Insulin-deficient Diabetes Research Articles

Hyperglycemia and reduced adiposity of streptozotocin-induced diabetic mice are not alleviated by oral benzylamine supplementation.

BACKGROUNDBenzylamine (Bza) oral administration delays the onset of hyperglycemia in insulin-resistant db-/- mice; a genetic model of obesity and type 2 diabetes.AIMTo extend the antihyperglycemic properties of oral benzylamine to a model of insulin-deficient type 1 diabetes.METHODSMale Swiss mice were rendered diabetic by streptozotocin treatment (STZ) and divided in two groups: one received 0.5% Bza as drinking solution for 24 d (STZ Bza-drinking) while the other was drinking water ad libitum. Similar groups were constituted in age-matched, nondiabetic mice. Food intake, liquid intake, body weight gain and nonfasting blood glucose levels were followed during treatment. At the end of treatment, fasted glycemia, liver and white adipose tissue (WAT) mass were measured, while glucose uptake assays were performed in adipocytes.RESULTSSTZ diabetic mice presented typical features of insulin-deficient diabetes: reduced body mass and increased blood glucose levels. These altered parameters were not normalized in the Bza-drinking group in spite of restored food and water intake. Bza consumption could not reverse the severe fat depot atrophy of STZ diabetic mice. In the nondiabetic mice, no difference was found between control and Bza-drinking mice for any parameter. In isolated adipocytes, hexose uptake was partially activated by 0.1 mmol/L Bza in a manner that was obliterated in vitro by the amine oxidase inhibitor phenelzine and that remained unchanged after Bza supplementation. Oxidation of 0.1 mmol/L Bza in WAT was lower in STZ diabetic than in normoglycemic mice.CONCLUSIONBza supplementation could not normalize the altered glucose handling of STZ diabetic mice with severe WAT atrophy. Consequently, its antidiabetic potential in obese and diabetic rodents does not apply to lipoatrophic type 1 diabetic mice.

Efficacy of glucagon-like peptide-1 and estrogen dual agonist in pancreatic islets protection and preclinical models of insulin-deficient diabetes

Efficacy of glucagon-like peptide-1 and estrogen dual agonist in pancreatic islets protection and preclinical models of insulin-deficient diabetes

Detecting Sarcopenia Risk by Diabetes Clustering: A Japanese Prospective Cohort Study.

Previous studies have assessed the usefulness of data-driven clustering for predicting complications in patients with diabetes mellitus. However, whether the diabetes clustering is useful in predicting sarcopenia remains unclear. To evaluate the predictive power of diabetes clustering for the incidence of sarcopenia in a prospective Japanese cohort. Three-year prospective cohort study. We recruited Japanese patients with type 1 or type 2 diabetes mellitus (n = 659) between January 2018 and February 2020 from the Fukushima Diabetes, Endocrinology, and Metabolism cohort. Kaplan-Meier and Cox proportional hazards models were used to measure the predictive values of the conventional and clustering-based classification of diabetes mellitus for the onset of sarcopenia. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia (AWGS) 2019 consensus update. Onset of sarcopenia. Cluster analysis of a Japanese population revealed 5 diabetes clusters: cluster 1 [severe autoimmune diabetes (SAID)], cluster 2 [severe insulin-deficient diabetes (SIDD)], cluster 3 (severe insulin-resistant diabetes, cluster 4 (mild obesity-related diabetes), and cluster 5 (mild age-related diabetes). At baseline, 38 (6.5%) patients met the AWGS sarcopenia criteria, and 55 had newly developed sarcopenia within 3 years. The SAID and SIDD clusters were at high risk of developing sarcopenia after correction for known risk factors. This study reveals that among the 5 diabetes clusters, the SAID and SIDD clusters are at a high risk for developing sarcopenia. Clustering-based stratification may be beneficial for predicting and preventing sarcopenia in patients with diabetes.

Clinical Characteristicsof Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis.

