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: The aim of the study was to investigate the impact of COVID-19 on the pulmonary function tests (PFT) in COVID-19 convalescents six months after recovery. Additionally, the research question was whether PFT should be performed routinely in post-COVID-19 patients. Methods: A total of 39 patients with a history of COVID-19 6 months prior to the study were included in the study (Group I). Individuals were hospitalized or treated in the outpatients department. The control group (Group II) consisted of 39 healthy patients without a COVID-19 history. Each subject completed a questionnaire interview and underwent laboratory and pulmonary function examinations. Results: Six months after COVID-19 recovery, patients mainly complained about cough (46%, n = 18), shortness of breath (23%, n = 9), weakness (13%, n = 5), and memory/concentration disorders (8%, n = 3). In the group of patients complaining of persistent cough present 6 months after COVID-19, the following PFT parameters were decreased: FEV1, FVC, FRC, TLC, and DLCO (p < 0.05) in comparison with patients without this symptom. Conclusions: Persistent shortness of breath is not necessarily associated with pulmonary function impairment in patients 6 months after SARS-CoV-2 infection, and hence it requires appropriate differential diagnosis. Patients with a cough persisting 6 months after the acute phase of COVID-19 may benefit from PFT.

While obesity has been correlated with welfare in the general population, there is not much data on the influence of body composition on welfare among the non-obese adult individuals. In this study, a total of 726 non-obese individuals from the general population were analyzed. The mean age was 46.8 ± 15.4 years and 42.1% of participants were male. The anthropometric measurements and dual energy X-ray absorptiometry (DEXA) were done. The mean value for the Satisfaction with Life Scale (SWLS) was 23.09 ± 5.43, for Euro Quality of Life Visual Analogue Scale (EQ-VAS) was 78.0 ± 14.5, and for the Beck Depression Inventory (BDI) was 6.7 ± 6.6. On the SWLS, the higher waist-hip ratio had a negative impact even after adjusting for age, gender, and concomitant diseases. EQ-VAS was inversely associated with android fat distribution and directly associated with muscle mass. BDI value was inversely associated with lower muscle mass, especially in lower limbs. The well-being of women was mainly associated with the distribution of adipose tissue and less with the distribution of muscle tissue—abdominal fat distribution has a particularly negative impact on well-being among women. In contrast, men’s well-being depends more on muscle mass and to a lesser extent on the distribution of fat tissue—a positive significant effect has lean mass and a circumference of thigh below gluteal fold.