Severe Insulin-deficient Diabetes Research Articles

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.

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.

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes.

This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.

Phenotypic clusters of type 2 diabetes mellitus among North Indians reveal higher levels of insulin deficiency along with insulin resistance

BackgroundType 2 diabetes is now considered a heterogenous disease. Distinct clusters have been identified with patterns varying between Europeans and South Asians as well as between South Indians who have described a novel cluster; Combined Insulin-Resistant and Deficient Diabetes, and individuals from West and East India who have reported that insulin deficiency is the primary driver of heterogeneity. Therefore, North Indian patients may also have a distinct, novel clustering pattern due to unique genetic, epigenetic, and environmental factors. We aim to identify clusters of type 2 diabetes in North Indians and to describe the different characteristics of these clusters. MethodsThe K value for the optimal number of clusters was obtained from two-step clustering. K means clustering was done with this K value using SPSS 29.0 software. Variables used for clustering were age, BMI, HbA1c, HOMA-beta, HOMA-IR, and waist circumference. ResultsFour phenotypically different clusters were identified in 469 individuals with type 2 diabetes. Cluster 1 was severe insulin deficient diabetes (15%), Cluster 2 was severe insulin resistant diabetes (22%), Cluster 3 was moderate obesity-related diabetes (35%), and Cluster 4 was moderate age-related diabetes (27%). Clusters 1 and 2 were similar to earlier studies but in different proportions. Clusters 3 and 4 characteristics were different from earlier studies, with greater impairment in beta cell function and higher HbA1c levels. Significant insulin resistance was noted in all clusters. ConclusionThe phenotypic clusters of type 2 diabetes identified in the present study were characterized by high levels of insulin deficiency along with important contributions from insulin resistance.

Disease severity-based subgrouping of type 2 diabetes does not parallel differences in quality of life: the Maastricht Study

Aims/hypothesisType 2 diabetes is a highly heterogeneous disease for which new subgroups (‘clusters’) have been proposed based on disease severity: moderate age-related diabetes (MARD), moderate obesity-related diabetes (MOD), severe insulin-deficient diabetes (SIDD) and severe insulin-resistant diabetes (SIRD). It is unknown how disease severity is reflected in terms of quality of life in these clusters. Therefore, we aimed to investigate the cluster characteristics and cluster-wise evolution of quality of life in the previously defined clusters of type 2 diabetes.MethodsWe included individuals with type 2 diabetes from the Maastricht Study, who were allocated to clusters based on a nearest centroid approach. We used logistic regression to evaluate the cluster-wise association with diabetes-related complications. We plotted the evolution of HbA1c levels over time and used Kaplan–Meier curves and Cox regression to evaluate the cluster-wise time to reach adequate glycaemic control. Quality of life based on the Short Form 36 (SF-36) was also plotted over time and adjusted for age and sex using generalised estimating equations. The follow-up time was 7 years. Analyses were performed separately for people with newly diagnosed and already diagnosed type 2 diabetes.ResultsWe included 127 newly diagnosed and 585 already diagnosed individuals. Already diagnosed people in the SIDD cluster were less likely to reach glycaemic control than people in the other clusters, with an HR compared with MARD of 0.31 (95% CI 0.22, 0.43). There were few differences in the mental component score of the SF-36 in both newly and already diagnosed individuals. In both groups, the MARD cluster had a higher physical component score of the SF-36 than the other clusters, and the MOD cluster scored similarly to the SIDD and SIRD clusters.Conclusions/interpretationDisease severity suggested by the clusters of type 2 diabetes is not entirely reflected in quality of life. In particular, the MOD cluster does not appear to be moderate in terms of quality of life. Use of the suggested cluster names in practice should be carefully considered, as the non-neutral nomenclature may affect disease perception in individuals with type 2 diabetes and their healthcare providers.Graphical

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.

Randomized open-label trial of semaglutide and dapagliflozin in patients with type 2 diabetes of different pathophysiology

The limited understanding of the heterogeneity in the treatment response to antidiabetic drugs contributes to metabolic deterioration and cardiovascular complications1,2, stressing the need for more personalized treatment1. Although recent attempts have been made to classify diabetes into subgroups, the utility of such stratification in predicting treatment response is unknown3. We enrolled participants with type 2 diabetes (n = 239, 74 women and 165 men) and features of severe insulin-deficient diabetes (SIDD) or severe insulin-resistant diabetes (SIRD). Participants were randomly assigned to treatment with the glucagon-like peptide 1 receptor agonist semaglutide or the sodium–glucose cotransporter 2 inhibitor dapagliflozin for 6 months (open label). The primary endpoint was the change in glycated haemoglobin (HbA1c). Semaglutide induced a larger reduction in HbA1c levels than dapagliflozin (mean difference, 8.2 mmol mol−1; 95% confidence interval, −10.0 to −6.3 mmol mol−1), with a pronounced effect in those with SIDD. No difference in adverse events was observed between participants with SIDD and those with SIRD. Analysis of secondary endpoints showed greater reductions in fasting and postprandial glucose concentrations in response to semaglutide in participants with SIDD than in those with SIRD and a more pronounced effect on postprandial glucose by dapagliflozin in participants with SIDD than in those with SIRD. However, no significant interaction was found between drug assignment and the SIDD or SIRD subgroup. In contrast, continuous measures of body mass index, blood pressure, insulin secretion and insulin resistance were useful in identifying those likely to have the largest improvements in glycaemic control and cardiovascular risk factors by adding semaglutide or dapagliflozin. Thus, systematic evaluation of continuous pathophysiological variables can guide the prediction of the treatment response to these drugs and provide more information than stratified subgroups (NCT04451837).

