Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

[Towards a new way to classify diabetes mellitus]. / Vers une nouvelle classification des diabètes.

Precision medicine makes it possible to classify patients into groups on the basis of molecular and genetic biomarkers, as well as clinical characteristics, in order to optimize therapeutic response. For example, several types of type 2 diabetes seem to coexist with classic insulin-dependent, autoimmune type 1 diabetes : diabetes with insulinopenia (generally severe), diabetes linked to aging or obesity (less severe), and diabetes with insulin resistance, whose patients will be those with the most numerous complications, notably macrovascular. In this article, we examine the possibilities offered by this new classification of diabetes with a view to personalized medicine.

La médecine de précision permet de classer les patients en groupes sur la base de biomarqueurs moléculaires et génétiques ainsi que de caractéristiques cliniques afin d'optimiser la réponse thérapeutique. Ainsi, plusieurs types de diabètes de type 2 semblent coexister à côté du classique diabète de type 1, insulinoprive et avec auto-immunité : des diabètes avec insulinopénie (généralement sévères), des diabètes liés au vieillissement ou à l'obésité (moins sévères), et des diabètes avec insulinorésistance dont les patients porteurs seront ceux qui auront le plus de complications, en particulier macrovasculaires. Dans cet article, nous abordons les possibilités offertes par cette nouvelle classification du diabète vers la perspective d'une médecine personnalisée.

The genetics of testosterone contributes to "femaleness/maleness" of cardiometabolic traits and type 2 diabetes.

The genetic architecture of testosterone is highly distinct between sexes. Moreover, obesity is associated with higher testosterone in females but lower testosterone in males. Here, we ask whether male-specific testosterone variants are associated with a male pattern of obesity and type 2 diabetes (T2D) in females, and vice versa. In the UK Biobank, we conducted sex-specific genome-wide association studies and computed polygenic scores for total (PGSTT) and bioavailable testosterone (PGSBT). We tested sex-congruent and sex-incongruent associations between sex-specific PGSTs and metabolic traits, as well as T2D diagnosis. Female-specific PGSBT was associated with an elevated cardiometabolic risk and probability of T2D, in both sexes. Male-specific PGSTT was associated with traits conferring a lower cardiometabolic risk and probability of T2D, in both sexes. We demonstrate the value in considering polygenic testosterone as sex-related continuous traits, in each sex.

The many faces of diabetes. Is there a need for re-classification? A narrative review.

The alarming rise in the worldwide prevalence of obesity and associated type 2 diabetes mellitus (T2DM) have reached epidemic portions. Diabetes in its many forms and T2DM have different physiological backgrounds and are difficult to classify. Bariatric surgery (BS) is considered the most effective treatment for obesity in terms of weight loss and comorbidity resolution, improves diabetes, and has been proven superior to medical management for the treatment of diabetes. The term metabolic surgery (MS) describes bariatric surgical procedures used primarily to treat T2DM and related metabolic conditions. MS is the most effective means of obtaining substantial and durable weight loss in individuals with obesity. Originally, BS was used as an alternative weight-loss therapy for patients with severe obesity, but clinical data revealed its metabolic benefits in patients with T2DM. MS is more effective than lifestyle or medical management in achieving glycaemic control, sustained weight loss, and reducing diabetes comorbidities. New guidelines for T2DM expand the use of MS to patients with a lower body mass index.Evidence has shown that endocrine changes resulting from BS translate into metabolic benefits that improve the comorbid conditions associated with obesity, such as hypertension, dyslipidemia, and T2DM. Other changes include bacterial flora rearrangement, bile acids secretion, and adipose tissue effect.This review aims to examine the physiological mechanisms in diabetes, risks for complications, the effects of bariatric and metabolic surgery and will shed light on whether diabetes should be reclassified.

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

BACKGROUND: A 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). METHODS: The 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. RESULTS: The 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. CONCLUSIONS: Leptin 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.

Consensus report: definition and interpretation of remission in type 2 diabetes.

Improvement of glucose levels into the normal range can occur in some people living with diabetes, either spontaneously or after medical interventions, and in some cases can persist after withdrawal of glucose-lowering pharmacotherapy. Such sustained improvement may now be occurring more often due to newer forms of treatment. However, terminology for describing this process and objective measures for defining it are not well established, and the long-term risks vs benefits of its attainment are not well understood. To update prior discussions of this issue, an international expert group was convened by the American Diabetes Association to propose nomenclature and principles for data collection and analysis, with the goal of establishing a base of information to support future clinical guidance. This group proposed 'remission' as the most appropriate descriptive term, and HbA1c <48 mmol/mol (6.5%) measured at least 3 months after cessation of glucose-lowering pharmacotherapy as the usual diagnostic criterion. The group also made suggestions for active observation of individuals experiencing a remission and discussed further questions and unmet needs regarding predictors and outcomes of remission.

Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity.

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM-related dementia is still less understood. Recent resting-state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM-CI). However, they mainly used a mass-univariate statistical analysis that was not suitable to reveal the altered FC "pattern" in T2DM-CI, due to lower sensitivity. In this study, we proposed to use high-order FC to reveal the abnormal connectomics pattern in T2DM-CI with a multivariate, machine learning-based strategy. We also investigated whether such patterns were different between T2DM-CI and T2DM without cognitive impairment (T2DM-noCI) to better understand T2DM-induced cognitive impairment, on 23 T2DM-CI and 27 T2DM-noCI patients, as well as 50 healthy controls (HCs). We first built the large-scale high-order brain networks based on temporal synchronization of the dynamic FC time series among multiple brain region pairs and then used this information to classify the T2DM-CI (as well as T2DM-noCI) from the matched HC based on support vector machine. Our model achieved an accuracy of 79.17% in T2DM-CI versus HC differentiation, but only 59.62% in T2DM-noCI versus HC classification. We found abnormal high-order FC patterns in T2DM-CI compared to HC, which was different from that in T2DM-noCI. Our study indicates that there could be widespread connectivity alterations underlying the T2DM-induced cognitive impairment. The results help to better understand the changes in the central neural system due to T2DM.

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.

Impact of mismatches in HbA1c vs glucose values on the diagnostic classification of diabetes and prediabetes.

AIMS: To determine whether HbA1c mismatches (HbA1c levels that are higher or lower than expected for the average glucose levels in different individuals) could lead to errors if diagnostic classification is based only on HbA1c levels. METHODS: In a cross-sectional study, 3106 participants without known diabetes underwent a 75-g oral glucose tolerance test (fasting glucose and 2-h glucose) and a 50-g glucose challenge test (1-h glucose) on separate days. They were classified by oral glucose tolerance test results as having: normal glucose metabolism; prediabetes; or diabetes. Predicted HbA1c was determined from the linear regression modelling the relationship between observed HbA1c and average glucose (mean of fasting glucose and 2-h glucose from the oral glucose tolerance test, and 1-h glucose from the glucose challenge test) within oral glucose tolerance test groups. The haemoglobin glycation index was calculated as [observed - predicted HbA1c ], and divided into low, intermediate and high haemoglobin glycation index mismatch tertiles. RESULTS: Those participants with higher mismatches were more likely to be black, to be men, to be older, and to have higher BMI (all P<0.001). Using oral glucose tolerance test criteria, the distribution of normal glucose metabolism, prediabetes and diabetes was similar across mismatch tertiles; however, using HbA1c criteria, the participants with low mismatches were classified as 97% normal glucose metabolism, 3% prediabetes and 0% diabetes, i.e. mostly normal, while those with high mismatches were classified as 13% normal glucose metabolism, 77% prediabetes and 10% diabetes, i.e. mostly abnormal (P<0.001). CONCLUSIONS: Measuring only HbA1c could lead to under-diagnosis in people with low mismatches and over-diagnosis in those with high mismatches. Additional oral glucose tolerance tests and/or fasting glucose testing to complement HbA1c in diagnostic classification should be performed in most individuals.

Challenges in the diagnosis of diabetes type in pediatrics.

The incidence of diabetes, both type 1 and type 2, is increasing. Health outcomes in pediatric diabetes are currently poor, with trends indicating that they are worsening. Minority racial/ethnic groups are disproportionately affected by suboptimal glucose control and have a higher risk of acute and chronic complications of diabetes. Correct clinical management starts with timely and accurate classification of diabetes, but in children this is becoming increasingly challenging due to high prevalence of obesity and shifting demographic composition. The growing obesity epidemic complicates classification by obesity's effects on diabetes. Since the prevalence and clinical characteristics of diabetes vary among racial/ethnic groups, migration between countries leads to changes in the distribution of diabetes types in a certain geographical area, challenging the clinician's ability to classify diabetes. These challenges must be addressed to correctly classify diabetes and establish an appropriate treatment strategy early in the course of disease for all. This may be the first step in improving diabetes outcomes across racial/ethnic groups. This review will discuss the pitfalls in the current diabetes classification scheme that is leading to increasing overlap between diabetes types and heterogeneity within each type. It will also present proposed alternative classification schemes and approaches to understanding diabetes type that may improve the timely and accurate classification of pediatric diabetes type.

