Severe Insulin-resistant Diabetes Research Articles

Data-driven subgroups of type 2 diabetes, metabolic response, and renal risk profile after bariatric surgery: a retrospective cohort study

A novel data-driven classification of type 2 diabetes has been proposed to personalise anti-diabetic treatment according to phenotype. One subgroup, severe insulin-resistant diabetes (SIRD), is characterised by mild hyperglycaemia but marked hyperinsulinaemia, and presents an increased risk of diabetic nephropathy. We hypothesised that patients with SIRD could particularly benefit from metabolic surgery. We retrospectively related the newly defined clusters with the response to metabolic surgery in participants with type 2 diabetes from independent cohorts in France (the Atlas Biologique de l'Obésite Sévère [ABOS] cohort, n=368; participants underwent Roux-en-Y gastric bypass or sleeve gastrectomy between Jan 1, 2006, and Dec 12, 2017) and Brazil (the metabolic surgery cohort of the German Hospital of San Paulo, n=121; participants underwent Roux-en-Y gastric bypass between April 1, 2008, and March 20, 2016). The study outcomes were type 2 diabetes remission and improvement of estimated glomerular filtration rate (eGFR). At baseline, 34 (9%) of 368 patients, 314 (85%) of 368 patients, and 17 (5%) of 368 patients were classified as having SIRD, mild obesity-related diabetes (MOD), and severe insulin deficient diabetes (SIDD) in the ABOS cohort, respectively, and in the São Paulo cohort, ten (8%) of 121 patients, 83 (69%) of 121 patients, and 25 (21%) of 121 patients were classified as having SIRD, MOD, and SIDD, respectively. At 1 year, type 2 diabetes remission was reported in 26 (81%) of 32 and nine (90%) of ten patients with SIRD, 167 (55%) of 306 and 42 (51%) of 83 patients with MOD, and two (13%) of 16 and nine (36%) of 25 patients with SIDD, in the ABOS and São Paulo cohorts, respectively. The mean eGFR was lower in patients with SIRD at baseline and increased postoperatively in these patients in both cohorts. In multivariable analysis, SIRD was associated with more frequent type 2 diabetes remission (odds ratio 4·3, 95% CI 1·8-11·2; p=0·0015), and an increase in eGFR (mean effect size 13·1 ml/min per 1·73 m2, 95% CI 3·6-22·7; p=0·0070). Patients in the SIRD subgroup had better outcomes after metabolic surgery, both in terms of type 2 diabetes remission and renal function, with no additional surgical risk. Data-driven classification might help to refine the indications for metabolic surgery. Agence Nationale de la Recherche, Investissement d'Avenir, Innovative Medecines Initiative, Fondation Cœur et Artères, and Fondation Francophone pour la Recherche sur le Diabète.

Roles of plasma leptin and resistin in novel subgroups of type 2 diabetes driven by cluster analysis

BackgroundA novel classification has been introduced to promote precision medicine in diabetes. The current study aimed to investigate the relationship between leptin and resistin levels with novel refined subgroups in patients with type 2 diabetes mellitus (T2DM).MethodsThe k-means analysis was conducted to cluster 541 T2DM patients into the following four subgroups: mild obesity-related diabetes (MOD), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild age-related diabetes (MARD). Individuals meeting the exclusion criteria were eliminated, the data for 285 patients were analyzed. Characteristics were determined using various clinical parameters. Both the leptin and resistin levels were determined using enzyme-linked immunosorbent assay.ResultsThe highest levels of plasma leptin were in the MOD group with relatively lower levels in the SIDD and SIRD groups (P < 0.001). The SIRD group had a higher resistin concentration than the MARD group (P = 0.024) while no statistical significance in resistin levels was found between the SIDD and MOD groups. Logistic regression demonstrated that plasma resistin was associated with a higher risk of diabetic nephropathy (odds ratios (OR) = 2.255, P = 0.001). According to receiver operating characteristic (ROC) curves, the area under the curve (AUC) of resistin (0.748, 95% CI 0.610–0.887) was significantly greater than that of HOMA2-IR (0.447, 95% CI 0.280–0.614) (P < 0.05) for diabetic nephropathy in the SIRD group.ConclusionsLeptin levels were different in four subgroups of T2DM and were highest in the MOD group. Resistin was elevated in the SIRD group and was closely related to diabetic nephropathy.

