Tetra methyl bisphenol F: another potential obesogen
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Obesity and its associated metabolic diseases are increasing globally. Sedentary lifestyle, high caloric diet, and genetic predisposition are known to contribute to the onset of obesity. It is increasingly recognized that exposure to environmental chemicals such as Bisphenol A (BPA) may also play a significant role. BPA has been correlated with an array of adverse health effects, including obesity and metabolic disorders. Due to public concern, manufacturers are replacing BPA with structural analogues for which there is limited toxicological data. The objective of this study was to assess the effects of these BPA analogues on adipogenesis. METHODS: The adipogenic effects of Tetra Methyl Bisphenol F (TMBPF), Bisphenol F (BPF), Bisphenol AP (BPAP), and fluorine-9-bisphenol (BHPF) were evaluated in murine 3T3-L1 cells. The cells were treated with BPA and its analogues at concentrations from 0.01 µM to 20 µM, throughout differentiation, in the absence of Dexamethasone (Dex). Lipid accumulation, mRNA and protein levels of adipogenic markers was assessed. RESULTS: We found that TMBPF, BPF and BPA increased 3T3-L1 lipid accumulation and the expression levels of adipogenic markers lipoprotein lipase (Lpl), fatty acid binding protein 4 (Fabp4) and perilipin (Plin) (1-20 µM; p < 0.05), whereas BHPF and BPAP had no effect in this model. Further, TMBPF induced adipogenesis to a greater extent than all the other chemicals including BPA (1-20 µM; p < 0.05). The effect mediated by TMBPF on expression levels of Fabp4, but not Plin, is likely mediated via peroxisome proliferator-activated receptor (PPAR) γ activation. CONCLUSIONS: Of the BPA analogues tested, BPF was most similar to BPA in its effects, while TMBPF was most adipogenic. In addition, TMBPF is likely a PPARγ agonist, it is likely an obesogenic chemical and may be a metabolic disruptor.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".