Is there a correlation between the changes in airway inflammation and the changes in respiratory mechanics after vaping in patients with asthma?

BACKGROUND: Electronic cigarettes (ECs) have been promoted as alternatives to traditional cigarettes. AIM: To investigate ECs' effects on respiratory system, especially in patients with respiratory diseases. METHODS: We randomly selected 25 smokers with stable moderate asthma and matched them with 25 healthy smokers. All were subjucted to pulmonary function tests (PFTs), impulse oscillometry (IOS), fraction exhaled Nitric Oxide (FeNO), exhaled breathe condensate (EBC) and biomarker measurements before and after vaping one nicotine-containing EC. RESULTS: The increase in FeNO 30 minutes after EC, reflecting airway inflammation, significantly correlated with increase of residual volume (RV), total lung capacity, respiratory impedance at 5 Hz (Z5Hz) and respiratory resistance at 5 and 20 Hz (R5Hz and R20Hz). No significant correlations were found between EBC biomarkers' changes and respiratory mechanics. CONCLUSION: This is the first study demonstrating that the changes in airway inflammation caused by EC have direct effects in respiratory mechanics of asthmatic patients.

Health Literacy, Self-Efficacy and Glycemic Control in Patients With Diabetes Type 2 in a Greek Population.

Aim  To investigate the relationship between health literacy (HL), self-efficacy (SE), and achievement of treatment goals in patients with type 2 diabetes mellitus (T2DM). Method The cross-sectional study was conducted with a random sample of patients with T2DM attending the diabetology clinic and the Home Care department of the General Hospital of Drama, Greece. They completed two questionnaires: the short form of the European Health Literacy Survey Questionnaire (HLS-EU-Q16) to measure HL and the Diabetes Management Self-Efficacy Scale (DMSES) for people with T2DM to measure SE. Medical history, demographic characteristics, and values related to glycemic control were also recorded. Linear regression analysis was used to search for the dependence of glycosylated hemoglobin (A1C) values with HL and SE and the dependence between them. Result About 120 patients with T2DM (response rate of 92.3%) were enrolled in the study. The mean age of the participants was 62.5 years [standard deviation (SD) = 10.6 years] and most of them were female (53.3%). A1C was found to be significantly negatively associated with diet, physical activity, and SE score. Also, a statistically significant positive correlation was found between HL and SE. HL was correlated with age, gender, education level, and A1C, with women and older people having lower HL, while conversely higher education level was significantly associated with higher HL. Higher A1C was significantly associated with lower HL. Also, SE partially mediates the relationship between HL and A1C, in a significant way. Conclusion The results of the study confirm the important role of HL and SE in the successful management of T2DM. Multi-level educational interventions for diabetic patients could improve HL and SE and promote diabetes self-management.

Smoking-Induced Disturbed Sleep. A Distinct Sleep-Related Disorder Pattern?

The relationship between smoking and sleep disorders has not been investigated sufficiently yet. Many aspects, especially regarding non-obstructive sleep apnea−hypopnea (OSA)-related disorders, are still to be addressed. All adult patients who visited a tertiary sleep clinic and provided information about their smoking history were included in this cross-sectional study. In total, 4347 patients were divided into current, former and never smokers, while current and former smokers were also grouped, forming a group of ever smokers. Sleep-related characteristics, derived from questionnaires and sleep studies, were compared between those groups. Ever smokers presented with significantly greater body mass index (BMI), neck and waist circumference and with increased frequency of metabolic and cardiovascular co-morbidities compared to never smokers. They also presented significantly higher apnea−hypopnea index (AHI) compared to never smokers (34.4 ± 24.6 events/h vs. 31.7 ± 23.6 events/h, p < 0.001) and were diagnosed more frequently with severe and moderate OSA (50.3% vs. 46.9% and 26.2% vs. 24.8% respectively). Epworth sleepiness scale (ESS) (p = 0.13) did not differ between groups. Ever smokers, compared to never smokers, presented more frequent episodes of sleep talking (30.8% vs. 26.6%, p = 0.004), abnormal movements (31.1% vs. 27.7%, p = 0.021), restless sleep (59.1% vs. 51.6%, p < 0.001) and leg movements (p = 0.002) during sleep. Those were more evident in current smokers and correlated significantly with increasing AHI. These significant findings suggest the existence of a smoking-induced disturbed sleep pattern.