Oleg S. Chaban

Alexithymia as a risk factor for type 2 diabetes mellitus in the metabolic syndrome: a cross-sectional study

Alexithymia is a clinical trait consisting of diminished introspective and interoceptive capacities that has been shown to implicate elevated autonomic outflow and to bias for hypertension. To estimate relative risk associated with alexithymia in the metabolic syndrome (MetS), we conducted a cross-sectional analysis of patients with manifest type 2 diabetes mellitus (T2DM) or familial diabetes risk (N=101; 67 females; age 45.6±13.96) in a nationwide sampled treatment cohort for MetS in the Ukrainian governmental health care system. Laboratory data of single components of the MetS according to International Diabetes Federation Consensus were dependent measures in multivariable regression models with self-reported alexithymia severity (TAS-20) and socio-demographic data. TAS-20 as the sole surviving psychometric predictor for T2DM in the simplest regression equation provided the best model fit: OR 1.073, Z=19.04, (95%CIs 1.065-1.081). For microalbuminuria, the best fitting model was OR 1.030, Z=3.49 (95%CIs 1.013-1.048). TAS-20 predicted also triglyceride level at Wald-χ(2)=1299.27, Z=36.05 (95%CIs 0.052-0.058) and blood pressure maximum at Wald-χ(2)=2309.05, Z=48.05 (95%CIs 2.402-2.606). Our results show that alexithymia severity contributes to MetS by covarying with several of its single components, and that it may be a substantial concurrent indicator of T2DM and cardiovascular risks in MetS.

Neuroendorine and Epigentic Mechanisms Subserving Autonomic Imbalance and HPA Dysfunction in the Metabolic Syndrome

Impact of environmental stress upon pathophysiology of the metabolic syndrome (MetS) has been substantiated by epidemiological, psychophysiological, and endocrinological studies. This review discusses recent advances in the understanding of causative roles of nutritional factors, sympathomedullo-adrenal (SMA) and hypothalamic-pituitary adrenocortical (HPA) axes, and adipose tissue chronic low-grade inflammation processes in MetS. Disturbances in the neuroendocrine systems for leptin, melanocortin, and neuropeptide Y (NPY)/agouti-related protein systems have been found resulting directly in MetS-like conditions. The review identifies candidate risk genes from factors shown critical for the functioning of each of these neuroendocrine signaling cascades. In its meta-analytic part, recent studies in epigenetic modification (histone methylation, acetylation, phosphorylation, ubiquitination) and posttranscriptional gene regulation by microRNAs are evaluated. Several studies suggest modification mechanisms of early life stress (ELS) and diet-induced obesity (DIO) programming in the hypothalamic regions with populations of POMC-expressing neurons. Epigenetic modifications were found in cortisol (here HSD11B1 expression), melanocortin, leptin, NPY, and adiponectin genes. With respect to adiposity genes, epigenetic modifications were documented for fat mass gene cluster APOA1/C3/A4/A5, and the lipolysis gene LIPE. With regard to inflammatory, immune and subcellular metabolism, PPARG, NKBF1, TNFA, TCF7C2, and those genes expressing cytochrome P450 family enzymes involved in steroidogenesis and in hepatic lipoproteins were documented for epigenetic modifications.

Trait anxiety but not state anxiety level associates with biomarkers for hypertension in the metabolic syndrome.

Various studies link hypertension with anxiety; however, it remains unclarified if such relations are present in the metabolic syndrome (MetS). We studied cross-sectionally the interrelations of self-reported anxiety (Spielberger STAI), and MetS components in MetS patients. We investigated a nationally sampled treatment cohort for MetS with familial Type 2 diabetes risk. N = 101 patients fulfilling International Diabetes Federation criteria for MetS participated. Both laboratory and nonlaboratory measures were included. Structural equation models (SEM) were adjusted. The final SEM had an R(2)  = .998 with the obesity component linking to waist, BMI, and degree of adiposity, and the hypertension component linking to systolic blood pressure, pulse pressure, total cholesterol, and trait anxiety. For state anxiety, no significant regressive causal path could be estimated. SEM supports the assumption of an interaction of pulse pressure, systolic blood pressure, cholesterol metabolism, and high trait anxiety in the pathophysiology of hypertension in MetS.

An Open Uncontrolled Pilot Trial of Online Cognitive-Behavioral Therapy for Insomnia for Ukrainian Alcohol-Dependent Patients

Heavy drinking patterns among adults are common in Ukraine (Webb et al. in Alcohol Alcohol 40(4):327, 2005). According to the WHO Global Information System on Alcohol and Health, the total recorded and unrecorded alcohol per capita consumption among adults (15+) in Ukraine was 14.3 (in liters of pure alcohol) in 2003–2005 which decreased to 13.9 in 2008–2010, with spirits being the main beverage group (48 % of all the alcohol) followed by beer (40 %) and wine (9 %). The 12-month prevalence of alcohol use disorders (including alcohol dependence and harmful use of alcohol) and alcohol dependence estimates (15+) among males were 9.3 and 4.2 % and females 1.1 and 0.5 %, respectively (WHO 2014). The same report found that the highest patterns of drinking score, i.e., the most risky patters of drinking, have been found in Russia and Ukraine.