IntroductionThis study aimed to explore the novel classification of inpatients with new-onset diabetes in Eastern China by the cluster-based classification method and compare the clinical characteristics among the different subgroups.MethodsA total of 1017 Inpatients with new-onset diabetes of five hospitals in Eastern China were included in the study. Clustering analysis was used to cluster the data into five subgroups according to six basic variables. The differences in clinical characteristics, treatments, and the prevalence of diabetes-related diseases among the five subgroups were analyzed by multiple groups comparisons and pairwise comparisons. The risk of diabetes-related diseases in the five subgroups was compared by calculating odd ratio (OR). P value < 0.05 was considered significant.ResultsFive subgroups were obtained by clustering analysis with the highest proportion of patients with severe insulin-deficient diabetes (SIDD) 451 (44.35%), followed by patients with mild age-related diabetes (MARD) 236 (23.21%), patients with mild obesity-related diabetes (MOD) 207 (20.35%), patients with severe insulin-resistant diabetes (SIRD) 81 (7.96%), and patients with severe autoimmune diabetes (SAID) 42 (4.13%). Five subtypes had their own unique characteristics and treatments. The prevalence and risk of diabetes-related complications and comorbidities were also significantly different among the five subtypes. Diabetic kidney disease (DKD) was the most common in SIRD group. Patients in SIDD, SIRD, and MARD groups were more likely to develop cardiovascular disease (CVD) and/or stroke, diabetic peripheral vascular disease (DPVD), and diabetic distal symmetric polyneuropathy (DSPN). The prevalence and risk of metabolic syndrome (MS) were the highest in MOD and SIRD groups. Patients in SAID group had the highest prevalence and risk of diabetic ketoacidosis (DKA). Patients with MOD were more likely to develop non-alcoholic fatty liver disease (NAFLD).ConclusionsThe inpatients with new-onset diabetes in Eastern China had the unique clustering distribution. The clinical characteristics, treatments, and diabetes-related complications and comorbidities of the five subgroups were different, which may provide the basis for precise treatments of diabetes.

Distribution and characteristics of newly-defined subgroups of type 2 diabetes in randomised clinical trials: Post hoc cluster assignment analysis of over 12,000 study participants

AimsNewly-defined subgroups of type 2 diabetes mellitus (T2DM) have been reported from real-world cohorts but not in detail from randomised clinical trials (RCTs). MethodsT2DM participants, uncontrolled on different pre-study therapies (n = 12.738; 82 % Caucasian; 44 % with diabetes duration > 10 years) from 14 RCTs, were assigned to new subgroups according to age at onset of diabetes, HbA1c, BMI, and fasting C-peptide using the nearest centroid approach. Subgroup distribution, characteristics and influencing factors were analysed. ResultsIn both, pooled and single RCTs, “mild-obesity related diabetes” predominated (45 %) with mean BMI of 35 kg/m2. “Severe insulin-resistant diabetes” was found least often (4.6 %) and prevalence of “mild age-related diabetes” (23.9 %) was mainly influenced by age at onset of diabetes and age cut-offs. Subgroup characteristics were widely comparable to those from real-world cohorts, but all subgroups showed higher frequencies of diabetes-related complications which were associated with longer diabetes duration. A high proportion of “severe insulin-deficient diabetes” (25.4 %) was identified with poor pre-study glycaemic control. ConclusionsClassification of RCT participants into newly-defined diabetes subgroups revealed the existence of a heterogeneous population of T2DM. For future RCTs, subgroup-based randomisation of T2DM will better define the target population and relevance of the outcomes by avoiding clinical heterogeneity.

Effects of antidiabetic agents on Alzheimer's disease biomarkers in experimentally induced hyperglycemic rat model by streptozocin.

