Mild Age-related Diabetes Research Articles

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.

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.

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.

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.

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.

Responses to Basal Insulin Glargine (300U/mL and 100U/mL) with or Without Pre-prandial Insulin in Pre-treated Subphenotypes of Type2 Diabetes: Insights from a Post Hoc Analysis.

This study aimed to evaluate glycemic outcomes in subphenotypes of type2 diabetes (T2D) with HbA1c > 7.0%, previously on basal insulin (pre-BI) alone (≥ 42U/day) or on basal-bolus therapy (pre-BB), and who were switched to either basal insulin glargine 300U/mL (IGlar-300) or 100U/mL (IGlar-100), with or without pre-prandial insulin. Participants from EDITION2 (pre-BI, n = 785), and EDITION1 (pre-BB, n = 792) trials were assigned retrospectively to subphenotypes of T2D: severe insulin deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity diabetes (MOD), and severe insulin resistant diabetes (SIRD). Key efficacy and safety parameters were analyzed at baseline, and after 26weeks, for IGlar-300 and IGlar-100 pooled groups according to subphenotypes. Outcomes were also compared with insulin-naïve subphenotypes on oral antihyperglycemic drugs (OADs) from the EDITION3 trial (pre-OAD, n = 858). Pre-BI and pre-BB treated subphenotypes with SIDD had a higher mean HbA1c (8.9% and 9.1%) at baseline compared to those of MARD (7.7% and 7.8%) and MOD (8.1% and 8.2%) and after 26weeks remained above target HbA1c (7.7% and 8.0%) despite mean glargine doses of 0.7 to 1.0U/kg/day and pre-prandial insulin use in the pre-BB SIDD subgroup. Pre-BB treated individuals with MARD and MOD achieved lower HbA1c levels (6.9% and 7.2%) than the pre-BI groups (7.3% and 7.5%) despite similar mean FPG levels (123-130mg/dL). Only 19-22% of participants with SIDD achieved HbA1c < 7.0% compared to 33-51% with MARD and MOD, respectively. Pre-BI and pre-BB treated subphenotypes experienced more hypoglycemia than pre-OAD treated subphenotypes. Individuals with T2D assigned post hoc to the SIDD subphenotype achieved suboptimal glycemic control with glargine regimens including basal-bolus therapy, alerting clinicians to improve further diabetes treatment, particularly post-prandial glycemic control, in individuals with SIDD.

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.

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.

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.

Data-driven subgroups of newly diagnosed type 2 diabetes and the relationship with cardiovascular diseases at genetic and clinical levels in Chinese adults

BackgroundTo subgroup Chinese patients with newly diagnosed type 2 diabetes (T2D) by K-means cluster analysis on clinical indicators, and to explore whether these subgroups represent different genetic features and calculated cardiovascular risks. MethodsThe K-means clustering analysis was performed on two cohorts (n = 590 and 392), both consisting of Chinese participants with newly diagnosed T2D. To assess genetic risks, multiple polygenic risk scores (PRSs) and mitochondrial DNA copy numbers (mtDNA-CN) were calculated for all participants. Furthermore, Framingham risk scores (FRS) of cardiovascular diseases in two cohorts were also calculated to verify the genetic risks. ResultsFour clusters were identified including the mild age-related diabetes (MARD)(35.08%), mild obesity-related diabetes (MOD) (34.41%), severe autoimmune diabetes (SAID) 19.15%, and severe insulin-resistant diabetes (SIRD) 11.36% subgroups in the MARCH (metformin, and acarbose in Chinese patients as the initial hypoglycemic treatment) cohort. There was a significant difference in PRS for cardiovascular diseases (CVD) across four subgroups in the MARCH cohort (p < 0.05). Compared with the SIDD and SIRD subgroups, patients in the MOD subgroup had a relatively lower PRS for CVD (p < 0.05) in the MARCH cohort. Females had a higher PRS compared to males, with no significant difference in FRS across the four clusters. The MOD subgroup had a significantly lower FRS which was consistent with the results of PRS. Similar results of PRS and FRS were also replicated in the CONFIDENCE (comparison of glycemic control and b-cell function among newly diagnosed patients with type 2 diabetes treated with exenatide, insulin or pioglitazone) cohort. ConclusionThere are different CVD risks in diabetic subgroups based on clinical and genetic evidence which may promote precision medicine.

Clusters of prediabetes and type 2 diabetes stratify all-cause mortality in a cohort of participants undergoing invasive coronary diagnostics.