The accuracy of provider diagnosed diabetes type in youth compared to an etiologic criteria in the SEARCH for Diabetes in Youth Study.

BACKGROUND: Although surveillance for diabetes in youth relies on provider-assigned diabetes type from medical records, its accuracy compared to an etiologic definition is unknown. METHODS: Using the SEARCH for Diabetes in Youth Registry, we evaluated the validity and accuracy of provider-assigned diabetes type abstracted from medical records against etiologic criteria that included the presence of diabetes autoantibodies (DAA) and insulin sensitivity. Youth who were incident for diabetes in 2002-2006, 2008, or 2012 and had complete data on key analysis variables were included (n = 4001, 85% provider diagnosed type 1). The etiologic definition for type 1 diabetes was ≥1 positive DAA titer(s) or negative DAA titers in the presence of insulin sensitivity and for type 2 diabetes was negative DAA titers in the presence of insulin resistance. RESULTS: Provider diagnosed diabetes type correctly agreed with the etiologic definition of type for 89.9% of cases. Provider diagnosed type 1 diabetes was 96.9% sensitive, 82.8% specific, had a positive predictive value (PPV) of 97.0% and a negative predictive value (NPV) of 82.7%. Provider diagnosed type 2 diabetes was 82.8% sensitive, 96.9% specific, had a PPV and NPV of 82.7% and 97.0%, respectively. CONCLUSION: Provider diagnosis of diabetes type agreed with etiologic criteria for 90% of the cases. While the sensitivity and PPV were high for youth with type 1 diabetes, the lower sensitivity and PPV for type 2 diabetes highlights the value of DAA testing and assessment of insulin sensitivity status to ensure estimates are not biased by misclassification.

Development and validation of algorithms to classify type 1 and 2 diabetes according to age at diagnosis using electronic health records.

BACKGROUND: Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly limited to white pediatric populations. We conducted a large study in Hong Kong among children and adults with diabetes to develop and validate algorithms using electronic health records (EHRs) to classify diabetes type against clinical assessment as the reference standard, and to evaluate performance by age at diagnosis. METHODS: We included all people with diabetes (age at diagnosis 1.5-100 years during 2002-15) in the Hong Kong Diabetes Register and randomized them to derivation and validation cohorts. We developed candidate algorithms to identify diabetes types using encounter codes, prescriptions, and combinations of these criteria ("combination algorithms"). We identified 3 algorithms with the highest sensitivity, positive predictive value (PPV), and kappa coefficient, and evaluated performance by age at diagnosis in the validation cohort. RESULTS: There were 10,196 (T1D n = 60, T2D n = 10,136) and 5101 (T1D n = 43, T2D n = 5058) people in the derivation and validation cohorts (mean age at diagnosis 22.7, 55.9 years; 53.3, 43.9% female; for T1D and T2D respectively). Algorithms using codes or prescriptions classified T1D well for age at diagnosis < 20 years, but sensitivity and PPV dropped for older ages at diagnosis. Combination algorithms maximized sensitivity or PPV, but not both. The "high sensitivity for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, or at least 1 insulin prescription within 90 days) had a sensitivity of 95.3% (95% confidence interval 84.2-99.4%; PPV 12.8%, 9.3-16.9%), while the "high PPV for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, and multiple daily injections with no other glucose-lowering medication prescription) had a PPV of 100.0% (79.4-100.0%; sensitivity 37.2%, 23.0-53.3%), and the "optimized" algorithm (ratio of type 1 to type 2 codes ≥ 4, and at least 1 insulin prescription within 90 days) had a sensitivity of 65.1% (49.1-79.0%) and PPV of 75.7% (58.8-88.2%) across all ages. Accuracy of T2D classification was high for all algorithms. CONCLUSIONS: Our validated set of algorithms accurately classifies T1D and T2D using EHRs for Hong Kong residents enrolled in a diabetes register. The choice of algorithm should be tailored to the unique requirements of each study question.

Fuzzy partitioning of clinical data for DMT2 patients.

The present study represents an original approach to data interpretation of clinical data for patients with diagnosis diabetes mellitus type 2 (DMT2) using fuzzy clustering as a tool for intelligent data analysis. Fuzzy clustering is often used in classification and interpretation of medical data (including in medical diagnosis studies) but in this study it is applied with a different goal: to separate a group of 100 patients with DMT2 from a control group of healthy volunteers and, further, to reveal three different patterns of similarity between the patients. Each pattern is described by specific descriptors (variables), which ensure pattern interpretation by appearance of underling disease to DMT2.

