Mild Obesity-related Diabetes Research Articles

The effect of the endothelin receptor antagonist atrasentan on insulin resistance in phenotypic clusters of patients with type 2 diabetes and chronic kidney disease.

Type 2 diabetes (T2D) patients with a clinical phenotype characterized by a high degree of insulin resistance are at increased risk of chronic kidney disease (CKD). We previously demonstrated that the endothelin receptor antagonist (ERA) atrasentan reduced insulin resistance in T2D. In this study, we compared the effect of atrasentan on insulin resistance across different phenotypic clusters of patients with T2D. We performed a post hoc analysis of the SONAR trial, a randomized, placebo-controlled trial of the ERA atrasentan in patients with T2D and CKD. Patients were stratified into four previously identified phenotypic clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD). Changes in insulin resistance, assessed by HOMA-IR, were compared between the phenotypic clusters using a mixed effects model. In total, 931 patients were included in the analysis. In the overall population, atrasentan compared to placebo reduced HOMA-IR by 12.9% [95%CI 3.5,21.4]. This effect of atrasentan was more pronounced in clusters characterized by insulin resistance or deficiency: (SIRD cluster 26.2% [95% CI 3.8,43.3] and SIDD cluster 18.5% [95%CI -3.8,35.9]), although the latter did not reach statistical significance. The effect of atrasentan compared to placebo was less pronounced in the other two clusters (MARD 12.2% [95% CI -1.7,24.12] and MOD -5.3% [95% CI -28.9,13.9]). Atrasentan significantly improved insulin sensitivity in patients with T2D and CKD, especially in those characterized by high insulin resistance (SIRD cluster). Further studies are warranted to investigate the long-term clinical outcomes of atrasentan treatment in these distinct phenotypic clusters.

Identification of new phenotypes in type 2 diabetes with acute myocardial infarction: toward a precision medicine?

Abstract Background Type 2 diabetes mellitus (T2DM) is a highly heterogeneous entity, with multiple subgroups differing by clinic presentation, disease progression and risk of complications such as myocardial infarction (MI). A new T2DM phenotyping classification has been recently proposed to help to improve treatment tailoring and target early treatments, and we aimed to identify the prevalence and characteristics of the new phenotypes. Methods All consecutive adults with a history T2DM hospitalized in a french Coronary Intensive Care Unit for an acute MI between February 1st, 2021 and January 31st, 2023 were included. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA1c, and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance). We stratified patients into five subgroups : 1/ SAID=severe autoimmune diabetes; 2/ SIDD=severe insulin-deficient diabetes; 3/ SIRD=severe insulin-resistant diabetes; 4/ MOD=mild obesity-related diabetes; and 5/ MARD=mild age-related diabetes. Results Among the 266 patients included, characteristics according to diabetes phenotypes are summarized in the Table. Conclusions Our prospective study showed that in real-world T2DM patients with acute MI, unclassical phenotypes, i.e. hyperinsulinaemic and insulinopaenic were common. Given the large discrepancies in characteristics, our findings suggest the relevance of the new substratification. However, its clinical utlity and translation in tailored treatment strategies and prognosis remains to be determined.Table.

Depression symptoms, wellbeing, health-related quality of life, and diabetes-related distress in novel subtypes of recent-onset diabetes in Germany: a 5-year observational follow-up study.

The subjective experiences of individuals living with diabetes is commonly assessed with patient-reported outcomes (PROs; eg, depression symptoms, wellbeing, health-related quality of life [HRQOL], and diabetes-related distress). Cluster analyses have identified novel diabetes subtypes differing in phenotypic and metabolic characteristics. We aimed to investigate associations between these subtypes and PROs and whether subtype predicted PROs 5 years later. Baseline (<12 months after a diabetes diagnosis) and 5-year follow-up data were collected from German Diabetes Study (GDS) participants. Multiple regressions were applied to analyse associations between diabetes subtypes and depression symptoms (Center for Epidemiologic Studies Depression Scale), wellbeing (WHO-5), HRQOL (SF-36), and diabetes-related distress (Problem Areas in Diabetes Scale). Cluster analyses at baseline (n=1391) identified participants with severe autoimmune diabetes (SAID, 417 [30%]), severe insulin-deficient diabetes (SIDD, 33 [2%]), severe insulin-resistant diabetes (SIRD, 150 [11%]), mild obesity-related diabetes (MOD, 354 [25%]), and mild age-related diabetes (MARD, 437 [31%]). At baseline, multiple regression analyses showed that participants with SIRD had higher depression symptoms than participants with MARD and lower physical HRQOL than all other subtypes. Participants with SAID reported higher depression symptoms and lower mental HRQOL than participants with MARD, higher physical HRQOL than participants with MARD and MOD, and higher diabetes-related distress than most other subtypes. At the 5-year follow-up, clustering predicted no statistically significant changes in PROs after adjustment for multiple testing, whereas descriptive analyses demonstrated that individuals with SIRD were more likely to experience clinically relevant depression symptoms (16% vs 6%) and low wellbeing (31% vs 14%), respectively, than individuals with MARD. Diabetes subtypes already differ in PROs at diabetes diagnosis. Our analyses had limited predictive power during follow-up. However, our findings suggest that clustering could predict future changes in depression symptoms. The GDS was initiated and financed by the German Diabetes Center, which is funded by the German Federal Ministry of Health, the Ministry of Culture and Science of the state of North Rhine-Westphalia, and by the German Federal Ministry of Education and Research to the German Center for Diabetes Research. For the German translation of the abstract see Supplementary Materials section.

