Gut Bacteria Genes May Predict Who Responds to Antidepressants

One of the most frustrating realities in modern psychiatry is that finding the right antidepressant is still largely a process of trial and error. Patients may spend months cycling through medications, enduring side effects while waiting for relief that may never come, because clinicians have had no reliable way to predict who will respond to a given drug. Now, a team of researchers in Canada and the United States has taken an unexpected step toward changing that, by looking not at the brain, but at the trillions of microbes living in the gut. In a study published in Translational Psychiatry, the group reports that genetic risk scores derived from heritable gut bacteria can predict whether patients with depression will achieve remission on specific antidepressant treatments.

The research, led by Sarah Asbury and Jane A. Foster of McMaster University together with Madhukar H. Trivedi of the University of Texas Southwestern Medical Center and colleagues, rests on a deceptively simple insight. Although the composition of a person’s gut microbiome is shaped by diet, environment, and countless life experiences, individual bacterial species are not entirely random residents. Twin studies and large population analyses have shown that the abundance of many gut microbes has a heritable component, meaning that human genetic variation influences which bacteria thrive in the intestine. If host genes help determine microbial makeup, then a person’s genome should carry statistical fingerprints of their microbiome, and those fingerprints might be quantified the same way geneticists already quantify inherited risk for diseases.

That quantification tool is the polygenic risk score, or PRS, a method that has become a workhorse of modern genetics. A PRS is built by aggregating the effects of thousands or millions of genetic variants across the genome, each weighted by how strongly it has been associated with a trait in a large discovery sample. Instead of applying this technique to a human disease, the team applied it to bacteria. Using data from the Texas Resilience Against Depression study, known as T-RAD, they computed polygenic risk scores for 25 heritable gut bacterial taxa, effectively generating a genome-based estimate of each participant’s propensity to host particular microbes. The approach is novel in neuropsychiatric research, where the gut-brain axis has attracted intense interest but has lacked genetically anchored biomarkers.

The gut-brain axis, the bidirectional communication network linking the intestinal microbiota with the central nervous system, has been implicated in depression through multiple mechanisms. Gut microbes produce and modify neurotransmitters, generate metabolites that cross the blood-brain barrier, modulate immune signaling, and influence the stress-response system. Animal studies have shown that transferring gut bacteria from depressed rodents or humans into germ-free animals can transmit depressive-like behaviors. What has been missing is a way to translate this biology into a clinically useful predictor, one that does not require repeatedly sampling a patient’s stool but can instead be read out from a blood or saliva sample used for standard genotyping.

To test whether microbial PRS could fill that gap, the researchers turned to EMBARC, the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care study, one of the most rigorously designed biomarker studies in depression research. EMBARC enrolled 226 participants with depression across four academic medical centers: UT Southwestern in Dallas, Massachusetts General Hospital in Boston, the University of Michigan in Ann Arbor, and Columbia University in New York. Participants were treated with sertraline, a widely prescribed selective serotonin reuptake inhibitor, and followed over time with careful measurement of depression severity. The design allowed the team to compute the same microbiome-related genetic scores in EMBARC participants and ask whether the scores identified in T-RAD held predictive value in an independent cohort, a validation strategy that lends considerably more weight to the findings than a single-cohort analysis would.

The results were striking in their specificity. In logistic regression analyses, three of the microbiome-related polygenic risk scores showed significant predictive value for remission after eight weeks of sertraline treatment, the standard early checkpoint in antidepressant trials. A fourth score predicted remission at sixteen weeks, following augmentation of sertraline with bupropion, a different mechanism of action often added when the first medication falls short. Beyond remission, four of the microbial scores were associated with greater changes in depression severity over time in patients receiving sertraline compared with placebo, suggesting that the genetic microbial signatures tracked not just a binary outcome but the trajectory of symptoms themselves.

For the authors, the significance lies less in any single bacterium than in the demonstration of principle. By applying established polygenic risk score methodology to the microbiome, the study shows that it is possible to leverage the gut microbiome to identify microbiome-related genetic predictors of antidepressant response without directly measuring the microbes at all. The scores function as a bridge between two data types that are rarely available together: detailed microbiome sequencing, which is expensive and technically demanding, and routine human genotyping, which is increasingly cheap and scalable. If the approach matures, a clinician could one day order a genetic test that implicitly encodes microbial information and use it to guide the initial choice of antidepressant, potentially sparing patients months of ineffective treatment.

The work also carries broader implications for how biomarkers are developed in psychiatry. The field has long struggled to find biological measures that predict treatment response, with candidates ranging from brain imaging patterns to blood-based gene expression panels. Most have failed to replicate across cohorts, a problem the present study explicitly tried to avoid by validating its scores in the independent EMBARC sample. The fact that the predictive signals were specific to particular drugs and timepoints, rather than generic markers of recovery, suggests the scores capture something biologically meaningful about how an individual’s gut-brain axis interacts with serotonergic and noradrenergic medications. That specificity, if it holds up, is exactly what precision psychiatry requires.

Caution is nonetheless warranted before the findings reach the clinic. The EMBARC validation cohort comprised 226 participants, a modest number by the standards of polygenic prediction, where effect sizes are typically small and large samples are needed to establish robust performance. The discovery work in T-RAD and the validation in EMBARC both involved participants recruited within specialized research programs, and it remains to be seen whether the same scores predict response in more heterogeneous primary care populations, in patients taking other antidepressants, or across different ancestries, since polygenic scores are known to lose accuracy when applied to populations different from those in which they were derived. The study was funded by the Center for Depression and Clinical Research at UT Southwestern and the Ontario Brain Institute, and both underlying studies were registered on clinicaltrials.gov, with T-RAD under NCT02919280 and EMBARC under NCT01407094, providing a transparent foundation for the replication efforts that must now follow.

Even with those caveats, the study opens a genuinely new frontier at the intersection of genomics, microbiome science, and treatment prediction. It suggests that the heritable architecture of the gut microbiome is not merely a curiosity but a usable signal, one that connects the biology of the gut-brain axis to the practical question every depressed patient and clinician faces: which treatment is most likely to work, and how soon. As the authors note, the result points toward a promising new approach to biomarker development in depression. If larger studies confirm and refine these microbial genetic predictors, the humble gut bacterium may earn an unlikely role in one of medicine’s most consequential decisions, helping to end the era of trial-and-error prescribing for millions of people worldwide.

Subject of Research: Using microbiome-related polygenic risk scores to predict antidepressant treatment response in depression

Article Title: Microbiome-related polygenic risk scores and treatment response in depression

Article References: Asbury, S., Liaqat, H., Chin Fatt, C., Seelam, N., Elsheikh, S. S. M., Müller, D. J., Vakorin, V. A., Moreno, S., Trivedi, M. H., & Foster, J. A. (2026). Microbiome-related polygenic risk scores and treatment response in depression. Translational Psychiatry. https://doi.org/10.1038/s41398-026-04472-x

Image Credits: AI Generated

DOI: 10.1038/s41398-026-04472-x

Keywords: depression, gut microbiome, polygenic risk score, antidepressant response, sertraline, bupropion, gut-brain axis, biomarkers, EMBARC, precision psychiatry, Translational Psychiatry, treatment prediction