Alzheimer's disease is the most common cause of dementia in the elderly population. It is characterized by the accumulation of amyloid β and intraneuronal neurofibrillary tangles in the brain. Increasing evidence shows that the disturbance of insulin signalling in the brain may contribute to the pathophysiology of Alzheimer's disease. In type 1 diabetes, these disruptions are caused by hypoinsulinemia, but in type 2 diabetes, they are caused by insulin resistance and decreased insulin secretion. Multiple studies have shown that diabetes is connected with an increased risk of acquiring Alzheimer's disease. The aim of this study was to investigate the impact of anti-diabetic agents on Alzheimer's disease progression and the levels of Alzheimer's biomarkers in a hyperglycaemic rat model, which was induced by intraperitoneal injection of streptozocin to produce insulin-deficient diabetes. Thirty-six male Wistar albino rats were allocated into six groups of six rats each. Group I was the negative control group. Intraperitoneal injections of streptozocin (42mg/kg) were used once for the five experimental groups. Group II served as the positive control group. The rats in Groups III, IV, V, and VI received metformin (300mg/kg), donepezil (10mg/kg), insulin glargine (3 unit/animal), and glibenclamide (10mg/kg), respectively, for 21 days. Inducing hyperglycaemia in rats significantly increased the levels of serum glucose, haemoglobin A1c, total cholesterol, triglycerides, high-density lipoprotein, interleukin 6, tumour necrosis factor alpha, amyloid β 42, total plasma tau, and neurofilament light. A significant increase was also found in brain amyloid β 42, nitric oxide, acetylcholinesterase, malondialdehyde, β secretase, and phosphorylated microtubule-associated protein tau. The greatest statistically significant reductions in serum glucose, haemoglobin A1c, triglycerides, amyloid β 42, total plasma tau, brain amyloid β 42, acetylcholinesterase, and malondialdehyde were observed in rats treated with metformin. In contrast, rats treated with donepezil demonstrated the greatest statistically significant reduction in serum tumour necrosis factor alpha, brain nitric oxide, and β secretase. The levels of neurofilament light and phosphorylated microtubule-associated protein tau in the brains of rats treated with insulin glargine were significantly lower than the other treatment groups. The total cholesterol and low-density lipoprotein levels in rats treated with glibenclamide exhibited the most statistically significant reductions of all the treatment groups. Metformin and donepezil, when administered at appropriate doses, were shown to successfully lower most plasma and brain biomarkers, including glucose, triglycerides, tumour necrosis factor alpha, amyloid β 42, nitric oxide, acetylcholinesterase, malondialdehyde, and β secretase in rats suffering from Diabetes Mellitus. As a result of this research, we suggest that metformin, either alone or in conjunction with donepezil, might be an excellent drug of choice for neuro-regeneration and risk reduction in Alzheimer's like disease.

The efficacy of canagliflozin in diabetes subgroups stratified by data-driven clustering or a supervised machine learning method: a post hoc analysis of canagliflozin clinical trial data.

Data-driven diabetes subgroups have shown distinct clinical characteristics and disease progression, although there is a lack of evidence that this information can guide clinical decisions. We aimed to investigate whether diabetes subgroups, identified by data-driven clustering or supervised machine learning methods, respond differently to canagliflozin. We pooled data from five randomised, double-blinded clinical trials of canagliflozin at an individual level. We applied the coordinates from the All New Diabetics in Scania (ANDIS) study to form four subgroups: mild age-related diabetes (MARD); severe insulin-deficient diabetes (SIDD); mild obesity-related diabetes (MOD) and severe insulin-resistant diabetes (SIRD). Machine learning models for HbA1c lowering (ML-A1C) and albuminuria progression (ML-ACR) were developed. The primary efficacy endpoint was reduction in HbA1c at 52 weeks. Concordance of a model was defined as the difference between predicted HbA1c and actual HbA1c decline less than 3.28 mmol/mol (0.3%). The decline in HbA1c resulting from treatment was different among the four diabetes clusters (pinteraction=0.004). In MOD, canagliflozin showed a robust glucose-lowering effect at week 52, compared with other drugs, with least-squares mean of HbA1c decline [95% CI] being 6.6 mmol/mol (4.1, 9.2) (0.61% [0.38, 0.84]) for sitagliptin, 7.1 mmol/mol (4.7, 9.5) (0.65% [0.43, 0.87]) for glimepiride, and 9.8 mmol/mol (9.0, 10.5) (0.90% [0.83, 0.96]) for canagliflozin. This superiority persisted until 104 weeks. The proportion of individuals who achieved HbA1c <53 mmol/mol (<7.0%) was highest in sitagliptin-treated individuals with MARD but was similar among drugs in individuals with MOD. The ML-A1C model and the cluster algorithm showed a similar concordance rate in predicting HbA1c lowering (31.5% vs 31.4%, p=0.996). Individuals were divided into high-risk and low-risk groups using ML-ACR model according to their predicted progression risk for albuminuria. The effect of canagliflozin vs placebo on albuminuria progression differed significantly between the high-risk (HR 0.67 [95% CI 0.57, 0.80]) and low-risk groups (HR 0.91 [0.75, 1.11]) (pinteraction=0.016). Data-driven clusters of individuals with diabetes showed different responses to canagliflozin in glucose lowering but not renal outcome prevention. Canagliflozin reduced the risk of albumin progression in high-risk individuals identified by supervised machine learning. Further studies with larger sample sizes for external replication and subtype-specific clinical trials are necessary to determine the clinical utility of these stratification strategies in sodium-glucose cotransporter 2 inhibitor treatment. The application for the clinical trial data source is available on the YODA website ( http://yoda.yale.edu/ ).

