Indoor microbiome and allergic diseases: From theoretical advances to prevention strategies

Abstract

The prevalence of allergic diseases, such as asthma, rhinitis, eczema, and sick building syndrome (SBS), has increased drastically in the past few decades. Current medications can only relieve the symptoms but not cure these diseases whose development is suggested to be greatly impacted by the indoor microbiome. However, no study comprehensively summarizes the progress and general rules in the field, impeding subsequent translational application. To close knowledge gaps between theoretical research and practical application, we conducted a comprehensive literature review to summarize the epidemiological, environmental, and molecular evidence of indoor microbiome studies. Epidemiological evidence shows that the potential protective indoor microorganisms for asthma are mainly from the phyla Actinobacteria and Proteobacteria, and the risk microorganisms are mainly from Bacilli, Clostridia, and Bacteroidia. Due to extremely high microbial diversity and geographic variation, different health-associated species/genera are detected in different regions. Compared with indoor microbial composition, indoor metabolites show more consistent associations with health, including microbial volatile organic compounds (MVOCs), lipopolysaccharides (LPS), indole derivatives, and flavonoids. Therefore, indoor metabolites could be a better indicator than indoor microbial taxa for environmental assessments and health outcome prediction. The interaction between the indoor microbiome and environmental characteristics (surrounding greenness, relative humidity, building confinement, and CO2 concentration) and immunology effects of indoor microorganisms (inflammatory cytokines and pattern recognition receptors) are briefly reviewed to provide new insights for disease prevention and treatment. Widely used tools in indoor microbiome studies are introduced to facilitate standard practice and the precise identification of health-related targets.

Symptom Clusters