The impact of ambient air pollution on lung function and respiratory symptoms in elite athletes

Impacts of environmental factors on the aetiological diagnosis and disease severity of community-acquired pneumonia in China: a multicentre, hospital-based, observational study

Environmental exposures are known to be associated with pathogen transmission and immune impairment, but the association of exposures with aetiology and severity of community-acquired pneumonia (CAP) are unclear. A retrospective observational study was conducted at nine hospitals in eight provinces in China from 2014 to 2019. CAP patients were recruited according to inclusion criteria, and respiratory samples were screened for 33 respiratory pathogens using molecular test methods. Sociodemographic, environmental and clinical factors were used to analyze the association with pathogen detection and disease severity by logistic regression models combined with distributed lag nonlinear models. A total of 3323 CAP patients were included, with 709 (21.3%) having severe illness. 2064 (62.1%) patients were positive for at least one pathogen. More severe patients were found in positive group. After adjusting for confounders, particulate matter (PM) 2.5 and 8-h ozone (O3-8h) were significant association at specific lag periods with detection of influenza viruses and Klebsiella pneumoniae respectively. PM10 and carbon monoxide (CO) showed cumulative effect with severe CAP. Pollutants exposures, especially PM, O3-8h, and CO should be considered in pathogen detection and severity of CAP to improve the clinical aetiological and disease severity diagnosis.

Analysis of influencing factors and a predictive model of small airway dysfunction in adults

BACKGROUND

Small airway dysfunction (SAD) is a widespread but less typical clinical manifestation of respiratory dysfunction. In lung diseases, SAD can have a higher-than-expected impact on lung function. The aim of this study was to explore risk factors for SAD and to establish a predictive model.

METHODS

We included 1233 patients in the pulmonary function room of TangDu Hospital from June 2021 to December 2021. We divided the subjects into a small airway disorder group and a non-small airway disorder group, and all participants completed a questionnaire. We performed univariate and multivariate analyses to identify the risk factors for SAD. Multivariate logistic regression was performed to construct the nomogram. The performance of the nomogram was assessed and validated by the Area under roc curve (AUC), calibration curves, and Decision curve analysis (DCA).

RESULTS

One. The risk factors for small airway disorder were advanced age (OR = 7.772,95% CI 2.284-26.443), female sex (OR = 1.545,95% CI 1.103-2.164), family history of respiratory disease (OR = 1.508,95% CI 1.069-2.126), history of occupational dust exposure (OR = 1.723,95% CI 1.177-2.521), history of smoking (OR = 1.732,95% CI 1.231-2.436), history of pet exposure (OR = 1.499,95% CI 1.065-2.110), exposure to O₃ (OR = 1.008,95% CI 1.003-1.013), chronic bronchitis (OR = 1.947,95% CI 1.376-2.753), emphysema (OR = 2.190,95% CI 1.355-3.539) and asthma (OR = 7.287,95% CI 3.546-14.973). 2. The AUCs of the nomogram were 0.691 in the training set and 0.716 in the validation set. Both nomograms demonstrated favourable clinical consistency. 3.There was a dose‒response relationship between cigarette smoking and SAD; however, quitting smoking did not reduce the risk of SAD.

CONCLUSION

Small airway disorders are associated with age, sex, family history of respiratory disease, occupational dust exposure, smoking history, history of pet exposure, exposure to O₃, chronic bronchitis, emphysema, and asthma. The nomogram based on the above results can effectively used in the preliminary risk prediction.