β cell regeneration and novel strategies for treatment of diabetes (Review).

The etiology of diabetes is primarily attributed to the lack of functioning β cells, which in-turn leads to insulin deficiency or insulin resistance, and this ultimately leads to β cell dysfunction. Restoring the number and function of β cells is an effective means of improving or even curing diabetes. β cell regeneration is a potential method for increasing the number of functioning β cells. In addition to self-duplication of pancreatic β cells, β cells can be regenerated from embryonic stem cells, human induced pluripotent stem cells and pancreatic stem cells. Based on these mechanisms, proliferation and differentiation into functional β cells in vitro is one of the most promising strategies for treatment of diabetes. Although β cell regeneration has significant potential in the treatment of insulin-deficient diabetes, and significant progress has been made in this regard, there remains challenges which prevent its use in the clinic.

Mouse models of type 1 diabetes and their use in skeletal research.

In this review, we describe the three primary mouse models of insulin-deficiency diabetes that have been used to study the effects of type 1 diabetes (T1D) on skeletal outcomes. These models include streptozotocin (chemically)-induced diabetes, autoimmune-mediated diabetes (the nonobese diabetes mouse), and a mutation in the insulin gene (the Akita mouse). We then describe the skeletal findings and/or skeletal phenotypes that have been delineated using these models. Humans with T1D have decreased bone mineral density and an increased risk for fragility fracture. Mouse models of insulin-deficiency diabetes (hereafter denoted as T1D) in many ways recapitulate these skeletal deficits. Utilizing techniques of microcomputed tomography, bone histomorphometry, biomechanical testing and fracture modeling, bone biomarker analysis, and Raman spectroscopy, mouse models of T1D have demonstrated abnormalities in bone mineralization, bone microarchitecture, osteoblast function, abnormal bone turnover, and diminished biomechanical properties of bone. Mouse models have provided significant insights into the underlying mechanisms involved in the abnormalities of bone observed in T1D in humans. These translational models have provided targets and pathways that may be modifiable to prevent skeletal complications of T1D.

1247-P: Subgroup Analysis of Diabetic Patients Living in Tibet Plateau

Background: recently, a novel subgroups of adult-onset diabetic patients has been reported. Their work implied that type 2 diabetic patients can be classified into four groups. However, the utility of this classification method in Tibet populations and the clinical character of each cluster of Tibetan patients is unknown. Methods: this is a cross-sectional study, patients newly diagnosed with type 2 diabetes was analyzed. Data was collected from hospital information system (HIS) , and homoeostasis model assessment 2 of β-cell function (HOMA2-B) and insulin resistance (HOMA2-IR) was calculated using fasting glucose and fasting C-peptide by HOMA calculator. The age at onset of diabetes, Hba1c, BMI, HOMA2-B and HOMA2-IR was used for clustering. K-means cluster method was used with k value of 4. The stability of each cluster was assessed by resampling the dataset and computing Jaccard similarities. All statistical analysis was performed on R and fpc package. Result: A total of 581 patients was enrolled, the four clusters with similar clinical aspect is observed in Tibetan population and the average jaccard similarities is over 0.7. The same as Ji's result, the largest subgroup is mild age-related diabetes cluster (MARD) 255 out of 581 (44.3%) patients is classified into this cluster. And 147 (25.6%) patients is classified into mild obesity related diabetes (MOD) . The proportion of severe insulin- resistant related diabetes (SIRD) (1.4%) is lower and those of severe insulin-deficient related diabetes (SIDD) (28.7%) is higher compared to Ji's study. This result implies that there may be more severe insulin-deficient patients and less severe insulin-resistant patients in Tibet plateau. Discussion: in this study, we found out that the cluster method developed by Emma Ahlqvist is suitable for Tibetan population, compared to the rest Chinese population, there're more severe insulin-deficient patient, and less severe insulin-resistance patients. Disclosure W.Suyuan: None. C.Zhang: None.

