Quantitative bias analysis of the association of type 2 diabetes mellitus with 2,2′,4,4′,5,5′-hexachlorobiphenyl (PCB-153)
Bibliographic record
Abstract
An association between serum concentrations of persistent organic pollutants (POPs), such as 2,2',4,4',5,5'-hexachlorobiphenyl (PCB-153), and risk of type 2 diabetes mellitus (T2DM) has been reported. Conditional on body mass index (BMI) and waist circumference (WC), a higher serum PCB-153 concentration may be a marker of T2DM risk because it reflects other aspects of obesity that are related to T2DM risk and to PCB-153 clearance. To estimate the amount of residual confounding by other aspects of obesity, we performed a quantitative bias analysis on the results of a specific study. A physiologically-based pharmacokinetic (PBPK) model was developed to predict serum levels of PCB-153 for a simulated population. T2DM status was assigned to simulated subjects based on age, sex, BMI, WC, and visceral adipose tissue mass. The distributions of age, BMI, WC, and T2DM prevalence of the simulated population were tailored to closely match the target population. Analysis of the simulated data showed that a small part of the observed association appeared to be due to residual confounding. For example, the predicted odds ratio of T2DM that would have been obtained had the results been adjusted for visceral adipose tissue mass, for the ≥90th percentile of PCB-153 serum concentration, was 6.60 (95% CI 2.46-17.74), compared with an observed odds ratio of 7.13 (95% CI 2.65-19.13). Our results predict that the association between PCB-153 and risk of type 2 diabetes mellitus would not be substantially changed by additional adjustment for visceral adipose tissue mass in epidemiologic analyses. Confirmation of these predictions with longitudinal data would be reassuring.
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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 | 0.039 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".