Usefulness of subclassification of adult diabetes mellitus among inpatients in Japan.

Aims/IntroductionWe aimed to replicate a new diabetes subclassification based on objective clinical information at admission in a diabetes educational inpatient program. We also assessed the educational outcomes for each cluster.MethodsWe included diabetes patients who participated in the educational inpatient program during 2009–2020 and had sufficient clinical information for the cluster analysis. We applied a data‐driven clustering method proposed in a previous study and further evaluated the clinical characteristics of each cluster. We investigated the association between the clusters and changes in hemoglobin A1c level from the start of the education program. We also assessed the risk of re‐admission for the educational program.ResultsWe divided a total of 651 patients into five clusters. Their clinical characteristics followed the same pattern as in previous studies. The intercluster ranking of the cluster center coordinates showed strong correlation coefficients with those of the previous studies (mean ρ = 0.88). Patients classified as severe insulin‐resistant diabetes (cluster 3) showed a more pronounced progression of renal dysfunction than patients classified as the other clusters. The patients classified as severe insulin‐deficient diabetes (cluster 2) had the highest rate of reduction in hemoglobin A1c level from the start of the program (P < 0.01) and a tendency toward a lower risk of re‐admission for the education program (hazard ratio 0.47, P = 0.09).ConclusionWe successfully replicated the diabetes subclassification using objective clinical information at admission for the education program. In addition, we showed that severe insulin‐deficient diabetes patients tended to have better educational outcomes than patients classified as the other clusters.

Differences in the prevalence of erectile dysfunction between novel subgroups of recent-onset diabetes

Aims/hypothesisIn men with diabetes, the prevalence of erectile dysfunction increases with advanced age and longer diabetes duration and is substantially higher in men with type 2 diabetes than those with type 1 diabetes. This study aimed to evaluate the prevalence of erectile dysfunction among the five novel subgroups of recent-onset diabetes and determine the strength of associations between diabetes subgroups and erectile dysfunction.MethodsA total of 351 men with recent-onset diabetes (<1 year) from the German Diabetes Study baseline cohort and 124 men without diabetes were included in this cross-sectional study. Erectile dysfunction was assessed with the International Index of Erectile Function (IIEF) questionnaire. Poisson regression models were used to estimate associations between diabetes subgroups (each subgroup tested against the four other subgroups as reference) and erectile dysfunction (dependent binary variable), adjusting for variables used to define diabetes subgroups, high-sensitivity C-reactive protein and depression.ResultsThe prevalence of erectile dysfunction was markedly higher in men with diabetes than in men without diabetes (23% vs 11%, p = 0.004). Among men with diabetes, the prevalence of erectile dysfunction was highest in men with severe insulin-resistant diabetes (SIRD) (52%), lowest in men with severe autoimmune diabetes (SAID) (7%), and intermediate in men with severe insulin-deficient diabetes (SIDD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD) (31%, 18% and 29%, respectively). Men with SIRD had an adjusted RR of 1.93 (95% CI 1.04, 3.58) for prevalent erectile dysfunction (p = 0.038). Similarly, men with SIDD had an adjusted RR of 3.27 (95% CI 1.18, 9.10) (p = 0.023). In contrast, men with SAID and those with MARD had unadjusted RRs of 0.26 (95% CI 0.11, 0.58) (p = 0.001) and 1.52 (95% CI 1.04, 2.22) (p = 0.027), respectively. However, these associations did not remain statistically significant after adjustment.Conclusions/interpretationThe high RRs for erectile dysfunction in men with recent-onset SIRD and SIDD point to both insulin resistance and insulin deficiency as major contributing factors to this complication, suggesting different mechanisms underlying erectile dysfunction in these subgroups.Graphical abstract

Assessing reproducibility and utility of clustering of patients with type 2 diabetes and established CV disease (SAVOR -TIMI 53 trial).

