Bisphenol A and indicators of obesity, glucose metabolism/type 2 diabetes and cardiovascular disease: A systematic review of epidemiologic research
Bibliographic record
Abstract
INTRODUCTION: Bisphenol A (BPA), a high-volume chemical with weak estrogenic properties, has been linked to obesity, cardiovascular diseases (CVD) and diabetes mellitus (DM). This review evaluates both the consistency and the quality of epidemiological evidence from studies testing the hypothesis that BPA exposure is a risk factor for these health outcomes. METHODS: We followed the current methodological guidelines for systematic reviews by using two independent researchers to identify, review and summarize the relevant epidemiological literature on the relation of BPA to obesity, CVD, DM, or related biomarkers. Each paper was summarized with respect to its methods and results with particular attention to study design and exposure assessment, which have been cited as the main areas of weakness in BPA epidemiologic research. As quantitative meta-analysis was not feasible, the study results were categorized qualitatively as positive, inverse, null, or mixed. RESULTS: Nearly all studies on BPA and obesity-, DM- or CVD-related health outcomes used a cross-sectional design and relied on a single measure of BPA exposure, which may result in serious exposure misclassification. For all outcomes, results across studies were inconsistent. Although several studies used the same data and the same or similar statistical methods, when the methods varied slightly, even studies that used the same data produced different results. CONCLUSION: Epidemiological study design issues severely limit our understanding of health effects associated with BPA exposure. Considering the methodological limitations of the existing body of epidemiology literature, assertions about a causal link between BPA and obesity, DM, or CVD are unsubstantiated.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".