Type 2 Diabetes Subtypes and Their Role in Metabolic Liver Disease and Fibrosis Progression.

BACKGROUND The relationship between different subgroups of type 2 diabetes (T2D) and the progression of metabolic dysfunction-associated steatotic liver disease (MASLD) and liver fibrosis has not been thoroughly studied. This study aims to determine the association between T2D subgroups and the risk of developing advanced liver fibrosis using the Fibrosis-4 (FIB-4) index, a non-invasive marker for assessing liver fibrosis risk. MATERIAL AND METHODS A total of 1205 patients with T2D were categorized into 4 distinct subgroups: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). The FIB-4 index was calculated for each patient to estimate the degree of liver fibrosis, with the following cutoff points: <1.3 indicating no or mild fibrosis, 1.3-2.67 suggesting moderate fibrosis, and >2.67 indicating advanced fibrosis (F3-F4). Logistic regression was used to compare the odds of advanced fibrosis across these subgroups. RESULTS The SIRD subgroup exhibited significantly higher odds of advanced liver fibrosis (F3-F4), compared with the other subgroups, as indicated by elevated FIB-4 scores (P<0.05). In contrast, the SIDD and MOD subgroups had lower odds of advanced fibrosis, while the MARD subgroup showed an intermediate association. CONCLUSIONS The findings suggest that the FIB-4 index, as a noninvasive assessment tool, effectively stratifies liver fibrosis risk among different T2D subgroups. This stratification can inform more personalized management strategies for patients with MASLD, underscoring the importance of accounting for the heterogeneity within T2D in clinical assessments of liver fibrosis risk.

Clinical application of cluster analysis in patients with newly diagnosed type 2 diabetes.

Early prevention and treatment of type 2 diabetes mellitus (T2DM) is still a huge challenge for patients and clinicians. Recently, a novel cluster-based diabetes classification was proposed which may offer the possibility to solve this problem. In this study, we report our performance of cluster analysis of individuals newly diagnosed with T2DM, our exploration of each subtype's clinical characteristics and medication treatment, and the comparison carried out concerning the risk for diabetes complications and comorbidities among subtypes by adjusting for influencing factors. We hope to promote the further application of cluster analysis in individuals with early-stage T2DM. In this study, a k-means cluster algorithm was applied based on five indicators, namely, age, body mass index (BMI), glycosylated hemoglobin (HbA1c), homeostasis model assessment-2 insulin resistance (HOMA2-IR), and homeostasis model assessment-2 β-cell function (HOMA2-β), in order to perform the cluster analysis among 567 newly diagnosed participants with T2DM. The clinical characteristics and medication of each subtype were analyzed. The risk for diabetes complications and comorbidities in each subtype was compared by logistic regression analysis. The 567 patients were clustered into four subtypes, as follows: severe insulin-deficient diabetes (SIDD, 24.46%), age-related diabetes (MARD, 30.86%), mild obesity-related diabetes (MOD, 25.57%), and severe insulin-resistant diabetes (SIRD, 20.11%). According to the results of the oral glucose tolerance test (OGTT) and biochemical indices, fasting blood glucose (FBG), 2-hour postprandial blood glucose (2hBG), HbA1c, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and triglyceride-glucose index (TyG) were higher in SIDD and SIRD than in MARD and MOD. MOD had the highest fasting C-peptide (FCP), 2-hour postprandial C-peptide (2hCP), fasting insulin (FINS), 2-hour postprandial insulin (2hINS), serum creatinine (SCr), and uric acid (UA), while SIRD had the highest triglycerides (TGs) and TyG-BMI. Albumin transaminase (ALT) and albumin transaminase (AST) were higher in MOD and SIRD. As concerms medications, compared to the other subtypes, SIDD had a lower rate of metformin use (39.1%) and a higher rate of α-glucosidase inhibitor (AGI, 61.7%) and insulin (74.4%) use. SIRD showed the highest frequency of use of sodium-glucose cotransporter-2 inhibitors (SGLT-2i, 36.0%) and glucagon-like peptide-1 receptor agonists (GLP-1RA, 19.3%). Concerning diabetic complications and comorbidities, the prevalence of diabetic kidney disease (DKD), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), dyslipidemia, and hypertension differed significantly among subtypes. Employing logistic regression analysis, after adjusting for unmodifiable (sex and age) and modifiable related influences (e.g., BMI, HbA1c, and smoking), it was found that SIRD had the highest risk of developing DKD (odds ratio, OR = 2.001, 95% confidence interval (CI): 1.125-3.559) and dyslipidemia (OR = 3.550, 95% CI: 1.534-8.215). MOD was more likely to suffer from NAFLD (OR = 3.301, 95%CI: 1.586-6.870). Patients with newly diagnosed T2DM can be successfully clustered into four subtypes with different clinical characteristics, medication treatment, and risks for diabetes-related complications and comorbidities, the cluster-based diabetes classification possibly being beneficial both for prevention of secondary diabetes and for establishment of a theoretical basis for precision medicine.