ObjectiveTo assess the reproducibility and clinical utility of clustering-based subtyping of patients with type 2 diabetes (T2D) and established cardiovascular (CV) disease.MethodsThe cardiovascular outcome trial SAVOR-TIMI 53 (n = 16,492) was used. Analyses focused on T2D patients with established CV disease. Unsupervised machine learning technique called “k-means clustering” was used to divide patients into subtypes. K-means clustering including HbA1c, age of diagnosis, BMI, HOMA2-IR and HOMA2-B was used to assign clusters to the following diabetes subtypes: severe insulin deficient diabetes (SIDD); severe insulin-resistant diabetes (SIRD); mild obesity-related diabetes (MOD); mild age-related diabetes (MARD). We refer these subtypes as “clustering-based diabetes subtypes”. A simulation study using randomly generated data was conducted to understand how correlations between the above variables influence the formation of the cluster-based diabetes subtypes. The predictive utility of clustering-based diabetes subtypes for CV events (3-point MACE), renal function reduction (eGFR decrease >30%) and diabetic disease progression (introduction of additional anti-diabetic medication) were compared with conventional risk scores. Hazard ratios (HR) were estimated by Cox-proportional hazard models.ResultsIn the SAVOR-TIMI 53 trial based dataset, the percentage of the clustering-based T2D subtypes were; SIDD (18%), SIRD (17%), MOD (29%), MARD (37%). Using the simulated dataset, the diabetes subtypes could be largely reproduced from a log-normal distribution when including known correlations between variables. The predictive utility of clustering-based diabetic subtypes on CV events, renal function reduction, and diabetic disease progression did not show an advantage compared to conventional risk scores.ConclusionsThe consistent reproduction of four clustering-based T2D subtypes can be explained by the correlations between the variables used for clustering. Subtypes of T2D based on clustering had limited advantage compared to conventional risk scores to predict clinical outcome in patients with T2D and established CV disease.

Physical Fitness and Cardiovascular Risk Factors in Novel Diabetes Subgroups.

ContextPhysical inactivity promotes insulin resistance and increases the risk of diabetes and cardiovascular disease. Recently introduced clustering based on simple clinical measures identified diabetes subgroups (clusters) with different risks of diabetes-related comorbidities and complications.ObjectiveThis study aims to determine differences in physical fitness and cardiovascular risk between diabetes subgroups and a glucose-tolerant control group (CON). We hypothesized that the severe insulin-resistant diabetes (SIRD) subgroup would be associated with lower physical fitness and increased cardiovascular risk.MethodsThe physical fitness and cardiovascular risk of 746 participants with recent-onset diabetes (diabetes duration of < 12 months, aged 18-69 years) and 74 CONs of the German Diabetes Study (GDS), a prospective longitudinal cohort study, were analyzed. Main outcome measures included physical fitness (VO2max from spiroerogometry), endothelial function (flow- and nitroglycerin-mediated dilation), and cardiovascular risk scores (Framingham Risk Scores for Coronary Heart Disease [FRS-CHD] and Atherosclerotic CardioVascular Disease [ASCVD] risk score).ResultsVO2max was lower in SIRD than in CON, severe autoimmune diabetes (SAID) (both P < .001), and mild age-related diabetes (MARD) (P < .01) subgroups, but not different compared to severe insulin-deficient diabetes (SIDD) (P = .98) and moderate obesity-related diabetes (MOD) subgroups (P = .07) after adjustment for age, sex, and body mass index. Endothelial function was similar among all groups, whereas SAID had lower FRS-CHD and ASCVD than SIRD, MOD, and MARD (all P < .001).ConclusionDespite comparable endothelial function across all groups, SIRD showed the lowest physical fitness. Of note, SAID had the lowest cardiovascular risk within the first year after diabetes diagnosis compared to the other diabetes subgroups.

