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.
Highlights
Diabetes Mellitus (DM) is a chronic and lifelong metabolic disorder characterized by elevated levels of glucose circulating in the blood that occurs either when the pancreas does not secrete enough insulin, due to destruction of pancreatic β-cells; when the body’s cells do not respond to insulin effectively; or by a combination of both mechanisms
The information was retrieved by two authors from selected articles to the a priori prepared tables, with the following columns: study design, source of the data taken for exploration, size and characteristics of targeted population, diagnostic criteria of DM, variables chosen for cluster analysis, and the number of clusters and their characteristics, as well as the data standardization, chosen clustering algorithm, methods for the determination on the number of clusters, and validation of clusters on an independent sample
After removing duplicates and screening the papers, 75 full-text articles were reviewed and 65 were excluded for the following reasons: 6 were review articles, 9 papers focused on exploring clusters of diabetic patients with specific comorbidities at baseline, 32 studies pursued other aims than finding subgroups of DM, 7 studies used other methodologies than unsupervised learning techniques, 9 studies conducted a similar analysis but with other specific aims, and 2 studies were conducted on mice
Summary
Diabetes Mellitus (DM) is a chronic and lifelong metabolic disorder characterized by elevated levels of glucose circulating in the blood that occurs either when the pancreas does not secrete enough insulin, due to destruction of pancreatic β-cells; when the body’s cells do not respond to insulin effectively; or by a combination of both mechanisms. The prevalence of DM has increased across the globe and is expected to rise to 592 million by 2035, incurring tremendous human, economic and social costs [1]. DM imposes a considerable burden on society in the form of low productivity, poor quality of life, increased healthcare expenditures, and premature mortality. Indirect costs accounted for 34.7% of the total burden [2]. DM significantly increases the risk of mortality: 1 in 12 of all-cause deaths may be attributable to DM [3,4,5]. Regardless of existence of effective treatments, DM outcomes are poor: DM patients show high frequency of serious and life-threatening micro- and macrovascular complications (strokes, acute coronary events, blindness, amputations, renal disease, heart failure) and premature mortality exceeding the general population [6]
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