Unravelling the Relationship between Body Mass Index and Polychlorinated Biphenyl Concentrations Using a Mechanistic Model
Bibliographic record
Abstract
Human biomonitoring (HBM) often reveals statistical associations between persistent organic pollutant (POP) concentrations and body mass index (BMI). Both negative and positive associations have been observed, which has been hypothesized to reflect variable toxicokinetics in lean and obese individuals during times of increasing and decreasing exposure. We examined this hypothesis and assessed the influence of the obesity epidemic on time trends in human exposure to polychlorinated biphenyls (PCB) at the population level using a mechanistic modeling approach and data from the National Health and Nutrition Examination Survey (NHANES) 1999-2004. Using model results for PCB-153, we simulated cross-sectional body burden versus BMI trends (CBBTs), as well as population level body burden versus time trends. Negative associations between PCB-153 concentrations and BMI are predicted for all birth cohorts in HBM studies conducted in the 1990s, while for future cross-sectional studies, we predict negative or positive relationships depending on the age group sampled. At the population level, demographic changes such as the obesity epidemic and population aging had only marginal influence on the simulated rate of decline in PCB-153 concentrations between 1980 and 2010. Mechanistic bioaccumulation models can help unravel relationships between age, BMI, and POP concentrations, informing efforts to understand potential obesogenic effects of POPs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".