The role of circulating galectin-1 in type 2 diabetes and chronic kidney disease: evidence from cross-sectional, longitudinal and Mendelian randomisation analyses

Aims/hypothesisGalectin-1 modulates inflammation and angiogenesis, and cross-sectional studies indicate that galectin-1 may be a uniting factor between obesity, type 2 diabetes and kidney function. We examined whether circulating galectin-1 can predict incidence of chronic kidney disease (CKD) and type 2 diabetes in a middle-aged population, and if Mendelian randomisation (MR) can provide evidence for causal direction of effects.MethodsParticipants (n = 4022; 58.6% women) in the Malmö Diet and Cancer Study–Cardiovascular Cohort enrolled between 1991 and 1994 (mean age 57.6 years) were examined. eGFR was calculated at baseline and after a mean follow-up of 16.6 ± 1.5 years. Diabetes status was ascertained through registry linkage (mean follow-up of 18.4 ± 6.1 years). The associations of baseline galectin-1 with incident CKD and type 2 diabetes were assessed with Cox regression, adjusting for established risk factors. In addition, a genome-wide association study on galectin-1 was performed to identify genetic instruments for two-sample MR analyses utilising the genetic associations obtained from the Chronic Kidney Disease Genetics (CKDGen) Consortium (41,395 cases and 439,303 controls) and the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium (74,124 cases and 824,006 controls). One genome-wide significant locus in the galectin-1 gene region was identified (sentinel SNP rs7285699; p = 2.4 × 10−11). The association between galectin-1 and eGFR was also examined in individuals with newly diagnosed diabetes from the All New Diabetics In Scania (ANDIS) cohort.ResultsGalectin-1 was strongly associated with lower eGFR at baseline (p = 2.3 × 10−89) but not with incident CKD. However, galectin-1 was associated with increased risk of type 2 diabetes (per SD increase, HR 1.12; 95% CI 1.02, 1.24). Two-sample MR analyses could not ascertain a causal effect of galectin-1 on CKD (OR 0.92; 95% CI 0.82, 1.02) or type 2 diabetes (OR 1.05; 95% CI 0.98, 1.14) in a general population. However, in individuals with type 2 diabetes from ANDIS who belonged to the severe insulin-resistant diabetes subgroup and were at high risk of diabetic nephropathy, genetically elevated galectin-1 was significantly associated with higher eGFR (p = 5.7 × 10−3).Conclusions/interpretationGalectin-1 is strongly associated with lower kidney function in cross-sectional analyses, and two-sample MR analyses suggest a causal protective effect on kidney function among individuals with type 2 diabetes at high risk of diabetic nephropathy. Future studies are needed to explore the mechanisms by which galectin-1 affects kidney function and whether it could be a useful target among individuals with type 2 diabetes for renal improvement.Graphical abstract

Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial.

Data analyses from Swedish individuals with newly diagnosed diabetes have suggested that diabetes could be classified into five subtypes that differ with respect to the progression of dysglycaemia and the incidence of diabetes consequences. We assessed this classification in a multiethnic cohort of participants with established and newly diagnosed diabetes, randomly allocated to insulin glargine vs standard care. In total, 7017 participants from the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial were assigned to the five predefined diabetes subtypes (namely, severe auto-immune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, mild age-related diabetes) based on the age at diabetes diagnosis, BMI, HbA1c, fasting C-peptide levels and the presence of glutamate decarboxylase antibodies at baseline. Differences between diabetes subtypes in cardiovascular and renal outcomes were investigated using Cox regression models for a median follow-up of 6.2years. We also compared the effect of glargine vs standard care on hyperglycaemia, defined by having a mean post-randomisation HbA1c ≥6.5%, between subtypes. The five diabetes subtypes were replicated in the ORIGIN trial and exhibited similar baseline characteristics in Europeans and Latin Americans, compared with the initially described clusters in the Swedish cohort. We confirmed differences in renal outcomes, with a higher incidence of events in the severe insulin-resistant diabetes subtype compared with the mild age-related diabetes subtype (i.e., chronic kidney disease stage 3A: HR 1.49 [95% CI 1.31, 1.71]; stage 3B: HR 2.25 [1.82, 2.78]; macroalbuminuria: HR 1.56 [1.22, 1.99]). No differences were observed in the incidence of retinopathy and cardiovascular diseases after adjusting for multiple hypothesis testing. Diabetes subtypes also differed in glycaemic response to glargine, with a particular benefit of receiving glargine (vs standard care) in the severe insulin-deficient diabetes subtype compared with the mild age-related diabetes subtype, with a decreased occurrence of hyperglycaemia by 13% (OR 1.36 [1.30, 1.41] on glargine; OR 1.49 [1.43, 1.57] on standard care; p for interaction subtype × intervention = 0.001). Cluster analysis enabled the characterisation of five subtypes of diabetes in a multiethnic cohort. Both the incidence of renal outcomes and the response to insulin varied between diabetes subtypes. These findings reinforce the clinical utility of applying precision medicine to predict comorbidities and treatment responses in individuals with diabetes. ORIGIN trial, ClinicalTrials.gov NCT00069784.

