One third of cases of new-onset diabetic ketosis in adults are associated with ketosis-prone type 2 diabetes-A systematic review and meta-analysis.

AIMS: Ketosis-prone type 2 diabetes was defined by the World Health Organization in 2019. According to the literature, the diagnosis is based on the presence of ketosis, islet autoantibody negativity and preserved insulin secretion. Our meta-analysis assessed the prevalence and clinical characteristics of ketosis-prone type 2 diabetes among patients hospitalised with diabetic ketoacidosis (DKA) or ketosis. METHODS: The systematic search was performed in five main databases as of 15 October 2021 without restrictions. We calculated the pooled prevalence of ketosis-prone type 2 diabetes (exposed group) within the diabetic population under examination, patients with ketoacidosis or ketosis, to identify the clinical characteristics, and we compared it to type 1 diabetes (the comparator group). The random effects model provided pooled estimates as prevalence, odds ratio and mean difference (MD) with 95% confidence intervals. RESULTS: Eleven articles were eligible for meta-analysis, thus incorporating 2010 patients of various ethnic backgrounds. Among patients presenting with DKA or ketosis at the onset of diabetes, 35% (95% CI: 24%-49%) had ketosis-prone type 2 diabetes. These patients were older (MD = 11.55 years; 95% CI: 5.5-17.6) and had a significantly higher body mass index (BMI) (MD = 5.48 kg/m2 ; 95% CI: 3.25-7.72) than those with type 1 diabetes. CONCLUSIONS: Ketosis-prone type 2 diabetes accounts for one third of DKA or ketosis at the onset of diabetes in adults. These patients are characterised by islet autoantibody negativity and preserved insulin secretion. They are older and have a higher BMI compared with type 1 diabetes. C-peptide and diabetes-related autoantibody measurement is essential to identify this subgroup among patients with ketosis at the onset of diabetes.

The relationship between islet autoantibody status and the genetic risk of type 1 diabetes in adult-onset type 1 diabetes.

AIMS/HYPOTHESIS: The reason for the observed lower rate of islet autoantibody positivity in clinician-diagnosed adult-onset vs childhood-onset type 1 diabetes is not known. We aimed to explore this by assessing the genetic risk of type 1 diabetes in autoantibody-negative and -positive children and adults. METHODS: We analysed GAD autoantibodies, insulinoma-2 antigen autoantibodies and zinc transporter-8 autoantibodies (ZnT8A) and measured type 1 diabetes genetic risk by genotyping 30 type 1 diabetes-associated variants at diagnosis in 1814 individuals with clinician-diagnosed type 1 diabetes (1112 adult-onset, 702 childhood-onset). We compared the overall type 1 diabetes genetic risk score (T1DGRS) and non-HLA and HLA (DR3-DQ2, DR4-DQ8 and DR15-DQ6) components with autoantibody status in those with adult-onset and childhood-onset diabetes. We also measured the T1DGRS in 1924 individuals with type 2 diabetes from the Wellcome Trust Case Control Consortium to represent non-autoimmune diabetes control participants. RESULTS: The T1DGRS was similar in autoantibody-negative and autoantibody-positive clinician-diagnosed childhood-onset type 1 diabetes (mean [SD] 0.274 [0.034] vs 0.277 [0.026], p=0.4). In contrast, the T1DGRS in autoantibody-negative adult-onset type 1 diabetes was lower than that in autoantibody-positive adult-onset type 1 diabetes (mean [SD] 0.243 [0.036] vs 0.271 [0.026], p<0.0001) but higher than that in type 2 diabetes (mean [SD] 0.229 [0.034], p<0.0001). Autoantibody-negative adults were more likely to have the more protective HLA DR15-DQ6 genotype (15% vs 3%, p<0.0001), were less likely to have the high-risk HLA DR3-DQ2/DR4-DQ8 genotype (6% vs 19%, p<0.0001) and had a lower non-HLA T1DGRS (p<0.0001) than autoantibody-positive adults. In contrast to children, autoantibody-negative adults were more likely to be male (75% vs 59%), had a higher BMI (27 vs 24 kg/m2) and were less likely to have other autoimmune conditions (2% vs 10%) than autoantibody-positive adults (all p<0.0001). In both adults and children, type 1 diabetes genetic risk was unaffected by the number of autoantibodies (p>0.3). These findings, along with the identification of seven misclassified adults with monogenic diabetes among autoantibody-negative adults and the results of a sensitivity analysis with and without measurement of ZnT8A, suggest that the intermediate type 1 diabetes genetic risk in autoantibody-negative adults is more likely to be explained by the inclusion of misclassified non-autoimmune diabetes (estimated to represent 67% of all antibody-negative adults, 95% CI 61%, 73%) than by the presence of unmeasured autoantibodies or by a discrete form of diabetes. When these estimated individuals with non-autoimmune diabetes were adjusted for, the prevalence of autoantibody positivity in adult-onset type 1 diabetes was similar to that in children (93% vs 91%, p=0.4). CONCLUSIONS/INTERPRETATION: The inclusion of non-autoimmune diabetes is the most likely explanation for the observed lower rate of autoantibody positivity in clinician-diagnosed adult-onset type 1 diabetes. Our data support the utility of islet autoantibody measurement in clinician-suspected adult-onset type 1 diabetes in routine clinical practice.