200-OR: IER3IP1 Is Critical for Maintaining Glucose Homeostasis through Regulating the Endoplasmic Reticulum Function and Survival of ß-Cells

Recessive mutations in IER3IP1 (Immediate Early Response 3 Interacting Protein 1) cause a triad of microcephaly, epilepsy, and permanent neonatal diabetes syndrome (MEDS) . IER3IP1 encodes an endoplasmic reticulum (ER) membrane protein, which is crucial for brain development, however the role of IER3IP1 in β cell function and glucose homeostasis remains unknown. Herein, we established a new β cell-specific IER3IP1 knockout (IER3IP1-βKO) mouse model and found that IER3IP1 deficiency in β cells causes severe early onset insulin deficient diabetes. Functional studies revealed a markedly dilated ER along with significantly increased proinsulin misfolding and elevated expression of the ER chaperones including PDI, ERO1, BiP, and P58IPK. Transcriptome analysis confirmed by qRT-PCR revealed a markedly decreased expression of genes associated with β cell maturation, cell cycle, and anti-apoptosis, whereas there was a significantly increased expression of anti-proliferation genes. Indeed, multiple independent approaches further demonstrated that IER3IP1-βKO impaired β cell maturation and proliferation, and promoted cell death. These data not only uncover a critical role of IER3IP1 in normal β cell function and survival, but they also provide insight into the mechanism (s) by which loss-of-function mutations of IER3IP1 lead to diabetes. Disclosure J.Yang: None. J.Sun: None. N.Li: None. T.Liu: None. S.Wang: None. X.Zhang: None. P.Arvan: n/a. M.Liu: None. J.Zhen: None. W.Feng: None. Z.Fan: None. Y.Huang: None. H.Shu: None. X.Li: None. J.Qiao: None. Y.Fan: None. Funding National Natural Science Foundation of China (81830025, 81620108004, 81870533, 81900720, 81800733, 82100865, 82070805, and 81870535)

1095-P: Clusters of Prediabetes and Type 2 Diabetes Stratify All-Cause Mortality in a Cohort of Participants Undergoing Invasive Coronary Diagnostics

Risk for complications and mortality among patients with type 2 diabetes (T2D) is heterogenous. Different trajectories can be identified in the prediabetic state, which comprises heterogenous metabolic clusters. It is not known whether such pathophysiologic clusters of prediabetes and diabetes affect 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) who have since been followed-up for a median of 14.5 (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 analyzed 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) age-, BMI-, HbA1c- and sex-adjusted. In the T2D group, 5were assigned to mild age-related diabetes (MARD) , 183 to severe insulin-resistant diabetes (SIRD) , 84 to mild obesity-related diabetes (MOD) , to severe insulin-deficient diabetes (SIDD) . Compared to the low-risk nondiabetes group, crude mortality was not different in MOD. An 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 already undergoing invasive coronary diagnostics. Novel clustering approaches based on glucose metabolism can identify persons who require special attention as they are in danger of increased mortality Disclosure K.Prystupa: None. G.Delgado: None. A.P.Moissl: None. M.E.Kleber: Employee; SYNLAB Holding Deutschland GmbH. M.Heni: Advisory Panel; Boehringer Ingelheim International GmbH, Research Support; Boehringer Ingelheim International GmbH, Sanofi, Speaker's Bureau; Amryt Pharma Plc, Boehringer Ingelheim International GmbH, Novo Nordisk. A.L.Birkenfeld: None. A.Fritsche: Advisory Panel; Boehringer Ingelheim International GmbH, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. W.Maerz: None. R.Wagner: Advisory Panel; Akcea Therapeutics, Daiichi Sankyo, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Lilly, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. Funding German Federal Ministry of Education and Research (BMBF) (01GI0925)

Novel Subgroups of Type 2 Diabetes Display Different Epigenetic Patterns That Associate With Future Diabetic Complications.