Leukocyte Counts and T-Cell Frequencies Differ Between Novel Subgroups of Diabetes and Are Associated With Metabolic Parameters and Biomarkers of Inflammation.

Frequencies of circulating immune cells are altered in those with type 1 and type 2 diabetes compared with healthy individuals and are associated with insulin sensitivity, glycemic control, and lipid levels. This study aimed to determine whether specific immune cell types are associated with novel diabetes subgroups. We analyzed automated white blood cell counts (n = 669) and flow cytometric data (n = 201) of participants in the German Diabetes Study with recent-onset (<1 year) diabetes, who were allocated to five subgroups based on data-driven analysis of clinical variables. Leukocyte numbers were highest in severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) and lowest in severe autoimmune diabetes (SAID). CD4+ T-cell frequencies were higher in SIRD versus SAID, MOD, and mild age-related diabetes (MARD), and frequencies of CCR4+ regulatory T cells were higher in SIRD versus SAID and MOD and in MARD versus SAID. Pairwise differences between subgroups were partially explained by differences in clustering variables. Frequencies of CD4+ T cells were positively associated with age, BMI, HOMA2 estimate of β-cell function (HOMA2-B), and HOMA2 estimate of insulin resistance (HOMA2-IR), and frequencies of CCR4+ regulatory T cells with age, HOMA2-B, and HOMA2-IR. In conclusion, different leukocyte profiles exist between novel diabetes subgroups and suggest distinct inflammatory processes in these diabetes subgroups.

Clinical Characteristics and Risk of Diabetic Complications in Data-Driven Clusters Among Type 2 Diabetes.

BackgroundThis study aimed to cluster newly diagnosed patients and patients with long-term diabetes and to explore the clinical characteristics, risk of diabetes complications, and medication treatment related to each cluster.Research Design and MethodsK-means clustering analysis was performed on 1,060 Chinese patients with type 2 diabetes based on five variables (HbA1c, age at diagnosis, BMI, HOMA2-IR, and HOMA2-B). The clinical features, risk of diabetic complications, and the utilization of elven types of medications agents related to each cluster were evaluated with the chi-square test and the Tukey–Kramer method.ResultsFour replicable clusters were identified, severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). In terms of clinical characteristics, there were significant differences in blood pressure, renal function, and lipids among clusters. Furthermore, individuals in SIRD had the highest prevalence of stages 2 and 3 chronic kidney disease (CKD) (57%) and diabetic peripheral neuropathy (DPN) (67%), while individuals in SIDD had the highest risk of diabetic retinopathy (32%), albuminuria (31%) and lower extremity arterial disease (LEAD) (13%). Additionally, the difference in medication treatment of clusters were observed in metformin (p = 0.012), α-glucosidase inhibitor (AGI) (p = 0.006), dipeptidyl peptidase 4 inhibitor (DPP-4) (p = 0.017), glucagon-like peptide-1 (GLP-1) (p <0.001), insulin (p <0.001), and statins (p = 0.006).ConclusionsThe newly diagnosed patients and patients with long-term diabetes can be consistently clustered into featured clusters. Each cluster had significantly different patient characteristics, risk of diabetic complications, and medication treatment.

