ACTA ENDOCRINOLOGICA (BUC)

Late November this year, Cluj-Napoca, the main academic center in Transylvania, hosted the 15th ESE Postgraduate Training Course in Endocrinology, Diabetes and Metabolism. Officially opened in Cluj on Friday, November 28 by ESE representatives, the ESE postgraduate course represents a “must attend event” for each endocrinologist, particularly the young, as these courses tremendously contribute to the development of our specialty by setting high standards and creating best practices, as pointed out by Prof. Carmen Georgescu, Local Organizing Committee Chair and Course Director. A most distinguished and renowned teaching faculty from around Europe was the key element towards achieving successful learning outcomes. The aim of a training course being to assure an up-to-date to known clinical problems and mostly to controversial issues of those, this meeting had all the features needed to provide for this aim. The layout of the course permitted a gentle passage from general management to individual cases, blending consensus opinions to individual case-finding strategy and treatment. To conclude, the 15th ESE Postgraduate Course was very fruitful and perfectly organized.

Objective. The aim of the study was to test whether a correlation exists between the Epworth Sleepiness Scale (ESS) and respiratory sleep parameters in patients with\r\ntype 2 diabetes.\r\nDesign. Subjects and Methods.The records of 83 consecutive patients (mean age 54.6? 9.8 years) with type 2 diabetes\r\nthat accepted to perform an in-hospital sleep study for screening of sleep apnea have been retrospectively evaluated.\r\nResults. There was a weak positive correlation between apnea/hypopnea index (AHI), oxygen desaturation index and ESS, and a weak negative correlation between ESS and mean O2 saturation. When data was separately analyzed in men and\r\nwomen, it could not be identified any correlation between sleep respiratory parameters and ESS in men. In women,\r\ncorrelation coefficients increased, proving a stronger relationship between ESS and AHI (r=0.65, p<0.001), mean O2 saturation (r=-0.52, p=0.005) and oxygen desaturation index (r=0.60, p=0.001). ESS had only a moderate level of accuracy in identifying patients with moderate and severe sleep\r\napnea (sensitivity 84.1%, specificity 74.1%, PPV 84.1%, NPV 74.1%). In women ESS showed a higher sensitivity than in men\r\n(92% vs. 80.6%), but a lower PPV (63% vs.78.1%) in predicting the presence of an AHI &#8805; 15.\r\nConclusions. In women with type 2 diabetes, it is possible to suspect the existence of SAS solely on the basis of the\r\nESS score. In male population, symptoms evaluated by questionnaires, such as the ESS, provide additional information which combined with clinical findings are helpful in selecting patients who are candidates for further detailed sleep studies.

Objective. to investigate the factors associated with postprandial glucose excursions in\r\npatients with type 2 diabetes.\r\nResearch Design and Methods. A complete medical history and physical examination\r\nwere assessed in 118 consecutive patients with type 2 diabetes attending the Diabetes\r\nOutpatient Clinic, Cluj-Napoca. Blood samples were collected in fasting state, and HbA1c\r\nand lipid profile were assessed. A six points blood glucose profile measured by patients at\r\nhome was performed. To determine variables associated with higher postprandial glycemic\r\nlevels, factor analysis followed by linear regression model was performed.\r\nResults. The study group had a median age of 59.2 years, 43.4% were females. The\r\nmedian duration of diabetes was 5 years. By factor analysis we have extracted 4 factors that\r\nexplained 75.6% of the variance of postprandial glycemia: factor 1 with positive loadings of\r\ntotal cholesterol and LDL cholesterol, factor 2 with positive loadings of body mass index\r\nand waist circumference, factor 3 with positive loadings of diabetes duration and age, factor\r\n4 with positive loadings of triglycerides and glycosylated hemoglobin (HbA1c). After\r\nadjustment for the sex and treatment, only factor 2 and factor 4 remained significantly associated\r\nwith postprandial glycemic values (p=0.003 and p<0.001), indicating that the postprandial\r\nglycemia is best predicted by a multiple regression that included body mass index, waist\r\ncircumference, tryglicerides and HbA1c as independent variables (r=0.54, p<0.001).\r\nConclusion. The results of our study shows that low body mass index and waist\r\ncircumference, high triglycerides and HbA1c levels are independently associated with\r\npostprandial glucose excursions.

Background. Recent experimental data show that increased plasma adiponectin in chronic kidney disease could be a response to inflammation.\r\nObjective. To identify factors influencing adiponectinemia in patients with type 2 diabetes (T2DM) and microalbuminuria or low grade proteinuria.\r\nDesign. 32 patients with urinary albumin excretion rate (UAER)> 30 mg/g creatinine but without significant proteinuria (< trace COMBUR) were included and compared to 59 normalbuminuric T2DM controls. History, anthropometric measurements, laboratory analysis, total plasma adiponectin were obtained.\r\nResults. In our patients with UAER of 273.51?57.26 mg/g creatinine and estimated glomerular filtration rate (eGFR) 64.92?4.56 mL/min, in simple regression, adiponectinemia\r\ncorrelates inversely to eGFR (p=0.02, r= -0.38), triglyceridemia (p=0.03, r=-0.37) and hemoglobin\r\n(Hb -p= 0.01, r=-0.45) and positively to HDL cholesterol (p=0.001, r=0.54) and UAER (p<0.0001, r=0.71); the two latter parameters remain significant in multiple regression. In controls, adiponectinemia correlates inversely to age (p=0.04, r=-0.26) and BMI (p=0.04, r=-0.24); these and UAER predict adiponectinemia in multiple regression. 11 patients have UAE superior to 300 mg/g creatinine and 21 are strictly microalbuminuric (mean UAER 653.16?97.02 and 83.68?10.28mg albumin/g creatinine respectively). In microalbuminuric patients serum C reactive protein (CRP) correlates positively (p=0.0008, r=0.68) and Hb negatively (p=0.04, r=-0.41) to adiponectinemia; in multiple regression adiponectinemia only depends on CRP. In proteinuric patients CRP and\r\nglycated Hb correlate to adiponectinemia in stepwise multiple regression.\r\nConclusion. Adiponectinemia is mainly predicted by UAER in our cohort whereas it depends on age and BMI in normalbuminuric T2DM controls; in strictly microalbuminuric\r\npatients CRP is a major predictor of adiponectinemia.

Obesity is a well-recognized risk factor for type 2 diabetes, cardiovascular disease, and several types of cancer. However, a proportion of the obese individuals display a significantly lower risk for metabolic complications than expected for their degree of body mass index, and this subtype of obesity was described as “metabolically healthy obesity” (MHO). No universally accepted criteria for the diagnosis of MHO exists and the prevalence of this subtype of obesity varies largely according to criteria used. Broadly, MHO is characterized by a lower amount of visceral fat, a more favorable inflammatory profile, and less insulin resistance as compared to the metabolically unhealthy obesity. Currently, controversies exist regarding the risk of cardiovascular events and all-cause mortality associated with MHO as compared to metabolically-healthy non-obese individuals. Further research is needed in order to identify the MHO phenotype and if MHO is truly healthy for a long period of time or if it is a transient state from normal metabolic/normal weight to abnormal metabolic/obese state. This review will discuss the MHO definition criteria; the differences between MHO and metabolically unhealthy obesity; the possible underlying mechanisms and clinical implications of MHO.