Identifying subtypes of type 2 diabetes mellitus based on real-world electronic medical record data in China

AimsTo replicate the European subtypes of type 2 diabetes mellitus (T2DM) in the Chinese diabetes population and investigate the risk of complications in different subtypes. MethodsA diabetes cohort using real-world patient data was constructed, and clustering was employed to subgroup the T2DM patients. Kaplan–Meier analysis and the Cox models were used to analyze the association between diabetes subtypes and the risk of complications. ResultsA total of 2,652 T2DM patients with complete clustering data were extracted. Among them, 466 (17.57 %) were classified as severe insulin-deficient diabetes (SIDD), 502 (18.93 %) as severe insulin-resistant diabetes (SIRD), 672 (25.34 %) as mild obesity-related diabetes (MOD), and 1,012 (38.16 %) as mild age-related diabetes (MARD). The risk of chronic kidney disease (CKD) and diabetic retinopathy (DR) were different in the four subtypes. Compared with MARD, SIRD had a higher risk of CKD (HR 2.40 [1.16, 4.96]), and SIDD had a higher risk of DR (HR 2.16 [1.11, 4.20]). The risk of stroke and coronary events had no difference. ConclusionsThe European T2DM subtypes can be replicated in the Chinese diabetes population. The risk of CKD and DR varied among different subtypes, indicating that proper interventions can be taken to prevent specific complications in different subtypes.

Comparison of clustering and phenotyping approaches for subclassification of type 2 diabetes and its association with remission in Indian population

Identification of novel subgroups of type 2 diabetes (T2D) has helped improve its management. Most classification techniques focus on clustering or subphenotyping but not on both. This study aimed to compare both these methods and examine the rate of T2D remission in these subgroups in the Indian population. K-means clustering (using age at onset, HbA1C, BMI, HOMA2 IR and HOMA2%B) and subphenotyping (using homeostatic model assessment (HOMA) estimates) analysis was done on the baseline data of 281 patients with recently diagnosed T2D who participated in a 1-year online diabetes management program. Cluster analysis revealed three distinct clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), and mild obesity-related diabetes (MOD) while subphenotyping showed four distinct categories: hyperinsulinemic, insulinopenic, classical, and nascent T2D. Comparison of the two approaches revealed that the clusters aligned with phenotypes based on shared characteristics of insulin sensitivity (IS) and beta cell function (BCF). Clustering correctly identified individuals in nascent group (high IS and BCF) as having mild obesity related diabetes which subphenotyping did not. Post-one-year intervention, higher remission rates were observed in the MOD cluster (p = 0.383) and the nascent phenotype showing high IS and BCF (p = 0.061, Chi-Square test). In conclusion, clustering based on a comprehensive set of parameters appears to be a superior method for classifying T2D compared with pathophysiological subphenotyping. Personalized interventions may be highly effective for newly diagnosed individuals with high IS and BCF and may result in higher remission rates in these individuals. Further large-scale studies are required to validate these findings.

