Investigation of the Relationship among Cortisol, Pro-inflammatoryCytokines, and the Degradation of Tryptophan into Kynurenine inPatients with Major Depression and Suicidal Behavior
Bibliographic record
Abstract
BACKGROUND: The increased degradation of tryptophan (Trp) along the kynurenine (Kyn) pathway due to inflammation and/or activation of the hypothalamic-pituitary-adrenal (HPA) axis has been reported among the biological factors involved in the pathophysiology of major depressive disorder (MDD) and suicide. However, the interaction among these multiple factors is not yet completely clarified. METHODS: We studied plasma levels of Trp, Kyn, cortisol and proinflammatory cytokines (IL-1, IL- 6, IL-12, IL-20) and calculated the ratio Kyn/Trp as an index of the breakdown of Trp into Kyn in 31 suicidal MDD patients and 67 non-suicidal MDD patients. RESULT: We confirmed that suicidal MDD patients have reduced plasma Trp, higher Kyn and Kyn/Trp ratio, and no difference in cortisol levels than non-suicidal MDD patients. IL-1 and IL-12 levels were significantly higher in suicidal MDD than in non-suicidal MDD (p=0.034 and p=0.023, respectively), whereas Il-6 and IL-20 levels were equal in the two groups. The Kyn/Trp ratio was positively correlated with a pro-inflammatory cytokines index (r=0.309, p=0.002) and cortisol (r=0.368, p=0.001). Notably, the variance in the Kyn/Trp ratio explained by the model including both cortisol and inflammatory parameters as dependent variables, substantially improved compared with the models in which the two parameters were considered separately. CONCLUSION: These findings show that both cortisol and proinflammatory cytokines are involved in the enhanced breakdown of Trp into Kyn occurring in suicidal MDD patients, thus adding new knowledge on the biological mechanisms leading to the activation of the Kyn pathway in MDD and suicide.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".