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Background/Objectives: Body composition (BC) affects the risk of developing metabolic dysfunction-associated steatotic liver disease (MASLD) and hypertension (HTN). Currently, dual-energy X-ray absorptiometry (DEXA) is considered the gold standard for assessing BC, even though it has some limitations, including immobility, ionizing radiation, and patient weight restrictions. The aim of the study was to evaluate the correlations of BC parameters measured by bioelectrical impedance analysis (BIA) with those measured by DEXA in patients with MASLD and HTN. Methods: Overall, 78 patients with MASLD and HTN underwent the following study procedures: compilation of an anamnesis, physical examination of a patient, laboratory tests, abdominal ultrasound, BIA, DEXA, and anthropometric measurements. Results: The agreement between BIA and DEXA in diagnosing reduced skeletal muscle mass (SMM) in patients with MASLD and HTN was moderate (kappa values were 0.440 and 0.404 in males and females, respectively). Significant strong direct correlations were found between fat mass (FM) and body fat percentage measured by BIA with corresponding measurements by DEXA (p < 0.001 for both). The area under the receiver operating characteristic curves (AUC) of SMM to body weight ratios calculated using BIA data were 0.834 and 0.929 for reduced appendicular SMM determined by DEXA in males and females with MASLD and HTN, respectively. Conclusions: In conclusion, BIA is an easy-to-use and widely available tool for assessing SMM and FM in patients with MASLD and HTN, demonstrating reliable agreement with DEXA measurement results and completely free of its limitations. [ABSTRACT FROM AUTHOR]

This study investigates the role of gut microbiota in cardiovascular diseases, with an additional focus on pro-atherogenic metabolites. We use advanced network analysis and machine learning techniques to identify key microbial features linked to coronary artery disease (CAD) and heart failure with reduced ejection fraction (HFrEF). This cross-sectional study included 189 participants divided into three groups: coronary artery disease (n = 93), heart failure with reduced ejection fraction (n = 43), and controls (n = 53). Assessments included physical exams, echocardiography, dietary surveys, blood analysis, and fecal analysis. Gut microbiota composition was analyzed using next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR). Statistical analysis methods for testing hypotheses and correlations, alpha and beta-diversity analyses, co-occurrence networks, and machine learning were conducted using Python libraries or R packages with multiple comparisons corrected using the Benjamini–Hochberg procedure. Significant gut microbiota alterations were observed, with higher Bacillota/Bacteroidota ratios in CAD and HFrEF groups compared to controls (p < 0.001). Significant differences were observed in α-diversity indices (Pielou, Chao1, Faith) between disease groups and controls (p < 0.001). β-diversity analyses also revealed distinct microbial profiles (p = 0.0015). Interestingly, trimethylamine N-oxide (TMAO) levels were lower in CAD and HFrEF groups compared to controls (p < 0.05), while indoxyl sulfate (IS) levels were comparable between the study groups. Co-occurrence network analysis and machine learning identified key microbial features linked to these conditions, highlighting complex interactions within the gut microbiota associated with cardiovascular disease. [ABSTRACT FROM AUTHOR]

Background: In Russia, the following two questionnaires are mainly used to study the burnout syndrome: Maslach Burnout Inventory (MBI) and Boyko's Burnout Inventory (BBI). Despite the fact that the questionnaires are based on different theoretical models and composition of the scales, they evaluate essentially the same construct. A few published sources provide data on correlations between the results of measuring burnout using these methods. However, the presence of a correlation does not imply the comparability of the methods. The goal of our study was to compare the results of MBI and BBI as well as to develop a methodology for reciprocal recalculation of their burnout estimates. Methods: MBI and BBI scales were employed to diagnose the burnout syndrome. Our study included 117 men aged 41–44 years. The total scores obtained by the two methods, as well as the subscale scores, were compared using the correlation analysis, the cross-comparative analysis, and the Bland–Altman plot method, while the associations between the results were estimated with odds ratio (OR) and 95% confidence interval (CI). Results: The total scores (integral indicators based on summing the scores of all subscales and taking into account differences in the weights of scale scores) demonstrated a high similarity in measuring the severity of the burnout. All three dimensions of burnout sensu MBI correlated with the total BBI score. Conclusion: The comparison (MBI vs. BBI) demonstrated the consistency of the results, which implied the possibility of comparing data yielded by the studies based on the two questionnaires (MBI and BBI). [ABSTRACT FROM AUTHOR]

Objective—Heart rate variability (HRV) and photoplethysmographic waveform variability (PPGV) are available approaches for assessing the state of cardiovascular autonomic regulation. The goal of our study was to compare the frequency-domain features and low-frequency (LF) synchronization of the PPGV and HRV with increasing severity of cardiovascular diseases. Methods—Our study included 998 electrocardiogram (ECG) and finger photoplethysmogram (PPG) recordings from subjects, classified into five categories: 53 recordings from healthy subjects, aged 28.1 ± 6.2 years, 536 recordings from patients with hypertension (HTN), 49.0 ± 8.8 years old, 185 recordings from individuals with stable coronary artery disease (CAD) (63.9 ± 9.3 years old), 104 recordings from patients with myocardial infarction (MI) that occurred three months prior to the recordings (PMI) (65.1 ± 11.0 years old), and 120 recordings from study subjects with acute myocardial infarction (AMI) (64.7 ± 11.5 years old). Spectral analyses of the HRV and PPGV were carried out, along with an assessment of the synchronization strength between LF oscillations of the HRV and of PPGV (synchronization index). Results—Changes in all frequency-domain indices and the synchronization index were observed along the following gradient: healthy subjects → patients with HTN → patients with CAD → patients with PMI → patients with AMI. Similar frequency-domain indices of the PPGV and HRV show little relationship with each other. Conclusions—The frequency-domain indices of the PPGV are highly sensitive to the development of any cardiovascular disease and, therefore, are superior to the HRV indices in this regard. The S index is an independent parameter from the frequency-domain indices. [ABSTRACT FROM AUTHOR]