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Improving causality in microbiome research: can human genetic epidemiology help?

Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studi...

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Detalles Bibliográficos
Autores principales: Wade, Kaitlin H., Hall, Lindsay J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217228/
https://www.ncbi.nlm.nih.gov/pubmed/32462081
http://dx.doi.org/10.12688/wellcomeopenres.15628.3
Descripción
Sumario:Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studies have been unconvincing in their ability to offer causal evidence due to their observational nature, where confounding by lifestyle and behavioural factors, reverse causation and bias are important limitations. Whilst randomized controlled trials have made steps towards understanding the causal role played by the gut microbiome in disease, they are expensive and time-consuming. This evidence that has not been translated between model systems impedes opportunities for harnessing the gut microbiome for improving population health. Therefore, there is a need for alternative approaches to interrogate causality in the context of gut microbiome research. The integration of human genetics within population health sciences have proved successful in facilitating improved causal inference (e.g., with Mendelian randomization [MR] studies) and characterising inherited disease susceptibility. MR is an established method that employs human genetic variation as natural “proxies” for clinically relevant (and ideally modifiable) traits to improve causality in observational associations between those traits and health outcomes. Here, we focus and discuss the utility of MR within the context of human gut microbiome research, review studies that have used this method and consider the strengths, limitations and challenges facing this research. Specifically, we highlight the requirements for careful examination and interpretation of derived causal estimates and host (i.e., human) genetic effects themselves, triangulation across multiple study designs and inter-disciplinary collaborations. Meeting these requirements will help support or challenge causality of the role played by the gut microbiome on human health to develop new, targeted therapies to alleviate disease symptoms to ultimately improve lives and promote good health.