Clustering of diabetes: implications for personalized treatment
Year: 2025
Type: article
Abstract: Background. Diabetes mellitus traditionally classified into type 1, type 2, gestational, and other specific types. A novel five-cluster model severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). This new classification was based on six clinical variants: age, body-mass index at diagnosis, glycosylated hemoglobin, glutamic acid decarboxylase antibodies, homeostasis model assessment 2 beta and insulin resistance. This model enhances personalized treatment and complication prediction. Objective. This review evaluates the clinical and therapeutic implications of the five-cluster classification, synthesizing evidence from 2010–2023 to assess its impact on treatment efficacy and personalized care. Methods. This review was limited to the last 15-years and was done using PubMed, Web of Science and Google Scholar. Terms used included “precision therapy,” “diabetes sub-categories,” “SAID,” “SIDD,” “SIRD,” “MOD” and “MIRD.” Only preclinical and clinical data in English were used. Results. The five-cluster model highlights specific clinical trajectories: SIRD is associated with nephropathy, while SAID and SIDD clusters have more complications (e.g., neuropathy, ketoacidosis) severity. The metabolic and organ dysfunction (MARD and MOD) that is more common in obesity, is less severe. Validation studies have brought attention to regional and ethnic disparities in the distribution of SIRD and MOD; specifically, it seems that South Asians have a higher incidence of SIRD than African cohorts, where MOD is more prevalent. Conclusion. By customizing therapeutic applications to metabolic profiles, the five-cluster method promotes precision medicine; therapy ought to be in line with biology.
Author: Khaled A. Abdel-Sater
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4414754662
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