Comparing Performances of Logistic Regression, Classification & Regression Trees and Artificial Neural Networks for Predicting Albuminuria in Type 2 Diabetes Mellitus

In this study, performances of classification methods were compared in order to predict the presence of albuminuria in type 2 diabetes mellitus patients. A retrospective analysis was performed in 266 subjects. We compared performances of logistic regression (LR), classification and regression trees (C&RT) and two artificial neural networks algorithms. Predictor variables were gender, urine creatinine, weight, blood urea, serum albumin, age, creatinine clearance, fasting plasma glucose, post-prandial plasma glucose, and HbA1c. For validation set, the best classification accuracy (84.85%), sensitivity (68.0%) and the highest Youden index (0.63) was found in the MLP model but the specificity was 95.12%. Additionally, the specificity of all the models was close to each other. For whole data set the results were found as 84.21%, 53.95%, 0.50 and 96.32% respectively. Consequently, the model had the highest predictive capability to predict the presence of albuminuria was MLP. According to this model, blood urea and serum albumin were the most important variables for predicting the albuminuria. On the basis of these considerations, we suggest that data should be better explored and processed by high performance modeling methods. Researchers should avoid assessment of data by using only one method in future studies focusing on albuminuria in type 2 diabetes mellitus patients or any other clinical condition

Efficacy and Safety of Insulin Glargine 300 U/mL in People with Type 2 Diabetes Uncontrolled on Basal Insulin: The 26-Week Interventional, Single-Arm ARTEMIS-DM Study

Introduction: The efficacy and safety of switching to insulin glargine 300 U/mL (Gla-300) in type 2 diabetes mellitus (T2DM) uncontrolled on basal insulin (BI) has been demonstrated in the North American and Western European populations; however, there is limited data from other geographical regions with different ethnicities. The ARTEMIS-DM study aimed to evaluate the efficacy and safety of Gla-300 in people with T2DM uncontrolled on BI from Asia, Latin America and Middle East Africa. Methods: The ARTEMIS-DM was a 26-week, prospective, interventional, single-arm, phase IV study (NCT03760991). Adults with T2DM previously uncontrolled (glycated haemoglobin [HbA1c] 7.5–10%) on BI were switched to Gla-300. The primary endpoint was change in HbA1c from baseline to 26 weeks. Key secondary endpoints were changes in HbA1c (week 12), fasting plasma glucose (FPG), self-monitored plasma glucose (SMPG) and BI dose from baseline to week 26. The safety and tolerability of Gla-300 were also assessed. Results: A total of 372 (50% male) participants were included, with mean (standard deviation [SD]) age 60.9 (10.0) years, duration of diabetes 13.11 (7.48) years and baseline HbA1c 8.67 (0.77)% (71.22 [8.44] mmol/mol). A total of 222 (59.7%) participants were using insulin glargine 100 U/mL and 107 (28.8%) were using neutral protamine Hagedorn insulin as previous BI. There were clinically significant reductions in mean HbA1c (− 0.82%; primary endpoint), FPG and SMPG levels at week 26. With a pre-defined titration algorithm, mean Gla-300 dose increased from 27.48 U (0.35 U/kg) at baseline to 39.01 U (0.50 U/kg) at week 26. Hypoglycaemia events occurred in 20.4% of the participants; 1 (0.3%) participant had a severe hypoglycaemia event. Conclusion: In people with T2DM uncontrolled on previous BI, switching to Gla-300 with optimal titration guided by an algorithm was associated with improved glycaemic control and low incidence of hypoglycaemia across multiple geographic regions. ClinicalTrials.gov identifier: NCT03760991.Fil: Sethi, Bipin. Care Hospital Hyderabad; IndiaFil: Al-Rubeaan, Khalid. Research and Scientific Center Sultan Bin Abdulaziz Humanitarian City; ArgentinaFil: Unubol, Mustafa. Adnan Menderes Universitesi; TurquíaFil: Mabunay, Maria A.. Sanofi; SingapurFil: Berthou, Baptiste. Sanofi; FranciaFil: Pilorget, Valerie. Sanofi; FranciaFil: Vethakkan, Shireene R.. University Malaya Medical Centre; MalasiaFil: Frechtel, Gustavo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; Argentin

The Comparisons of Four Splitting Rules for Fitting a Classification Tree with Simulation and an Application Related to Albuminuria Data in Type 2 Diabetes Mellitus

The objective of this study was to compare the performances of splitting rules for predicting an ordinal response with simulation and a real data set. In the case of simulations, we compared across the methods using different sample sizes and the number of independent variables by employing the Monte Carlo simulation method. In the real data application, an analysis was performed with 265 cases. The results showed that the performances of the generalized Gini with the linear and quadratic costs of misclassification were better suited for analysis based on the gamma ordinal association measure and misclassification error rate than the other approaches. According to the gamma ordinal association measure, the generalized Gini (linear and quadratic) to the major risk factors determined for albuminuria in type 2 diabetes mellitus patients showed a slightly better performance than the other approaches. The predictive capability of splitting rules based on generalized Gini for predicting an ordinal response can be used for different sample sizes, number of independent variables and potential future suitable classification data problems. Consequently, our study will move towards choosing the generalized Gini (linear or quadratic) as the splitting rule and evaluate the data by using the Classification Trees (CT) in future studies, focusing on predicting an ordinal response

