[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.

Risk factors for chronic kidney disease in middle eastern patients with type 2 diabetes mellitus: A cross-sectional study using the KDIGO classification.

AIMS: Chronic kidney disease (CKD) is prevalent in patients with type 2 diabetes mellitus (T2DM). This study aimed to investigate risk factors for CKD progression across the kidney disease-Improving Global Outcomes (KDIGO)categories in a Middle Eastern population beyond hyperglycemia as emphasized by KDIGO guidelines which classifying CKD by cause and severity. METHODS: This cross-sectional study targeted 1603 patients with T2DM. Risk factors for CKD progression were determined using odds ratios (ORs) and 95 % confidence intervals (CIs). RESULTS: Overall, 35.5 %, 31.7 %, and 32.8 % of patients were classified as low-risk, moderate-risk, and high-/very high-/highest-risk, respectively. Several factors were associated with high/very high/highest risk categorization, including being aged >45 years (OR: 1.85, 95 % CI: 1.36-2.49; P < 0.001), male gender (OR: 1.87, 95 % CI: 1.38-2.54; P < 0.001), hypertension (OR: 3.66, 95 % CI: 2.32-5.78; P < 0.001), and T2DM duration of ≥15 years (OR: 3.2, 95 % CI: 2.27-4.5; P < 0.001). Patients with more concurrent risk factors were notably represented in the high/very high/highest risk category. CONCLUSIONS: Male patients, older patients, and those with comorbid hypertension, longstanding T2DM, and additional concurrent risk factors have a significantly higher risk of advanced CKD. Such findings should be considered when planning management approaches for patients with CKD.

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.

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.

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.

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.

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.

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

OBJECTIVE: To assess the reproducibility and clinical utility of clustering-based subtyping of patients with type 2 diabetes (T2D) and established cardiovascular (CV) disease. METHODS: The 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. RESULTS: In 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. CONCLUSIONS: The 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.

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.

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 ß-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.

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.

Toward an Improved Classification of Type 2 Diabetes: Lessons From Research into the Heterogeneity of a Complex Disease.

CONTEXT: Accumulating evidence indicates that type 2 diabetes (T2D) is phenotypically heterogeneous. Defining and classifying variant forms of T2D are priorities to better understand its pathophysiology and usher clinical practice into an era of "precision diabetes." EVIDENCE ACQUISITION AND METHODS: We reviewed literature related to heterogeneity of T2D over the past 5 decades and identified a range of phenotypic variants of T2D. Their descriptions expose inadequacies in current classification systems. We attempt to link phenotypically diverse forms to pathophysiology, explore investigative methods that have characterized "atypical" forms of T2D on an etiological basis, and review conceptual frameworks for an improved taxonomy. Finally, we propose future directions to achieve the goal of an etiological classification of T2D. EVIDENCE SYNTHESIS: Differences among ethnic and racial groups were early observations of phenotypic heterogeneity. Investigations that uncover complex interactions of pathophysiologic pathways leading to T2D are supported by epidemiological and clinical differences between the sexes and between adult and youth-onset T2D. Approaches to an etiological classification are illustrated by investigations of atypical forms of T2D, such as monogenic diabetes and syndromes of ketosis-prone diabetes. Conceptual frameworks that accommodate heterogeneity in T2D include an overlap between known diabetes types, a "palette" model integrated with a "threshold hypothesis," and a spectrum model of atypical diabetes. CONCLUSION: The heterogeneity of T2D demands an improved, etiological classification scheme. Excellent phenotypic descriptions of emerging syndromes in different populations, continued clinical and molecular investigations of atypical forms of diabetes, and useful conceptual models can be utilized to achieve this important goal.

Urinary C-peptide/creatinine ratio: A useful biomarker of insulin resistance and refined classification of type 2 diabetes mellitus.

