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Background: Pulmonary Langerhans cell histiocytosis (PLCH) is a rare interstitial lung disease (ILD) associated with smoking, whose definitive diagnosis requires the exclusion of other forms of ILD and a compatible surgical lung biopsy. Bronchoalveolar lavage (BAL) is commonly proposed for the diagnosis of ILD, including PLCH, but the diagnostic value of this technique is limited. Here, we have analyzed the levels of a panel of cytokines and chemokines in BAL from PLCH patients, in order to identify a distinct immune profile to discriminate PLCH from other smoking related-ILD (SR-ILD), and comparing the results with idiopathic pulmonary fibrosis (IPF) as another disease in which smoking is considered a risk factor. Methods: BAL samples were collected from thirty-six patients with different ILD, including seven patients with PLCH, sixteen with SR-ILD and thirteen with IPF. Inflammatory profiles were analyzed using the Human Cytokine Membrane Antibody Array. Principal component analysis (PCA) was performed to reduce dimensionality and protein–protein interaction (PPI) network analysis using STRING 11.5 database were conducted. Finally, Random forest (RF) method was used to build a prediction model. Results: We have found significant differences (p < 0.05) on thirty-two cytokines/chemokines when comparing BAL from PLCH patients with at least one of the other ILD. Four main groups of similarly regulated cytokines were established, identifying distinct sets of markers for each cluster. Exploratory analysis using PCA (principal component analysis) showed clustering and separation of patients, with the two first components capturing 69.69% of the total variance. Levels of TARC/CCL17, leptin, oncostatin M (OSM) and IP-10/CXCL10 were associated with lung function parameters, showing positive correlation with FVC. Finally, random forest (RF) algorithm demonstrates that PLCH patients can be differentiated from the other ILDs based solely on inflammatory profile (accuracy 96.25%). Conclusions: Our results show that patients with PLCH exhibit a distinct BAL immune profile to SR-ILD and IPF. PCA analysis and RF model identify a specific immune profile useful for discriminating PLCH. [ABSTRACT FROM AUTHOR]

Background and objective: The relationship between IPF development and environmental factors has not been completely elucidated. Analysing geographic regions of idiopathic pulmonary fibrosis (IPF) cases could help identify those areas with higher aggregation and investigate potential triggers. We hypothesize that cross‐analysing location of IPF cases and areas of consistently high air pollution concentration could lead to recognition of environmental risk factors for IPF development. Methods: This retrospective study analysed epidemiological and clinical data from 503 patients registered in the Observatory IPF.cat from January 2017 to June 2019. Incident and prevalent IPF cases from the Catalan region of Spain were graphed based on their postal address. We generated maps of the most relevant air pollutant PM2.5 from the last 10 years using data from the CALIOPE air quality forecast system and observational data. Results: In 2018, the prevalence of IPF differed across provinces; from 8.1 cases per 100 000 habitants in Barcelona to 2.0 cases per 100 000 in Girona. The ratio of IPF was higher in some areas. Mapping PM2.5 levels illustrated that certain areas with more industry, traffic and shipping maintained markedly higher PM2.5 concentrations. Most of these locations correlated with higher aggregation of IPF cases. Compared with other risk factors, PM2.5 exposure was the most frequent. Conclusion: In this retrospective study, prevalence of IPF is higher in areas of elevated PM2.5 concentration. Prospective studies with targeted pollution mapping need to be done in specific geographies to compile a broader profile of environmental factors involved in the development of pulmonary fibrosis. [ABSTRACT FROM AUTHOR]