Flash glucose monitoring data analysed by detrended fluctuation function on beta-cell function and diabetes classification.

AIM: We aimed to use data-driven glucose pattern analysis to unveil the correlation between the metrics reflecting glucose fluctuation and beta-cell function, and to identify the possible role of this metric in diabetes classification. MATERIALS AND METHODS: In total, 78 participants with type 1 diabetes and 59 with type 2 diabetes were enrolled in this study. All participants wore a flash glucose monitoring system, and glucose data were collected. A detrended fluctuation function (DFF) was utilized to extract glucose fluctuation information from flash glucose monitoring data and a DFF-based glucose fluctuation metric was proposed. RESULTS: For the entire study population, a significant negative correlation between the DFF-based glucose fluctuation metric and fasting C-peptide was observed (r = -0.667; P <.001), which was larger than the correlation coefficient between the fasting C-peptide and mean amplitude of plasma glucose excursions (r = -0.639; P < .001), standard deviation (r = -0.649; P <.001), mean blood glucose (r = -0.519; P < .001) and time in range (r = 0.593; P < .001). As glucose data analysed by DFF revealed a clear bimodal distribution among the total participants, we randomly assigned the 137 participants into discovery cohorts (n = 100) and validation cohorts (n = 37) for 10 times to evaluate the consistency and effectiveness of the proposed metric for diabetes classification. The confidence interval for area under the curve according to the receiver operating characteristic analysis in the 10 discovery cohorts achieved (0.846, 0.868) and that for the 10 validation cohorts was (0.799, 0.862). In addition, the confidence intervals for sensitivity and specificity in the discovery cohorts were (75.5%, 83.0%), (81.3%, 88.5%) and (71.8%, 88.3%), (76.5%, 90.3%) in the validation cohorts, indicating the potential capacity of DFF in distinguishing type 1 and type 2 diabetes. CONCLUSIONS: Our study first proposed the possible role of data-driven analysis acquired glucose metric in predicting beta-cell function and diabetes classification, and a large-scale, multicentre study will be needed in the future.

Distinction of cardiometabolic profiles among people ≥75 years with type 2 diabetes: a latent profile analysis.