Penile ulcer ATRA related in patient with acute promyelocytic leukemia

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Adrenal incidentaloma and the Janus Kinase 2 V617F mutation: A case-based review of the literature

Adrenal incidentaloma was detected in an 81-year-old male patient and a 37-year-old female patient who had been diagnosed with essential thrombocytosis. Each patient′s Janus Kinase 2 (JAK2) V617F mutation was positive, and they were evaluated as having non-functional adrenal incidentaloma. The JAK2 activates the signal transducers and activators of transcription (STAT) proteins which then activate the phosphoinositol-3 kinases, Ras, mitogen-activated protein (MAP) kinases, and transcription. Constitutive activation causes cell proliferation and dysregulation of apoptosis. It is thought that STAT3 activation-mediated JAK family kinases have a central role in the solid tumor cell series. Permanent activation of STAT3 and STAT5 causes tumor cell proliferation, survival, metastasis, and an increase in tumor-mediated inflammation in solid and hematologic tumors. According to our literature screening, irregular JAK signaling, seen at the pathogenesis of many solid and hematologic tumors, has not been previously evaluated with regard to adrenal tumors. As a result, our cases are the first coexistence of JAK V617F mutation with adrenal incidentaloma in the literature. Because of this, we think that JAK2 mutation must be evaluated to clarify the etiology of adrenal incidentalomas

Subacute THYROiditis Related to SARS-CoV-2 VAccine and Covid-19 (THYROVAC Study): A Multicenter Nationwide Study.

Context The aims of the study are to compare characteristics of subacute thyroiditis (SAT) related to different etiologies, and to identify predictors of recurrence of SAT and incident hypothyroidism. Methods This nationwide, multicenter, retrospective cohort study included 53 endocrinology centers in Turkey. The study participants were divided into either COVID-19-related SAT (Cov-SAT), SARS-CoV-2 vaccine-related SAT (Vac-SAT), or control SAT (Cont-SAT) groups. Results Of the 811 patients, 258 (31.8%) were included in the Vac-SAT group, 98 (12.1%) in the Cov-SAT group, and 455 (56.1%) in the Cont-SAT group. No difference was found between the groups with regard to laboratory and imaging findings. SAT etiology was not an independent predictor of recurrence or hypothyroidism. In the entire cohort, steroid therapy requirement and younger age were statistically significant predictors for SAT recurrence. C-reactive protein measured during SAT onset, female sex, absence of antithyroid peroxidase (TPO) positivity, and absence of steroid therapy were statistically significant predictors of incident (early) hypothyroidism, irrespective of SAT etiology. On the other hand, probable predictors of established hypothyroidism differed from that of incident hypothyroidism. Conclusion Since there is no difference in terms of follow-up parameters and outcomes, COVID-19- and SARS-CoV-2 vaccine-related SAT can be treated and followed up like classic SATs. Recurrence was determined by younger age and steroid therapy requirement. Steroid therapy independently predicts incident hypothyroidism that may sometimes be transient in overall SAT and is also associated with a lower risk of established hypothyroidism

Impact of Obesity on the Metabolic Control of Type 2 Diabetes: Results of the Turkish Nationwide Survey of Glycemic and Other Metabolic Parameters of Patients with Diabetes Mellitus (TEMD Obesity Study)

Background: Obesity is the main obstacle for metabolic control in patients with type 2 diabetes. Turkey has the highest prevalence of obesity and type 2 diabetes in Europe. The effect of obesity on the metabolic control, and the macro-and microvascular complications of patients are not apparent. Objectives: This nationwide survey aimed to investigate the prevalence of overweight and obesity among patients with type 2 diabetes and to search for the impact of obesity on the metabolic control of these patients. We also investigated the independent associates of obesity in patients with type 2 diabetes. Methods: We consecutively enrolled patients who were under follow-up for at least 1 year in 69 tertiary healthcare units in 37 cities. The demographic, anthropometric, and clinical data including medications were recorded. Patients were excluded if they were pregnant, younger than 18 years, had decompensated liver disease, psychiatric disorders interfering with cognition or compliance, had bariatric surgery, or were undergoing renal replacement therapy. Results: Only 10% of patients with type 2 diabetes (n = 4,648) had normal body mass indexes (BMI), while the others were affected by overweight (31%) or obesity (59%). Women had a significantly higher prevalence of obesity (53.4 vs. 40%) and severe obesity (16.6 vs. 3.3%). Significant associations were present between high BMI levels and lower education levels, intake of insulin, antihypertensives and statins, poor metabolic control, or the presence of microvascular complications. Age, gender, level of education, smoking, and physical inactivity were the independent associates of obesity in patients with type 2 diabetes. Conclusion: The TEMD Obesity Study shows that obesity is a major determinant of the poor metabolic control in patients with type 2 diabetes. These results underline the importance of prevention and management of obesity to improve health care in patients with type 2 diabetes. Also, the results point out the independent sociodemographic and clinical associates of obesity, which should be the prior targets to overcome, in the national fight with obesity. (c) 2019 The Author(s) Published by S. Karger AG, Base