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study

Aims/hypothesisFive clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic.MethodsIn three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres. Finally, we compared the time to insulin requirement for each cluster.ResultsFive distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6–90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression.Conclusions/interpretationClusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.Graphical abstract

Non-alcoholic fatty liver disease in type 2 diabetes - A specific entity?

Individuals with obesity or type 2 diabetes (T2D) have an increased risk of developing non-alcoholic fatty liver disease (NAFLD). In insulin-resistant states, altered adipose tissue function may be the initial abnormality underlying NAFLD. Hepatic lipid oversupply interferes with insulin signalling and mitochondrial function. In obese individuals, adaptation of hepatic mitochondrial respiration fails with the progression of NAFLD and can activate pro-inflammatory pathways. T2D as well as type 1 diabetes are associated with altered hepatic mitochondrial function. Screening for NAFLD remains challenging especially in those with diabetes because liver enzymes are often in the normal range and the performance of NAFLD scores is limited. Patients with T2D and severe insulin-resistant diabetes (SIRD) have the highest prevalence of NAFLD at diagnosis and the greatest risk of progression. In this subgroup, the single-nucleotide-polymorphism (SNP) rs738409(G) of the patatin-like phospholipase domain-containing protein 3 (PNPLA3) gene is associated with high liver fat content and adipose tissue insulin resistance. This frequent SNP is also known to be associated with lean NAFLD so that genetic testing for this and other SNPs could improve future screening strategies to identify high-risk individuals. Although lifestyle modifications are effective, this approach is limited owing to difficulties with compliance and several classes of drugs are being tested to treat NAFLD. Antihyperglycaemic drugs such as glucagon-like peptide 1 receptor agonists (GLP-1 RA), sodium-glucose cotransporter 2 inhibitors (SGLT2i) and pioglitazone are promising and halt the progression of NAFLD. In conclusion, although NAFLD in diabetes may not be a separate entity, there are specific features to its pathogenesis and clinical management.

High Insulin Resistance in Pancreatic Adenocarcinoma

Abstract Introduction: Diabetes mellitus and obesity is associated with increased risk of pancreatic cancer which has been postulated to be due to pancreatic beta cell dysfunction and increased insulin resistance. Pancreatic cancer also exerts its effect on pancreatic beta cells, reducing insulin secretion, affecting glucose uptake and increasing insulin resistance. We present a case of pancreatic adenocarcinoma with severe insulin resistance and uncontrolled diabetes which reversed after tumor removal. Clinical Description: 79 year old male patient with no prior diagnosis of diabetes, history of hypertension, atrial fibrillation, cerebrovascular accident got admitted to the hospital with painless jaundice, dark colored urine and pruritus for a month. He also endorsed polyuria, polydipsia and weight loss for 2 months. He underwent Magnetic Resonance Cholangiopancreatography and was diagnosed with a 2.8 cm pancreatic head mass, consistent with adenocarcinoma along with intraductal papillary mucinous neoplasm. He was diagnosed with new onset of diabetes, A1c 12.6% during that hospitalization. He weighed about 75 kg and height 170cm (BMI 26.6). Labs showed normal renal function, deranged liver enzymes with direct hyperbilirubinemia, elevated transaminases and alkaline phosphatase. C-peptide level was 1.2 ng/ml for a blood glucose of 197 mg/dl. He was discharged on once daily insulin Glargine 25 units and mealtime insulin Lispro 10 units three times a day (TID) requiring 0.7 units/kg. During follow-up, his insulin requirement started to increase despite proper insulin injection technique and medication compliance. He required glargine 150 units/day and U500 insulin 50 units TID requiring about 4 units/kg. He underwent Whipple’s procedure, partial pancreas resection after a month of his diagnosis. Patient was started on clear liquid diet and his blood glucose started to get better on post-op day 1 with just correctional insulin. He was discharged on Repaglinide 0.5mg TID with each meal and all his insulin was discontinued. His blood glucose was in range of 80 to 160 mg/dl with Repaglinide during clinical follow-up with regular diabetic diet. His severe insulin resistance got reversed after the resection of pancreatic neoplasm. Conclusion: The pathogenesis of pancreatic cancer associated diabetes has not been studied well. Basic science research found that adrenomedullin, an amionopeptide, is up-regulated in patients with pancreatic cancer and causes insulin resistance in β Cells. Cancer theories also found about metabolic reprogramming and metabolic cross talk happens between pancreatic cancer and peripheral tissue, inhibiting cellular glucose intake and inducing insulin resistance. More research is required to understand these paraneoplastic phenomenon caused by diabetogenic tumor-secreted product in pancreatic cancer associated diabetes.

