Abstract
BACKGROUND: Discrimination is a recognized psychosocial stressor that has been linked to various negative health outcomes. This study explored the impact of discrimination on gut health, specifically focusing on microbiome changes, predicted metagenomic differences, transcriptomic profiles, and the potential for using a multi-omic approach to predict discrimination to identify discrimination status for an individual. Methods: We conducted a comprehensive investigation involving male and premenopausal female participants, using the Everyday Discrimination Scale to classify them into either high or low discrimination. Multiple questionnaires were administered to evaluate participants' physiological, psychological, and perceived stressors. Two diet questionnaires were also administered. Stool samples were collected for microbiome analysis and RNA sequencing. Microbial composition changes were analyzed using the Shannon index and Chao1 richness estimator for alpha diversity and the Aitchison distance metric for beta diversity. Differential abundance was evaluated using MaAsLin2, followed by metatranscriptomics sequencing and annotation. A multi-omic approach utilizing random forest was used to assess the predictability of discrimination. RESULTS: The study results showed that high discrimination was linked to higher gut microbiome species richness (Chao1, p = 0.02) and significant beta diversity differences (p = 0.04). Prevotella and Ruminococcaceae were both less abundant in the high discrimination group. High discrimination participants also reported higher levels of depression, anxiety, perceived stress, early life adversity, visceral sensitivity, and neuroticism than those in the low discrimination group. Gene expression analysis revealed distinctive patterns, with significant changes in genes associated with environmental sensing (two-component system) and metabolic pathways. In a plot comparing gene transcription to DNA content, certain genes showed higher expression levels in participants who experienced both high and low levels of discrimination. Our random forest classifier demonstrated the capability to accurately differentiate individuals with high and low discrimination in our training cohort (AUC = 0.91). CONCLUSION: These findings illuminate the substantial impact of discrimination on gut health, encompassing microbiome composition, gene expression, and functional pathways. These findings suggest that discrimination is associated with internal biological changes that can be associated with negative health outcomes, opening research to examine novel pathways that can be used to mitigate the negative health effects of discrimination.