Mediation analysis methods to model human microbiome mediating disease-leading causal pathways in children

Dartmouth College, Hanover NH

Investigators

Linked publications, trials & patents

Abstract

Abstract Emerging evidence suggests that human microbiome, particularly the gut microbiome composed of collective genomes of as many as 100 trillion commensal, symbiotic and pathogenic microorganisms, could be mediating disease-leading causal pathways initiated by environmental toxicants or other factors such as drug usage. Arsenic exposure through drinking water could initiate perturbation of gut microbiome, and therefore, children could inherit perturbed microbiome composition if their mothers have arsenic exposure during perinatal period. The unhealthy microbiome composition could, in turn, induce children's asthma, infection and allergy, which could explain that arsenic exposure during pregnancy is related to children's infection. Taken together, arsenic exposure could be the initiation of causal pathways leading to children's infection through perturbed mother's microbiome being passed to children. There are many other possible initiation factors such as diet, gene mutation and antibiotics leading to different health outcomes. Despite this exciting evidence for microbiome mediating disease-leading causal pathways, it remains challenging to model mediating processes due to the complex nature of microbiome. These mediations could happen simply through changes in some individual microbes, though the perturbation of microbiome homeostasis, or through a mixture of both. Therefore, there is an urgent need to have appropriate mediation analysis methods in place for estimating and testing the mediational effects of human microbiome for these different situations. Although high-throughput sequencing technologies are able to quantify microbiome, none of the existing mediation analysis methods is adequate enough to model the mediational effects of microbiome due to the unique features of microbiome data despite the extensive developments of mediation methods in the literature. To address these issues, we will develop general mediation analysis frameworks to identify mediation through changes in individual bacterial taxa and model mediation though the perturbation of overall microbiome composition.