Bisphenol A induces differentiation of human preadipocytes in the absence of glucocorticoid and is inhibited by an estrogen-receptor antagonist
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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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) | 0.003 | 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".