Machine learning-based reproducible prediction of type 2 diabetes subtypes

Aims/hypothesisClustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist’s classification is currently the most vigorously validated method because of its superior ability to predict diabetes complications but it does not have strong consistency over time and requires HOMA2 indices, which are not routinely available in clinical practice and standard cohort studies. We developed a machine learning (ML) model to classify individuals with type 2 diabetes into Ahlqvist’s subtypes consistently over time.MethodsCohort 1 dataset comprised 619 Japanese individuals with type 2 diabetes who were divided into training and test sets for ML models in a 7:3 ratio. Cohort 2 dataset, comprising 597 individuals with type 2 diabetes, was used for external validation. Participants were pre-labelled (T2Dkmeans) by unsupervised k-means clustering based on Ahlqvist’s variables (age at diagnosis, BMI, HbA1c, HOMA2-B and HOMA2-IR) to four subtypes: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD). We adopted 15 variables for a multiclass classification random forest (RF) algorithm to predict type 2 diabetes subtypes (T2DRF15). The proximity matrix computed by RF was visualised using a uniform manifold approximation and projection. Finally, we used a putative subset with missing insulin-related variables to test the predictive performance of the validation cohort, consistency of subtypes over time and prediction ability of diabetes complications.ResultsT2DRF15 demonstrated a 94% accuracy for predicting T2Dkmeans type 2 diabetes subtypes (AUCs ≥0.99 and F1 score [an indicator calculated by harmonic mean from precision and recall] ≥0.9) and retained the predictive performance in the external validation cohort (86.3%). T2DRF15 showed an accuracy of 82.9% for detecting T2Dkmeans, also in a putative subset with missing insulin-related variables, when used with an imputation algorithm. In Kaplan–Meier analysis, the diabetes clusters of T2DRF15 demonstrated distinct accumulation risks of diabetic retinopathy in SIDD and that of chronic kidney disease in SIRD during a median observation period of 11.6 (4.5–18.3) years, similarly to the subtypes using T2Dkmeans. The predictive accuracy was improved after excluding individuals with low predictive probability, who were categorised as an ‘undecidable’ cluster. T2DRF15, after excluding undecidable individuals, showed higher consistency (100% for SIDD, 68.6% for SIRD, 94.4% for MOD and 97.9% for MARD) than T2Dkmeans.Conclusions/interpretationThe new ML model for predicting Ahlqvist’s subtypes of type 2 diabetes has great potential for application in clinical practice and cohort studies because it can classify individuals with missing HOMA2 indices and predict glycaemic control, diabetic complications and treatment outcomes with long-term consistency by using readily available variables. Future studies are needed to assess whether our approach is applicable to research and/or clinical practice in multiethnic populations.Graphical

Proteomics profiling and association with cardiorenal complications in type 2 diabetes subtypes in Asian population

AimAmong multi-ethnic Asians, type 2 diabetes (T2D) clustered in three subtypes; mild obesity-related diabetes (MOD), mild age-related diabetes with insulin insufficiency (MARD-II) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII) had differential cardio-renal complication risk. We assessed the proteomic profiles to identify subtype specific biomarkers and its association with diabetes complications. Methods1448 plasma proteins at baseline were measured and compared across the T2D subtypes. Multivariable cox regression was used to assess associations between significant proteomics features and cardio-renal complications. ResultsAmong 645 T2D participants (SIRD-RII [19%], MOD [45%], MARD-II [36%]), 295 proteins expression differed significantly across the groups. These proteins were enriched in cell adhesion, neurogenesis and inflammatory response processes. In SIRD-RII group, ADH4, ACY1, THOP1, IGFBP2, NEFL, ENTPD2, CALB1, HAO1, CTSV, ITGAV, SCLY, EDA2R, ERBB2 proteins significantly associated with progressive CKD and LILRA5 protein with incident heart failure (HF). In MOD group, TAFA5, RSPO3, EDA2R proteins significantly associated with incident HF. In MARD-II group, FABP4 protein significantly associated with progressive CKD and PTPRN2 protein with major adverse cardiovascular events. Genetically determined NEFL and CALB1 were associated with kidney function decline. ConclusionsEach T2D subtype has unique proteomics signature and association with clinical outcomes and underlying mechanisms.

Circulating small non-coding RNAs are associated with the insulin-resistant and obesity-related type 2 diabetes clusters.

