Cluster Analysis in Diabetes Research: A Systematic Review Enhanced by a Cross-Sectional Study
Taurbekova B. Sarsenov R. Yaqoob M.M. Atageldiyeva K. Semenova Y. Fazli S. Starodubov A. Angalieva A. Sarria-Santamera A.
May 2025Multidisciplinary Digital Publishing Institute (MDPI)
Journal of Clinical Medicine
2025#14Issue 10
Background: Diabetes mellitus is a heterogeneous metabolic disorder that poses substantial challenges in the management of patients with diabetes. Emerging research underscores the potential of unsupervised cluster analysis as a promising methodological approach for unraveling the complex heterogeneity of diabetes mellitus. This systematic review evaluated the effectiveness of unsupervised cluster analysis in identifying diabetes phenotypes, elucidating the risks of diabetes-related complications, and distinguishing treatment responses. Methods: We searched MEDLINE Complete, PubMed, and Web of Science and reviewed forty-one relevant studies. Additionally, we conducted a cross-sectional study using K-means cluster analysis of real-world clinical data from 558 patients with diabetes. Results: A key finding was the consistent reproducibility of the five clusters across diverse populations, encompassing various patient origins and ethnic backgrounds. MOD and MARD were the most prevalent clusters, while SAID was the least prevalent. Subgroup analysis stratified by ethnic group indicated a higher prevalence of SIDD among individuals of Asian descent than among other ethnic groups. These clusters shared similar phenotypic traits and risk profiles for complications, with some variations in their distribution and key clinical variables. Notably, the SIRD subtype was associated with a wide spectrum of kidney-related clinical presentations. Alternative clustering techniques may reveal additional clinically relevant diabetes subtypes. Our cross-sectional study identified five subgroups, each with distinct profiles of glycemic control, lipid metabolism, blood pressure, and renal function. Conclusions: Overall, the results suggest that unsupervised cluster analysis holds promise for revealing clinically meaningful subgroups with distinct characteristics, complication risks, and treatment responses that may remain undetected using conventional approaches.
classification , cluster analysis , diabetes mellitus , diabetic complication , phenotype
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Department of Biomedical Sciences, School of Medicine, Nazarbayev University, 5/1 Kerey and Zhanibek Khandar Str., Astana, 010000, Kazakhstan
Department of Biology, School of Sciences and Humanities, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana, 010000, Kazakhstan
Department of Renal Medicine and Transplantation, The Royal London Hospital, Barts Health NHS Trust, Whitechapel Road, London, E1 1BB, United Kingdom
William Harvey Research Institute, Faculty of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, United Kingdom
Department of Medicine, School of Medicine, Nazarbayev University, 5/1 Kerey and Zhanibek Khandar Str., Astana, 010000, Kazakhstan
Department of Surgery, School of Medicine, Nazarbayev University, 5/1 Kerey and Zhanibek Khandar Str., Astana, 010000, Kazakhstan
Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana, 010000, Kazakhstan
«B.B.NURA» Hospitals Group, Office 815, 33/1 Mangilik El Str., Astana, 010000, Kazakhstan
Women’s Health Department, City Multidisciplinary Hospital No. 2, 6 Turar Ryskulov Str., Astana, 010000, Kazakhstan
Department of Biomedical Sciences
Department of Biology
Department of Renal Medicine and Transplantation
William Harvey Research Institute
Department of Medicine
Department of Surgery
Department of Computer Science
«B.B.NURA» Hospitals Group
Women’s Health Department
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