Introduction: The global burden of chronic airway diseases represents an important public health concern. The role of oxidative stress and inflammation in the pathogenesis of these diseases is well known. The aim of this study is to evaluate the behavior of both inflammatory and oxidative stress biomarkers in patients with chronic bronchitis, current asthma and past asthma in the frame of a population-based study. Methods: For this purpose, data collected from the Gene Environment Interactions in Respiratory Diseases (GEIRD) Study, an Italian multicentre, multicase-control study, was evaluated. Cases and controls were identified through a two-stage screening process of individuals aged 20-65 years from the general population. Out of 16,569 subjects selected from the general population in the first stage of the survey, 2259 participated in the clinical evaluation. Oxidative stress biomarkers such as 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG), 8-isoprostane and glutathione and inflammatory biomarkers such as Fractional Exhaled Nitric Oxide (FENO) and white blood cells were evaluated in 1878 subjects. Results: Current asthmatics presented higher levels of FENO (23.05 ppm), leucocytes (6770 n/µL), basophils (30.75 n/µL) and eosinophils (177.80 n/µL), while subjects with chronic bronchitis showed higher levels of GSH (0.29 mg/mL) and lymphocytes (2101.6 n/µL). The multivariable multinomial logistic regression confirmed high levels of leucocytes (RRR = 1.33), basophils (RRR = 1.48), eosinophils (RRR = 2.39), lymphocytes (RRR = 1.26) and FENO (RRR = 1.42) in subjects with current asthma. Subjects with past asthma had a statistically significant higher level of eosinophils (RRR = 1.78) with respect to controls. Subjects with chronic bronchitis were characterized by increased levels of eosinophils (RRR = 2.15), lymphocytes (RRR = 1.58), GSH (RRR = 2.23) and 8-isoprostane (RRR = 1.23). Conclusion: In our study, current asthmatics show a greater expression of the inflammatory profile compared to subjects who have had asthma in the past and chronic bronchitis. On the other hand, chronic bronchitis subjects showed a higher rate of expression of oxidative stress biomarkers compared to asthmatic subjects. In particular, inflammatory markers such as circulating inflammatory cells and FENO seem to be more specific for current asthma, while oxidative stress biomarkers such as glutathione and 8-isoprostane appear to be more specific and applicable to patients with chronic bronchitis.

Summary: Background: Fat intake has been associated with respiratory diseases, with conflicting results.Objective: We studied the association between asthma and rhinitis with dietary fats, and their food sources in an Italian population.Methods: Clinical and nutritional information was collected for 871 subjects (aged 20‐84) from the population‐based multi‐case‐control study Genes Environment Interaction in Respiratory Diseases (GEIRD): 145 with current asthma (CA), 77 with past asthma (PA), 305 with rhinitis and 344 controls. Food intake was collected using the EPIC (European Investigation into Cancer and Nutrition) Food Frequency Questionnaire. The associations between fats and respiratory diseases were estimated by multinomial models. Fats and their dietary sources were analysed both as continuous variables and as quartiles.Results: Monounsaturated fatty acids and oleic acid were associated with a reduced risk of CA in both continuous (RRR = 0.68, 95%CI: 0.48; 0.96; RRR = 0.69; 95%CI: 0.49; 0.97, per 10 g, respectively) and per‐quartile analyses (p for trend = 0.028 and 0.024, respectively). Olive oil was associated with a decreased risk of CA (RRR = 0.80; 95%CI: 0.65; 0.98 per 10 g). An increased risk of rhinitis was associated with moderate total fat and SFA intake.Conclusions: High dietary intakes of oleic acid and of olive oil are associated with a lower risk of asthma but not of rhinitis.

Health-related quality of life (HRQL) in respiratory diseases has been generally investigated in clinical settings, focusing on a single disorder. In this study on a general population sample, we assessed the relationship between HRQL and several respiratory diseases studied simultaneously (COPD, current (CA) and past (PA) asthma, allergic (AR) and non-allergic (NAR) rhinitis and chronic bronchitis (CB). Controls (n = 328) and cases of NAR (n = 95), AR (n = 163), CB (n = 48), CA (n = 224), PA (n = 126) and COPD (n = 28) were recruited in the centre of Verona in the frame of the Italian multi-case control GEIRD (Gene Environment Interactions in Respiratory Diseases) study; HRQL was measured through the SF-36 questionnaire. The relationships between HRQL (in terms of Physical (PCS) and Mental Component Scores (MCS)), respiratory diseases, and covariates were evaluated. With respect to controls, the adjusted PCS median score was worse in subjects suffering from current asthma (− 1.7; 95%CI:-2.8;-0.6), CB (− 3.8; 95%CI:-5.7;-1.9), and COPD (− 5.6; 95%CI:-8.1;-3.1). MCS was worse in current asthmatics (− 2.2; 95%CI:-4.1;-0.3), CB (− 5.5; 95%CI:-8.7;-2.2), and COPD cases (− 4.6; 95%CI:-8.8;-0.5) as well. To our knowledge, this is the first study in the general population that analyzed HRQL performing a simultaneous comparison of HRLQ in several respiratory disorders. We found that subjects suffering from COPD, CA, and CB had the poorest HRQL. Clinicians should carefully consider the possible impact of respiratory disorders as CB and not only that of CA and COPD.

Background: The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on nursing homes/long-term care facilities. This study examined the relationship between geography, size, design, organizational characteristics, and implementation of infection prevention and control (IPC) measures and the extent of COVID-19 outbreaks in nursing homes in the Autonomous Province of Trento (Italy) during the time frame of March-May 2020. Methods: The analysis included 57 nursing homes (5145 beds). The association between median cumulative incidence of COVID-19 cases among residents and characteristics of nursing homes was assessed by Mann–Whitney U test, Kruskal–Wallis test or Spearman rho. To evaluate the potential confounding of geographical area, a 2-level random intercept logistic model was fitted, with level 1 units (patients in nursing homes) nested into level 2 units (nursing homes), and “being a COVID-19 case” as the dependent variable. Results: Median cumulative incidence was not significantly associated with any of the variables, except for geographical region (p = 0.002). COVID-19 cases clustered in the part of the province bordering the Italian region most affected by the pandemic (Lombardy) (45.2% median cumulative incidence). Conclusions: Structural/organizational factors and standard IPC measures may not predict the epidemiology of COVID-19 outbreaks and be sufficient alone to protect nursing homes against them.