Successful long-term use of pioglitazone in Berardinelli-Seip lipodystrophy-associated diabetes.

SummaryBerardinelli–Seip congenital lipodystrophy (BSCL) is a rare autosomal recessive disease, characterized by the absence of subcutaneous adipose tissue, leptin deficiency and severe metabolic complications, such as insulin resistance, diabetes mellitus, and dyslipidemia. The most common mutation occurs in BCSL2 which encodes seipin, a protein involved in adipogenesis. We report a patient with BSCL who was diagnosed with diabetes at 11 years old. He was started on metformin 1000 mg twice daily, which lowered glycated hemoglobin (HbA1c) to less than 7%. Four months later, HbA1c raised above 7.5%, indicating secondary failure to metformin. Therefore, we added the peroxisome proliferator-activated receptor-gamma (PPARG) agonist, pioglitazone. Since then and for the last 5 years his HbA1c has been within the normal range. These findings indicate that pioglitazone should be considered as a valid alternative in the treatment of diabetes in BSCL patients. To the best of our knowledge, this is the first specific report of successful long-term treatment with pioglitazone in a patient with BSCL.Learning pointsBerardinelli–Seip congenital lipodystrophy (BSCL) is a recessive genetic disorder associated with severe insulin resistance and early onset diabetes, usually around puberty. Failure of oral antidiabetic medication occurs within the first years of treatment in BSCL patients.When failure to achieve metabolic control with metformin occurs, pioglitazone may be a safe option, lowering insulin resistance and improving both the metabolic control and lipodystrophic phenotype.Herein we show that pioglitazone can be a safe and efficient alternative in the long-term treatment of BSCL patients with diabetes.

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

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

Identification of two novel subgroups in patients with diabetes mellitus and their association with clinical outcomes: A two‐step cluster analysis

Aims/IntroductionThe aim of this study was to determine whether distinct subphenotypes of patients with type 2 diabetes in the European classification exist in Chinese populations, and to further establish novel subphenotypes more suitable for Chinese populations.Material and MethodsThe research retrospectively analyzed 5414 patients with type 2 diabetes from the National Clinical Research Center for Metabolic Diseases Diabetes Center in China, and a two‐step cluster analysis was carried out. First, we confirmed the European classification in Chinese populations by six parameters, including age at disease onset, body mass index, glycosylated hemoglobin, homeostatic model assessment 2 to estimate β‐cell function and insulin resistance, and glutamate decarboxylase antibodies. Furthermore, triglycerides and uric acid were added to refine the cluster analysis, and Cox regression was used to evaluate the risk of diabetic complications.ResultsJust three clusters were replicated in our cohort according to Emma Ahlqvist's European classification. When other variables were added to the cluster analysis, seven subgroups were identified, including five clusters of the European classification and two novel subgroups, namely, uric acid‐related diabetes and inheritance‐related diabetes. Compared with patients with inheritance‐related diabetes, patients with severe insulin‐resistant diabetes showed a higher risk of diabetic peripheral neuropathy, hypertension and chronic kidney disease, and the uric acid‐related diabetes subgroup showed a higher risk of coronary heart disease, cerebral vascular disease and end‐stage renal disease. Patients with severe insulin‐deficient diabetes showed a higher risk of diabetic retinopathy and diabetic foot than those with inheritance‐related diabetes. Furthermore, there were sex‐specific associations between subgroups and clinical outcomes. No significant difference was observed in the prevalence of cancer in each subgroup.ConclusionsSeven subgroups of type 2 diabetes were identified in Chinese populations, with distinct characteristics and disparate clinical outcomes. This etiology‐based stratification might contribute to the diagnosis and management of type 2 diabetes.

