The gut microbiome's influence on human health and disease is increasingly recognized. While studies have linked gut dysbiosis to various conditions, inconsistencies in findings due to varying methodologies and lack of unified databases hinder a comprehensive understanding. This study aimed to characterize gut microbiota across multiple diseases using publicly available datasets to identify patterns of disease-associated microbial shifts. A meta-analysis approach was employed to mitigate biases inherent in individual studies, leveraging the power of combined data to identify robust signatures.

Extensive literature supports the gut microbiota's role in regulating host health. Dysbiosis, or an imbalance in the gut microbiota, has been associated with numerous diseases, including autoimmune disorders, cardiovascular diseases, infectious diseases, psychiatric disorders, and cancers. However, the lack of standardized methods and databases makes it challenging to compare findings across studies. This necessitates a large-scale meta-analysis to identify consistent patterns of microbial alterations associated with various diseases.

The study utilized publicly available fecal metagenomes from 36 case-control studies focusing on diverse diseases in the Chinese population. After rigorous quality control, 6314 samples were included. Taxonomic profiling was performed using MetaPhlAn 4. Statistical analyses, including Wilcoxon rank-sum tests, PERMANOVA, random forest classification, and LASSO regression, were employed to identify disease-associated microbial signatures. A meta-analysis approach, combining random effects meta-analysis and MaAsLin 2, was used to identify differentially abundant species between disease and control groups. Furthermore, a random forest classifier was trained to predict disease status based on the identified microbial signatures and validated using independent cohorts.

The study revealed significant alterations in gut microbial richness and diversity across various diseases. Many diseases showed reduced species richness and diversity, while a few exhibited increases. A meta-analysis identified 277 disease-associated species, with many control-enriched species belonging to phyla like Firmicutes (including SCFA producers such as *Roseburia*, *Ruminococcus*, and *Faecalibacterium*) and Bacteroides. Disease-enriched species were predominantly opportunistic pathogens like *Streptococcus*, *Enterococcus*, *Escherichia coli*, *Fusobacterium*, and *Flavonifractor plautii*. A random forest classifier, trained on these 277 species, achieved an AUC of 0.776 for classifying cases versus controls and 0.825 for distinguishing high-risk patients from controls. Validation using independent cohorts showed varying performance depending on the disease, with higher accuracy for high-risk diseases like colorectal cancer and end-stage renal disease.

The findings demonstrate consistent alterations in gut microbial composition associated with a wide range of diseases. The identification of universal microbial signatures, enriched in either controls or disease states, highlights common mechanisms underlying disease pathogenesis. The high predictive accuracy of the random forest classifier underscores the potential of gut microbiome profiling for disease diagnosis and risk stratification. The differences in classifier performance across various diseases suggest that the strength of the microbiome-disease association varies and warrants further investigation. These findings could lead to personalized disease management strategies.

This population-scale meta-analysis revealed universal gut microbial signatures associated with multiple common diseases. The identification of both protective and pathogenic species offers valuable insights into disease mechanisms and potential therapeutic targets. Future research could focus on validating these findings in larger, more diverse populations and investigating the functional implications of these microbial signatures. Developing targeted interventions based on these findings could lead to improved disease management and prevention strategies.

The study primarily included data from the Chinese population, limiting the generalizability of the findings to other ethnic groups. The reliance on publicly available data may introduce biases related to sample selection and data quality. Further investigation is needed to determine the causal relationship between the identified microbial signatures and disease pathogenesis.