BACKGROUND: Older patients with type 2 diabetes mellitus represent a heterogeneous group in terms of metabolic profile. It makes glucose-lowering-therapy (GLT) complex to manage, as it needs to be individualised according to the patient profile. This study aimed to identify and characterize subgroups existing among older patients with diabetes. METHODS: Retrospective observational cohort study of outpatients followed in a Belgian diabetes clinic. Included participants were all aged ≥75 years, diagnosed with type 2 diabetes, Caucasian, and had a Homeostasis Model Assessment (HOMA2). A latent profile analysis was conducted to classify patients using the age at diabetes diagnosis and HOMA2 variables, i.e. insulin sensitivity (HOMA2%-S), beta-cell-function (HOMA2%-ß), and the product between both (HOMA2%-ßxS; as a measure of residual beta-cell function). GLT was expressed in defined daily dose (DDD). RESULTS: In total, 147 patients were included (median age: 80 years; 37.4% women; median age at diabetes diagnostic: 62 years). The resulting model classified patients into 6 distinct cardiometabolic profiles. Patients in profiles 1 and 2 had an older age at diabetes diagnosis (median: 68 years) and a lesser decrease in HOMA2%-S, as compared to other profiles. They also presented with the highest HOMA2%-ßxS values. Patients in profiles 3, 4 and 5 had a moderate decrease in HOMA2%-ßxS. Patients in profile 6 had the largest decrease in HOMA2%-ß and HOMA2%-ßxS. This classification was associated with significant differences in terms of HbA1c values and GLT total DDD between profiles. Thus, patients in profiles 1 and 2 presented with the lowest HbA1c values (median: 6.5%) though they received the lightest GLT (median GLT DDD: 0.75). Patients in profiles 3 to 5 presented with intermediate values of HbA1c (median: 7.3% and GLT DDD (median: 1.31). Finally, patients in profile 6 had the highest HbA1c values (median: 8.4%) despite receiving the highest GLT DDD (median: 2.28). Other metabolic differences were found between profiles. CONCLUSIONS: This study identified 6 groups among patients ≥75 years with type 2 diabetes by latent profile analysis, based on age at diabetes diagnosis, insulin sensitivity, absolute and residual ß-cell function. Intensity and choice of GLT should be adapted on this basis in addition to other existing recommendations for treatment individualisation.

Secondary analysis of newly diagnosed type 2 diabetes subgroups and treatment responses in the MARCH cohort.

OBJECTIVE: To incorporate new clusters in the MARCH (Metformin and AcaRbose in Chinese patients as the initial Hypoglycemic treatment) cohort of newly diagnosed type 2 diabetes (T2D) patients and compare the anti-glycemic effects of metformin and acarbose across different clusters. METHODS: K-means cluster analysis was performed based on six clinical indicators. The diabetic clusters in the MARCH cohort were retrospectively associated with the response to metformin and acarbose. RESULTS: A total of 590 newly diagnosed T2D patients were classified by data-driven clusters into the MARD (mild obesity-related diabetes) (34.1 %), MOD (mild obesity-related diabetes) (34.1 %), SIDD (severe insulin-deficient diabetes) (20.3 %) and SIRD (severe insulin-resistant diabetes) (11.5 %) subgroups at baseline. At 24 and 48 weeks, 346 participants had finished the follow-up. After the adjustment of age, gender, weight, baseline HbA1c, baseline fasting glucose and 2-h postprandial blood glucose (2hPG), metformin mainly decreased the fasting glucose (0.07 ± 0.89 vs -0.26 ± 0.83, P = 0.043) in the MARD subgroup presented with OGTT (oral glucose tolerance test) results compared with acarbose group at 24 weeks. Acarbose led to a greater decrease in 2hPG in the MOD subgroup compared with metformin group (0.08 ± 0.86 vs -0.24 ± 0.92, P = 0.037) at 24 weeks. There was a also significant interaction between cluster and treatment efficacy in HbA1c (glycated hemoglobin) reduction in metformin and acarbose groups at 24 and 48 weeks (pinteraction<0.001). CONCLUSIONS: Metformin and acarbose affected different metabolic variables depending on the diabetes subtype.

Classification of type 1 diabetes: A pathogenic and treatment-based classification.

AIM: To improve the diagnosis and classification of patients who fail to satisfy current type 1 diabetes diagnostic criteria. METHODS: Review of the literature and current diagnostic guidelines. DISCUSSION: We propose a novel, clinically useful classification based on islet autoantibody status and non-fasting C-peptide levels. Notably, we discuss the subgroup of latent autoimmune diabetes in the young and propose a new subgroup classification of autoantibody negative type 1 diabetes in remission. CONCLUSION: A novel classification system is proposed. Further work is needed to accurately diagnose and manage minority type 1 diabetes subgroups.

