ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Clinical Diabetes
This article is part of the Research TopicNovel Strategies for the Clinical Management of Cardiovascular-Kidney-Metabolic SyndromeView all 15 articles
Stratifying Metabolic-Related Risk Factors Using Latent Class Analysis to Explore the Risk of Renal Composite Endpoints in Patients with type 2 diabetes mellitus and associated chronic kidney disease
Provisionally accepted
Xiaojie Chen1,2*
Danfeng Liu3Weiting He3Runli Jia3Yaxi Zhu3Xuan Zhao3Hanchen Hou3
Qijun Wan2*
Wenjian Wang3*- 1Shenzhen Second People's Hospital, Shenzhen, China
- 2Department of Nephrology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- 3Guangdong Provincial People's Hospital, Guangzhou, Guangdong Province, China
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Background Metabolic syndrome is a key independent risk factor for the progression of chronic kidney disease (CKD) in patients with Type 2 diabetes (T2DM). Traditional studies often focus on isolated metabolic markers, but our research aims to comprehensively assess the metabolic landscape of these patients. Existing approaches have been limited in integrating multiple metabolic parameters and stratifying patients based on the severity of metabolic dysregulation, hindering the understanding of disease progression. Methods This single-center, retrospective cohort study was conducted at Guangdong Provincial People's Hospital, enrolling 860 participants from January 2010 to December 2023. A total of 65.0% were male, and 35.0% were female. Using Latent Class Analysis (LCA), we stratified CKD patients with T2DM into two distinct classes based on a comprehensive set of baseline clinical metabolic indicators, including glycated hemoglobin (HbA1c), lipid profiles, serum uric acid, blood pressure and body mass index (BMI). Cox proportional hazards models were used to assess renal outcome risks across these identified metabolic phenotypes. Results LCA revealed that Class 2 exhibited significantly higher values for clinical parameters including systolic and diastolic blood pressure, BMI, total cholesterol, triglycerides, LDL-C, HbA1c, and protein-to-creatinine ratio. Longitudinal analysis showed increased hazard ratios for renal outcomes at 3, 5, and 10 years for Class 2 (HR: 1.718, 1.662, and 1.826, respectively; all P < 0.05). Conclusions This study highlights the utility of comprehensive metabolic profiling through LCA for stratifying CKD patients with T2DM, identifying two distinct subgroups with differential renal prognoses, and offering insights for precision nephrology interventions and personalized risk management.
Keywords: Associated Chronic Kidney Disease, latent class analysis, Metabolic-Related Risk Factors, Renal Composite Endpoints, type 2 diabetes mellitus
Received: 24 Mar 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Chen, Liu, He, Jia, Zhu, Zhao, Hou, Wan and Wang. 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:
Xiaojie Chen
Qijun Wan
Wenjian Wang
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