Objectives: This study aimed to explore the demographic and clinical features of tinnitus individuals and analyse its correlation with associated comorbidities. Methods: The study population comprised 147 participants (66 men, 81 women; median age: 52 years) who experienced persistent tinnitus. Comprehensive assessments were carried out, including audiological examinations, scoring using the Tinnitus Handicap Inventory, and thorough medical evaluations. Statistical analyses were applied to explore the correspondences between tinnitus, hearing loss, and various comorbidities, including cardiovascular conditions, metabolic disorders, gastroesophageal reflux disease, autoimmune diseases, pulmonary diseases, and allergic rhinitis. Results: The analysis indicated a slight predominance of females, comprising 55.1% of the participants, with a median onset of tinnitus around the age of 50. Chronic tinnitus was noted, lasting approximately 46 months. Hearing loss was noted in 52.4% of patients, with bilateral tinnitus being the most prevalent type, affecting 44.2% of individuals. Dyslipidaemia was found to significantly predict bilateral tinnitus (p = 0.003*) and left-sided tinnitus (p = 0.023*). Additionally, atherosclerosis was associated with hearing impairment (p = 0.006*) and right-sided tinnitus (p = 0.044*). Dyslipidaemia was also significantly correlated with elevated intensity values (p = 0.04*). Furthermore, atherosclerosis was significantly associated with higher levels of hearing loss (p < 0.00001*). Conclusions: The study emphasises the complex nature of tinnitus and its links to cardiovascular, metabolic, and other comorbidities, highlighting the necessity for comprehensive, interdisciplinary management.

Objectives: This study aimed to analyse blood glucose and lipid levels in subjective tinnitus compared to healthy controls. Method: A total of 414 patients with subjective tinnitus and 274 healthy participants were enrolled. All participants underwent comprehensive laboratory testing, which included measurements of fasting blood glucose, HbA1c, and blood lipids. Results: The tinnitus group had significantly higher glucose levels than the control group (p = 0.024). Additionally, the HbA1c levels were higher in the tinnitus group (p = 0.006). Tinnitus patients exhibited elevated levels of total cholesterol (p < 0.001), triglycerides (p < 0.001), and LDL (p < 0.001). However, HDL levels showed no significant difference (p = 0.984). Although the triglyceride–glucose index tends to show higher values in the tinnitus group, this difference is statistically insignificant (p = 0.688). ROC indicated that LDL had the highest sensitivity for predicting tinnitus (AUC = 0.620; p < 0.001, 95% CI: 0.573–0.668). Other parameters showed significant results, excluding the triglyceride–glucose index (AUC = 0.514; p = 0.688, 95% CI: 0.443–0.585), which was not a significant predictor. Glucose levels significantly correlate with age (rho = 0.338, p < 0.001) and tinnitus onset (rho = 0.107, p = 0.034). Additionally, age showed a significant correlation with total cholesterol levels (rho = 0.156, p = 0.002) and triglycerides (rho = 0.121, p = 0.020) and tinnitus onset (rho = 0.344, p < 0.001). Logistic regression analysis revealed that the presence of tinnitus was significantly associated with elevated HbA1c levels (p = 0.007) and TG levels (p = 0.001). Furthermore, the occurrence of chronic tinnitus was significantly influenced by elevated glucose levels (p = 0.026). Conclusions: The results showed increased glucose and blood lipid levels in tinnitus. LDL levels exhibited the highest sensitivity in predicting tinnitus. However, the TyG index was not a significant predictor. Given the cross-sectional design of the study, which may limit the results, further longitudinal studies are necessary.

Purpose: This study aimed to determine the effect of body mass index (BMI) percentile, asthma, sex, and age on the paediatric obstructive sleep apnoea (OSA) severity. Furthermore, to determine the possible predictive role of the BMI percentile and age in severe OSA. Methods: This retrospective study included 921 children aged 2–18 years diagnosed with OSA by polysomnography. Analysis of Covariance (ANCOVA), Spearman’s correlation, Receiver Operating Characteristics (ROC) analyses were performed and area under the curve (AUC) was determined. Results: We observed a significant association between a higher BMI percentile and the severity of OSA (p < 0.001, ρ = 0.15). The correlation also was significant under (p = 0.007, ρ = 0.11) and over 7 (p = 0.0002, ρ = 0.23) years of age. There was no association between the severity of OSA and the presence of asthma (p = 0.9) or sex (p = 0.891), respectively. Age was significantly related to OSA severity (p = 0.01, ρ = 0.08). Although both the BMI percentile (0.59 AUC [0.54–0.65]) and age (0.58 AUC [0.52–0.63]) predicted severe OSA, according to the sensitivity and specificity values of the ROC curve, the association presents a slight clinical relevance. Conclusions: OSA severity is determined by the BMI percentile and age in children; however, these factors are unsuitable for predicting severe OSA in clinical practice. Based on our results, obesity is also a significant risk factor for OSA in younger children. Our study highlights that older, overweight, and obese children have a higher risk for severe OSA.

