Disease Of The Sultans: Metabolic Syndrome In Ottoman Dynasty

Metabolic syndrome is generally considered as a complication of modernity. Here we searched for the presence of metabolic syndrome components among the Ottoman emperors who lived between 1258 and 1926. Collections of historical archives, which were published as books specifically about morbidity and mortality of Ottoman emperors were reviewed to diagnose metabolic syndrome according to modified criteria by American College of Endocrinology and American Association of Clinical Endocrinologists. Nineteen of 36 dynasty members (53%) had fatal or non-fatal cardiovascular events. Twenty-nine of the dynasty (81%) members were either depicted as truncal obese or reported to have obesity. Thirteen emperors (36%) satisfied diagnostic criteria for metabolic syndrome, retrospectively. Overall, 42% of non-commanding emperors, but 26% of commanding-emperors (who were assumed to be athletically grown and physically more active) were found to have metabolic syndrome (p=0.553). We suggest firstly here that sedentary palace lifestyle exacerbated metabolic syndrome in Ottoman dynasty especially in elderly members, thereafter complicated by cardiovascular events, even in pre-modern era. (Anadolu Kardiyol Derg 2010; 10:270-3)WoSScopu

Expert Panel Recommendations for Use of Standardized Glucose Reporting System Based on Standardized Glucometrics Plus Visual Ambulatory Glucose Profile (AGP) Data in Clinical Practice

This expert panel of diabetes specialists aimed to provide guidance to healthcare providers on the best practice in the use of innovative continuous glucose monitoring (CGM) techniques through a practical and implementable document that specifically addresses the rationale for and also analysis and interpretation of the new standardized glucose reporting system based on standardized CGM metrics and visual ambulatory glucose profile (AGP) data. This guidance document presents recommendations and a useful algorithm for the use of a standardized glucose reporting system in the routine diabetes care setting

Discrimination between non-functioning pituitary adenomas and hypophysitis using machine learning methods based on magnetic resonance imaging-derived texture features

Purpose Hypophysitis is a heterogeneous condition that includes inflammation of the pituitary gland and infundibulum, and it can cause symptoms related to mass effects and hormonal deficiencies. We aimed to evaluate the potential role of machine learning methods in differentiating hypophysitis from non-functioning pituitary adenomas. Methods The radiomic parameters obtained from T1A-C images were used. Among the radiomic parameters, parameters capable of distinguishing between hypophysitis and non-functioning pituitary adenomas were selected. In order to avoid the effects of confounding factors and to improve the performance of the classifiers, parameters with high correlation with each other were eliminated. Machine learning algorithms were performed with the combination of gray-level run-length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray-level co-occurrence entropy. Results A total of 34 patients were included, 17 of whom had hypophysitis and 17 had non-functioning pituitary adenomas. Among the 38 radiomics parameters obtained from post-contrast T1-weighted images, 10 tissue features that could differentiate the lesions were selected. Machine learning algorithms were performed using three selected parameters; gray level run length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray level co-occurrence entropy. Error matrices were calculated by using the machine learning algorithm and it was seen that support vector machines showed the best performance in distinguishing the two lesion types. Conclusions Our analysis reported that support vector machines showed the best performance in distinguishing hypophysitis from non-functioning pituitary adenomas, emphasizing the importance of machine learning in differentiating the two lesions

Clinical characteristics and outcomes of COVID-19 in patients with type 2 diabetes in Turkey: A nationwide study (TurCoviDia)

Background Coronavirus disease 2019 (COVID-19) has been reported to be associated with a more severe course in patients with type 2 diabetes mellitus (T2DM). However, severe adverse outcomes are not recorded in all patients. In this study, we assessed disease outcomes in patients with and without T2DM hospitalized for COVID-19

Clinical characteristics and outcomes of COVID

Background Coronavirus disease 2019 (COVID-19) has been reported to be associated with a more severe course in patients with type 2 diabetes mellitus (T2DM). However, severe adverse outcomes are not recorded in all patients. In this study, we assessed disease outcomes in patients with and without T2DM hospitalized for COVID-19

Clinical outcomes of non-diabetic COVID-19 patients with different blood glucose levels: a nationwide Turkish study (TurCoGlycemia)

Purpose New coronavirus disease 2019 (COVID-19) has a worse prognosis in patients with diabetes. However, there are insufficient data about the effect of hyperglycemia on COVID-19 prognosis in non-diabetic patients. This study aimed to investigate the relationship between random blood glucose levels measured at the time of diagnosis and prognosis of COVID-19 disease in non-diabetic patients. Methods A nationwide retrospective cohort of non-diabetic patients with confirmed COVID-19 infection from 11 March to 30 May 2020 in the Turkish Ministry of Health database was investigated. The patients were stratified into three groups according to blood glucose levels which were <100 mg/dL in group-1, in the range of 100-139 mg/dl in group-2, and the range of 140-199 mg/dl in group-3. Clinical characteristics and outcomes were compared among the groups. The primary outcome was mortality. Results A total of 12,817 non-diabetic patients (median age [IQR]: 44 [25] years, females: 50.9%) were included. Patients in group-2 (5%) and group-3 (14%) had higher mortality rates than patients in group-1 (2.1%). The rates of hospitalization, hospital stays longer than 8 days, intensive care unit (ICU) admission, ICU stay more than 6 days, and mechanical ventilation were also significantly higher in group-3 patients. Likewise, glucose levels in the range of 140-199 mg/dL were an independent associate of mortality and composite of ICU admission and/or mechanical ventilation. Conclusion Hyperglycemia at the time of COVID-19 diagnosis is associated with poor prognosis in non-diabetic patients. Clinicians should be more careful in the treatment of non-diabetic COVID-19 patients with hyperglycemia

Higher rate of COVID-19 mortality in patients with type 1 than type 2 diabetes: a nationwide study

Introduction: COVID-19 disease has a worse prognosis in patients with diabetes, but comparative data about the course of COVID-19 in patients with type 1 (T1DM) and type 2 diabetes (T2DM) are lacking. The purpose of this study was to find out the relative clinical severity and mortality of COVID-19 patients with T1DM and T2DM

The clinical outcomes of COVID-19 infection in patients with a history of thyroid cancer: A nationwide study

Background There are scarce published data in differentiated thyroid cancer patients about new coronavirus disease 2019 (COVID-19) disease outcomes and mortality. Here, we evaluated COVID-19 infection outcomes and mortality in thyroid cancer patients with COVID-19 infection