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[Preprint]. 2024 Mar 19:2024.03.19.585820.
doi: 10.1101/2024.03.19.585820.

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  1   11   12   13 ; Alzheimer Gut Microbiome Project Consortium
Affiliations

Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression

Amanda Hazel Dilmore et al. bioRxiv. .

Update in

  • Medication use is associated with distinct microbial features in anxiety and depression.
    Dilmore AH, Kuplicki R, McDonald D, Kumar M, Estaki M, Youngblut N, Tyakht A, Ackermann G, Blach C, MahmoudianDehkordi S, Dunlop BW, Bhattacharyya S, Guinjoan S, Mandaviya P, Ley RE, Kaddaruh-Dauok R, Paulus MP, Knight R; Alzheimer Gut Microbiome Project Consortium. Dilmore AH, et al. Mol Psychiatry. 2025 Jun;30(6):2545-2557. doi: 10.1038/s41380-024-02857-2. Epub 2025 Jan 10. Mol Psychiatry. 2025. PMID: 39794490 Free PMC article.

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 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 and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and 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. The findings suggest that future research on the gut microbiota's role in NPD and its interactions with pharmacological treatments are needed.

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Conflict of interest statement

9.Conflicts of Interest: Daniel McDonald is a consultant for, and has equity in, BiomeSense, Inc. Mehrbod Estaki is the chief science officer and has equity at Innovate Phytoceuticals Inc. He is a scientific advisor and holds equity at Melius Microbiomics Inc. Rima Kaddurah-Daouk 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. Rob Knight 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. Pieter Dorrestein (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. Pieter Dorrestein also consulted for DSM animal health in 2023.

Figures

Figure 1 -
Figure 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) Independent effect sizes of (categorical) demographic and psychiatric variables on gut microbiota beta diversity across the entire cohort. (D) Independent effect sizes of (categorical) demographic and psychiatric variables on gut microbiota beta diversity across only unmedicated individuals. (E) Independent effect sizes of (categorical) demographic and psychiatric variables on gut microbiota beta diversity across only medicated individuals. (F) Effect sizes of categorical and quantitative variables were modeled together using the following formulas in vegan’s Adonis function: Antidepressant Use + Cohort + Anxiolytic Use + Sex + Age + BMI + MDD or ANXD / MDD / ANXD.
Figure 2 -
Figure 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.
Figure 3 -
Figure 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.
Figure 4 -
Figure 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.
Figure 5 -
Figure 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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