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. 2025 Jun;30(6):2545-2557.
doi: 10.1038/s41380-024-02857-2. Epub 2025 Jan 10.

Medication use is associated with distinct microbial features in anxiety and depression

Amanda Hazel Dilmore  1   2 , Rayus Kuplicki  3 , Daniel McDonald  1 , Megha Kumar  1 , Mehrbod Estaki  1 , Nicholas Youngblut  4 , Alexander Tyakht  4 , Gail Ackermann  1 , Colette Blach  5   6   7 , Siamak MahmoudianDehkordi  5 , Boadie W Dunlop  8 , Sudeepa Bhattacharyya  9 , Salvador Guinjoan  3 , Pooja Mandaviya  10 , Ruth E Ley  4 , Rima Kaddaruh-Dauok  5   6   7 , Martin P Paulus  3 , Rob Knight  11   12   13   14 ; Alzheimer Gut Microbiome Project Consortium
Affiliations

Medication use is associated with distinct microbial features in anxiety and depression

Amanda Hazel Dilmore et al. Mol Psychiatry. 2025 Jun.

Abstract

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing (16S) and shallow shotgun sequencing (WGS) were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and Neurocomputational Mechanisms of Affiliation and Personality Study Center for Biomedical Research Excellence (NeuroMAP CoBRE) cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. Though the effect sizes were larger in females than males, similar trends emerged for both sexes. These findings encourage future research on the gut microbiota's role in NPD and its interactions with pharmacological treatments.

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Conflict of interest statement

Competing interests: DM is a consultant for, and has equity in, BiomeSense, Inc. ME is the chief science officer and has equity at Innovate Phytoceuticals Inc. He is a scientific advisor and holds equity at Melius Microbiomics Inc. BWD has received research support from Beckley PsyTech, Boehringer Ingelheim, Compass Pathways, NIMH, Otsuka, Reunion Neuroscience, and the Usona Institute, and has served as a consultant for Biohaven, Cerebral Therapeutics, Myriad Neuroscience, NRx Pharmaceuticals, and Otsuka. RK-D is an inventor on key patents in the field of Metabolomics and holds equity in Metabolon. In addition, she holds patents licensed to Chymia LLC and PsyProtix with royalties and ownership. PD (of the AGMP consortium) is an advisor and holds equity in Cybele and Sirenas and a Scientific co-founder, advisor and holds equity to Ometa, Enveda, and Arome with prior approval by UC San Diego. PD also consulted for DSM animal health in 2023. RK is a scientific advisory board member, and consultant for BiomeSense, Inc., has equity and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo, and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a cofounder of Micronoma, and has equity and is a scientific advisory board member. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. The companies listed here had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the paper; and decision to submit the paper for publication.

Figures

Fig. 1
Fig. 1. Medications have a stronger effect on gut microbiota beta diversity than ANXD or MDD diagnosis.
A Principal coordinates (PCoA) plot of Unweighted UniFrac distances shows statistically significant, but minimal visible separation between participants with MDD and HC. B The same plot colored by antidepressant use shows a stronger statistical and visual separation between participants using antidepressants and those not using them. C The same plot colored by ANXD diagnosis shows some separation by diagnosis, but not to a statistically significant extent. D Independent effect sizes of (categorical) demographic and psychiatric variables on gut microbiota beta diversity across the entire cohort. E Independent effect sizes of (categorical) demographic and psychiatric variables on gut microbiota beta diversity across only females. F Independent effect sizes of (categorical) demographic and psychiatric variables on gut microbiota beta diversity across only males. G Independent effect sizes of (categorical) demographic and psychiatric variables on gut microbiota beta diversity across only unmedicated individuals. H Independent effect sizes of (categorical) demographic and psychiatric variables on gut microbiota beta diversity across only medicated individuals.
Fig. 2
Fig. 2. Identification & validation of microbes enriched in ANXD.
A Visualization of the participants used in the discovery cohort. Unmedicated participants with ANXD were compared to unmedicated HC. B The top 15 microbes credibly associated with ANXD or HC are displayed. C Log ratios were created comparing the relative abundance of all credible microbes associated with ANXD to all credible microbes associated with HC. Thickness of the line is correlated with the negative log of the p-value between the two groups. D Log ratio displayed by medication category. E Scatterplot between ANXD log ratio and OASIS score.
Fig. 3
Fig. 3. Identification & validation of microbes enriched in MDD.
A Visualization of the participants used in the discovery cohort. Unmedicated participants with MDD were compared to unmedicated HC. B The top 15 microbes credibly associated with MDD or HC are displayed. C Log ratios were created comparing the relative abundance of all credible microbes associated with MDD to all credible microbes associated with HC. D The same log ratio displayed by medication category. E Scatterplot between MDD log ratio and PHQ-9 score. F Empirical cumulative distribution plot between the MDD log ratio in American Gut participants with or without a self-reported MDD diagnosis.
Fig. 4
Fig. 4. Identification & validation of microbes enriched with anxiolytic and/or antidepressant use.
A Visualization of the participants used in the discovery cohort. Unmedicated participants with an ANXD or MDD diagnosis were compared to individuals with an ANXD or MDD diagnosis who were taking anxiolytics. B The top 15 microbes credibly associated with anxiolytics or no medication use are displayed. C Visualization of the participants used in the discovery cohort. Unmedicated participants with an ANXD or MDD diagnosis were compared to individuals with an ANXD or MDD diagnosis who were taking antidepressants. D The top 15 microbes credibly associated with antidepressants or no medication use are displayed. E Log ratios were created comparing the relative abundance of all credible microbes associated with anxiolytics to all credible microbes associated with no medication usage. F Log ratios were created comparing the relative abundance of all credible microbes associated with antidepressants to all credible microbes associated with no medication usage.
Fig. 5
Fig. 5. Random Forest Classifiers demonstrate high accuracy and precision when predicting ANXD and/or MDD diagnosis.
A Area under the receiver operating characteristic curve (AUC) is displayed for a diagnosis classifier distinguishing ANXD diagnosis, MDD diagnosis, or ANXD and MDD diagnosis from HC. Four types of feature selection were applied in addition to the raw data. B Random Forest Classifier was applied to only unmedicated participants. C Average precision (APR) scores were also calculated across the different classifications and feature selection methods. D APR scores in the unmedicated subset.
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