Heterogeneous metabolic clusters have been identified in diabetic and prediabetic states. It is not known whether such pathophysiologic clusters impact survival in at-risk persons being evaluated for coronary heart disease. The LURIC Study recruited patients referred for coronary angiography at a median age of 63 (IQR 56-70) with a follow-up of 16.1 (IQR 9.6, 17.7) years. Clustering of 1269 subjects without diabetes was performed with oGTT-derived glucose and insulin; fasting triglyceride, high-density lipoprotein, BMI, waist and hip circumference. Patients with T2D (n = 794) were clustered using age, BMI, glycemia, homeostasis model assessment, and islet autoantibodies. Associations of clusters with mortality were analysed using Cox regression. Individuals without diabetes were classified into six subphenotypes, with 884 assigned to subjects at low-risk (cluster 1,2,4) and 385 at high-risk (cluster 3,5,6) for diabetes. We found significantly increased mortality in clusters 3 (hazard ratio (HR)1.42), 5 (HR 1.43), and 6 (HR 1.46) after adjusting for age, BMI, HbA1c and sex. In the T2D group, 508 were assigned to mild age-related diabetes (MARD), 183 to severe insulin-resistant diabetes (SIRD), 84 to mild obesity-related diabetes (MOD), 19 to severe insulin-deficient diabetes (SIDD). Compared to the low-risk non-diabetes group, crude mortality was not different in MOD. Increased mortality was found for MARD (HR 2.2), SIRD (HR 2.2), and SIDD (HR 2.5). Metabolic clustering successfully stratifies survival even among persons undergoing invasive coronary diagnostics. Novel clustering approaches based on glucose metabolism can identify persons who require special attention as they are at risk of increased mortality.

Polygenic scores of diabetes-related traits in subgroups of type 2 diabetes in India: a cohort study.

A machine-learning approach identified five subgroups of diabetes in Europeans which included 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) with partially distinct genetic aetiologies. We previously validated four of the non-autoimmune subgroups in people with young-onset type 2 diabetes (T2D) from the Indian WellGen study. Here, we aimed to apply European-derived centroids and genetic risk scores (GRSs) to the unselected (for age) WellGen to test their applicability and investigate the genetic aetiology of the Indian T2D subgroups. We applied European derived centroids and GRSs to T2D participants of Indian ancestry (WellGen, n=2217, 821 genotyped) and compared them with normal glucose tolerant controls (Pune Maternal Nutrition Study, n=461). SIDD was the predominant subgroup followed by MOD, whereas SIRD and MARD were less frequent. Weighted-GRS for T2D, obesity and lipid-related traits associated with T2D. We replicated some of the previous associations of GRS for T2D, insulin secretion, and BMI with SIDD and MOD. Unique to Indian subgroups was the association of GRS for (a) proinsulin with MOD and MARD, (b) liver-lipids with SIDD, SIRD and MOD, and (c) opposite effect of beta-cell GRS with SIDD and MARD, obesity GRS with MARD compared to Europeans. Genetic variants of fucosyltransferases were associated with T2D and MOD in Indians but not Europeans. The similarities emphasise the applicability of some of the European-derived GRSs to T2D and its subgroups in India while the differences highlight the need for large-scale studies to identify aetiologies in diverse ancestries. The data provide robust evidence for genetically distinct aetiologies for the T2D subgroups and at least partly mirror those seen in Europeans. Vetenskapsrådet, Diabetes Wellness, and Hjärt-Lungfonden (Sweden), DST (India), Wellcome Trust, Crafoord Foundation and Albert Påhlsson Foundation.

1418-P: Identification and Follow-up of T2D by Subtypes and Their Cardiovascular Risk in Mexican Population—The Experience of the MIDO State Registry in Guanajuato

Type 2 diabetes (T2D) is a complex and heterogeneous disease that has been categorized based on its underlying physiological mechanisms to inform treatment strategies. However, patients with T2D often exhibit a wide range of phenotypic and metabolic profiles, and the subclassification of this disease has only recently been developed, enabling the possibility of personalized management and improved outcomes. The aim of this research was to identify and follow-up T2D subtypes and their cardiovascular risk (CVR) in the Mexican population. In this study, 14,124 patients with incident T2D were assessed to characterize their phenotypic and metabolic profiles and to identify their CVR as part of the MIDO State Registry in Guanajuato, Mexico between April 2014, and April 2022. Of the 14,124 patients with incident T2D, when clustered into subtypes according to their phenotypic and metabolic profile, 3,530 (24.9%) belonged to the mild age-related diabetes (MARD) subtype, 4,304 (30.5%) to the mild obesity-related diabetes (MOD), 3,180 (22.1%) to the severe autoimmune diabetes (SIRD), and 3,111 (11.5%) to the severe insulin-resistant diabetes (SIDD). When the CVR was assessed, 1,767 (50.1%) individuals had a medium CVR and 1,763 (49.9%) had a high CVR in the MARD, 3,313 (76.9%) had a medium CVR and 990 (23.1%) had a high CVR in the MOD, 1,699 (54.6%) had a medium CVR and 1,733 (45.4%) had a high CVR in the SIRD, and 2,290 (72.0%) had a medium CVR and 890 (27.9%) had a high CVR in the SIDD. Of the 13,192 patients that were followed for 6 months, up to 61.0% of the MARD, 61.4% of the MOD, 61.2% of the SIRD, and 40.9% of the SIDD achieved metabolic control (HbA1c &amp;lt;7%). Classifying the subtypes of T2D allows for the design of personalized treatment strategies aiming to delay the progression of the disease and prevent cardiovascular complications. In Mexico, this is a novel approach that seeks to improve the overall outcomes of patients with incident T2D. Disclosure H.Gallardo-rincón: None. R.Tapia-conyer: None. A.Montoya: None. L.Morales-juarez: None. M.J.Rios-blancas: None. D.Álvarez-hernández: None. L.Martinez-juarez: None. D.Diaz-martínez: None. F.Magos-vazquez: None. E.Vargas: None.

