Intersections in Neuropsychiatric and Metabolic Disorders: Possible Role of TRPA1 Channels
Bibliographic record
Abstract
Neuropsychiatric disorders (NPDs) are a huge burden to the patient, their family, and society. NPDs have been greatly associated with cardio-metabolic comorbidities such as obesity, type-2 diabetes mellitus, dysglycaemia, insulin resistance, dyslipidemia, atherosclerosis, and other cardiovascular disorders. Antipsychotics, which are frontline drugs in the treatment of schizophrenia and off-label use in other NPDs, also add to this burden by causing severe metabolic perturbations. Despite decades of research, the mechanism deciphering the link between neuropsychiatric and metabolic disorders is still unclear. In recent years, transient receptor potential Ankyrin 1 (TRPA1) channel has emerged as a potential therapeutic target for modulators. TRPA1 agonists/antagonists have shown efficacy in both neuropsychiatric disorders and appetite regulation and thus provide a crucial link between both. TRPA1 channels are activated by compounds such as cinnamaldehyde, allyl isothiocyanate, allicin and methyl syringate, which are present naturally in food items such as cinnamon, wasabi, mustard, garlic, etc. As these are present in many daily food items, it could also improve patient compliance and reduce the patients' monetary burden. In this review, we have tried to present evidence of the possible involvement of TRPA1 channels in neuropsychiatric and metabolic disorders and a possible hint towards using TRPA1 modulators to target appetite, lipid metabolism, glucose and insulin homeostasis and inflammation associated with NPDs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".