Meta-analysis of gut microbiome studies identifies disease-specific and shared responses

By Claire Duvallet, Sean M. Gibbons, Thomas Gurry, Rafael A. Irizarry, Eric J. Alm. Nature Communications, 2017

881 citations recorded by the source corpus.

Hundreds of clinical studies have demonstrated associations between the human microbiome and disease, yet fundamental questions remain on how we can generalize this knowledge. Results from individual studies can be inconsistent, and comparing published data is further complicated by a lack of standard processing and analysis methods. Here we introduce the MicrobiomeHD database, which includes 28 published case–control gut microbiome studies spanning ten diseases. We perform a cross-disease meta-analysis of these studies using standardized methods. We find consistent patterns characterizing disease-associated microbiome changes. Some diseases are associated with over 50 genera, while most show only 10–15 genus-level changes. Some diseases are marked by the presence of potentially pathogenic microbes, whereas others are characterized by a depletion of health-associated bacteria. Furthermore, we show that about half of genera associated with individual studies are bacteria that respond to more than one disease. Thus, many associations found in case–control studies are likely not disease-specific but rather part of a non-specific, shared response to health and disease. Reported associations between the human microbiome and disease are often inconsistent. Here, Duvallet et al. perform a meta-analysis of 28 gut microbiome studies spanning ten diseases, and find associations that are likely not disease-specific but potentially part of a shared response to disease.

Subfields: General Health Professions, Infectious Diseases, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/46eb481f6838f85f727a00101879b0f4c90cc622

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In Molecular Biology

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Citations in the corpus

• Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, methodology): “Additionally, the authors performed their own comparative analysis of the mentioned ML methods using three datasets available from MicrobiomeHD database5 (Duvallet et al., 2017).”
• The Human Gut Microbiome: From Association to Modulation. (2018, neutral): “Two recent meta-analyses of microbiome-disease association studies found that 268 between-study variation required explicit or implicit batch effect correction (Duvallet et al., 2017; 269 Pasolli et al., 2016).”
• The Gut-Brain Axis: How Microbiota and Host Inflammasome Influence Brain Physiology and Pathology (2020, background): “Interestingly, among those, there are also diseases affecting host organs in physical distance from the gut (8, 9), like the central nervous system (CNS) (7, 10–22).”
• Antibiotics as Major Disruptors of Gut Microbiota (2020, background): “…of the gut microbiota and diseases, including recurrent diarrhea associated with C. difficile, some bowel disorders (including IBD), colorectal cancer, non-alcoholic steatohepatitis, type 2 diabetes, obesity, and advanced chronic liver disease (Duvallet et al., 2017; Wirbel et al., 2019).”
• A practical guide to methods controlling false discoveries in computational biology (2018, neutral): “These studies were processed as described in [57].”