A machine learning-based classification of adult-onset diabetes identifies patients at risk of liver-related complications

Diabetes mellitus is a major risk factor for fatty liver disease development and progression. A novel machine learning method identified five clusters of patients with diabetes, with different characteristics and risk of diabetic complications using six clinical and biological variables. We evaluated whether this new classification could identify individuals with an increased risk of liver-related complications. We used a prospective cohort of patients with a diagnosis of type 1 or type 2 diabetes without evidence of advanced fibrosis at baseline recruited between 2000 and 2020. We assessed the risk of each diabetic cluster of developing liver-related complications (i.e. ascites, encephalopathy, variceal haemorrhage, hepatocellular carcinoma), using competing risk analyses. We included 1,068 patients, of whom 162 (15.2%) were determined to be in the severe autoimmune diabetes subgroup, 266 (24.9%) had severe insulin-deficient diabetes, 95 (8.9%) had severe insulin-resistant diabetes (SIRD), 359 (33.6%) had mild obesity-related diabetes, and 186 (17.4%) were in the mild age-related diabetes subgroup. In multivariable analysis, patients in the SIRD cluster and those with excessive alcohol consumption at baseline had the highest risk for liver-related events. The SIRD cluster, excessive alcohol consumption, and hypertension were independently associated with clinically significant fibrosis, evaluated by liver biopsy or transient elastography. Using a simplified classification, patients assigned to the severe and mild insulin-resistant groups had a three- and twofold greater risk, respectively, of developing significant fibrosis compared with those in the insulin-deficient group. A novel clustering classification adequately stratifies the risk of liver-related events in a population with diabetes. Our results also underline the impact of the severity of insulin resistance and alcohol consumption as key prognostic risk factors for liver-related complications. Diabetes represents a major risk factor for NAFLD development and progression. This study examined the ability of a novel machine-learning approach to identify at-risk diabetes subtypes for liver-related complications. Our results suggest that patients that had severe insulin resistance had the highest risk of liver-related outcomes and fibrosis progression. Moreover, excessive alcohol consumption at the diagnosis of diabetes was the strongest risk factor for developing liver-related events.

Response to insulin glargine 100 U/mL treatment in newly-defined subgroups of type 2 diabetes: Post hoc pooled analysis of insulin-naïve participants from nine randomised clinical trials

AimsTo assess insulin glargine 100 U/mL (IGlar-100) treatment outcomes according to newly-defined subgroups of type 2 diabetes mellitus (T2DM). MethodsInsulin-naïve T2DM participants (n = 2684) from nine randomised clinical trials initiating IGlar-100 were pooled and assigned to subgroups “Mild Age-Related Diabetes (MARD)”, “Mild Obesity Diabetes (MOD)”, “Severe Insulin Resistant Diabetes (SIRD)”, and “Severe Insulin Deficient Diabetes (SIDD)”, according to age at onset of diabetes, baseline HbA1c, BMI, and fasting C-peptide using sex-specific nearest centroid approach. HbA1c, FPG, hypoglycemia, insulin dose, and body weight were analysed at baseline and 24 weeks. ResultsSubgroup distribution was MARD 15.3 % (n = 411), MOD 39.8 % (n = 1067), SIRD 10.5 % (n = 283), SIDD 34.4 % (n = 923). From baseline HbA1c 8.0–9.6% adjusted least square mean reductions after 24 weeks were similar between subgroups (1.4–1.5 %). SIDD was less likely to achieve HbA1c < 7.0 % (OR: 0.40 [0.29, 0.55]) than MARD. While the final IGlar-100 dose (0.36 U/kg) in MARD was lower than in other subgroups (0.46–0.50 U/kg), it had the highest hypoglycemia risk. SIRD had lowest hypoglycemia risk and SIDD exhibited greatest body weight gain. ConclusionsIGlar-100 lowered hyperglycemia similarly in all T2DM subgroups, but level of glycemic control, insulin dose, and hypoglycemia risk differed between subgroups.