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CLINICAL TRIAL article

Front. Endocrinol.

Sec. Clinical Diabetes

Volume 16 - 2025 | doi: 10.3389/fendo.2025.1541663

This article is part of the Research Topic Digital Technology in the Management and Prevention of Diabetes: Volume II View all 9 articles

Predict and Prevent Microvascular Complications of Type 2 Diabetes: A Cross-Sectional and Longitudinal Study in Chinese Communities

Provisionally accepted
Zhaoxiang Liu Zhaoxiang Liu 1Lianhao Zhou Lianhao Zhou 2Wenhui Zhao Wenhui Zhao 1Lixia Jin Lixia Jin 1Jinping Zhang Jinping Zhang 3Yajing Zhang Yajing Zhang 4Yufeng Li Yufeng Li 4Guixia Deng Guixia Deng 5Jiquan He Jiquan He 6Xinghua Zhao Xinghua Zhao 7Wenli Zheng Wenli Zheng 8Yong Tian Yong Tian 9Ji Wu Ji Wu 2Jianzhong Xiao Jianzhong Xiao 1*Jiandong Gao Jiandong Gao 2*
  • 1 Beijing Tsinghua Changgung Hospital, Beijing, China
  • 2 Department of Electronic Engineering, School of Information Science and Technology, Tsinghua University, Beijing, China
  • 3 China-Japan Friendship Hospital, Beijing, Beijing Municipality, China
  • 4 Beijing Pinggu District Hospital, Beijing, China
  • 5 Beijing Pinggu District Yukou Community Central Health Center, beijing, China
  • 6 Beijing Pinggu District Xiagezhuang Township Hospital, beijing, China
  • 7 Beijing Huairou District Yangsong Township Hospital, beijing, China
  • 8 Beijing Daxing District Yinghai Community Central Health Center, beijing, China
  • 9 Beijing Huairou Hospital of Traditional Chinese Medicine, Beijing, China

The final, formatted version of the article will be published soon.

    Purpose: This study investigates the incidence, predictors, and preventive strategies for microvascular complications in type 2 diabetes patients in community settings.Methods:Data were collected from 3,008 type 2 diabetes patients enrolled across 31 clinics in Beijing and Hebei. Prevalence and incidence of diabetic kidney disease (DKD), diabetic retinopathy (DR), and diabetic peripheral neuropathy (DPN) were assessed. Predictors were identified using XGBoost and Cox regression, and the impact of lifestyle and multifactorial interventions (MFI) was analyzed.The prevalence of DKD, DR, and DPN were 39.5%, 26.2%, and 27.1%, respectively, with incidences of 74, 21, and 28 per 1000-person year. XGBoost identified that diabetes duration, age, HbA1c, FBG, triglyceride, BP, serum creatinine, proteinuria, aspirin and statin use were associated with those microvascular complications. The risk of DKD increased more rapidly as HbA1c exceeded 7.5% and decreased as blood pressure was maintained below 120/70 mmHg. Cox regression models showed that community-based intervention, including lifestyle modifications, were associated with a lower risk of DR and DPN. The study also found that higher variability in HbA1c and albumin-to-creatinine ratio (ACR) was associated with an increased risk of microvascular complications..: Community-based interventions significantly reduce the of DR and DPN, highlighting the need for individualized glycemic and BP management in primary care. The findings emphasize the importance of comprehensive management strategies to prevent the development and progression of microvascular complications in type 2 diabetes patients.

    Keywords: type 2 diabetes, machine learning, Diabetes kidney disease, Diabetic Retinopathy, Diabetic peripheral neuropathy

    Received: 08 Dec 2024; Accepted: 10 Mar 2025.

    Copyright: © 2025 Liu, Zhou, Zhao, Jin, Zhang, Zhang, Li, Deng, He, Zhao, Zheng, Tian, Wu, Xiao and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Jianzhong Xiao, Beijing Tsinghua Changgung Hospital, Beijing, China
    Jiandong Gao, Department of Electronic Engineering, School of Information Science and Technology, Tsinghua University, Beijing, 100084, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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