OBJECTIVEType 2 diabetes (T2D) was recently reclassified into severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD), which have different risk of complications. We explored whether DNA methylation differs between these subgroups and whether subgroup-unique methylation risk scores (MRSs) predict diabetic complications.RESEARCH DESIGN AND METHODSGenome-wide DNA methylation was analyzed in blood from subjects with newly diagnosed T2D in discovery and replication cohorts. Subgroup-unique MRSs were built, including top subgroup-unique DNA methylation sites. Regression models examined whether MRSs associated with subgroups and future complications.RESULTSWe found epigenetic differences between the T2D subgroups. Subgroup-unique MRSs were significantly different in those patients allocated to each respective subgroup compared with the combined group of all other subgroups. These associations were validated in an independent replication cohort, showing that subgroup-unique MRSs associate with individual subgroups (odds ratios 1.6–6.1 per 1-SD increase, P < 0.01). Subgroup-unique MRSs were also associated with future complications. Higher MOD-MRS was associated with lower risk of cardiovascular (hazard ratio [HR] 0.65, P = 0.001) and renal (HR 0.50, P < 0.001) disease, whereas higher SIRD-MRS and MARD-MRS were associated with an increased risk of these complications (HR 1.4–1.9 per 1-SD increase, P < 0.01). Of 95 methylation sites included in subgroup-unique MRSs, 39 were annotated to genes previously linked to diabetes-related traits, including TXNIP and ELOVL2. Methylation in the blood of 18 subgroup-unique sites mirrors epigenetic patterns in tissues relevant for T2D, muscle and adipose tissue.CONCLUSIONSWe identified differential epigenetic patterns between T2D subgroups that associated with future diabetic complications. These data support a reclassification of diabetes and the need for precision medicine in T2D subgroups.

Integrating polygenic risk scores in the prediction of type 2 diabetes risk and subtypes in British Pakistanis and Bangladeshis: A population-based cohort study.

Type 2 diabetes (T2D) is highly prevalent in British South Asians, yet they are underrepresented in research. Genes & Health (G&H) is a large, population study of British Pakistanis and Bangladeshis (BPB) comprising genomic and routine health data. We assessed the extent to which genetic risk for T2D is shared between BPB and European populations (EUR). We then investigated whether the integration of a polygenic risk score (PRS) for T2D with an existing risk tool (QDiabetes) could improve prediction of incident disease and the characterisation of disease subtypes. In this observational cohort study, we assessed whether common genetic loci associated with T2D in EUR individuals were replicated in 22,490 BPB individuals in G&H. We replicated fewer loci in G&H (n = 76/338, 22%) than would be expected given power if all EUR-ascertained loci were transferable (n = 101, 30%; p = 0.001). Of the 27 transferable loci that were powered to interrogate this, only 9 showed evidence of shared causal variants. We constructed a T2D PRS and combined it with a clinical risk instrument (QDiabetes) in a novel, integrated risk tool (IRT) to assess risk of incident diabetes. To assess model performance, we compared categorical net reclassification index (NRI) versus QDiabetes alone. In 13,648 patients free from T2D followed up for 10 years, NRI was 3.2% for IRT versus QDiabetes (95% confidence interval (CI): 2.0% to 4.4%). IRT performed best in reclassification of individuals aged less than 40 years deemed low risk by QDiabetes alone (NRI 5.6%, 95% CI 3.6% to 7.6%), who tended to be free from comorbidities and slim. After adjustment for QDiabetes score, PRS was independently associated with progression to T2D after gestational diabetes (hazard ratio (HR) per SD of PRS 1.23, 95% CI 1.05 to 1.42, p = 0.028). Using cluster analysis of clinical features at diabetes diagnosis, we replicated previously reported disease subgroups, including Mild Age-Related, Mild Obesity-related, and Insulin-Resistant Diabetes, and showed that PRS distribution differs between subgroups (p = 0.002). Integrating PRS in this cluster analysis revealed a Probable Severe Insulin Deficient Diabetes (pSIDD) subgroup, despite the absence of clinical measures of insulin secretion or resistance. We also observed differences in rates of progression to micro- and macrovascular complications between subgroups after adjustment for confounders. Study limitations include the absence of an external replication cohort and the potential biases arising from missing or incorrect routine health data. Our analysis of the transferability of T2D loci between EUR and BPB indicates the need for larger, multiancestry studies to better characterise the genetic contribution to disease and its varied aetiology. We show that a T2D PRS optimised for this high-risk BPB population has potential clinical application in BPB, improving the identification of T2D risk (especially in the young) on top of an established clinical risk algorithm and aiding identification of subgroups at diagnosis, which may help future efforts to stratify care and treatment of the disease.

Pathophysiology, phenotypes and management of type 2 diabetes mellitus in Indian and Chinese populations.

