Fluoxetine‐induced hepatic lipid accumulation is mediated by prostaglandin endoperoxide synthase 1 and is linked to elevated 15‐deoxy‐Δ<sup>12,14</sup>PGJ<sub>2</sub>
Bibliographic record
Abstract
Abstract Major depressive disorder and other neuropsychiatric disorders are often managed with long‐term use of antidepressant medication. Fluoxetine, an SSRI antidepressant, is widely used as a first‐line treatment for neuropsychiatric disorders. However, fluoxetine has also been shown to increase the risk of metabolic diseases such as non‐alcoholic fatty liver disease. Fluoxetine has been shown to increase hepatic lipid accumulation in vivo and in vitro. In addition, fluoxetine has been shown to alter the production of prostaglandins which have also been implicated in the development of non‐alcoholic fatty liver disease. The goal of this study was to assess the effect of fluoxetine exposure on the prostaglandin biosynthetic pathway and lipid accumulation in a hepatic cell line (H4‐II‐E‐C3 cells). Fluoxetine treatment increased mRNA expression of prostaglandin biosynthetic enzymes (Ptgs1, Ptgs2, and Ptgds), PPAR gamma (Pparg), and PPAR gamma downstream targets involved in fatty acid uptake (Cd36, Fatp2, and Fatp5) as well as production of 15‐deoxy‐Δ12,14PGJ2 a PPAR gamma ligand. The effects of fluoxetine to induce lipid accumulation were attenuated with a PTGS1 specific inhibitor (SC‐560), whereas inhibition of PTGS2 had no effect. Moreover, SC‐560 attenuated 15‐deoxy‐Δ12,14PGJ2 production and expression of PPAR gamma downstream target genes. Taken together these results suggest that fluoxetine‐induced lipid abnormalities appear to be mediated via PTGS1 and its downstream product 15d‐PGJ2 and suggest a novel therapeutic target to prevent some of the adverse effects of fluoxetine treatment.
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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 | 0.000 | 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) | 0.002 | 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".