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.

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.

432-P: Role of Patatin-Like Phospholipase Domain–Containing 3 Gene for Kidney Dysfunction in Diabetes Endotypes

Recent studies identified distinct endotypes of diabetes with differences in metabolic features and in risk for diabetes-related comorbidities. Of note, persons allocated to the severe insulin resistant diabetes (SIRD) endotype, who show increased prevalence of nonalcoholic fatty liver disease (NAFLD) and chronic kidney disease (CKD), are more frequently carriers of the G-allele in the single-nucleotide polymorphism (SNP) rs738409 in the patatin-like phospholipase domain containing 3 (PNPLA3) gene. This SNP associates with increased risk of NAFLD yet the association between the presence of the PNPLA3 SNP and CKD remains controversial. The present study examined whether this SNP differently associates with CKD in endotypes of recent-onset diabetes. Participants with newly diagnosed diabetes (n=707) from the prospective German Diabetes Study underwent k-means clustering, genotyping, magnetic resonance spectroscopy to determine hepatocellular lipid content (HCL) and laboratory analyses to calculate the glomerular filtration rates (eGFR). SIRD had the lowest eGFR and highest HCL compared to severe insulin deficient, moderate obesity-related, moderate age-related and severe autoimmune diabetes clusters (all p&amp;lt;0.05). HCL was negatively associated with eGFR (r=-0.287, p&amp;lt;0.01) across all groups. Further stratification by PNPLA3 G-allele carrier status did not reveal any association between HCL and eGFR in any of the diabetes types, irrespective of G-allele carrier status. However, with declining eGFR the proportion of G-allele carriers increased from 44% for eGFR &amp;gt;60 ml/min to 52% for eGFR &amp;lt;60 ml/min (p&amp;lt;0.05). In conclusion, increased hepatic lipid content is associated with reduced kidney function across all diabetes endotypes. This association is independent of the presence of the PNPLA3 polymorphism in newly diagnosed diabetes, but there might be role for PNPLA3 for the severity of CKD. Disclosure O.P. Zaharia: None. K. Strassburger: None. B. Knebel: None. Y. Kupriyanova: None. J. Kotzka: None. K. Bódis: None. M. Schön: None. M. Bombrich: None. C. Möser: None. K. Prystupa: Other Relationship; Berlin-Chemie AG. H. Al-Hasani: None. V. Schrauwen-Hinderling: None. K. Jandeleit-Dahm: None. R. Wagner: Speaker's Bureau; Novo Nordisk, Sanofi. Advisory Panel; Daiichi Sankyo. M. Roden: Advisory Panel; Eli Lilly and Company. Research Support; Boehringer-Ingelheim, Novo Nordisk, Novartis. Consultant; TARGET PharmaSolutions, Inc. Research Support; Sanofi. Funding German Research Foundation; European Foundation for the Study of Diabetes; German Diabetes Association; German Federal Ministry of Education and Research; Heinrich Heine University

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.

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

Subtypes of type 2 diabetes and their association with outcomes in Korean adults - A cluster analysis of community-based prospective cohort

BackgroundLittle is known about the subtypes of type 2 diabetes (T2D) and their association with clinical outcomes in Asians. MethodsWe performed data-driven cluster analysis in patients with newly diagnosed drug-naive T2D (n = 756) from the Korean Genome and Epidemiology Study. Clusters were based on five variables (age at diagnosis, BMI, HbA1c, and HOMA2 β-cell function, and insulin resistance). ResultsWe identified four clusters of patients with T2D according to k-means clustering: cluster 1 (22.4 %, severe insulin-resistant diabetes [SIRD]), cluster 2 (32.7 %, mild age-related diabetes [MARD]), cluster 3 (32.7 %, mild obesity-related diabetes [MOD]), and cluster 4 (12.3 %, severe insulin-deficient diabetes [SIDD]). During 14 years of follow-up, individuals in the SIDD cluster had the highest risk of initiation of glucose-lowering therapy compared to individuals in the other three clusters. Individuals in the MARD and SIDD clusters showed the highest risk of chronic kidney disease and cardiovascular disease, and individuals in the MOD clusters showed the lowest risk after adjusting for other risk factors (P < 0.05). ConclusionsPatients with T2D can be categorized into four subgroups with different glycemic deterioration and risks of diabetes complications. Individualized management might be helpful for better clinical outcomes in Asian patients with different T2D subgroups.