The Perinatal Microbiota-Gut-Brain Axis: Implications for Postpartum Depression
Bibliographic record
Abstract
BACKGROUND: Pregnancy and childbirth are accompanied by widespread maternal physiological adaptations and hormonal shifts that have been suggested to result in a period of vulnerability for the development of mood disorders such as postpartum depression (PPD). There is also evidence of peripartum changes in the composition of the gut microbiota, but the potential contribution of intestinal microbes to the adaptations, or subsequent vulnerabilities, during this period are unknown. SUMMARY: Here, we outline key pathways involved in peripartum adaptations including GABAergic signaling, oxytocin, and immunomodulation that are also associated with susceptibility to mood disorders and present evidence that these pathways are modulated by gut microbes. We also discuss the therapeutic potential of the microbiota-gut-brain axis in PPD and identify future directions for research to help realize this potential. KEY MESSAGES: Peripartum adaptations are associated with shifts in gut microbial composition. Disruption of GABAergic, oxytocin, and immunomodulatory pathways may contribute to vulnerability of mood disorders including PPD. These key adaptive pathways are modulated by intestinal microbes suggesting a role for the gut microbiota in determining susceptibility to PPD. More research is needed to confirm relationship between gut microbes and PPD and to gain the mechanistic understanding required to realize the therapeutic potential of microbiota-gut-brain axis in this mood disorder.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".