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‐Δ 12,14 PGJ 2 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‐Δ 12,14 PGJ 2 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‐PGJ 2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".