age-related diabetes (MARD), severe insulin-deficient diabetes (SIDD), and mild obesity-related diabetes (MOD) . These subtypes differ in clinical presentation, complication risks, and response to pharmacologic treatments, suggesting their potential for guiding individualized treatment. [2] While this classification has been initially investigated in European populations undergoing bariatric surgery,[3] its relevance and applicability in Chinese diabetic patients undergoing surgery remain unexplored. Ethnic differences are known to influence diabetes phenotypes and treatment response. In the Chinese population, T2DM develops ata lower body mass index (BMI), often accompanied by intrinsic β-cell dysfunction, increased visceral adiposity, and heightened insulin resistance. [4] Furthermore, East Asian patients have been shown to exhibit more favorable glycemic responses following bariatric surgery. [5] In this multicenter study, we applied the clustering framework to two independent cohorts of Chinese T2DM patients | Access this article online | | | --- | --- | | Quick Response Code: | Website:
[www.cmj.org](www.cmj.org) | | | DOI:
10.1097/CM9.0000000000003879 | undergoing bariatric surgery to evaluate its association with diabetes remission, and to explore its clinical utility in surgical diabetes management. The discovery cohort comprised 207 patients with T2DM who underwent sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) between 2011 and 2020 at five centers: Zhongshan Hospital (Fudan University), Drum Tower Hospital, Second Xiangya Hospital, Daping Hospital, and Shanghai Tenth People’s Hospital. The study inclusion criteria were a diagnosis of T2DM at baseline and availability of 1-year postoperative follow-up data. Patients were excluded if they lacked glycemic outcomes or had missing baseline clustering variables [Supplementary Figure 1 and Supplementary Table 1, [http://links.lww](http://links.lww). com/CM9/C666]. An independent validation cohort comprised 57 T2DM patients who underwent SG at Beijing Friendship Hospital between 2021 and 2023, with 1-year diabetes assessment. The study protocol was approved

FAQs

Why use a clustering framework instead of conventional predictive scoring models?

Conventional models are built from univariate variables and may not reflect T2DM heterogeneity or distinguish distinct diabetes subgroups. Clustering provides a phenotype-driven classification that can better inform surgical outcomes.

Which diabetes subtypes are included in the clustering framework?

The framework classifies T2DM into four subtypes: severe insulin-resistant diabetes (SIRD), mild age-related diabetes (MARD), severe insulin-deficient diabetes (SIDD), and mild obesity-related diabetes (MOD).

How was remission association evaluated after bariatric surgery?

The study applied the clustering framework to two independent Chinese cohorts undergoing bariatric surgery, then assessed the association between subtype membership and 1-year diabetes remission. It also explored the clinical utility of this approach for surgical diabetes management.

Sep 29, 2026

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