Nearly half of all adults with type 2 diabetes mellitus (T2DM) live in India and China. These populations have an underlying predisposition to deficient insulin secretion, which has a key role in the pathogenesis of T2DM. Indian and Chinese people might be more susceptible to hepatic or skeletal muscle insulin resistance, respectively, than other populations, resulting in specific forms of insulin deficiency. Cluster-based phenotypic analyses demonstrate a higher frequency of severe insulin-deficient diabetes mellitus and younger ages at diagnosis, lower β-cell function, lower insulin resistance and lower BMI among Indian and Chinese people compared with European people. Individuals diagnosed earliest in life have the most aggressive course of disease and the highest risk of complications. These characteristics might contribute to distinctive responses to glucose-lowering medications. Incretin-based agents are particularly effective for lowering glucose levels in these populations; they enhance incretin-augmented insulin secretion and suppress glucagon secretion. Sodium–glucose cotransporter 2 inhibitors might also lower blood levels of glucose especially effectively among Asian people, while α-glucosidase inhibitors are better tolerated in east Asian populations versus other populations. Further research is needed to better characterize and address the pathophysiology and phenotypes of T2DM in Indian and Chinese populations, and to further develop individualized treatment strategies.

Cardiorenal Risk Profiles Among Data-Driven Type 2 Diabetes Sub-Phenotypes: A Post-Hoc Analysis of the China Health and Nutrition Survey.

Background and AimEvidence about recently proposed data-driven clusters of type 2 diabetes (T2D) is mainly about its prognostic effects and Western populations. We tested the applicability of this clustering approach among the Chinese population. We further investigated the cardiorenal risk profiles among different T2D sub-phenotypes cross-sectionally and before diabetes diagnosis.MethodsWith the use of data from the China Health and Nutrition Survey (1989–2009), 6,728 participants with available fasting blood samples and completed questionnaires in the 2009 survey were included. Glycemic statuses (normoglycemia, prediabetes, and new-onset T2D) were defined according to the 2020 American Diabetes Association criteria. Data-driven cluster analysis was conducted among new-onset T2D based on five variables: age at onset, body mass index (BMI), hemoglobin A1c, homeostasis model estimates of β-cell function, and insulin resistance. Linear regression models were used to cross-sectionally examine the differences of cardiorenal risk factors (body fat distribution, blood pressure, lipid profiles, and kidney function) between glycemic statuses. Mixed-effects models were used to explore a maximum of 20-year trajectories of cardiovascular risk factors (body fat distribution and blood pressure) before diabetes diagnosis.ResultsAmong 557 (8.3%) new-onset T2D, four sub-phenotypes were found, with 57 (10.2%) assigned to the severe insulin-resistant diabetes (SIRD), 72 (12.9%) to the severe insulin-deficient diabetes (SIDD), 167 (30.0%) to the mild obesity-related diabetes (MOD), and 261 (46.9%) to the mild age-related diabetes (MARD). People clustered within different T2D sub-phenotypes had different cardiorenal risk profiles. Three T2D sub-phenotypes (SIRD, SIDD, and MOD) had worse cardiorenal abnormalities, while the risk burden in the MARD sub-phenotype was similar to that in prediabetes. Compared with people with other T2D sub-phenotypes, people in the MOD sub-phenotype had a faster increment in BMI, waist, upper arm circumference, and triceps skinfold up to 10 years before diagnosis. Blood pressure was less distinct in different T2D sub-phenotypes; however, SIDD and MOD clusters had higher blood pressure levels before diabetes diagnosis.ConclusionsData-driven T2D sub-phenotyping is applicable in the Chinese population. Certain sub-phenotypes such as MARD only have a minor cardiorenal risk burden, and distinct cardiovascular risk development occurs long before diabetes diagnosis. Our findings can help improve early prevention and targeted treatment for diabetes.