To uncover differences in small non-coding RNAs (sncRNAs) in individuals with type 2 diabetes (T2D) categorized into five clusters based on individual characteristics, which may aid in the identification of those prone to rapid progression. In the Hoorn Diabetes Care System (DCS) cohort, participants were clustered by age, body mass index (BMI), and glycated haemoglobin, C-peptide and high-density lipoprotein (HDL) cholesterol levels, yielding severe insulin-deficient diabetes, severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes, and mild diabetes with high HDL cholesterol clusters (n = 412). Utilizing plasma sncRNA-sequencing, we identified distinct cluster-specific sncRNAs. Validation was performed in a smaller DCS Hoorn dataset (n = 138). To elucidate their potential functions, we examined tissue expression, identified potential targets or (co-)regulated proteins, conducted gene set enrichment analyses on the targets through Reactome, and examined tissue expression of the (co-)regulated proteins. The insulin-resistant cluster exhibited aberrant expression of 10 sncRNAs, while the high BMI cluster featured eight differentially expressed sncRNAs. Multiple (co-)regulated proteins were identified for sncRNAs associated with both clusters. Proteins associated with both clusters showed enrichment for metabolism. Proteins that specifically and only associated with the SIRD cluster showed enrichment for immune-related signalling. Furthermore, MOD cluster-specific associated proteins showed enrichment for the complement system. Our research showed differential sncRNA levels among type 2 diabetes clusters. This may reflect and could deepen our understanding of molecular mechanisms, in development, progression, and risk factors for each cluster.

Fetal/maternal-determined birth weight and adulthood type 2 diabetes and its subtypes: a Mendelian randomization study.

Lower birth weight (BW) might increase the risk of adulthood type 2 diabetes, but its associations with the highly heterogeneous type 2 diabetes subtypes remain to be studied. In addition, whether the associations between lower BW and adulthood type 2 diabetes risks depend on fetal or maternal effect is largely unknown. In this study, we performed a two-sample Mendelian Randomization analysis to study the associations between overall, fetal-determined, and maternal-determined BW and the risks of type 2 diabetes and its subtypes, namely mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), severe insulin-deficient diabetes (SIDD), and severe insulin-resistant diabetes (SIRD). Lower BW was genetically associated with increased risks of type 2 diabetes (odds ratio (OR): 1.86; 95% confidence interval (CI): 1.53, 2.26), MARD (OR: 2.15; 95%CI: 1.43, 3.23), MOD (OR: 1.75; 95%CI: 1.10, 2.77), SIDD (OR: 1.86; 95%CI: 1.11, 3.10), and SIRD (OR: 1.66; 95%CI: 1.06, 2.60). When examining the fetal-determined genetic effects independently, lower BW remained associated with type 2 diabetes and its subtypes, except for MOD. Using maternal-determined BW-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it raised offspring risks of type 2 diabetes. Fetal-determined but not maternal-determined lower BW were associated with increased risks of adulthood type 2 diabetes and its subtypes. Our results underscored the importance of early targeted management among people with a low BW in the prevention of type 2 diabetes.

Etiologies underlying subtypes of long-standing type 2 diabetes.

Attempts to subtype, type 2 diabetes (T2D) have mostly focused on newly diagnosed European patients. In this study, our aim was to subtype T2D in a non-white Emirati ethnic population with long-standing disease, using unsupervised soft clustering, based on etiological determinants. The Auto Cluster model in the IBM SPSS Modeler was used to cluster data from 348 Emirati patients with long-standing T2D. Five predictor variables (fasting blood glucose (FBG), fasting serum insulin (FSI), body mass index (BMI), hemoglobin A1c (HbA1c) and age at diagnosis) were used to determine the appropriate number of clusters and their clinical characteristics. Multinomial logistic regression was used to validate clustering results. Five clusters were identified; the first four matched Ahlqvist et al subgroups: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), and a fifth new subtype of mild early onset diabetes (MEOD). The Modeler algorithm allows for soft assignments, in which a data point can be assigned to multiple clusters with different probabilities. There were 151 patients (43%) with membership in cluster peaks with no overlap. The remaining 197 patients (57%) showed extensive overlap between clusters at the base of distributions. Despite the complex picture of long-standing T2D with comorbidities and complications, our study demonstrates the feasibility of identifying subtypes and their underlying causes. While clustering provides valuable insights into the architecture of T2D subtypes, its application to individual patient management would remain limited due to overlapping characteristics. Therefore, integrating simplified, personalized metabolic profiles with clustering holds greater promise for guiding clinical decisions than subtyping alone.

Developing Research for Five Subtypes of Diabetes with Specific Characteristics

Recently, sub-classifications for adult-onset diabetes have been presented based on cluster analysis. There are 5 exclusive subtypes, with clusters ranging from 1 to 5: 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), respectively. The related variables known include GAD antibody, age at diagnosis, BMI, HbA1c, and HOMA2-B and HOMA2-IR. From the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial, the numerical numbers of the 5 subtypes are 241-1594-914-1595-2673, respectively. The prevalence percentages for the 5 subtypes in 3 countries are as follows: Japan: 5.4-19.0-7.2-28.9-39.5, Germany: 22.0-3.0-11.0-29.0-35.0, and Finland: 9.9-8.9-11.2-22.8-47.3, respectively.

Differential ischemic stroke risk linked to novel subtypes of type 2 diabetes: insights from a Mendelian randomization analysis.

