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The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review

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  • Antonio Sarría-Santamera

    (Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan
    Spanish Network of Health Services Research and Chronic Diseases, REDISSEC, 28001 Madrid, Spain)

  • Binur Orazumbekova

    (Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan)

  • Tilektes Maulenkul

    (Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan)

  • Abduzhappar Gaipov

    (Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan)

  • Kuralay Atageldiyeva

    (Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan)

Abstract

Diabetes Mellitus is a chronic and lifelong disease that incurs a huge burden to healthcare systems. Its prevalence is on the rise worldwide. Diabetes is more complex than the classification of Type 1 and 2 may suggest. The purpose of this systematic review was to identify the research studies that tried to find new sub-groups of diabetes patients by using unsupervised learning methods. The search was conducted on Pubmed and Medline databases by two independent researchers. All time publications on cluster analysis of diabetes patients were selected and analysed. Among fourteen studies that were included in the final review, five studies found five identical clusters: Severe Autoimmune Diabetes; Severe Insulin-Deficient Diabetes; Severe Insulin-Resistant Diabetes; Mild Obesity-Related Diabetes; and Mild Age-Related Diabetes. In addition, two studies found the same clusters, except Severe Autoimmune Diabetes cluster. Results of other studies differed from one to another and were less consistent. Cluster analysis enabled finding non-classic heterogeneity in diabetes, but there is still a necessity to explore and validate the capabilities of cluster analysis in more diverse and wider populations.

Suggested Citation

  • Antonio Sarría-Santamera & Binur Orazumbekova & Tilektes Maulenkul & Abduzhappar Gaipov & Kuralay Atageldiyeva, 2020. "The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review," IJERPH, MDPI, vol. 17(24), pages 1-18, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:24:p:9523-:d:464825
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    References listed on IDEAS

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    1. Michal Markovich Gordon & Asher M Moser & Eitan Rubin, 2012. "Unsupervised Analysis of Classical Biomedical Markers: Robustness and Medical Relevance of Patient Clustering Using Bioinformatics Tools," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
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    Cited by:

    1. Yerbolat Saruarov & Gulnaz Nuskabayeva & Mehmet Ziya Gencer & Karlygash Sadykova & Mira Zhunissova & Ugilzhan Tatykayeva & Elmira Iskandirova & Gulmira Sarsenova & Aigul Durmanova & Abduzhappar Gaipov, 2023. "Associations of Clusters of Cardiovascular Risk Factors with Insulin Resistance and Β-Cell Functioning in a Working-Age Diabetic-Free Population in Kazakhstan," IJERPH, MDPI, vol. 20(5), pages 1-11, February.
    2. Antonio Bernabe-Ortiz & Diego B. Borjas-Cavero & Jimmy D. Páucar-Alfaro & Rodrigo M. Carrillo-Larco, 2022. "Multimorbidity Patterns among People with Type 2 Diabetes Mellitus: Findings from Lima, Peru," IJERPH, MDPI, vol. 19(15), pages 1-11, July.
    3. Maja Rubinowicz-Zasada & Ewa Kucio & Anna Polak & Petr Stastny & Krzysztof Wierzbicki & Piotr Król & Cezary Kucio, 2021. "The Combined Effect of Neuromuscular Electrical Stimulation and Insulin Therapy on Glycated Hemoglobin Concentrations, Lipid Profiles and Hemodynamic Parameters in Patients with Type-2-Diabetes and He," IJERPH, MDPI, vol. 18(7), pages 1-13, March.

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