Responses to Basal Insulin Glargine (300 U/mL and 100 U/mL) with or Without Pre-prandial Insulin in Pre-treated Subphenotypes of Type 2 Diabetes: Insights from a Post Hoc Analysis.

INTRODUCTION: This study aimed to evaluate glycemic outcomes in subphenotypes of type 2 diabetes (T2D) with HbA1c > 7.0%, previously on basal insulin (pre-BI) alone (≥ 42 U/day) or on basal-bolus therapy (pre-BB), and who were switched to either basal insulin glargine 300 U/mL (IGlar-300) or 100 U/mL (IGlar-100), with or without pre-prandial insulin. METHODS: Participants from EDITION 2 (pre-BI, n = 785), and EDITION 1 (pre-BB, n = 792) trials were assigned retrospectively to subphenotypes of T2D: severe insulin deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity diabetes (MOD), and severe insulin resistant diabetes (SIRD). Key efficacy and safety parameters were analyzed at baseline, and after 26 weeks, for IGlar-300 and IGlar-100 pooled groups according to subphenotypes. Outcomes were also compared with insulin-naïve subphenotypes on oral antihyperglycemic drugs (OADs) from the EDITION 3 trial (pre-OAD, n = 858). RESULTS: Pre-BI and pre-BB treated subphenotypes with SIDD had a higher mean HbA1c (8.9% and 9.1%) at baseline compared to those of MARD (7.7% and 7.8%) and MOD (8.1% and 8.2%) and after 26 weeks remained above target HbA1c (7.7% and 8.0%) despite mean glargine doses of 0.7 to 1.0 U/kg/day and pre-prandial insulin use in the pre-BB SIDD subgroup. Pre-BB treated individuals with MARD and MOD achieved lower HbA1c levels (6.9% and 7.2%) than the pre-BI groups (7.3% and 7.5%) despite similar mean FPG levels (123-130 mg/dL). Only 19-22% of participants with SIDD achieved HbA1c < 7.0% compared to 33-51% with MARD and MOD, respectively. Pre-BI and pre-BB treated subphenotypes experienced more hypoglycemia than pre-OAD treated subphenotypes. CONCLUSION: Individuals with T2D assigned post hoc to the SIDD subphenotype achieved suboptimal glycemic control with glargine regimens including basal-bolus therapy, alerting clinicians to improve further diabetes treatment, particularly post-prandial glycemic control, in individuals with SIDD.

A framework to classify gestational diabetes diagnosed by routine antenatal 75g glucose tolerance testing.

OBJECTIVE: To create an objective framework to classify gestational diabetes mellitus diagnosed by routine antenatal 75 g diabetes testing results to provide an alternative to current treatment-based classification. METHODS: A framework was created to classify gestational diabetes according to the severity of glycemic abnormalities after routine antenatal 75 g GTT (classes 1 through 4, determined by fasting and post-test glycemic abnormalities). A retrospective cohort chart review was used to correlate clinically how often diet therapy alone maintained glycemic targets throughout pregnancy in each class. Chi-square analysis was used to assess inter-class differences in the success of diet therapy alone maintaining glycemic targets throughout pregnancy. RESULTS: Seventy-four of 228 (33%), 35/228 (15%), 76/228 (33%), and 43/228 (19%) of the study population were classified as Class 1, 2, 3, or 4, respectively. Of eighty-nine patients who maintained glycemic targets throughout pregnancy with diet alone 51/89 (57%) were Class 1, 20/89 (22.5%) were Class 2, 11/89 (12.5%) were Class 3, and 7/89 (8%) were Class 4. Chi-square analysis showed statistically significant inter-class differences in the likelihood of diet therapy alone maintaining glycemic targets throughout pregnancy. CONCLUSION: In this framework classifying gestational diabetes according to the severity of glycemic abnormalities after routine antenatal 75 g GTT (an objective proxy for disease severity), the higher the assigned class, the less likely that diet therapy alone maintained glycemic targets throughout pregnancy (a clinical proxy for disease severity).

Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm.

Over the past few decades, the prevalence of chronic illnesses in humans associated with high blood sugar has dramatically increased. Such a disease is referred to medically as diabetes mellitus. Diabetes mellitus can be categorized into three types, namely types 1, 2, and 3. When beta cells do not secrete enough insulin, type 1 diabetes develops. When beta cells create insulin, but the body is unable to use it, type 2 diabetes results. The last category is called gestational diabetes or type 3. This happens during the trimesters of pregnancy in women. Gestational diabetes, however, disappears automatically after childbirth or may continue to develop into type 2 diabetes. To improve their treatment strategies and facilitate healthcare, an automated information system to diagnose diabetes mellitus is required. In this context, this paper presents a novel system of classification of the three types of diabetes mellitus using a multi-layer neural network no-prop algorithm. The algorithm uses two major phases in the information system: the training phase and the testing phase. In each phase, the relevant attributes are identified using the attribute-selection process, and the neural network is trained individually in a multi-layer manner, starting with normal and type 1 diabetes, then normal and type 2 diabetes, and finally healthy and gestational diabetes. Classification is made more effective by the architecture of the multi-layer neural network. To provide experimental analysis and performances of diabetes diagnoses in terms of sensitivity, specificity, and accuracy, a confusion matrix is developed. The maximum specificity and sensitivity values of 0.95 and 0.97 are attained by this suggested multi-layer neural network. With an accuracy score of 97% for the categorization of diabetes mellitus, this proposed model outperforms other models, demonstrating that it is a workable and efficient approach.

Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches.

OBJECTIVES: We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type. STUDY DESIGN AND SETTING: We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE). RESULTS: Approaches' accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability. CONCLUSION: Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.

Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification.

This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.

[Is there a need for a revised classification in diabetes mellitus?] / Indokolt-e a klasszifikáció módosítása diabetesben?

Diabetes mellitus is a cluster of diseases with heterogeneous etiopathogenesis and clinical nature. The exact classification of certain cases is of decisive importance in terms of the optimal choice of treatment. However, the classification is still not completely resolved, despite the available, ever-expanding tool park and rapidly expanding knowledge. Therefore, new recommendations are made to clarify the grouping. This article reviews the classification guidelines created between 1965 and 2019 based on international consensus, with the coordination of the World Health Organization (WHO), as well as the proposals made based on recent tests and observations. It states that for daily practice, the present WHO guideline is still the most orienting. In addition, in cases of uncertain classification, it is essential to follow up the patients and repeat the examinations as necessary, until the nature of the specific form of the disease is clarified. Orv Hetil. 2022; 163(48): 1909-1916.

HNF1B-MODY Masquerading as Type 1 Diabetes: A Pitfall in the Etiological Diagnosis of Diabetes.

Hepatocyte nuclear factor-1B (HNF1B) maturity-onset diabetes of the young (MODY), also referred to as "renal cysts and diabetes syndrome" or MODY-5, is a rare form of monogenic diabetes that is caused by a deletion or a point mutation in the HNF1B gene, a developmental gene that plays a key role in regulating urogenital and pancreatic development. HNF1B-MODY has been characterized by its association with renal, hepatic and other extrapancreatic features. We present the case of a 39-year-old female patient who was first diagnosed with type 1 diabetes, but then, owing to the absence of anti-islet autoantibodies and to the disease's progression, was labeled later on as having atypical type 2 diabetes. She was finally recognized as having HNF1B-MODY, a diagnosis that had been suggested by the lack of metabolic syndrome and by the presence of unexplained chronically disturbed liver function tests and hypomagnesemia. There was a 10-year delay between the onset of diabetes and the molecular diagnosis. An atypical form of diabetes, especially in patients with multisystem involvement, should raise suspicion for an alternative etiology. A timely diagnosis of HNF1B-MODY is of utmost importance since it can greatly impact diabetes management and disease progression as well as family history.

Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture.

Diabetes is a deadly chronic disease that occurs when the pancreas is not able to produce ample insulin or when the body cannot use insulin effectively. If undetected, it may lead to a host of health complications. Hence, accurate and explainable early-stage detection of diabetes is essential for the proper administration of treatment options in leading a healthy and productive life. For this, we developed an interpretable TabNet model tuned via Bayesian optimization (BO). To achieve model-specific interpretability, the attention mechanism of TabNet architecture was used, which offered the local and global model explanations on the influence of the attributes on the outcomes. The model was further explained locally and globally using more robust model-agnostic LIME and SHAP eXplainable Artificial Intelligence (XAI) tools. The proposed model outperformed all benchmarked models by obtaining high accuracy of 92.2% and 99.4% using the Pima Indians diabetes dataset (PIDD) and the early-stage diabetes risk prediction dataset (ESDRPD), respectively. Based on the XAI results, it was clear that the most influential attribute for diabetes classification using PIDD and ESDRPD were Insulin and Polyuria, respectively. The feature importance values registered for insulin was 0.301 (PIDD) and for polyuria 0.206 was registered (ESDRPD). The high accuracy and ancillary interpretability of our objective model is expected to increase end-users trust and confidence in early-stage detection of diabetes.

Type 2 diabetes subgroups and potential medication strategies in relation to effects on insulin resistance and beta-cell function: A step toward personalised diabetes treatment?

BACKGROUND: Type 2 diabetes is a syndrome defined by hyperglycaemia that is the result of various degrees of pancreatic ß-cell failure and reduced insulin sensitivity. Although diabetes can be caused by multiple metabolic dysfunctions, most patients are defined as having either type 1 or type 2 diabetes. Recently, Ahlqvist and colleagues proposed a new method of classifying patients with adult-onset diabetes, considering the heterogenous metabolic phenotype of the disease. This new classification system could be useful for more personalised treatment based on the underlying metabolic disruption of the disease, although to date no prospective intervention studies have generated data to support such a claim. SCOPE OF REVIEW: In this review, we first provide a short overview of the phenotype and pathogenesis of type 2 diabetes and discuss the current and new classification systems. We then review the effects of different anti-diabetic medication classes on insulin sensitivity and ß-cell function and discuss future treatment strategies based on the subgroups proposed by Ahlqvist et al. MAJOR CONCLUSIONS: The proposed novel type 2 diabetes subgroups provide an interesting concept that could lead to a better understanding of the pathophysiology of the broad group of type 2 diabetes, paving the way for personalised treatment choices based on understanding the root cause of the disease. We conclude that the novel subgroups of adult-onset diabetes would benefit from anti-diabetic medications that take into account the main pathophysiology of the disease and thereby prevent end-organ damage. However, we are only beginning to address the personalised treatment of type 2 diabetes, and studies investigating the effects of current and novel drugs in subgroups with different metabolic phenotypes are needed to develop personalised treatment of the syndrome.

Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model.

The World Health Organization (WHO) estimated that in 2016, 1.6 million deaths caused were due to diabetes. Precise and on-time diagnosis of type-II diabetes is crucial to reduce the risk of various diseases such as heart disease, stroke, kidney disease, diabetic retinopathy, diabetic neuropathy, and macrovascular problems. The non-invasive methods like machine learning are reliable and efficient in classifying the people subjected to type-II diabetics risk and healthy people into two different categories. This present study aims to develop a stacking-based integrated kernel extreme learning machine (KELM) model for identifying the risk of type-II diabetic patients based on the follow-up time on the diabetes research center dataset. The Pima Indian Diabetic Dataset (PIDD) and a Diabetic Research Center dataset are used in this study. A min-max normalization is used to preprocess the noisy datasets. The Hybrid Particle Swarm Optimization-Artificial Fish Swarm Optimization (HAFPSO) algorithm used satisfies the multi-objective problem by increasing the Classification Accuracy (CA) and decreasing the kernel complexity of the optimal learners (NBC) selected. At last, the model is integrated by utilizing the KELM as a meta-classifier which combines the predictions of the twenty Base Learners as a whole. The proposed classification method helps the clinicians to predict the patients who are at a high risk of type-II diabetes in the future with the highest accuracy of 98.5%. The proposed method is tested with different measures such as accuracy, sensitivity, specificity, Mathews Correlation Coefficient, and Kappa Statistics are calculated. The results obtained show that the KELM-HAFPSO approach is a promising new tool for identifying type-II diabetes.