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

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

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

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

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

Diabetes Mellitus is a chronic and lifelong disease that incurs a huge burden to healthcare systems. Its prevalence is on the rise worldwide. Diabetes is more complex than the classification of Type 1 and 2 may suggest. The purpose of this systematic review was to identify the research studies that tried to find new sub-groups of diabetes patients by using unsupervised learning methods. The search was conducted on Pubmed and Medline databases by two independent researchers. All time publications on cluster analysis of diabetes patients were selected and analysed. Among fourteen studies that were included in the final review, five studies found five identical clusters: Severe Autoimmune Diabetes; Severe Insulin-Deficient Diabetes; Severe Insulin-Resistant Diabetes; Mild Obesity-Related Diabetes; and Mild Age-Related Diabetes. In addition, two studies found the same clusters, except Severe Autoimmune Diabetes cluster. Results of other studies differed from one to another and were less consistent. Cluster analysis enabled finding non-classic heterogeneity in diabetes, but there is still a necessity to explore and validate the capabilities of cluster analysis in more diverse and wider populations.

Are the Different Diabetes Subgroups Correlated With All-Cause, Cancer-Related, and Cardiovascular-Related Mortality?

Numerous studies have shown that cardiovascular disease (CVD) represents the most important cause of mortality among people with diabetes mellitus (DM). However, no studies have evaluated the risk of CVD-related mortality among different DM subgroups. We aimed to examine all-cause, CVD-related, and cancer-related mortality for different DM subgroups. We included participants (age ≥ 20 years) from the National Health and Nutrition Examination Survey III (NHANES III) data set. We evaluated the risks of all-cause and cause-specific (CVD and cancer) mortality among 5 previously defined diabetes subgroups: 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). The hazard ratios (HRs) for all-cause and cause-specific (CVD and cancer) mortality were measured for each of the 5 DM subgroups. We also evaluated the odds ratios (ORs) for retinopathy and nephropathy in each subgroup. A total of 712 adults were enrolled and the median follow-up time was 12.71 years (range, 0.25-18.08 years). The number of deaths in the 5 subgroups (SAID, SIDD, SIRD, MOD, and MARD) were 50, 75, 64, 7, and 18, respectively, and the number of CVD-related deaths in the 5 subgroups was 29, 30, 26, 2, and 11, respectively. Compared to the MOD subgroup, the adjusted HRs and 95% CIs of CVD-related mortality for the SAID, SIDD, SIRD, and MARD subgroups were 3.23 (95% CI, 0.77-13.61), 2.87 (95% CI, 0.68-12.06), 2.23 (95% CI, 0.53-9.50), and 4.75 (95% CI, 1.05-21.59), respectively (the HR for the MARD subgroup had a P value of .04). In addition, compared to the MARD subgroup, the adjusted ORs and 95% CIs for retinopathy in the SAID and SIDD groups were 2.38 (95% CI, 1.13-5.01, P = .02) and 3.34 (95% CI, 1.17-6.88, P = .001), respectively. The ORs for nephropathy were nonsignificant. Our study of patients from the NHANES III data set indicated that among the different DM subgroups, the MARD subgroup tended to have a higher CVD-related mortality than the MOD subgroup. The all-cause and cancer-related mortality rates were similar across the different diabetes subgroups. In addition, compared to the MARD subgroup, the SAID and SIDD subgroups had a higher retinopathy risk, but there was no difference in nephropathy among the subgroups.