The Asthma Obese Phenotype

Asthma is a very heterogeneous disease, with two major asthma phenotypes, the allergic and the late onset asthma, differentiated by the triggers, the cellular dominance, the Th1/Th2 inflammation pattern and the local and serological markers. As there were many overlapping biological markers between these two phenotypes, different types of tentative classification followed. A clinical one makes a difference between the predominant eosinophilic one (with better response to glucocorticoid) and the predominant neutrophilic one with more severe evolution and low rate of therapeutical improvement. Another approach was based on cluster analysis of asthma characteristics (onset, atopic status, and body mass index (BMI)), sensitivity to methacholine test, peak flow variability, bronchodilatation response, postbronchodilator level of FEV1, sputum eosinophil and neutrophil count, FeNO test, clinical symptom scores, treatment scheme to control symptoms, exacerbations, and severity. Emerging data suggest a distinct late onset obese-asthma phenotype, with a specific pathophysiology, comorbidities, and clinical evolution. This chapter reviews the main characteristics of this phenotype: the specific lung function impairment, the underlying inflammation, the adipokine profile, the comorbidities and the therapeutical approach. The mutual influence between obesity and asthma will be illustrated, whenever scientific data are available

Coronasomnia in Employees without a Direct Contact with COVID-19 Infected Patients in Their Workplace

Background: The aim of this analysis was to explore coronasomnia in second line workers. Methods: Data were collected via in an online questionnaire. Patients with new onset or aggravation of insomnia were defined as cases and those without insomnia as controls. Differences among groups were studied by nonparametric tests; the correlation among variables was assessed using regression, followed by Bonferroni adjustment. Results: There were 377 responders, grouped into 129 cases and 248 controls. Younger age (Odds Ratio = 0.97, p = 0.021), women (OR = 2.46, p = 0.016), workers belonging to a vulnerable group (OR = 2.36, p = 0.007), and those with previous history of insomnia (OR = 38.76, p = 0.00) were associated with coronasomnia. Increased home duties were directly related to insomnia (OR = versus home support which were indirectly associated). The constant preoccupation for SARS-CoV-2 media reports (OR = 3.6, p = 0.00009) and media consumption were components of the coronasomnia. In the adjusted models, the personal medical history, and the anxiety created by media alerts maintained their significance. Conclusion: Preventive measures to reduce the occurrence of insomnia in times of social stress in nonessential occupations should focus on health vulnerable groups, persons with previous history of insomnia and who develop anxiety from media

Smoking Obstructive Sleep Apnea: Arguments for a Distinctive Phenotype and a Personalized Intervention

Background: This is the first study that aims to define smoking, with obstructive sleep apnea (OSA), as a phenotype (SOSA). Moreover, we wanted to demonstrate the deleterious effects of the continuation of smoking on OSA. Methods: The cross-sectional study highlighted four dimensions of SOSA: the demographic and anthropometric features, the symptoms, the comorbidities, and the sleep study parameters. This study compared these characteristics between current smokers (CS), those who have never smoked (NS), and ex-smokers (ES) with OSA. Results: More men (83.95% in CS, versus 66.67% in NS) and an earlier onset of OSA (average age = 50.05 in CS, versus 52.26 in NS, p = 0.04) were recorded among CS. The distinguishing symptom of CS was daytime sleepiness, with an Epworth score that was significantly higher than in NS. Chronic obstructive pulmonary disease (COPD) was significantly more prevalent in CS (38.27%) than in NS (1.51%) (p < 0.001). The severity of OSA, consisting of a higher apnea-hypopnea index, a higher oxygen desaturation index, and a longer time spent below 90% oxygen saturation during sleep was significantly influenced by smoking. Conclusions: The SOSA phenotype includes younger male patients with a higher waist circumference, suggesting central obesity. They have a higher prevalence of COPD and a greater severity of OSA, in correlation with the number of pack-years of smoking

Impaired Melatonin Secretion, Oxidative Stress and Metabolic Syndrome in Night Shift Work

Metabolic syndrome has been associated in many studies with working in shifts. Even if the mechanistic details are not fully understood, forced sleep deprivation and exposure to light, as happens during night shifts, or irregular schedules with late or very early onset of the working program, lead to a sleep–wake rhythm misalignment, metabolic dysregulation and oxidative stress. The cyclic melatonin secretion is regulated by the hypothalamic suprachiasmatic nuclei and light exposure. At a central level, melatonin promotes sleep and inhibits wake-signals. Beside this role, melatonin acts as an antioxidant and influences the functionality of the cardiovascular system and of different metabolic processes. This review presents data about the influence of night shifts on melatonin secretion and oxidative stress. Assembling data from epidemiological, experimental and clinical studies contributes to a better understanding of the pathological links between chronodisruption and the metabolic syndrome related to working in shifts

Smoking Obstructive Sleep Apnea: Arguments for a Distinctive Phenotype and a Personalized Intervention

Background: This is the first study that aims to define smoking, with obstructive sleep apnea (OSA), as a phenotype (SOSA). Moreover, we wanted to demonstrate the deleterious effects of the continuation of smoking on OSA. Methods: The cross-sectional study highlighted four dimensions of SOSA: the demographic and anthropometric features, the symptoms, the comorbidities, and the sleep study parameters. This study compared these characteristics between current smokers (CS), those who have never smoked (NS), and ex-smokers (ES) with OSA. Results: More men (83.95% in CS, versus 66.67% in NS) and an earlier onset of OSA (average age = 50.05 in CS, versus 52.26 in NS, p = 0.04) were recorded among CS. The distinguishing symptom of CS was daytime sleepiness, with an Epworth score that was significantly higher than in NS. Chronic obstructive pulmonary disease (COPD) was significantly more prevalent in CS (38.27%) than in NS (1.51%) (p < 0.001). The severity of OSA, consisting of a higher apnea-hypopnea index, a higher oxygen desaturation index, and a longer time spent below 90% oxygen saturation during sleep was significantly influenced by smoking. Conclusions: The SOSA phenotype includes younger male patients with a higher waist circumference, suggesting central obesity. They have a higher prevalence of COPD and a greater severity of OSA, in correlation with the number of pack-years of smoking