Efficacy of glucagon-like peptide-1 and estrogen dual agonist in pancreatic islets protection and pre-clinical models of insulin-deficient diabetes

SummaryWe study the efficacy of a glucagon-like peptide-1 (GLP-1) and estrogen dual agonist (GLP1-E2) in pancreatic islet protection. GLP1-E2 provides superior protection from insulin-deficient diabetes induced by multiple low-dose streptozotocin (MLD-STZ-diabetes) and by the Akita mutation in mice than a GLP-1 monoagonist. GLP1-E2 does not protect from MLD-STZ-diabetes in estrogen receptor-α (ERα)-deficient mice and fails to prevent diabetes in Akita mice following GLP-1 receptor (GLP-1R) antagonism, demonstrating the requirement of GLP-1R and ERα for GLP1-E2 antidiabetic actions. In the MIN6 β cell model, GLP1-E2 activates estrogen action following clathrin-dependent, GLP-1R-mediated internalization and lysosomal acidification. In cultured human islet, proteomic bioinformatic analysis reveals that GLP1-E2 amplifies the antiapoptotic pathways activated by monoagonists. However, in cultured mouse islets, GLP1-E2 provides antiapoptotic protection similar to monoagonists. Thus, GLP1-E2 promotes GLP-1 and E2 antiapoptotic signals in cultured islets, but in vivo, additional GLP1-E2 actions in non-islet cells expressing GLP-1R are instrumental to prevent diabetes.

100 YEARS OF INSULIN: Towards improved precision and a new classification of diabetes mellitus

Type 2 diabetes (T2D) is one of the fastest increasing diseases worldwide. Although it is defined by a single metabolite, glucose, it is increasingly recognized as a highly heterogeneous disease with varying clinical manifestations. Identification of different subtypes at an early stage of disease when complications might still be prevented could hopefully allow for more personalized medicine. An important step toward precision medicine would be to target the right resources to the right patients, thereby improving patient health and reducing health costs for the society. More well-defined disease populations also offer increased power in experimental, genetic and clinical studies. In a recent study, we used six clinical variables (glutamate decarboxylase autoantibodies, age at onset of diabetes, glycated hemoglobin, BMI and simple measures of insulin resistance and insulin secretion (so called HOMA estimates) to cluster adult-onset diabetes patients into five subgroups. These subgroups have been robustly reproduced in several populations worldwide and are associated with different risks of diabetic complications and responses to treatment. Importantly, the group with severe insulin-deficient diabetes had increased risk of retinopathy and neuropathy, whereas the severe insulin-resistant diabetes group has the highest risk for diabetic kidney disease (DKD) and fatty liver. This emphasizes the key role of insulin resistance in the pathogenesis of DKD and fatty liver in T2D. In conclusion, this novel subclassification, breaking down T2D in clinically meaningful subgroups, provides the prerequisite framework for expanded personalized medicine in diabetes beyond what is already available for monogenic and to some extent type 1 diabetes.

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.

Novel Subgroups and Chronic Complications of Diabetes in Middle-Aged and Elderly Chinese:A Prospective Cohort Study.

BackgroundDiabetes mellitus, especially type 2 diabetes mellitus (T2DM), is regarded as highly heterogeneous. Novel diabetes phenotypes by cluster analysis have been proposed in Europeans but may show different cluster features in Asians. The applicability of cluster analysis in middle-aged and elderly Chinese community T2DM patients needs further investigation.MethodsParticipants were recruited from Jiading community in Shanghai, China. We adopted k-means cluster analysis in 1130 patients (aged ≥ 40 years) with newly-diagnosed T2DM at baseline. Cluster analysis was performed based on seven variables, including fasting plasma glucose, 2 hours postprandial blood glucose, age at diagnosis, body mass index, hemoglobin A1c, homoeostatic model assessment estimates of β-cell function and insulin resistance. All subjects were re-examined at 4.4 years later. Metabolic associated fatty liver disease was diagnosed using B-ultrasound, hepatic fibrosis by non-invasive scores, renal and cardiovascular status by subclinical biomarkers. Multivariable logistic regression models were used to compare the risks of complications between clusters.ResultsPatients were classified into 4 clusters. 381 (33.7%), 456 (40.4%), 87 (7.7%), and 206 (18.2%) patients were separately assigned to mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), severe insulin-deficient and insulin-resistant diabetes (SIDRD), or severe obesity-related and insulin-resistant diabetes (SOIRD), respectively. Participants in MARD, SOIRD, and SIDRD clusters were associated with significantly increased risks of different complications. SOIRD and SIDRD showed novel features in Chinese T2DM patients that were different from those in Europeans.ConclusionsThe refined diabetes phenotypic approach was applicable to Chinese middle-aged and elderly T2DM patients. Patients in different clusters presented significantly different characteristics, progression of metabolic features, and risks of diabetic complications.