BACKGROUND: The urinary C-peptide/creatinine ratio (UCPCR) is low in patients with type 1 diabetes mellitus, but it has not been well characterized in patients with type 2 diabetes mellitus (T2DM). We aimed to measure the UCPCRs in patients with T2DM and explore the relationships among UCPCR, insulin resistance (IR), and chronic vascular complications of diabetes. METHODS: A cross-sectional study was performed of 1299 Chinese hospitalized patients with T2DM. Binary logistic regression was used to evaluate the relationships between the chronic vascular complications of diabetes and UCPCR. K-means analysis was used to allocate participants to subgroups with five to six variables (age at diagnosis, body mass index [BMI], glycosylated hemoglobin, homoeostasis model assessment 2-estimated beta-cell function (HOMA2-B), and HOMA2-insulin resistance (HOMA2-IR), with or without UCPCR). RESULTS: UCPCR positively correlated with HOMA2-IR (r = 0.448, P < .001). After adjustment for sex, age, duration of diabetes, and other cardiovascular risk factors, UCPCR was positively associated with diabetic kidney disease (DKD) (odds ratio [OR] = 1.198, 95% CI 1.019-1.408, P = .029) and coronary heart disease (CHD) (OR = 1.312, 95% CI 1.079-1.594, P = .006). When UCPCR was added, cluster analysis using the six variables identified five subgroups of T2DM, characterized by differing age at diagnosis, BMI, beta-cell function, IR, and prevalence of vascular complications. CONCLUSIONS: UCPCR is positively associated with IR, DKD, and CHD and represents a promising biomarker that could refine the classification of T2DM.

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

AIMS/INTRODUCTION: The 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 METHODS: The 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 ß-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. RESULTS: Just 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. CONCLUSIONS: Seven 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.

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.

An Approach to Screen Genotoxic-Susceptible Diabetic Population of Various Prakriti Groups for Personalized Disease Management.

Background: Ayurveda classifies human populations into three predominant groups as Vata, Pitta, and Kapha based on their "Prakriti'. Any disturbance in the equilibrium of Prakriti can cause various diseases. Objectives: The aim of the study was to link genotoxic variation among the three Prakriti having type 2 diabetes. Design: Type 2 diabetic patients and healthy individuals belonging to three predominant Prakriti were selected through the Prakriti Questionnaire screening as per the guidelines of the CSIR-TRISUTRA unit modified for type 2 diabetes disease. Settings/Location: Sixty individuals from three predominant Prakriti, each consisting of 10 diabetic patients and 10 healthy individuals, were chosen. Subjects: Clinically diagnosed outdoor patients of JBRMCH suffering from type 2 diabetes for 5 years (fasting blood glucose >140 mg/dL; HbA1C > 7.0) and healthy individuals were the subjects for study. Inclusion Criteria: Age limit: 30-70 years, Sex: Both, Habitant: Participants residing in West Bengal for the last five generations, Religion: Unspecified, Social entity: Both urban and rural, Education: High school to college, Economic status: Lower middle to middle classes. Exclusion Criteria: Participants were nonsmokers and nonalcoholics. An individual having a medical history of long-term illness or dwandaja Prakriti type was excluded here. Outcome Measures: Reactive oxygen species (ROS) generation, blood DNA content, DNA damage, apoptosis of blood cells, and interaction of DNA with various carcinogens were observed. Results: The yield of ROS and total cell damage were significantly higher in the diabetic Vata (p < 0.001) group compared with other Prakriti Decreased DNA content and increased DNA damage were observed in type 2 diabetic patients who belonged to Vata (p < 0.01) Prakriti. DNA of Vata Prakriti was more prone to lead and arsenic. Conclusions: The diabetic Vata Prakriti is a genetically susceptible group as it has a tendency to get affected by increased DNA damage, which could help in creating personalized management of diabetes among individual Prakriti.

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.

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.

Unraveling the molecular heterogeneity in type 2 diabetes: a potential subtype discovery followed by metabolic modeling.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a complex multifactorial disease with a high prevalence worldwide. Insulin resistance and impaired insulin secretion are the two major abnormalities in the pathogenesis of T2DM. Skeletal muscle is responsible for over 75% of the glucose uptake and plays a critical role in T2DM. Here, we sought to provide a better understanding of the abnormalities in this tissue. METHODS: The muscle gene expression patterns were explored in healthy and newly diagnosed T2DM individuals using supervised and unsupervised classification approaches. Moreover, the potential of subtyping T2DM patients was evaluated based on the gene expression patterns. RESULTS: A machine-learning technique was applied to identify a set of genes whose expression patterns could discriminate diabetic subjects from healthy ones. A gene set comprising of 26 genes was found that was able to distinguish healthy from diabetic individuals with 94% accuracy. In addition, three distinct clusters of diabetic patients with different dysregulated genes and metabolic pathways were identified. CONCLUSIONS: This study indicates that T2DM is triggered by different cellular/molecular mechanisms, and it can be categorized into different subtypes. Subtyping of T2DM patients in combination with their real clinical profiles will provide a better understanding of the abnormalities in each group and more effective therapeutic approaches in the future.