Mild Obesity-related Diabetes Research Articles

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.

Correlates of Skeletal Muscle Mass and Differences Between Novel Subtypes in Recent-Onset Diabetes.

Low skeletal muscle mass (SMM) is associated with long-standing diabetes but little is known about SMM in newly diagnosed diabetes. We aimed to identify correlates of SMM in recent-onset diabetes and to compare SMM between novel diabetes subtypes. SMM was normalized to body mass index (SMM/BMI) in 842 participants with known diabetes duration of less than 1 year from the German Diabetes Study (GDS). Cross-sectional associations between clinical variables, 79 biomarkers of inflammation, and SMM/BMI were assessed, and differences in SMM/BMI between novel diabetes subtypes were analyzed with different degrees of adjustment for confounders. Male sex and physical activity were positively associated with SMM/BMI, whereas associations of age, BMI, glycated hemoglobin A1c, homeostatic model assessment for β-cell function, and estimated glomerular filtration rate with SMM/BMI were inverse (all P < .05; model r2 = 0.82). Twenty-three biomarkers of inflammation showed correlations with SMM/BMI after adjustment for sex and multiple testing (all P < .0006), but BMI largely explained these correlations. In a sex-adjusted analysis, individuals with severe autoimmune diabetes had a higher SMM/BMI whereas individuals with severe insulin-resistant diabetes and mild obesity-related diabetes had a lower SMM/BMI than all other subtypes combined. However, differences were attenuated after adjustment for the clustering variables. SMM/BMI differs between diabetes subtypes and may contribute to subtype differences in disease progression. Of note, clinical variables rather than biomarkers of inflammation explain most of the variation in SMM/BMI.

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.

Childhood adiposity and novel subtypes of diabetes in adults: a Mendelian randomisation and genome-wide genetic correlation study

BackgroundFive novel subtypes of adult-onset diabetes were identified in 2018. We aimed to investigate whether childhood adiposity increases the risks of these subtypes using a Mendelian randomisation design, and to explore genetic overlaps between body size (self-reported perceived body size [ie, thinner, about average, or plumper] in childhood, and BMI measured in adulthood) and these subtypes. MethodsThe Mendelian randomisation and genetic correlation analyses were based on summary statistics from European genome-wide association studies of childhood body size (n=453 169), adult BMI (n=359 983), latent autoimmune diabetes in adults (n=8581), severe insulin-deficient diabetes (n=3937), severe insulin-resistant diabetes (n=3874), mild obesity-related diabetes (n=4118), and mild age-related diabetes (n=5605). We identified 267 independent genetic variants as instrumental variables for childhood body size in the Mendelian randomisation analysis of latent autoimmune diabetes in adults and 258 independent genetic variants as instrumental variables for other diabetes subtypes. The inverse variance-weighted method was used as the primary estimator in the Mendelian randomisation analysis, supplemented by other Mendelian randomisation estimators. We calculated overall genetic correlations (rg) between childhood or adult adiposity and different subtypes using linkage disequilibrium score regression. FindingsA large childhood body size was associated with increased risk of latent autoimmune diabetes in adults (odds ratio [OR] 1·62, 95% CI 1·95–2·52), severe insulin-deficient diabetes (OR 2·45, 1·35–4·46), severe insulin-resistant diabetes (OR 3·08, 1·73–5·50), and mild obesity-related diabetes (OR 7·70, 4·32–13·7), but not mild age-related diabetes in the main Mendelian randomisation analysis. Other Mendelian randomisation estimators gave similar results and did not support the existence of horizontal pleiotropy. There was genetic overlap between childhood body size and mild obesity-related diabetes (rg 0·282; p=0·0003), and between adult BMI and all diabetes subtypes. InterpretationThis study provides genetic evidence that higher childhood adiposity is a risk factor for all subtypes of adult-onset diabetes, except mild age-related diabetes. It is therefore important to prevent and intervene in childhood overweight or obesity. There is shared genetic contribution to childhood obesity and mild obesity-related diabetes. FundingThe study was supported by the China Scholarship Council, the Swedish Research Council (grant number 2018-03035), Research Council for Health, Working Life and Welfare (grant number 2018-00337), and Novo Nordisk Foundation (grant number NNF19OC0057274).

Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial.

Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n=241), severe insulin-deficient diabetes (SIDD, n=1594), severe insulin-resistant diabetes (SIRD, n=914), mild obesity-related diabetes (MOD, n=1595) and mild age-related diabetes (MARD, n=2673). Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection. A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p<4.3 × 10-5). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID. We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping. ORIGIN trial, ClinicalTrials.gov NCT00069784.

Altered blood gene expression in the obesity-related type 2 diabetes cluster may be causally involved in lipid metabolism: a Mendelian randomisation study

Aims/hypothesisThe aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes.MethodsParticipants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA1c, C-peptide and HDL-cholesterol. Whole blood RNA-seq was used to identify differentially expressed lncRNAs and mRNAs in a cluster compared with all others. Differentially expressed genes were validated in the Innovative Medicines Initiative DIabetes REsearCh on patient straTification (IMI DIRECT) study. Expression quantitative trait loci (eQTLs) for differentially expressed RNAs were obtained from a publicly available dataset. To estimate the causal effects of RNAs on traits, a two-sample Mendelian randomisation analysis was performed using public genome-wide association study (GWAS) data.ResultsEleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA AL354696.2 was upregulated in the SIDD cluster and GPR15 mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (n=23) and lipid metabolism traits (n=10). GPR146 showed a causal effect on plasma HDL-cholesterol levels (p = 2×10–15), without evidence for reverse causality.Conclusions/interpretationMultiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits.Graphical abstract