Breast Cancer and Exposure to Organochlorines in the CECILE Study: Associations with Plasma Levels Measured at the Time of Diagnosis and Estimated during Adolescence
Bibliographic record
Abstract
Exposure to environmental chemicals with hormonal effects, such as organochlorine compounds (OCs), during developmental periods of breast cells may have an impact on the incidence of breast cancer later in life. However, the assessment of exposure to these chemicals that occurred in early life at the time of breast cancer development in adult women is a difficult challenge in epidemiological studies. Plasma levels of the OCs p,p'-dichlorodiphenyl dichloroethene (DDE) and polychlorinated biphenyl congener 153 (PCB153) were measured in 695 cases and 1055 controls of a population-based case-control study conducted in France (CECILE study). Based on these values, we used a physiologically-based pharmacokinetic (PBPK) model to estimate PCB153 levels at age 11⁻20 years when the women were adolescents. Overall, there was no clear association between breast cancer risk and measured levels of DDE and PCB153 at the time of diagnosis, but there was a trend of decreasing odds ratios of breast cancer with increasing DDE and PCB153 levels in women aged 50 years and over. The PBPK model revealed that PCB153 concentrations estimated during adolescence were highest in the youngest women born after 1960 who reached adolescence at a time when environmental contamination was maximum, and very low in the oldest women who attained adolescence before the contamination peak. Negative associations between breast cancer and PCB153 estimates during adolescence were also found. The negative associations between DDE and PCB153 levels measured at the time of diagnosis or estimated during adolescence in our study were unexplained. Further investigations are needed to clarify whether this finding is real or related to study artifacts. However, this study suggests that using PBPK models in epidemiological studies to back-estimate OC exposures during early life stages may be useful to address critical questions on cancer development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".