Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases.

Assessing disease similarity is an essential initial step preceding disease-based approach for drug repositioning. Our study provides a modest first step in underscoring the potential of integrating microbiome insights into the disease similarity assessment. Recent microbiome research has predominantly focused on analyzing individual disease to understand its unique characteristics, which by design excludes comorbidities individuals. We analyzed shotgun metagenomic data from existing studies and identified previously unknown similarities between diseases. Our research represents a pioneering effort that utilize both interpretable machine learning and differential abundance analysis to assess microbial similarity between diseases.