Plasticizers and Cardiovascular Health: Role of Adipose Tissue Dysfunction
Bibliographic record
Abstract
Since the 1950s, the production of plastics has increased 200-fold, reaching 360 million tonnes in 2019. Plasticizers, additives that modify the flexibility and rigidity of the product, are ingested as they migrate into food and beverages. Human exposure is continuous and widespread; between 75 and 97% of urine samples contain detectable levels of bisphenols and phthalates, the most common plasticizers. Concern over the toxicity of plasticizers arose in the late 1990s, largely focused around adverse developmental and reproductive effects. More recently, many studies have demonstrated that exposure to plasticizers increases the risk for obesity, type 2 diabetes, and cardiovascular disease (CVD). In the 2000s, many governments including Canada, the United States and European countries restricted the use of certain plasticizers in products targeted towards infants and children. Resultant consumer pressure motivated manufacturers to substitute plasticizers with analogues, which have been marketed as safe. However, data on the effects of these new substitutes are limited and data available to-date suggest that many exhibit similar properties to the chemicals they replaced. The adverse effects of plasticizers have largely been attributed to their endocrine disrupting properties, which modulate hormone signaling. Adipose tissue has been well-documented to be a target of the disrupting effects of both bisphenols and phthalates. Since adipose tissue function is a key determinant of cardiovascular health, adverse effects of plasticizers on adipocyte signaling and function may underlie their link to cardiovascular disease. Herein, we discuss the current evidence linking bisphenols and phthalates to obesity and CVD and consider how documented impacts of these plasticizers on adipocyte function may contribute to the development of CVD.
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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) | 0.002 | 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 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".