Therapeutic modulation of the kynurenine pathway in severe mental illness and comorbidities: A potential role for serotonergic psychedelics
Bibliographic record
Abstract
Mounting evidence points towards a crucial role of the kynurenine pathway (KP) in the altered gut-brain axis (GBA) balance in severe mental illness (SMI, namely depression, bipolar disorder, and schizophrenia) and cardiometabolic comorbidities. Preliminary evidence shows that serotonergic psychedelics and their analogues may hold therapeutic potential in addressing the altered KP in the dysregulated GBA in SMI and comorbidities. In fact, aside from their effects on mood, psychedelics elicit therapeutic improvement in preclinical models of obesity, metabolic syndrome, and vascular inflammation, which are highly comorbid with SMI. Here, we review the literature on the therapeutic modulation of the KP in the dysregulated GBA in SMI and comorbidities, and the potential application of psychedelics to address the altered KP in the brain and systemic dysfunction underlying SMI and comorbidities. Psychedelics might therapeutically modulate the KP in the altered GBA in SMI and comorbidities either directly, via altering the metabolic pathway by influencing the rate-limiting enzymes of the KP and affecting the levels of available tryptophan, or indirectly, by affecting the gut microbiome, gut metabolome, metabolism, and the immune system. Despite promising preliminary evidence, the mechanisms and outcomes of the KP modulation with psychedelics in SMI and systemic comorbidities remain largely unknown and require further investigation. Several concerns are discussed surrounding the potential side effects of this approach in specific cohorts of individuals with SMI and systemic comorbidities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".