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

2020 ◽  
Vol 17 (24) ◽  
pp. 9523
Author(s):  
Antonio Sarría-Santamera ◽  
Binur Orazumbekova ◽  
Tilektes Maulenkul ◽  
Abduzhappar Gaipov ◽  
Kuralay Atageldiyeva
Keyword(s):  
Diabetes Mellitus ◽  
Systematic Review ◽  
Cluster Analysis ◽  
Autoimmune Diabetes ◽  
Insulin Resistant ◽  
Age Related ◽  
Mild Obesity ◽  
Diabetes Patients

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.

scholarly journals A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

Diabetologia ◽  
2022 ◽  
Author(s):  
Christian Herder ◽  
Michael Roden
Keyword(s):  
Clinical Features ◽  
Autoimmune Diabetes ◽  
Routine Care ◽  
Insulin Resistant ◽  
Open Questions ◽  
Age Related ◽  
The Stability ◽  
Mild Obesity ◽  
Insight Into

AbstractThe current classification of diabetes, based on hyperglycaemia, islet-directed antibodies and some insufficiently defined clinical features, does not reflect differences in aetiological mechanisms and in the clinical course of people with diabetes. This review discusses evidence from recent studies addressing the complexity of diabetes by proposing novel subgroups (subtypes) of diabetes. The most widely replicated and validated approach identified, in addition to severe autoimmune diabetes, four subgroups designated severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes and mild age-related diabetes subgroups. These subgroups display distinct patterns of clinical features, disease progression and onset of comorbidities and complications, with severe insulin-resistant diabetes showing the highest risk for cardiovascular, kidney and fatty liver diseases. While it has been suggested that people in these subgroups would benefit from stratified treatments, RCTs are required to assess the clinical utility of any reclassification effort. Several methodological and practical issues also need further study: the statistical approach used to define subgroups and derive recommendations for diabetes care; the stability of subgroups over time; the optimal dataset (e.g. phenotypic vs genotypic) for reclassification; the transethnic generalisability of findings; and the applicability in clinical routine care. Despite these open questions, the concept of a new classification of diabetes has already allowed researchers to gain more insight into the colourful picture of diabetes and has stimulated progress in this field so that precision diabetology may become reality in the future. Graphical abstract

scholarly journals Differences in Biomarkers of Inflammation Between Novel Subgroups of Recent-Onset Diabetes

2021 ◽  
Author(s):  
Christian Herder ◽  
Haifa Maalmi ◽  
Klaus Strassburger ◽  
Oana-Patricia Zaharia ◽  
Jacqueline M. Ratter ◽  
...  
Keyword(s):  
Autoimmune Diabetes ◽  
Serum Levels ◽  
Recent Onset ◽  
Insulin Resistant ◽  
Onset Diabetes ◽  
Age Related ◽  
Multiple Biomarkers ◽  
Adult Onset Diabetes ◽  
Clustering Approach ◽  
Mild Obesity

A novel clustering approach identified five subgroups of diabetes with distinct progression trajectories of complications. We hypothesized that these subgroups differ in multiple biomarkers of inflammation. Serum levels of 74 biomarkers of inflammation were measured in 414 individuals with recent adult-onset diabetes from the German Diabetes Study (GDS) allocated to five subgroups based on data-driven analysis. Pairwise differences between subgroups for biomarkers were assessed with generalized linear mixed models before (model 1) and after adjustment (model 2) for the clustering variables. Participants were assigned to five subgroups: severe autoimmune diabetes (SAID, 21%), severe insulin-deficient diabetes (SIDD, 3%), severe insulin-resistant diabetes (SIRD, 9%), mild obesity-related diabetes (MOD, 32%) and mild age-related diabetes (MARD, 35%). In model 1, 23 biomarkers showed ≥1 pairwise difference between subgroups (Bonferroni-corrected p<0.0007). Biomarker levels were generally highest in SIRD and lowest in SIDD. All 23 biomarkers correlated with ≥1 of the clustering variables. In model 2, three biomarkers (CASP-8, EN-RAGE, IL-6) showed at least one pairwise difference between subgroups (e.g. lower CASP8, EN-RAGE and IL-6 in SIDD vs. all other subgroups, all p<0.0007). Thus, novel diabetes subgroups show multiple differences in biomarkers of inflammation, underlining a prominent role of inflammatory pathways in particular in SIRD.

scholarly journals Differences in Biomarkers of Inflammation Between Novel Subgroups of Recent-Onset Diabetes

