Background and Objectives: The key pathogenetic mechanism of glucose metabolism disorders, insulin resistance (IR), can be assessed using the Homeostasis Model Assessment of IR (HOMA-IR). However, its application in clinical practice is limited due to the absence of cut-offs. In this study, we aimed to define the cut-offs for the Czech population. Methods: After undergoing anthropometric and biochemical studies, the sample of 3539 individuals was divided into either nondiabetics, including both subjects with normal glucose tolerance (NGT, n = 1947) and prediabetics (n = 1459), or diabetics (n = 133). The optimal HOMA-IR cut-offs between subgroups were determined to maximize the sum of the sensitivity and specificity for diagnosing type 2 diabetes mellitus (T2DM) or prediabetes. The predictive accuracy was illustrated using receiver operating characteristic (ROC) curves. Logistic regression was performed to assess the association between a target variable (presence/absence of T2DM) depending on the HOMA-IR score as well as on the age and sex. Results: The HOMA-IR cut-off between nondiabetics and diabetics for both sexes together was 3.63, with a sensitivity of 0.56 and a specificity of 0.86. The area under the ROC curve was 0.73 for T2DM diagnosing in both sexes. The HOMA-IR cut-off between the NGT subjects and prediabetics was 1.82, with a sensitivity of 0.60 and a specificity of 0.66. Logistic regression showed that increased HOMA-IR is a risk factor for the presence of T2DM (odds ratio (OR) 1.2, 95% confidence interval (CI) 1.14–1.28, p < 0.0001). The predictive ability of HOMA-IR in diagnosing T2DM is statistically significantly lower in females (OR 0.66, 95% CI 0.44–0.98). The results are valid for middle-aged European adults. Conclusions: The results suggest the existence of HOMA-IR cut-offs signaling established IR. Introduction of the instrument into common clinical practice, together with the known cut-offs, may contribute to preventing T2DM.

High vaccination coverage among healthcare workers (HCWs) is crucial for managing the COVID-19 pandemic. The aim was to determine the demand for vaccination among all employees (n = 4553) of a tertiary care hospital after several weeks of the vaccine’s availability, and to analyze motives for acceptance and reasons for hesitancy through an anonymous online questionnaire. Upon the completion of data collection, the hospital’s vaccination coverage was at 69.8%. A total of 3550 completed questionnaires were obtained (2657 from vaccinated, 893 from unvaccinated employees). Significant predictors of vaccine acceptance were: age (odds ratio (OR) 1.01, 95% confidence interval (CI) 1.01–1.02), sex (OR (females) 0.58, 95% CI 0.45–0.75), job type (OR (non-physician HCWs) 0.54, 95% CI 0.41–0.72; OR (non-HCWs) 0.51, 95% CI 0.37–0.71), fear of COVID-19 (OR 1.4, 95% CI 1.34–1.46), history of COVID-19 (OR 0.41, 95% CI 0.34–0.49) and of influenza vaccination (OR 2.74, 95% CI 2.12–3.57). The most frequent motive for acceptance was the effort to protect family members (84%), while concerns about vaccine safety and side effects (49.4%), followed by distrust in the vaccine’s efficacy (41.1%) were the top reasons for hesitancy. To increase vaccination coverage among HCWs, it is necessary to raise awareness of vaccine safety and efficacy.

Introduction: Insulin resistance (IR) is a key and early pathogenetic mechanism of cardiometabolic diseases with huge potential if being early detected and mitigated, for lowering the burden of the diseases. Available data are conflicting to what extent adult thyroid dysfunction is associated with IR. Therefore, we aimed to investigate the association and to identify which thyroid parameters are predictors of IR. Material and Methods: After undergoing basic anthropometric and biochemical studies including thyroid hormones, oral glucose tolerance test (OGTT) and insulin, 1425 middle-aged individuals were divided into three groups according to the thyroid parameters: overt hypothyroidism (OH), subclinical hypothyroidism (SH) and euthyroidism (EU). Results: The homeostasis model assessment of IR (HOMA-IR), fasting insulin and 2-hour glucose levels of OGTT showed a steady, yet insignificant, increase from EU through SH to OH. The strongest noted correlations were those of insulin levels with free triiodothyronine/free thyroxine (FT3/FT4) ratio (r = 0.206, p < 0.001) and FT3 (r = 0.205, p < 0.001). Also in the case of HOMA-IR, the only statistically significant correlations were observed with FT3 (r = 0.181, p < 0.001) and the FT3/FT4 ratio (r = 0.165, p < 0.001). Among other thyroid hormones, linear logistic regression proved the FT3/FT4 ratio as the only significant predictor of HOMA-IR (linear coefficient = 5.26, p = 0.027) and insulin levels (linear coefficient = 18.01, p = 0.023), respectively. Thyroid-stimulating hormone was not associated with IR in either correlation or regression analysis. Conclusions: The FT3/FT4 ratio should be more emphasised in the diagnosis and treatment of thyroid disorders. Patients could benefit from a pharmacological reduction of the FT3/FT4 ratio, potentially leading to a decrease in insulin resistance, and thus a risk of the cardiometabolic diseases. Introduction: Insulin resistance (IR) is a key and early pathogenetic mechanism of cardiometabolic diseases with huge potential if being early detected and mitigated, for lowering the burden of the diseases. Available data are conflicting to what extent adult thyroid dysfunction is associated with IR. Therefore, we aimed to investigate the association and to identify which thyroid parameters are predictors of IR. Material and Methods: After undergoing basic anthropometric and biochemical studies including thyroid hormones, oral glucose tolerance test (OGTT) and insulin, 1425 middle-aged individuals were divided into three groups according to the thyroid parameters: overt hypothyroidism (OH), subclinical hypothyroidism (SH) and euthyroidism (EU). Results: The homeostasis model assessment of IR (HOMA-IR), fasting insulin and 2-hour glucose levels of OGTT showed a steady, yet insignificant, increase from EU through SH to OH. The strongest noted correlations were those of insulin levels with free triiodothyronine/free thyroxine (FT3/FT4) ratio (r = 0.206, p < 0.001) and FT3 (r = 0.205, p < 0.001). Also in the case of HOMA-IR, the only statistically significant correlations were observed with FT3 (r = 0.181, p < 0.001) and the FT3/FT4 ratio (r = 0.165, p < 0.001). Among other thyroid hormones, linear logistic regression proved the FT3/FT4 ratio as the only significant predictor of HOMA-IR (linear coefficient = 5.26, p = 0.027) and insulin levels (linear coefficient = 18.01, p = 0.023), respectively. Thyroid-stimulating hormone was not associated with IR in either correlation or regression analysis. Conclusions: The FT3/FT4 ratio should be more emphasised in the diagnosis and treatment of thyroid disorders. Patients could benefit from a pharmacological reduction of the FT3/FT4 ratio, potentially leading to a decrease in insulin resistance, and thus a risk of the cardiometabolic diseases.

