Posttraumatic Stress Disorder: Does the Gut Microbiome Hold the Key?
Bibliographic record
Abstract
Gut bacteria strongly influence our metabolic, endocrine, immune, and both peripheral and central nervous systems. Microbiota do this directly and indirectly through their components, shed and secreted, ranging from fermented and digested dietary and host products to functionally active neurotransmitters including serotonin, dopamine, and γ-aminobutyric acid. Depression has been associated with enhanced levels of proinflammatory biomarkers and abnormal responses to stress. Posttraumatic stress disorder (PTSD) appears to be marked in addition by low cortisol responses, and these factors seem to predict and predispose individuals to develop PTSD after a traumatic event. Dysregulation of the immune system and of the hypothalamic-pituitary-adrenal axis observed in PTSD may reflect prior trauma exposure, especially early in life. Early life, including the prenatal period, is a critical time in rodents, and may well be for humans, for the functional and structural development of the immune and nervous systems. These, in turn, are likely shaped and programmed by gut and possibly other bacteria. Recent experimental and clinical data converge on the hypothesis that imbalanced gut microbiota in early life may have long-lasting immune and other physiologic effects that make individuals more susceptible to develop PTSD after a traumatic event and contribute to the disorder. This suggests that it may be possible to target abnormalities in these systems by manipulation of certain gut bacterial communities directly through supplementation or indirectly by dietary and other novel approaches.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".