Fatty acid binding proteins and their involvement in anxiety and mood disorders
Bibliographic record
Abstract
Anxiety and mood disorders represent the most prevalent neuropsychiatric conditions. Nevertheless, current pharmacotherapies often have a host of adverse side effects. Emerging evidence suggests modulation of lipid signaling pathways - particularly those involved in the endocannabinoid (eCB) system, may offer promising new targets for the treatment of anxiety and depression. Polyunsaturated fatty acids (PUFA) and their metabolic derivatives, including the eCB ligands, have garnered significant attention for their roles in neuropsychiatric disease mechanisms. Intracellular transportation of these lipids is facilitated by fatty acid binding proteins (FABP), which are increasingly recognized as key regulators of lipid signaling. Accumulating evidence indicates that FABPs may impact the development of neuropsychiatric disorders by mediating the signaling pathways of PUFAs and eCB ligands. In this review, we investigate the role of FABPs in two major categories of neuropsychiatric conditions - anxiety disorders and clinical depression. We begin by examining several neuropathophysiological mechanisms through which FABPs can impact these conditions, focusing on their role as lipid chaperones. These mechanisms include the trafficking of eCB ligands, as well as oleoylethanolamide and palmitoylethanolamide; modulation of inflammatory responses through PUFA transport and PPAR activation; regulation of PUFA availability to support neurogenesis; influence on stress-related pathways, including NMDA receptor activation and the hypothalamic-pituitary-adrenal axis; and the facilitation of dopamine receptor trafficking and localization. Next, we discuss preclinical evidence linking FABP function to anxiety- and depression-related behaviours. Finally, we propose that pharmacologically targeting FABP-mediated pathways holds considerable potential as a novel therapeutic strategy for addressing the symptoms associated with mood and anxiety disorders.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".