Microbiome-related polygenic risk scores and treatment response in depression
Abstract
Recent advances in microbiome research have underscored the intricate connection between the human gut microbiota and mental health disorders, such as depression. This study pioneers a unique investigative approach within the field of the gut-brain axis, focusing on the gut microbiome’s capacity to serve as a biomarker for predicting antidepressant response, a novel concept in neuropsychiatric research. Using the unique Texas Resillience Against Depression (T-RAD) study, we computed polygenic risk scores (PRSs) for 25 heritable gut bacteria from individual-level genotype and microbiome data. PRSs were also computed in 226 participants in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC). This design allowed us to validate microbial PRSs identified in T-RAD. The ability of microbial PRS to predict treatment outcome was tested in the EMBARC cohort using individual-level genotype data and treatment outcome. Logistic regression analysis in EMBARC demonstrated that 3 microbiome PRS showed a significant predictive value for remission after 8 weeks of treatment with sertraline and 1 microbiome PRS that showed a significant predictive value for remission at 16 weeks following augmentation of sertraline treatment with bupropion. Moreover, 4 microbiome PRS that were associated with greater changes in depression severity over time in response to sertraline treatment compared to placebo. By applying established polygenic risk score methodology, this work demonstrates the singular potential to leverage the gut-microbiome to identify microbiome-related genetic predictors of antidepressant response. This result points to a promising new approach to biomarker development for treatment prediction in depression.
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BibTeX
@article{asbury2026microbiomerelated,
title = {{Microbiome-related polygenic risk scores and treatment response in depression}},
author = {Sarah Asbury and Hayyan Liaqat and Cherise Chin Fatt and Namita Seelam and Samar S. M. Elsheikh and Daniel J. Müller and Vasily A. Vakorin and Sylvain Moreno and Madhukar H. Trivedi and Jane A. Foster},
journal = {Translational Psychiatry},
year = {2026},
month = oct,
doi = {10.1038/s41398-026-04472-x},
url = {https://www.nature.com/articles/s41398-026-04472-x},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Microbiome-related polygenic risk scores and treatment response in depression
AU - Sarah Asbury
AU - Hayyan Liaqat
AU - Cherise Chin Fatt
AU - Namita Seelam
AU - Samar S. M. Elsheikh
AU - Daniel J. Müller
AU - Vasily A. Vakorin
AU - Sylvain Moreno
AU - Madhukar H. Trivedi
AU - Jane A. Foster
PY - 2026
DA - 2026/10/03
JO - Translational Psychiatry
DO - 10.1038/s41398-026-04472-x
UR - https://www.nature.com/articles/s41398-026-04472-x
AB - Recent advances in microbiome research have underscored the intricate connection between the human gut microbiota and mental health disorders, such as depression. This study pioneers a unique investigative approach within the field of the gut-brain axis, focusing on the gut microbiome’s capacity to serve as a biomarker for predicting antidepressant response, a novel concept in neuropsychiatric research. Using the unique Texas Resillience Against Depression (T-RAD) study, we computed polygenic risk scores (PRSs) for 25 heritable gut bacteria from individual-level genotype and microbiome data. PRSs were also computed in 226 participants in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC). This design allowed us to validate microbial PRSs identified in T-RAD. The ability of microbial PRS to predict treatment outcome was tested in the EMBARC cohort using individual-level genotype data and treatment outcome. Logistic regression analysis in EMBARC demonstrated that 3 microbiome PRS showed a significant predictive value for remission after 8 weeks of treatment with sertraline and 1 microbiome PRS that showed a significant predictive value for remission at 16 weeks following augmentation of sertraline treatment with bupropion. Moreover, 4 microbiome PRS that were associated with greater changes in depression severity over time in response to sertraline treatment compared to placebo. By applying established polygenic risk score methodology, this work demonstrates the singular potential to leverage the gut-microbiome to identify microbiome-related genetic predictors of antidepressant response. This result points to a promising new approach to biomarker development for treatment prediction in depression.
SN - 2158-3188
ER -