Since the declaration of the SARS-CoV-2 pandemic confirmed by World Health Organization, work on the development of vaccines has been stimulated. When vaccines are commonly available, a major problem is persistent vaccine hesitancy in many European countries. The main goal of our study was to understand the multidimensional factors inducing this phenomenon in Poland. Our study was carried out at the third wave’s peak of the pandemic, with record rates of daily cases and deaths associated with COVID-19. The results indicate that vaccine hesitancy/acceptability should always be considered in an interdisciplinary manner and according to identified factors where most negative attitudes could be altered. Our analyses included the assessment of a representative quota sample of adult Poles (N = 1000). The vaccine hesitancy in the studied group reached 49.2%. We performed stepwise logistic regression modeling to analyze variables set into six trajectories (groups) predicting the willingness to vaccinate. Apart from typical, socio-demographic and economic determinants, we identified the fear of vaccines’ side effects, beliefs in conspiracy theories and physical fitness. We were also able to establish the order of importance of factors used in a full model of all impact trajectories.

Despite knowledge of classical coronary artery disease (CAD) risk factors, the morbidity and mortality associated with this disease remain high. Therefore, new factors that may affect the development of CAD, such as the gut microbiome, are extensively investigated. This study aimed to evaluate gut microbiome composition in CAD patients in relation to the control group. We examined 169 CAD patients and 166 people in the control group, without CAD, matched in terms of age and sex to the study group. Both populations underwent a detailed health assessment. The microbiome analysis was based on the V3–V4 region of the 16S rRNA gene (NGS method). Among 4074 identified taxonomic units in the whole population, 1070 differed between study groups. The most common bacterial types were Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Furthermore, a higher Firmicutes/Bacteroidetes ratio in the CAD group compared with the control was demonstrated. Firmicutes/Bacteroidetes ratio, independent of age, sex, CAD status, LDL cholesterol concentration, and statins treatment, was related to altered phosphatidylcholine concentrations obtained in targeted metabolomics. Altered alpha-biodiversity (Kruskal–Wallis test, p = 0.001) and beta-biodiversity (Bray–Curtis metric, p < 0.001) in the CAD group were observed. Moreover, a predicted functional analysis revealed some taxonomic units, metabolic pathways, and proteins that might be characteristic of the CAD patients’ microbiome, such as increased expressions of 6-phospho-β-glucosidase and protein-N(pi)-phosphohistidine-sugar phosphotransferase and decreased expressions of DNA topoisomerase, oxaloacetate decarboxylase, and 6-beta-glucosidase. In summary, CAD is associated with altered gut microbiome composition and function.

Background: The aim of this study was to assess risk factors for tooth loss in the population of the city of Bialystok, in north-eastern Poland, taking into account the entire population and different age groups. The study included 1138 subjects divided into three subgroups: 20–44 years, 45–64 years, and 65–79 years. Participants were classified according to the number of teeth lost (0–8 vs. 9–28). Socio-economic variables, smoking history, and dental habits were collected through a questionnaire. Medical examinations provided data on the body mass index and the fasting blood glucose level. Data were statistically analysed using Mann-Whitney U, Student’s t, chi² tests, and binary logistic regression, p < 0.05. Results: For the general population, being female (OR 1.38, 1.07–1.79, p = 0.015), having secondary education (OR 4.18, Cl 2.97–5.87, p < 0.000), higher body mass index (OR 1.13, Cl 1.10–1.17, p < 0.000), higher fasting blood glucose level (OR 1.03 1.03–1.04, p < 0.000), being former smoker (OR 1.72, Cl 1.29–2.31, p < 0.000), ever smoker (OR 1.69, Cl 1.29–2.20, p < 0.000), current smoker (OR 1.62, Cl 1.15–2.29, p < 0.006), longer smoking period (OR 1.11, Cl 1.09–1.14, p < 0.000), last visit to the dentist over a year ago (OR 1.92, Cl 0.44–2.58, p < 0.000) and tooth brushing less than two times a day (OR 1.6, Cl 1.14–2.23, p < 0.006) were associated with losing more than 8 teeth. In the subgroup aged 20–44 years, only smoking duration was a risk factor for tooth loss (p = 0.02). For the middle-aged and oldest groups, education level (respectively p < 0.001, and p = 0.001), body mass index (respectively, p < 0.001, and p = 0.037), smoking status ever/former/current (respectively p < 0.001 and p = 0.002), smoking status never/ever (respectively p < 0.001 and p = 0.009), smoking duration (p < 0.001) were related to tooth loss. Additionally, in the elderly group, fasting blood glucose level (p = 0.044) and frequency of dental visits (p = 0.007) were related to tooth loss. We concluded that in the evaluated population, tooth loss was associated with socio-demographic, medical, and behavioural factors.