A Bayesian hierarchical logistic regression model of multiple informant family health histories.

BACKGROUND: Family health history (FHH) inherently involves collecting proxy reports of health statuses of related family members. Traditionally, such information has been collected from a single informant. More recently, research has suggested that a multiple informant approach to collecting FHH results in improved individual risk assessments. Likewise, recent work has emphasized the importance of incorporating health-related behaviors into FHH-based risk calculations. Integrating both multiple accounts of FHH with behavioral information on family members represents a significant methodological challenge as such FHH data is hierarchical in nature and arises from potentially error-prone processes. METHODS: In this paper, we introduce a statistical model that addresses these challenges using informative priors for background variation in disease prevalence and the effect of other, potentially correlated, variables while accounting for the nested structure of these data. Our empirical example is drawn from previously published data on families with a history of diabetes. RESULTS: The results of the comparative model assessment suggest that simply accounting for the structured nature of multiple informant FHH data improves classification accuracy over the baseline and that incorporating family member health-related behavioral information into the model is preferred over alternative specifications. CONCLUSIONS: The proposed modelling framework is a flexible solution to integrate multiple informant FHH for risk prediction purposes.

Novel insights into genetics and clinics of the HNF1A-MODY.

MODY (Maturity Onset Diabetes of the Young) is a type of diabetes resulting from a pathogenic effect of gene mutations. Up to date, 13 MODY genes are known. Gene HNF1A is one of the most common causes of MODY diabetes (HNF1A-MODY; MODY3). This gene is polymorphic and more than 1200 pathogenic and non-pathogenic HNF1A variants were described in its UTRs, exons and introns. For HNF1A-MODY, not just gene but also phenotype heterogeneity is typical. Although there are some clinical instructions, HNF1A-MODY patients often do not meet every diagnostic criteria or they are still misdiagnosed as type 1 and type 2 diabetics. There is a constant effort to find suitable biomarkers to help with in distinguishing of MODY3 from Type 1 Diabetes (T1D) and Type 2 Diabetes (T2D). DNA sequencing is still necessary for unambiguous confirmation of clinical suspicion of MODY. NGS (Next Generation Sequencing) methods brought discoveries of multiple new gene variants and new instructions for their pathogenicity classification were required. The most actual problem is classification of variants with uncertain significance (VUS) which is a stumbling-block for clinical interpretation. Since MODY is a hereditary disease, DNA analysis of family members is helpful or even crucial. This review is updated summary about HNF1A-MODY genetics, pathophysiology, clinics functional studies and variant classification.

The Importance of DS-14 and HADS Questionnaires in Quantifying Psychological Stress in Type 2 Diabetes Mellitus.

Background and Objectives: The comorbid association between type 2 diabetes mellitus (T2DM) and a psychological profile characterized by depression and/or anxiety has been reported to increase the risk of coronary heart disease (CAD), the most striking macrovascular complication of diabetes. The purpose of the present study was to quantify anxiety, depression and the presence of type D personality, and to correlate the scores obtained with cardiovascular risk factors and disease severity in diabetic patients. Materials and methods: The retrospective study included 169 clinically stable diabetic patients divided into two groups: group 1 without macrovascular complications (n = 107) and group 2 with CAD, stroke and/or peripheral vascular disease (n = 62). A biochemical analysis and an assessment of psychic stress by applying the Hospital Anxiety and Depression Scale (HADS)and the Type D scale (DS-14) to determine anxiety, depression and D personality scores were done in all patients. Statistical analysis was made using SPSSv17 and Microsoft Excel, non-parametric Kruskal-Wallis and Mann-Whitney tests. Results: Following application of the HAD questionnaire for the entire group (n = 169), anxiety was present in 105 patients (62.2%), and depression in 96 patients (56.8%). Group 2 showed significantly higher anxiety scores compared to group 1 (p = 0.014), while depression scores were not significantly different. Per entire group, analysis of DS-14 scores revealed social inhibition (SI) present in 56 patients (33%) and negative affectivity (NA) in 105 patients (62%). TheDS-14 SI score was significantly higher in group 2 compared to group 1 (p = 0.036). Type D personality, resulting from scores above 10 in both DS-14 parameter categories, was present in 51 patients of the study group (30%). There was a direct and significant correlation (r = 0.133, p = 0.025) between the Hospital Anxiety and Depression Scale-Anxiety (HAD-A) score and the LDL-c values. Conclusions: The results of this study demonstrated that more than a half of patients with diabetes had anxiety and/or depression and one third had Type D personality, sustaining that monitoring of emotional state and depression should be included in the therapeutic plan of these patients. New treatment strategies are needed to improve the well-being of diabetic patients with psychological comorbidities.

Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis.

BACKGROUND: Type 2 diabetes (T2D) is a heterogeneous disease for which (1) disease-causing pathways are incompletely understood and (2) subclassification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper, we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four separate subsets of individuals with T2D. METHODS AND FINDINGS: In an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization (bNMF) clustering to genome-wide association study (GWAS) results for 94 independent T2D genetic variants and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta cell function, differing from each other by high versus low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity mediated (high body mass index [BMI] and waist circumference [WC]), "lipodystrophy-like" fat distribution (low BMI, adiponectin, and high-density lipoprotein [HDL] cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster genetic risk scores were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease (CAD), and stroke. We evaluated the potential for clinical impact of these clusters in four studies containing individuals with T2D (Metabolic Syndrome in Men Study [METSIM], N = 487; Ashkenazi, N = 509; Partners Biobank, N = 2,065; UK Biobank [UKBB], N = 14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with approximately 30% of all individuals assigned to just one cluster top decile. Limitations of this study include that the genetic variants used in the cluster analysis were restricted to those associated with T2D in populations of European ancestry. CONCLUSION: Our approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports the use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.

Pathophysiology-based phenotyping in type 2 diabetes: A clinical classification tool.

BACKGROUND: Type 2 diabetes may be a more heterogeneous disease than previously thought. Better understanding of pathophysiological subphenotypes could lead to more individualized diabetes treatment. We examined the characteristics of different phenotypes among 5813 Danish patients with new clinically diagnosed type 2 diabetes. METHODS: We first identified all patients with rare subtypes of diabetes, latent autoimmune diabetes of adults (LADA), secondary diabetes, or glucocorticoid-associated diabetes. We then used the homeostatic assessment model to subphenotype all remaining patients into insulinopenic (high insulin sensitivity and low beta cell function), classical (low insulin sensitivity and low beta cell function), or hyperinsulinemic (low insulin sensitivity and high beta cell function) type 2 diabetes. RESULTS: Among 5813 patients diagnosed with incident type 2 diabetes in the community clinical setting, 0.4% had rare subtypes of diabetes, 2.8% had LADA, 0.7% had secondary diabetes, 2.4% had glucocorticoid-associated diabetes, and 93.7% had WHO-defined type 2 diabetes. In the latter group, 9.7% had insulinopenic, 63.1% had classical, and 27.2% had hyperinsulinemic type 2 diabetes. Classical patients were obese (median waist 105 cm), and 20.5% had cardiovascular disease (CVD) at diagnosis, while insulinopenic patients were fairly lean (waist 92 cm) and 17.5% had CVD (P = 0.14 vs classical diabetes). Hyperinsulinemic patients were severely obese (waist 112 cm), and 25.5% had CVD (P < 0.0001 vs classical diabetes). CONCLUSIONS: Patients clinically diagnosed with type 2 diabetes are a heterogeneous group. In the future, targeted treatment based on pathophysiological characteristics rather than the current "one size fits all" approach may improve patient prognosis.

A metagenome-wide association study of gut microbiota in type 2 diabetes.

Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.

Increasing incidence of type 2 diabetes in New Zealand children <15 years of age in a regional-based diabetes service, Auckland, New Zealand.

AIM: It is important to understand whether type 2 diabetes mellitus (T2DM) is increasing in childhood for health-care planning and clinical management. The aim of this study is to examine the incidence of T2DM in New Zealand children, aged <15 years from a paediatric diabetes centre, Auckland, New Zealand. METHODS: Retrospective analysis of prospectively collected data from a population-based referral cohort from 1995 to 2015. RESULTS: Hundred and four children presented with T2DM over the 21-year period. The female:male ratio was 1.8:1, at mean (standard deviation) age 12.9 (1.9) years, body mass index standard deviation score +2.3 (0.5), blood sugar 15.3 (8.5) mmol/L, HbA1c 76 (28) mmol/mol. At diagnosis, 90% had acanthosis nigricans and 48% were symptomatic. In all, 33% were Maori, 46% Pacific Island, 15% Asian/Middle Eastern and 6% European. There was a progressive secular increase of 5% year on year in incidence. The overall annual incidence of T2DM <15 years of age was 1.5/100 000 (1.2-1.9) (95% confidence interval), with higher rates in Pacific Island (5.9/100 000) and Maori (4.1/100 000). CONCLUSIONS: The incidence of T2DM in children <15 years of age in New Zealand has increased progressively at 5%/year over the last 21 years. The risk was disproportionately associated with girls and children from high-risk ethnic groups.