# Meta-Analysis of the Gut Microbiome: An African American Representation

## TL;DR

This study highlights the underrepresentation of African Americans in gut microbiome research and finds significant ethnic differences in microbial diversity and composition.

## Contribution

The study provides a comprehensive meta-analysis of the gut microbiome in African Americans, emphasizing the importance of ethnicity in microbiome variation.

## Key findings

- African Americans showed lower microbial diversity compared to other groups.
- Ethnicity had a stronger effect on beta diversity than diet, age, sex, or BMI.
- Clostridium sensu stricto 1 was enriched, while Dialister was depleted in healthy African Americans.

## Abstract

The human gut hosts approximately 100 trillion microbes, forming a complex ecosystem critical to the body’s metabolism, nutrition, and immune function. Despite growing research, African Americans remain underrepresented in clinical studies. This study addresses the gap through a comprehensive meta-analysis of gut microbiome datasets. Fecal sample data from amplicon sequencing were analyzed using a bioinformatics pipeline that incorporated DADA2 for sequence processing and Phyloseq for diversity analysis within RStudio (v2024.09.0+375). Statistical approaches, including Wilcoxon tests, Kruskal–Wallis tests, PERMANOVA, and ANCOM-BC, identified significant microbial differences. Results revealed that African Americans exhibited lower microbial diversity. Beta diversity metrics demonstrated a stronger effect of ethnicity compared to diet, age, sex, and BMI, highlighting its significance in microbiome variation. Similarly, ANCOM-BC identified Clostridium sensu stricto 1 significantly enriched in healthy African Americans, while Dialister was depleted, a finding with potential clinical relevance given previous research linking reduced Dialister abundance with depression. Additionally, machine learning approaches were found to potentially complement traditional statistical methods by handling class imbalance and identifying complex microbial associations. By addressing critical gaps in microbiome research, this study underscores the importance of inclusive datasets in enhancing disease risk prediction and ensuring that microbiome-based health interventions are equitable and broadly applicable.