Abstract
Background/Objectives: Mental health conditions, including stress, anxiety, depression, and sleep problems, represent a significant health concern globally. Mounting evidence suggests a link between mental health and the gut microbiome via the gut-brain axis. However, discrepancies in human microbiome data exist due to the heterogeneity in study design and analytical approaches. Thus, this study aimed to explore the gut microbial characteristics associated with self-reported mental health symptoms using multiple analytical methods. Methods: A total of 44 healthy adults, defined as individuals without any major chronic medical conditions, were assessed for mental health symptoms using self-reported questionnaire data. To evaluate gut microbial characteristics, stool samples were collected at six time points over 28 days and underwent 16S rRNA gene sequencing. Differential abundance was assessed via ANCOM-BC, and a random forest classifier was implemented to rank features important for the classification of mental health symptoms. Participants who did not report anxiety, stress, depression, or sleep problems served as the reference group for microbiome comparisons. Results: The proportion of participants with self-reported mental health symptoms was 11.4% (stress), 27.3% (depression), 31.8% (anxiety), and 15.9% (sleep problems). Participants reporting mental health symptoms showed differences in gut microbiome composition compared to asymptomatic participants, including variation in alpha- and beta-diversity. Differential analysis identified specific taxa with higher or lower relative abundance in participants reporting specific mental health symptoms. Random forest feature ranking identified partially overlapping taxa across methods, suggesting candidate associations warranting further investigation. Conclusions: These exploratory findings suggest that self-reported mental health symptoms in otherwise healthy adults are associated with differences in the gut microbiome. The taxa identified in this study represent candidates for validation in larger, independent cohorts.