Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research. Academic Article uri icon

Overview

abstract

  • Evaluating the relationship between the human gut microbiome and disease requires computing reliable statistical associations. Here, using millions of different association modeling strategies, we evaluated the consistency-or robustness-of microbiome-based disease indicators for 6 prevalent and well-studied phenotypes (across 15 public cohorts and 2,343 individuals). We were able to discriminate between analytically robust versus nonrobust results. In many cases, different models yielded contradictory associations for the same taxon-disease pairing, some showing positive correlations and others negative. When querying a subset of 581 microbe-disease associations that have been previously reported in the literature, 1 out of 3 taxa demonstrated substantial inconsistency in association sign. Notably, >90% of published findings for type 1 diabetes (T1D) and type 2 diabetes (T2D) were particularly nonrobust in this regard. We additionally quantified how potential confounders-sequencing depth, glucose levels, cholesterol, and body mass index, for example-influenced associations, analyzing how these variables affect the ostensible correlation between Faecalibacterium prausnitzii abundance and a healthy gut. Overall, we propose our approach as a method to maximize confidence when prioritizing findings that emerge from microbiome association studies.

publication date

  • March 2, 2022

Research

keywords

  • Bacteria
  • Biomedical Research
  • Gastrointestinal Microbiome
  • Metagenome
  • Metagenomics

Identity

PubMed Central ID

  • PMC8890741

Scopus Document Identifier

  • 85125614014

Digital Object Identifier (DOI)

  • 10.1038/nature13568

PubMed ID

  • 35235560

Additional Document Info

volume

  • 20

issue

  • 3