Bisphenol A induces differentiation of human preadipocytes in the absence of glucocorticoid and is inhibited by an estrogen-receptor antagonist
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Obesity is a major health concern in the developed world, and increasing evidence suggests that exposures to common environmental substances may enhance the risk for the development of this disease. OBJECTIVES: The current study examines the effect of the ubiquitous plastic monomer bisphenol A (BPA) on the differentiation of primary human preadipocytes in vitro and the role of the estrogen and glucocorticoid receptors. METHODS: In this study, the mechanism of BPA-induced adipogenesis in preadipocytes from donors with healthy body mass index in the absence of exogenous glucocorticoid was evaluated. The effects of estradiol, the estrogen-receptor (ER) antagonist ICI and the glucocorticoid receptor (GR) antagonist RU486 on BPA-induced adipogenesis were examined. The expression levels of key adipogenic factors were assessed. RESULTS: Treatment of preadipocytes with 1-50 μM BPA induced a dose-dependent increase in differentiation and lipid accumulation as determined by lipid staining and triacylglyceride quantification. BPA also induced expression of the adipogenic markers aP2, adipsin, peroxisome proliferator-activated receptor γ and the CCAAT-enhancer-binding proteins α and β. Co-treatment of cells with ICI inhibited the BPA-induced increase in aP2 levels, while treatment with ICI or estradiol alone had no effect. Treatment of cells with the GR antagonist RU486 had no effect on BPA-induced differentiation as evaluated by aP2 levels. CONCLUSIONS: This study is one of the first to show that BPA induces human adipocyte differentiation in the absence of exogenous glucocorticoid through a non-classical ER pathway rather than through GR activation. These studies add to the growing evidence that endocrine-disrupting chemicals such as BPA have the potential to modulate adipogenesis and impact human biology.
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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) | 0.000 | 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) | 0.000 | 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 it