Analysis of blood microbiome dysbiosis in pulmonary sarcoidosis by decision tree model

Abstract

AbstractPulmonary sarcoidosis is a complex inflammatory disease characterized by granulomas in the lung tissue, leading to breathing difficulties and chest pain. Its etiology remains not fully understood, with factors such as allergies, autoimmune responses and genetics playing a role. This study explores the potential of blood microbiome dysbiosis, defined as an imbalance in the microbial ecosystem, as a missing piece of the puzzle in understanding the etiology of the disease. Our objective was to apply a decision-tree supervised machine learning hierarchical model to distinguish potential patterns of microbiome dysbiosis in blood samples from patients with pulmonary sarcoidosis as compared to healthy age-matched controls. Blood microbiome analysis, being individually-specific and stable, offers a unique perspective. Utilizing 16S rRNA gene amplicon sequencing, we analyzed the blood microbiome composition characterized by non-normally distributed and sparse data. Because of the rarity of the disease in Bulgaria, we studied a relatively small patient group, n = 7. The findings were compared to 21 healthy age-matched controls. Bioinformatics and statistical analysis play a pivotal role in microbiome analysis, especially when discerning associations between taxonomic composition and disorders such as pulmonary sarcoidosis. By analyzing the microbial diversity, we identified alterations in the blood microbiome composition between healthy individuals and those with sarcoidosis, which potentially may trigger the disease. Advanced machine learning techniques provided additional power to the analysis, that might be overlooked by the usual group statistics, confirming the differentiation of the diversity within the studied microbiome.

Keywords

• Pulmonary sarcoidosis
• blood microbiome dysbiosis
• classification
• decision tree model