Aggressivity and hostility traits affect different cardiovascular risk profiles in the metabolic syndrome

What is nowadays known as the Metabolic Syndrome (MetS), is amultiplex disorder, which consists of single symptoms that, whenstanding alone confer little risk, but when occurring in coincidence, ex-ponentiallyincreasetheprobabilitytomanifesttype2diabetes(T2DM),and for cardiovascular disease (CVD) [1,2] The combination of angertraits and MetS confers longitudinally a more than fourfold (OddsRatio 4.21) increase in the incidence of myocardial infarction [3,4].Totest the assumption that personality traits may relate to single bio-markers in MetS, we estimated the association of anger traits to bio-markers of CVD risk. To this end we investigated a nationally sampledtreatment cohort cross-sectionally—in size and composition compara-ble to research samples typically used in biomarker detection studiesin MetS, with approximately counterbalancing diabetic and non-diabetic patients by design. Following the steps correlation, principalcomponent analysis (PCA), hierarchical (HRA) and multivariablelogistic regression (MVLR), the comprehensive variance–covariancematrices were decomposed by structural equation modeling (SEM).Our overall SEM approach was the specification of a measurement-based causal model, with using the latent constructs indicated by atwo-factor solution (see below) for aggressivity (overt anger) and hos-tility (covert anger) in the preceding PCA.The current sample was drawn nationwide from patients treatedwithin the Ukrainian Ministry of Transport health care system, and re-ferred from Ukraine to a specialized unit in Kiev. This pool of patientsconstituted a national treatment cohort for metabolic disorders, whowere annually examined at Railway Hospital No. 2 in Kiev, Ukraine,for reasons of preventive control. The total treatment cohort had beencompiled on the basis of documented familial risk factors for T2DMand known endocrinological problems. Inclusion criteria were validMetSranges, and exclusion criteria were other internal medical, neuro-logical and psychiatric disorders. The final sample comprised N =101volunteering individuals (mean age 45.6 ± 0.14 SEM years; educationlevels 4.73 ± 0.11, 4 = junior college level, 67 females).The investigation was conducted in compliance with the HelsinkiDeclaration (www.wma.net/e/ethicsunit/helsinki.htm). The InstitutionalReviewBoardoftheNationalMedicalUniversityoftheUkrainehaden-dorsedallprocedures.Allsubjectsgavewritteninformedconsenttothescientific use of their data, and were reimbursed for their participation.The patientshad tofulfillthree ormorecriteriaforMetS,conformingtoInternational Diabetes Federation (IDF; www.idf.org/publications) con-sensus as documented in the patient files. Laboratory analyses of all bi-ological specimens were performed inhouse with enzymatic methodsusing commercially available reagents. The Buss–Durkee Hostility In-ventory was used to assess different facets of anger and hostility traitsby self-report.The internal consistencies for the two main factor-analytically de-rived index measures (Aggressivity Index α = 0.871, Hostility-Indexα = 0.906) were sufficient for further statistical analyses. The resultsof the PCA suggesting a two-factor solution (73% of variance accountedfor), with Aggressivity Index characterizing latent component 1, andHostility Index characterizing latent component 2, are reported inTable 1. The results of HRA and MVLR models corroborated the exis-tenceoftwodistinctregressivepathsinthebiologicalvariableswithre-specttothelatentcomponentsfoundinPCA.Thebest-fittingSEMwasaconstrained model (setting the latent variables to 1) using robust Ordi-nary Least Squares path estimation with adjustment of standard errorsfor different clustering in the two sexes, so yielding an overall unique

Anger Traits Associated With Cardiovascular Risk Biomarkers in the Metabolic Syndrome

Background:Recent studies have shown that different personality traits contribute to mortality in different subtypes of cardiovascular disease (CVD). Anger traits have been shown to promote the constellation of the metabolic syndrome (MetS), which in turn increases CVD risks. Objective:To determine covariation of anger traits with CVD biomarkers, we examined patients (N = 101; 34 men and 67 women; age, 45.6 ± 13.96 years) in a nationally sampled treatment cohort for MetS in the Ukrainian governmental healthcare system. Methods:Data collection was conducted in 2007. Laboratory data of single components of the MetS according to International Diabetes Federation Consensus were dependent measures in regression models with self-reported overt aggressivity and covert hostility in the Buss-Durkee Hostility Inventory and sociodemographic data. Structural equation models (SEMs) were tested. Results:The SEM results are in favor of a sex-adjusted 2-factor solution R2 = 0.723), as indicated by equation-level Bentler-Raykov goodness-of-fit coefficients of 0.81 to 0.97 for paths to biological variables. Two latent components, 1 relating to aggressivity and the other to hostility, combine lipid/obesity-related measures and cholesterol-related measures, respectively. Conclusions:The SEM results suggest that CVD-risk biomarker variables in this MetS sample (a) associate into 2 distinct profiles and (b) that 1 profile associates with overt anger, whereas the other associates with covert hostility. These results could contribute to more personalized prevention and care in CVD patients.