Objectives: The aim of this study was to investigate the relationships between tinnitus pitch and loudness, the frequency of hearing loss, hearing level, and the Tinnitus Handicap Inventory (THI) scores reported by patients. Methods: In total, 139 patients (50 men and 89 women; mean age ± SD, 60.19 ± 11.47 years) who suffered from chronic primary tinnitus associated with sensorineural hearing loss were included in the study. Participants underwent pure-tone audiometry and tinnitus pitch matching assessments. Additionally, all participants completed the Hungarian version of the THI questionnaire. Correlations were assessed using simple linear, Spearman’s, and Pearson’s correlation tests, and a linear regression model was applied. The Mann–Whitney U test was also used. Results: This study identified a significant correlation between the pitch of tinnitus and the frequency of hearing loss (p = 0.000 *; rho = 0.549). There was also a significant correlation between tinnitus noise and hearing levels (p = 0.000 *; rho = 0.375). Age was shown to significantly affect tinnitus loudness (p = 0.016 *) and hearing levels (p = 0.000 *) as determined by a linear regression model. Tinnitus duration only significantly influenced tinnitus loudness (p = 0.022). There was no significant effect of sex on tinnitus or audiometry parameters. Total THI scores were influenced solely by tinnitus loudness (p = 0.021 *). Furthermore, sex did have an effect on total THI scores, with women reporting higher scores (p = 0.000 *). Conclusions: This study concluded that there is a significant correlation between the pitch and loudness of tinnitus and hearing levels, suggesting a connection in their underlying mechanisms. The intensity of tinnitus and hearing level are primarily affected by ageing processes. Furthermore, the severity of self-perceived tinnitus is mainly related to the loudness of tinnitus.

Purpose: The gold standard diagnostic modality of Obstructive Sleep Apnea (OSA) is polysomnography (PSG), which is resource-intensive, requires specialized facilities, and may not be accessible to all patients. There is a growing body of research exploring the potential of artificial intelligence (AI) to offer more accessible, efficient, and cost-effective alternatives for the diagnosis of OSA. Methods: We conducted a scoping review of studies applying AI techniques to diagnose and assess OSA in adult populations. A comprehensive search was performed in the Web of Science database using terms related to “obstructive sleep apnea,” “artificial intelligence,” “machine learning,” and related approaches. Results: A total of 344 articles met the inclusion criteria. The findings highlight various methodologies of disease evaluation, including binary classification distinguishing between OSA-positive and OSA-negative individuals in 118 articles, OSA event detection in 211 articles, severity evaluation in 38 articles, topographic diagnostic evaluation in 8 articles, and apnea-hypopnea index (AHI) estimation in 26 articles. 40 distinct types of data sources were identified. The three most prevalent data types were electrocardiography (ECG), used in 108 articles, photoplethysmography (PPG) in 62 articles, and respiratory effort and body movement in 44 articles. The AI techniques most frequently applied were convolutional neural networks (CNNs) in 104 articles, support vector machines (SVMs) in 91 articles, and K-Nearest Neighbors (KNN) in 57 articles. Of these studies, 229 used direct patient recruitment, and 115 utilized existing datasets. Conclusion: While AI demonstrates substantial potential with high accuracy rates in certain studies, challenges remain such as model transparency, validation across diverse populations, and seamless integration into clinical practice. These challenges may stem from factors such as overfitting to specific datasets, limited generalizability, and the need for standardized protocols in clinical settings.

Air pollution is the biggest environmental problem in modern societies, causing considerable health damage and requiring substantial financial resources for health care. The goal of the study is to demonstrate the adverse economic consequences of air pollution on example of a small, open Central European country, Hungary, and to provide quantified financial arguments for macroeconomic decision-making for the development of a long-term energy strategy. On the basis of the Cobb-Douglas production function and Solow-Swann model of dynamic economic systems a simple and robust model was constructed to estimate and predict economic losses, caused by the pollution. On base of results it is obvious, that on base of macroeconomic theory and combination of various, publicly available, quality-controlled statistical resources quantifiable models can be constructed to characterise the economic consequences of air pollution, but it should be taken into consideration, that the reliability of economic models considerably depends on their initial parameters and practical validity of assumptions, based on which the underlying economic theories were built. The most important economic burden of air pollution is caused by the loss of working–age population, resulting in a decrease of 4.1–9.4 % a year in Gross Domestic Product (GDP) in the next fifty years. The additional burden of health care costs amounts to 0.1 % of GDP. Reducing air pollution is not only a quality of life improvement but also an investment into the economic development. Notwithstanding of statistical biases it could be proven the importance of combination health economic and econometric methods in preparation of more efficient environmental-related socio-economic decisions.