This study employs a two-sample Mendelian randomization (MR) approach to investigate the variation in ischemic stroke risk across novel subtypes of adult-onset type 2 diabetes. Leveraging pooled genome-wide association study (GWAS) data from the Swedish ANDIS cohort, we explored the association of four newly identified type 2 diabetes subtypes-severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD)-with ischemic stroke risk. The outcome data for ischemic stroke and its three subtypes (large artery, cardioembolic, and small vessel stroke) were sourced from the MEGASTROKE Consortium. Our analysis applied multiple MR methods, focusing on the inverse-variance weighted (IVW) technique, complemented by thorough sensitivity analyses to examine heterogeneity and potential horizontal pleiotropy. Our findings reveal a significant causal relationship between the SIDD subtype and small vessel stroke (OR = 1.06, 95% CI: 1.01-1.11, p = 0.025), while no causal associations were observed for SIRD with any stroke subtype. MOD was causally linked to small vessel stroke (OR = 1.07, 95% CI: 1.02-1.12, p = 0.004) and large artery stroke (OR = 1.07, 95% CI: 1.01-1.13, p = 0.015). Similarly, MARD showed a causal relationship with small vessel stroke (OR = 1.09, 95% CI: 1.03-1.16, p = 0.006) and overall ischemic stroke risk (OR = 1.04, 95% CI: 1.01-1.08, p = 0.010). Our study highlights distinct causal links between specific type 2 diabetes subtypes and ischemic stroke risks, emphasizing the importance of subtype-specific prevention and treatment strategies.

Estimating the effect of lipid-lowering agents on novel subtypes of adult-onset diabetes.

The aims of the present study were to assess the effects of lipid-lowering drugs [HMG-CoA reductase inhibitors, proprotein convertase subtilisin/kexin type 9 inhibitors, and Niemann-Pick C1-Like 1 (NPC1L1) inhibitors] on novel subtypes of adult-onset diabetes through a Mendelian randomisation study. We first inferred causal associations between lipid-related traits [including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoproteins A-I, and apolipoproteins B] and novel subtypes of adult-onset diabetes. The expression quantitative trait loci of drug target genes for three classes of lipid-lowering drugs, as well as genetic variants within or nearby drug target genes associated with LDL-C, were then utilised as proxies for the exposure of lipid-lowering drugs. Mendelian randomisation analysis was performed using summary data from genome-wide association studies of LDL-C, severe autoimmune diabetes, severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes. There was an association between HMGCR-mediated LDL-C and the risk of SIRD [odds ratio (OR)=0.305, 95% confidence interval (CI)=0.129-0.723; p=0.007], and there was an association of PCSK9-mediated LDL-C with the risk of SIDD (OR=0.253, 95% CI=0.120-0.532; p<0.001) and MOD (OR=0.345, 95% CI=0.171-0.696; p=0.003). Moreover, NPC1L1-mediated LDL-C (OR=0.109, 95% CI=0.019-0.613; p=0.012) and the increased expression of NPC1L1 gene in blood (OR=0.727, 95% CI=0.541-0.977; p=0.034) both showed a significant association with SIRD. These results were further confirmed by sensitivity analyses. In summary, the different lipid-lowering medications have a specific effect on the increased risk of different novel subtypes of adult-onset diabetes.

Association of dietary patterns with diabetes-related comorbidities varies among diabetes endotypes

Background and aimsDifferences of dietary pattern adherence across the novel diabetes endotypes are unknown. This study assessed adherence to pre-specified dietary patterns and their associations with cardiovascular risk factors, kidney function, and neuropathy among diabetes endotypes. Methods and resultsThe cross-sectional analysis included 765 individuals with recent-onset (67 %) and prevalent diabetes (33 %) from the German Diabetes Study (GDS) allocated into severe autoimmune diabetes (SAID, 35 %), severe insulin-deficient diabetes (SIDD, 3 %), severe insulin-resistant diabetes (SIRD, 5 %), mild obesity-related diabetes (MOD, 28 %), and mild age-related diabetes (MARD, 29 %). Adherence to a Mediterranean diet score (MDS), Dietary Approaches to Stop Hypertension (DASH) score, overall plant-based diet (PDI), healthful (hPDI) and unhealthful plant-based diet index (uPDI) was derived from a food frequency questionnaire and associated with cardiovascular risk factors, kidney function, and neuropathy using multivariable linear regression analysis. Differences in dietary pattern adherence between endotypes were assessed using generalized mixed models.People with MARD showed the highest, those with SIDD and MOD the lowest adherence to the hPDI. Adherence to the MDS, DASH, overall PDI, and hPDI was inversely associated with high-sensitivity C-reactive protein (hsCRP) among people with MARD (β (95%CI): −9.18 % (−15.61; −2.26); −13.61 % (−24.17; −1.58); −19.15 % (−34.28; −0.53); −16.10 % (−28.81; −1.12), respectively). Adherence to the PDIs was associated with LDL cholesterol among people with SAID, SIRD, and MOD. ConclusionsMinor differences in dietary pattern adherence (in particular for hPDI) and associations with markers of diabetes-related complications (e.g. hsCRP) were observed between endotypes. So far, evidence is insufficient to derive endotype-specific dietary recommendations. Trial registrationClinicaltrials.gov: NCT01055093.

