Metabolic Syndrome as a Predictor of Adrenal Functional Status: A Discriminant Multivariate Analysis Versus Logistic Regression Analysis.

Patients harboring adrenal tumors are characterized by higher prevalence of metabolic syndrome (MetS) components and a higher incidence of cardiovascular complications, especially in cases of subclinical or overt hormonal hypersecretion. Early detection and referral of those patients in tertiary centers could prevent unfavorable outcomes. In this cross-sectional, retrospective study, we evaluated 111 consecutive patients with adrenal incidentalomas and 14 patients with known hypersecretory adrenal lesions (autonomous cortisol secretion, primary aldosteronism, and pheochromocytoma), who were investigated in our clinic. Based on the different distribution of MetS components in patients with non-functional and functional adrenal lesions we introduced a predictive model of hormonal hypersecretion using those components. We performed multivariate discriminant analysis and compared predictive results with conventional multiple logistic regression analysis. Diabetes, impaired glucose tolerance, impaired fasting glucose, hypertension, body mass index, HDL-cholesterol levels, triglyceride levels, drug treatment for lipid disorder (statins, fenofibrate, and fish oils, alone or in combination), and maximal adrenal lesion diameter were used as discriminating covariates. Multivariate discriminant function exhibited a sensitivity of 77.27% and specificity of 73.08% in predicting adrenal hormonal hypersecretion. Receiver operating characteristic curve of discriminant predictive function had an area under the curve value of 0.785, S.E. 0.04. Logistic function delivered comparable results. MetS components exhibit a good predictive feature of hormonal hypersecretion in patients with adrenal tumors. Predictive functions may help in the search for an easy and generally available algorithm to validly predict the functional activity of adrenal masses.

S100B Levels in Patients with Type 2 Diabetes Mellitus and Co-Occurring Depressive Symptoms.

Depression is a comorbid condition in patients with Type 2 Diabetes mellitus (T2DM). S100B, a glia derived protein, is linked to depression and has been suggested as a biomarker for depression outcomes in several populations. However, to date there is no data about S100B levels and depression in patients with T2DM. Objective. We hypothesized that S100B serum levels are increased in patients with T2DM and recently diagnosed, drug-free depressive symptoms, and could be used for the diagnosis of depression in T2DM. Methods. Overall 52 patients (62 ± 12 years, female 66, 7%) with no history of depression deriving from the Diabetes out-patient clinic of our University Hospital underwent a one-to-one interview with a psychiatrist and filled a self-assessment (Zung) questionnaire. Serum S00B levels were compared between 30 (63±12 years, female 66, 7%) diabetic patients without depressive symptoms vs 22 patients (62 ±12 years, female 68, 2%) with T2DM and depressive symptoms. Results. There was no difference in serum levels of S100B between patients with T2DM without depressive symptoms vs diabetic patients suffering from depressive symptoms (2.1 (1.9-10.9) pg/ml vs 2.4 (1.9-14.8) pg/ml, p=0. 637+). Moreover, linear regression analysis did not show any association between lnS100B levels and depressive symptoms (ß = 0.084, 95% CI 0.470-0.871, and p=0.552), Zung self-assessment score (ß = 0.048, 95% CI -0.024-0.033, and p=0.738), and other patients' characteristics. Conclusions. In patients with T2DM there is no correlation between S100B serum levels and newly detected mild depressive symptoms. The brain biochemistry pathways of depression in T2DM warrant further investigation in a larger scale population.