Increased incidence of non‐alcoholic fatty liver disease in male rat offspring exposed to fluoxetine during fetal and neonatal life involves the NLRP3 inflammasome and augmented de novo hepatic lipogenesis
Bibliographic record
Abstract
Up to 10% of women take selective serotonin reuptake inhibitors (SSRI) during pregnancy. Children exposed to SSRIs in utero have an increased risk of being overweight suggesting that fetal exposure to SSRIs can cause permanent metabolic changes. We have previously shown in rats that fetal and neonatal exposure to the SSRI antidepressant fluoxetine results in metabolic perturbations including increased hepatic triglyceride content; a hallmark of non-alcoholic fatty liver disease (NAFLD). Therefore, the aim of this study was to identify the mechanism(s) underlying the fluoxetine-induced increase in intrahepatic triglyceride content. Female nulliparous Wistar rats were given vehicle or fluoxetine (10 mg/kg/day) orally for 2 weeks prior to mating until weaning. At 6 months of age, we assessed whether SSRI exposure altered components of the hepatic triglyceride biosynthesis pathway in the offspring and examined the molecular mechanisms underlying these changes. Male SSRI-exposed offspring had a significant increase in the steady-state mRNA levels of Elovl6 and Dgat1 and core components of the NLRP3 inflammasome (apoptosis-associated speck-like protein containing a caspase activation recruitment domain [ASC] and caspase-1). Augmented expression of Asc in the SSRI-exposed offspring coincided with increased histone acetylation in the proximal promoter region. Given that we have previously demonstrated that antenatal exposure to SSRIs can lead to fatty liver in the offspring, this raises concerns regarding the long-term metabolic sequelae of fetal SSRI exposure. Moreover, this study suggests that elevated hepatic triglyceride levels observed in the SSRI-exposed offspring may be due, in part, to activation of the NLRP3 inflammasome and augmentation of de novo lipogenesis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".