Secondary analysis of newly diagnosed type 2 diabetes subgroups and treatment responses in the MARCH cohort

ObjectiveTo incorporate new clusters in the MARCH (Metformin and AcaRbose in Chinese patients as the initial Hypoglycemic treatment) cohort of newly diagnosed type 2 diabetes (T2D) patients and compare the anti-glycemic effects of metformin and acarbose across different clusters. MethodsK-means cluster analysis was performed based on six clinical indicators. The diabetic clusters in the MARCH cohort were retrospectively associated with the response to metformin and acarbose. ResultsA total of 590 newly diagnosed T2D patients were classified by data-driven clusters into the MARD (mild obesity-related diabetes) (34.1 %), MOD (mild obesity-related diabetes) (34.1 %), SIDD (severe insulin-deficient diabetes) (20.3 %) and SIRD (severe insulin-resistant diabetes) (11.5 %) subgroups at baseline. At 24 and 48 weeks, 346 participants had finished the follow-up. After the adjustment of age, gender, weight, baseline HbA1c, baseline fasting glucose and 2-h postprandial blood glucose (2hPG), metformin mainly decreased the fasting glucose (0.07 ± 0.89 vs −0.26 ± 0.83, P = 0.043) in the MARD subgroup presented with OGTT (oral glucose tolerance test) results compared with acarbose group at 24 weeks. Acarbose led to a greater decrease in 2hPG in the MOD subgroup compared with metformin group (0.08 ± 0.86 vs −0.24 ± 0.92, P = 0.037) at 24 weeks. There was a also significant interaction between cluster and treatment efficacy in HbA1c (glycated hemoglobin) reduction in metformin and acarbose groups at 24 and 48 weeks (pinteraction<0.001). ConclusionsMetformin and acarbose affected different metabolic variables depending on the diabetes subtype.

Glycated haemoglobin, HOMA2-B, C-peptide to glucose ratio and type 2 diabetes clusters as predictors for therapy failure in individuals with type 2 diabetes without insulin therapy: A registry analysis.

Some people with type 2 diabetes mellitus (T2D) and declining β-cell function do require insulin over time. Various laboratory parameters, indices of glucose metabolism or phenotypes of T2D (clusters) have been suggested, which might predict future therapy failure (TF), indicating the need for insulin therapy initiation. This analysis evaluated glycated haemoglobin (HbA1c), homeostatic model assessment (HOMA)2-B, C-peptide to glucose ratio (CGR) and diabetes clusters as predictive parameters for the occurrence of glycaemic TF in individuals diagnosed with T2D without previous insulin therapy. In total, 159 individuals with T2D [41% female, median age 50 (IQR: 53-69) years, diabetes duration 9 (5-15) years], without insulin therapy were prospectively evaluated for the occurrence of a composite primary endpoint, including HbA1c increasing or remaining >8.0% (64 mmol/mol) 3 months after baseline on non-insulin glucose-lowering agents, insulin initiation or hospital admissions because of acute hyperglycaemic events. Diabetes clusters were formed according to previously described characteristics. Only severe autoimmune diabetes clusters were excluded because of a small amount of glutamate decarboxylase antibody-positive participants. The other clusters were distributed as mild age-related diabetes 33%; severe insulin-deficient diabetes 31%; mild obesity-related diabetes 20%; and severe insulin-resistant diabetes 15%. During a median observation of 57 months, higher tertiles of HbA1c at baseline, HOMA2-B, as well as a lower CGR were significantly predictive for the occurrence of the primary endpoint. The probability of meeting the primary endpoint was the highest for mild obesity-related diabetes [hazard ratio 3.28 (95% confidence interval 1.75-6.2)], followed by severe insulin-deficient diabetes [hazard ratio 2.03 (95% confidence interval 1.1-3.7)], mild age-related diabetes and the lowest for severe insulin-resistant diabetes. The best performance to predict TF with an area under the curve (AUC) of 0.77 was HbA1c at baseline, followed by HOMA2-B (AUC 0.69) and CGR (AUC 0.64). HbA1c, indices of insulin secretion capacity (HOMA2-B and CGR) and T2D clusters might be applicable tools to guide practitioners in the decision of whether insulin is required in people already diagnosed with T2D. These findings need to be validated in prospective studies.