Background: Chronic wounds present a significant clinical, social, and economic challenge. This study aimed to objectify the risk factors of healing outcomes and the duration of chronic wounds from various etiologies. Methods: Patients treated for non-healing wounds at the surgical outpatient clinic of the Olomouc Military Hospital were involved. Data from patients treated between 8/2021 and 9/2023 were selected. Patients were mostly treated as outpatients, with microbiological follow-up indicated in cases of advanced signs of inflammation. Results: There were 149 patients who met our selection criteria (the mean age was 64.4 years). Predominant causes of wounds involved diabetes (30.9%), post-trauma (25.5%), pressure ulcers (14.8%), surgical site infections (14.8%), and vascular ulcers (14.1%). Patient outcomes included wound resolution in 77.2% of patients (with a mean healing time of 110.9 days), amputation in 14.1%, and wound-related death in 8.7% of patients. Non-healing cases (amputation/death) were predicted by several local factors including an initial depth greater than 1 cm, wound secretion, inflammatory base, and a maximum wound size. Systemic factors included most strongly clinically manifested atherosclerosis and its risk factors. Of the 110 swabs performed, 103 identified at least 1 bacterial genus. The dominant risk factor for a prolonged healing duration was bacterial infection. Wounds contaminated by Proteus or Pseudomonas had prolonged healing times of 87 days (p = 0.02) and 72 days (p = 0.045), respectively. Conclusions: The early identification of local and systemic risk factors contributes to the successful resolution of chronic wounds and a reduced duration of healing.

Secondhand smoke (SHS), composed of mainstream and sidestream smoke, is a known human carcinogen. It contains a variety of harmful substances at even higher concentrations than mainstream smoke itself, which is inhaled during firsthand smoking. Exposure to SHS, affecting more than a third of the worldwide population, increases the likelihood of lung cancer by roughly 30%, with specific contributions depending on the histological type of cancer. This study aimed to present the harmful potential of SHS through case reports and describe the burden of SHS via a literature review. From a collection of lung cancer case reports occurring in never smokers from the Olomouc district over the last 10 years, 2 cases with no risk factors for lung cancer except for significant exposure to SHS were identified. Both cases were of young women who lived in households where their parents smoked during childhood. They suffered from rarer histological types of lung cancer in which the association with SHS has not yet been analyzed. As the literature confirms, SHS has the most adverse effects in individuals exposed during childhood. It is necessary to both take measures to reduce the prevalence of SHS, especially among children in households and pay due attention to the smoking history of patients, including current and previous exposure to SHS, when obtaining anamnestic data. Furthermore, the effect of SHS on rarer histological types of lung cancer should be studied.

Background and objectives: The visceral adiposity index (VAI), estimating visceral adiposity dysfunction through a simple formula, could serve as a useful tool for identifying individuals at higher cardiometabolic risk. Its relationship with insulin resistance (IR), assessed using the homeostasis model assessment of IR (HOMA-IR), and metabolic syndrome (MetS) components remains unclear. The study aimed to investigate the association of VAI with both HOMA-IR and MetS. Materials and Methods: After undergoing anthropometric and biochemical studies, 783 individuals were divided into three groups according to a number of present MetS components. The VAI cut-offs signaling MetS and HOMA-IR were determined by maximizing the sum of the sensitivity and specificity. Correlation analysis was performed to explore the associations between VAI and other tested parameters. A logistic stepwise regression analysis was applied to identify statistically significant determinants of HOMA-IR. Given the variability of reference values, two thresholds of HOMA-IR were applied, namely 2.0 and 3.8. Results: VAI increased significantly between the groups with a rising number of MetS components. The VAI cut-off for MetS was 2.37, with a sensitivity of 0.86 and a specificity of 0.78. The same cut-off point identified subjects with HOMA-IR = 3.8, with a sensitivity of 0.79 and a specificity of 0.66. The VAI cut-off for HOMA-IR = 2.0 was 1.89, with a sensitivity of 0.74 and a specificity of 0.68. The strongest correlations of VAI were noted with HOMA-IR (r = 0.51) and insulin (r = 0.49), respectively, while the strongest correlation of HOMA-IR was with waist circumference (r = 0.54). Not one of the routine parameters was a significant predictor in the regression analysis. Conclusions: The obtained results show an existing association of VAI with HOMA-IR. The high sensitivity and specificity of the cut-offs may allow the application of VAI in common clinical practice.