Heterogeneity of Metabolic Defects in Type 2 Diabetes and Its Relation to Reactive Oxygen Species and Alterations in Beta-Cell Mass.

Type 2 diabetes (T2D) is a complex and heterogeneous disease which affects millions of people worldwide. The classification of diabetes is at an interesting turning point and there have been several recent reports on sub-classification of T2D based on phenotypical and metabolic characteristics. An important, and perhaps so far underestimated, factor in the pathophysiology of T2D is the role of oxidative stress and reactive oxygen species (ROS). There are multiple pathways for excessive ROS formation in T2D and in addition, beta-cells have an inherent deficit in the capacity to cope with oxidative stress. ROS formation could be causal, but also contribute to a large number of the metabolic defects in T2D, including beta-cell dysfunction and loss. Currently, our knowledge on beta-cell mass is limited to autopsy studies and based on comparisons with healthy controls. The combined evidence suggests that beta-cell mass is unaltered at onset of T2D but that it declines progressively. In order to better understand the pathophysiology of T2D, to identify and evaluate novel treatments, there is a need for in vivo techniques able to quantify beta-cell mass. Positron emission tomography holds great potential for this purpose and can in addition map metabolic defects, including ROS activity, in specific tissue compartments. In this review, we highlight the different phenotypical features of T2D and how metabolic defects impact oxidative stress and ROS formation. In addition, we review the literature on alterations of beta-cell mass in T2D and discuss potential techniques to assess beta-cell mass and metabolic defects in vivo.

Ketosis prone diabetes presenting as fulminant type 1 diabetes.

Patients with ketosis prone diabetes have been reported primarily in Africans and African Americans. At presentation, both insulin secretion and insulin action are impaired in ketosis prone diabetes patients. Fulminant diabetes is a subtype of type 1 diabetes reported mainly in the Asian populations characterized by diabetic ketosis or ketoacidosis occurring soon after the onset of hyperglycemic symptoms with inappropriately low HbA1c (< 8.5%). We report here the first case of a ketosis prone diabetes presenting as fulminant diabetes.

[Study of acute inaugural ketosis-prone diabetes in a Hospital in Central-Eastern Tunisian]. / Etude sur le diabète aigu cétosique inaugural dans un hôpital du Centre-Est Tunisien.

Ketosis-prone diabetes is an acute complication of diabetes resulting from ketone accumulation in the blood. Despite the high rate of ketosis-prone diabetes described, there is very little information on the epidemiology of this inaugural complication of diabetes in Tunisia. This study aims to determine the epidemiological and clinical features and the laboratory tests parameters of inaugural ketoses in a Hospital in Tunisian. We conducted a retrospective, cross-sectional exhaustive study of patients admitted with inaugural ketosis over the period January 2010 - August 2016. The study population was divided into 2 groups according to the presence or not of anti-pancreatic autoimmunity: the DAI group consisted of all patients with autoimmunity, the DNAI group consisted of all patients without autoimmunity. Our study included 391 patients, with a sex ratio of 226 men/125 women, the average age was 34 ± 14.33 years. There was a male predominance (68%) in the general population. The age of disease onset was significantly lower in the DAI group. A factor that contributed to ketosis onset was found in 77.7% of the overall study population, it was significantly more frequent in the DAI group than in the DNAI group. The most common factor was viral infections. Thyroid antibodies were significantly higher in the DAI group. Ketosis is a common factor leading to inaugural decompensation of diabetes in Tunisia. Young adult male is the most affected group of population reported in the literature, with the absence of autimmunity, and a clinical profile of type 2 diabetes.