Characteristics of glucolipid metabolism and complications in novel cluster-based diabetes subgroups: a retrospective study

BackgroundGlucolipid metabolism plays an important role in the occurrence and development of diabetes mellitus. However, there is limited research on the characteristics of glucolipid metabolism and complications in different subgroups of newly diagnosed diabetes. This study aimed to investigate the characteristics of glucolipid metabolism and complications in novel cluster-based diabetes subgroups and explore the contributions of different glucolipid metabolism indicators to the occurrence of complications and pancreatic function.MethodsThis retrospective study included 547 newly diagnosed type 2 diabetes patients. Age, body mass index (BMI), glycated hemoglobin (HbA1C), homeostasis model assessment-2 beta-cell function (HOMA2-β), and homeostasis model assessment-2 insulin resistance (HOMA2-IR) were used as clustering variables. The participants were divided into 4 groups by k-means cluster analysis. The characteristics of glucolipid indicators and complications in each subgroup were analyzed. Regression analyses were used to evaluate the impact of glucolipid metabolism indicators on complications and pancreatic function.ResultsTotal cholesterol (TC), triglycerides (TG), triglyceride glucose index (TyG), HbA1C, fasting plasma glucose (FPG), and 2-h postprandial plasma glucose (2hPG) were higher in the severe insulin-resistant diabetes (SIRD) and severe insulin-deficient diabetes (SIDD) groups. Fasting insulin (FINS), fasting C-peptide (FCP), 2-h postprandial insulin (2hINS), 2-h postprandial C-peptide (2hCP), and the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) were higher in mild obesity-related diabetes (MOD) and SIRD. 2hCP, FCP, and FINS were positively correlated with HOMA2-β, while FPG, TyG, HbA1C, and TG were negatively correlated with HOMA2-β. FINS, FPG, FCP, and HbA1C were positively correlated with HOMA2-IR, while high-density lipoprotein (HDL) was negatively correlated with HOMA2-IR. FINS (odds ratio (OR),1.043;95% confidence interval (CI) 1.006 ~ 1.081), FCP (OR,2.881;95%CI 2.041 ~ 4.066), and TyG (OR,1.649;95%CI 1.292 ~ 2.104) contributed to increase the risk of nonalcoholic fatty liver disease (NAFLD); 2hINS (OR,1.015;95%CI 1.008 ~ 1.022) contributed to increase the risk of atherosclerotic cardiovascular disease (ASCVD); FCP (OR,1.297;95%CI 1.027 ~ 1.637) significantly increased the risk of chronic kidney disease (CKD).ConclusionsThere were differences in the characteristics of glucolipid metabolism as well as complications among different subgroups of newly diagnosed type 2 diabetes. 2hCP, FCP, FINS, FPG, TyG, HbA1C, HDL and TG influenced the function of insulin. FINS, TyG, 2hINS, and FCP were associated with ASCVD, NAFLD, and CKD in newly diagnosed T2DM patients.

Rates of diabetic retinopathy among cluster analysis—identified type 2 diabetic mellitus subgroups

PurposeTo determine whether phenotypic clustering of patients with diabetes mellitus (DM) is associated with more advanced diabetic retinopathy (DR).MethodsRetrospective cohort study of 495 patients with no prior DR treatment seen at a tertiary care clinic 2014–2020. Four previously identified clusters from Ahlqvist’s 2018 paper were reproduced utilizing baseline hemoglobin A1c, body mass index, and age at DM diagnosis. Age-adjusted Cox proportional hazard ratios were used to compare clusters with reference as the lowest risk cluster.ResultsAll four type 2 DM clusters were replicated with our cohort. There was a significant difference in racial distribution among clusters (p = 0.018) with severe insulin-resistant diabetes (SIRD) having the higher percentage of Caucasians and lower percentage of Hispanics compared to other groups and a higher percentage of African Americans comprising the severe insulin-deficient diabetes (SIDD) cluster than other groups. Rates of proliferative diabetic retinopathy were higher in mild obesity-related diabetes (MOD) (28%), SIDD (24%), mild age-related diabetes (MARD) (20%), and lowest in SIRD (7.9%), overall p = 0.004. Rates of vitreous hemorrhage were higher in MOD (p = 0.032) and MARD (0.005) compared to SIRD.ConclusionBaseline clinical measures may be useful in risk stratifying patients for progression to retinopathy requiring intervention.