Diversity-disease relationships and shared species analyses for human microbiome-associated diseases View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-20

AUTHORS

Zhanshan Ma, Lianwei Li, Nicholas J. Gotelli

ABSTRACT

Diversity indices have been routinely computed in the study of human microbiome-associated diseases (MADs). However, it is still unclear whether there is a consistent diversity-disease relationship (DDR) for the human MADs, and whether there are consistent differences in the taxonomic composition of microbiomes sampled from healthy versus diseased individuals. Here we reanalyzed raw data and used a meta-analysis to compare the microbiome diversity and composition of healthy versus diseased individuals in 41 comparisons extracted from 27 previously published studies of human MADs. In the DDR analysis, the average effect size across studies did not differ from zero for a comparison of healthy versus diseased individuals. In 30 of 41 comparisons (73%) there was no significant difference in microbiome diversity of healthy versus diseased individuals, or of different disease classes. For the species composition analysis (shared species analysis), the effect sizes were significantly different from zero. In 33 of 41 comparisons (80%), there were fewer OTUs (operational taxonomic units) shared between healthy and diseased individuals than expected by chance, but with 49% (20 of 41 comparisons) statistically significant. These results imply that the taxonomic composition of disease-associated microbiomes is often distinct from that of healthy individuals. Because species composition changes with disease state, some microbiome OTUs may serve as potential diagnostic indicators of disease. However, the overall species diversity of human microbiomes is not a reliable indicator of disease. More... »

PAGES

1-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41396-019-0395-y

DOI

http://dx.doi.org/10.1038/s41396-019-0395-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112880507

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30894688


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