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

The ISME Journal (2019) | Download Citation


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.

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Data availability

All datasets analyzed in this study are available in public domain and see Table S1 for the detailed access information for each of the 27 datasets.

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This study received funding from the following sources: National Science Foundation of China (Grant No. 71473243), Cloud-Ridge Industry Technology Leader Grant, A China-US International Cooperation Project on Genomics/Metagenomics Big Data.

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  1. Computational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China

    • Zhanshan (Sam) Ma
    •  & Lianwei Li
  2. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China

    • Zhanshan (Sam) Ma
  3. Department of Biology, University of Vermont Burlington, Burlington, VT, 05405, USA

    • Nicholas J. Gotelli


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ZSM and NJG defined the research objective. ZSM and LWL conducted data analysis and interpreted the results. ZSM wrote the manuscript and NJG revised the manuscript. All authors read and approved the final manuscript.

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The authors declare that they have no conflict of interest.

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Correspondence to Zhanshan (Sam) Ma or Nicholas J. Gotelli.

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