2021 ◽  
Author(s):  
Christian Herder ◽  
Haifa Maalmi ◽  
Klaus Strassburger ◽  
Oana-Patricia Zaharia ◽  
Jacqueline M. Ratter ◽  
...  
Keyword(s):  
Autoimmune Diabetes ◽  
Serum Levels ◽  
Recent Onset ◽  
Insulin Resistant ◽  
Onset Diabetes ◽  
Age Related ◽  
Multiple Biomarkers ◽  
Adult Onset Diabetes ◽  
Clustering Approach ◽  
Mild Obesity

A novel clustering approach identified five subgroups of diabetes with distinct progression trajectories of complications. We hypothesized that these subgroups differ in multiple biomarkers of inflammation. Serum levels of 74 biomarkers of inflammation were measured in 414 individuals with recent adult-onset diabetes from the German Diabetes Study (GDS) allocated to five subgroups based on data-driven analysis. Pairwise differences between subgroups for biomarkers were assessed with generalized linear mixed models before (model 1) and after adjustment (model 2) for the clustering variables. Participants were assigned to five subgroups: severe autoimmune diabetes (SAID, 21%), severe insulin-deficient diabetes (SIDD, 3%), severe insulin-resistant diabetes (SIRD, 9%), mild obesity-related diabetes (MOD, 32%) and mild age-related diabetes (MARD, 35%). In model 1, 23 biomarkers showed ≥1 pairwise difference between subgroups (Bonferroni-corrected p<0.0007). Biomarker levels were generally highest in SIRD and lowest in SIDD. All 23 biomarkers correlated with ≥1 of the clustering variables. In model 2, three biomarkers (CASP-8, EN-RAGE, IL-6) showed at least one pairwise difference between subgroups (e.g. lower CASP8, EN-RAGE and IL-6 in SIDD vs. all other subgroups, all p<0.0007). Thus, novel diabetes subgroups show multiple differences in biomarkers of inflammation, underlining a prominent role of inflammatory pathways in particular in SIRD.

The Association between Antidiabetic Agents and Leukocyte Telomere Length in the Novel Classification of Type 2 Diabetes Mellitus

Gerontology ◽  
2020 ◽  
pp. 1-9
Author(s):  
Jiaojiao Huang ◽  
Xuemin Peng ◽  
Kun Dong ◽  
Jing Tao ◽  
Yan Yang
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Telomere Length ◽  
The Novel ◽  
Telomere Attrition ◽  
Insulin Resistant ◽  
Age Related

<b><i>Aims:</i></b> This study aimed to explore the new role of telomere length (TL) in the novel classification of type 2 diabetes mellitus (T2DM) patients driven by cluster analysis. <b><i>Materials and Methods:</i></b> A total of 541 T2DM patients were divided into 4 subgroups by <i>k</i>-means analysis: mild obesity-related diabetes (MOD), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), and mild age-related diabetes (MARD). After patients with insufficient data were excluded, further analysis was conducted on 246 T2DM patients. The TL was detected using telomere restriction fragment, and the related diabetic indexes were also measured by clinical standard procedures. <b><i>Results:</i></b> The MARD group had significantly shorter TLs than the MOD and SIDD groups. Then, we subdivided all T2DM patients into the MARD and NONMARD groups, which included the MOD, SIDD, and SIRD groups. The TLs of the MARD group, associated with age, were discovered to be significantly shorter than those of the NONMARD group (<i>p</i> = 0.0012), and this difference in TL disappeared after metformin (<i>p</i> = 0.880) and acarbose treatment (<i>p</i> = 0.058). The linear analysis showed that metformin can more obviously reduce telomere shortening in the MARD group (<i>r</i> = 0.030, 95% CI 0.010–0.051,<i> p</i> = 0.004), and acarbose can more apparently promote telomere attrition in the SIRD group (<i>r</i> = –0.069, 95% CI –0.100 to –0.039, <i>p</i>&#x3c; 0.001) compared with other T2DM patients after adjusting for age and gender. <b><i>Conclusions:</i></b> The MARD group was found to have shorter TLs and benefit more from the antiaging effect of metformin than other T2DM. Shorter TLs were observed in the SIRD group after acarbose use.

scholarly journals Type 2 Diabetes Mellitus (T2DM) may have Four Subtypes Beneficial for Adequate Treatment

2021 ◽  
Vol 4 (1) ◽  
pp. 38-41
Author(s):  
Bando H
Keyword(s):  
Type 2 Diabetes ◽  
Diabetes Type 2 ◽  
Adequate Treatment ◽  
Insulin Resistant ◽  
Age Related ◽  
Cluster A ◽  
Diabetic Treatment ◽  
Mild Obesity