The aim of the study was to determine oral health–related quality of life (OHRQoL) using the measures Geriatric/General Oral Health Assessment (GOHAI) and Oral Health Impact Profile (OHIP-14) in relation to missing teeth in the Polish population aged 20–79. This was a cross-sectional study carried out among 1112 randomly selected participants. The mean age was 48.72 and mean number of teeth was 20.12. Altogether, in the GOHAI, the percentage that gave a positive response to each question ranged from 3.3% to 48.0%; in the OHIP-14, these answers ranged from 2.4% to 25.1%. The GOHAI measure was statistically significant, with more grouping variables than the OHIP-14 measure. Both measures showed significant associations with gender, age, dry mouth, education, professional status, number of teeth, and upper and lower total dentures. We detected a significant relationship between oral health–related quality of life and the factors influencing the presence or absence of dentition. Missing teeth were statistically associated with GOHAI, OHIP-14, advanced age, self-reported dry mouth, lower education, higher Body Mass Index (BMI), lower professional status, diabetes, myocardial infraction, and total dentures in upper or/and lower jaws. However, edentulous individuals had two times higher risk of having an OHIP-14 score above the median. This suggests that oral health practitioners should work to prevent oral diseases that lead to tooth loss in their patients, starting from an early age.

Background: Cardiovascular diseases (CVD) are still the leading cause of death in developed countries. The aim of this study was to calculate the potential for CV risk reduction when using three different prevention strategies to evaluate the effect of primary prevention. Methods: A total of 931 individuals aged 20–79 years old from the Bialystok PLUS Study were analyzed. The study population was divided into CV risk classes. The Systematic Coronary Risk Estimation (SCORE), Framingham Risk Score (FRS), and LIFE-CVD were used to assess CV risk. The optimal prevention strategy assumed the attainment of therapeutic goals according to the European guidelines. The moderate strategy assumed therapeutic goals in participants with increased risk factors: a reduction in systolic blood pressure by 10 mmHg when it was above 140 mmHg, a reduction in total cholesterol by 25% when it was above 190 mg/dL, and a reduction in body mass index below 30. The minimal prevention strategy assumed that CV risk would be lowered by lifestyle modifications. The greatest CV risk reduction was achieved in the optimal model and then in the minimal model, and the lowest risk reduction was achieved in the moderate model, e.g., using the optimal model of prevention (Model 1). In the total population, we achieved a reduction of −1.74% in the 10-year risk of CVD death (SCORE) in relation to the baseline model, a −0.85% reduction when using the moderate prevention model (Model 2), and a −1.11% reduction when using the minimal prevention model (Model 3). However, in the low CV risk class, the best model was the minimal one (risk reduction of −0.72%), which showed even better results than the optimal one (reduction of −0.69%) using the FRS. Conclusion: A strategy based on lifestyle modifications in a population without established CVD could be more effective than the moderate strategy used in the present study. Moreover, applying a minimal strategy to the low CV risk class population may even be beneficial for an optimal model.

Lipid-lowering in patients with coronary artery disease (CAD) is related to a lower risk of cardiovascular events. We evaluated factors related to the management of hypercholesterolemia in patients with established CAD. Patients were interviewed 6–18 months after hospitalization for an acute coronary syndrome (ACS) or a myocardial revascularization procedure. Statins were prescribed at discharge to 94.4% of patients, while 68.1% of the patients hospitalized for an ACS were prescribed a high-dose statin. Hospitalization in a teaching hospital, percutaneous coronary intervention, cholesterol measurement during hospitalization and the male sex were related to prescription of statins at discharge. The intensity of lipid-lowering therapy in the post-discharge period increased in 17.3%, decreased in 11.7%, and did not change in 71.0% of the patients. The prescription of a lipid-lowering drug (LLD) at discharge (odds ratio 5.88 [95% confidence intervals 3.05–11.34]) and a consultation with a cardiologist (2.48 [1.51–4.08]) were related to the use of LLDs, while age (1.32 [1.10–1.59] per 10 years), loneliness (0.42 [0.19–0.94]), professional activity (1.56 [1.13–2.16]), and diabetes (1.66 [1.27–2.16]) were related to achieving an LDL cholesterol goal 6–18 months after discharge. In conclusion, health-system-related factors are associated with the LLD utilization, whereas mainly patient-related factors are related to the control of hypercholesterolemia following hospitalization for CAD.