Diabetes includes various heterogeneous factors. Similar to subtypes of type 1 diabetes, type 2 diabetes may show four subtype clusters. They are cluster A: severe insulin-deficient diabetes, B: severe insulin-resistant diabetes, C: mild obesity-related diabetes, and D: mild age-related diabetes. Comparing them, the prevalence of nephropathy and cardiovascular events was highest in the cluster A. Reference data are i) the ratio of cluster A-D is 18.7%, 23.7%, 21.1%, 36.4%, ii) HbA1c for A-D is 11.05%, 8.17%, 8.49%, 7.95%, iii) event ratio of MACE is 14.4%, 10.6%, 11.4%, 9.1%. Future diabetic treatment is hopefully provided suitable for each subtype.

scholarly journals Non-autoimmune diabetes mellitus and the risk of virus infections: a systematic review and meta-analysis of case-control and cohort studies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Lontchi-Yimagou ◽  
Charly Feutseu ◽  
Sebastien Kenmoe ◽  
Alexandra Lindsey Djomkam Zune ◽  
Solange Fai Kinyuy Ekali ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Systematic Review ◽  
Cohort Studies ◽  
H1n1 Virus ◽  
Autoimmune Diabetes ◽  
Meta Analysis ◽  
Case Control ◽  
Cochrane Central Register ◽  
Virus Infections ◽  
Autoimmune Diabetes Mellitus

AbstractA significant number of studies invoked diabetes as a risk factor for virus infections, but the issue remains controversial. We aimed to examine whether non-autoimmune diabetes mellitus enhances the risk of virus infections compared with the risk in healthy individuals without non-autoimmune diabetes mellitus. In this systematic review and meta-analysis, we assessed case-control and cohort studies on the association between non-autoimmune diabetes and viruses. We searched PubMed, Embase, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and Web of Science with no language restriction, to identify articles published until February 15, 2021. The main outcome assessment was the risk of virus infection in individuals with non-autoimmune diabetes. We used a random-effects model to pool individual studies and assessed heterogeneity (I2) using the χ2 test on Cochrane’s Q statistic. This study is registered with PROSPERO, number CRD42019134142. Out of 3136 articles identified, we included 68 articles (90 studies, as the number of virus and or diabetes phenotype varied between included articles). The summary OR between non-autoimmune diabetes and virus infections risk were, 10.8(95% CI: 10.3–11.4; 1-study) for SARS-CoV-2; 3.6(95%CI: 2.7–4.9, I2 = 91.7%; 43-studies) for HCV; 2.7(95% CI: 1.3–5.4, I2 = 89.9%, 8-studies;) for HHV8; 2.1(95% CI: 1.7–2.5; 1-study) for H1N1 virus; 1.6(95% CI: 1.2–2.13, I2 = 98.3%, 27-studies) for HBV; 1.5(95% CI: 1.1–2.0; 1-study) for HSV1; 3.5(95% CI: 0.6–18.3 , I2 = 83.9%, 5-studies) for CMV; 2.9(95% CI: 1–8.7, 1-study) for TTV; 2.6(95% CI: 0.7–9.1, 1-study) for Parvovirus B19; 0.7(95% CI: 0.3–1.5 , 1-study) for coxsackie B virus; and 0.2(95% CI: 0–6.2; 1-study) for HGV. Our findings suggest that, non-autoimmune diabetes is associated with increased susceptibility to viruses especially SARS-CoV-2, HCV, HHV8, H1N1 virus, HBV and HSV1. Thus, these viruses deserve more attention from diabetes health-care providers, researchers, policy makers, and stakeholders for improved detection, overall proper management, and efficient control of viruses in people with non-autoimmune diabetes.

The challenges of diagnosing diabetes in childhood

Diagnosis ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mairi Pucci ◽  
Marco Benati ◽  
Claudia Lo Cascio ◽  
Martina Montagnana ◽  
Giuseppe Lippi
Keyword(s):  
Diabetes Mellitus ◽  
Children And Young People ◽  
Narrative Literature ◽  
Diagnosis And Classification ◽  
Narrative Literature Review ◽  
Optimal Approach ◽  
Diagnostic Step

AbstractDiabetes is one of the most prevalent diseases worldwide, whereby type 1 diabetes mellitus (T1DM) alone involves nearly 15 million patients. Although T1DM and type 2 diabetes mellitus (T2DM) are the most common types, there are other forms of diabetes which may remain often under-diagnosed, or that can be misdiagnosed as being T1DM or T2DM. After an initial diagnostic step, the differential diagnosis among T1DM, T2DM, Maturity-Onset Diabetes of the Young (MODY) and others forms has important implication for both therapeutic and behavioral decisions. Although the criteria used for diagnosing diabetes mellitus are well defined by the guidelines of the American Diabetes Association (ADA), no clear indications are provided on the optimal approach to be followed for classifying diabetes, especially in children. In this circumstance, both routine and genetic blood test may play a pivotal role. Therefore, the purpose of this article is to provide, through a narrative literature review, some elements that may aid accurate diagnosis and classification of diabetes in children and young people.

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