Abstract PO4-09-03: Environmental Metal Exposures and Breast Cancer Risk: A Prospective Study of Nationally Representative Canadian Data
Bibliographic record
Abstract
Abstract Introduction: The impact of metal exposure on breast cancer risk remains unclear. Studies have explored metals independently with limited investigation into chronic exposures and mixture analyses. This project describes exposure to eight heavy and essential metals and evaluates the association between metals and breast cancer risk, independently and in a mixture, among Canadian women. Methods: Demographic information and concentrations of urinary or blood metal biomarkers from 2007-2017 of the Canadian Health Measures Survey (CHMS) were analyzed. Incident breast cancers were ascertained through linkage to the Canadian Cancer Registry. Metal exposure was described using weighted percentiles and categorized by tertiles. Cox proportional hazards regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for metal exposure and breast cancer risk. Quantile g-computation was used to estimate the joint association between metal exposure and breast cancer risk. Results: This analysis included 5,100 women (mean age 44.6 years) with an average follow-up of 6.6 years. Higher urinary arsenic (> 13.0 µg/L) and cadmium (> 10.0 µg/L) had a significant increased risk of breast cancer (HR Arsenic T3 vs. T1 = 2.05; 95%CI 1.05-3.94; HR Cadmium T3 vs. T1 = 1.71; 95%CI 1.01 – 5.87). Analyses into the joint association between the metal exposure mixture and breast cancer risk are ongoing, and matrix results will be presented. Conclusion: This represents the first evaluation of metal exposure and breast cancer risk in a nationally representative cohort. Our findings suggest that arsenic and cadmium, even at low levels, may be associated with an increased risk of breast cancer. These findings can inform population-level interventions to reduce the burden of cancer in Canada. Citation Format: Katherine Pullella, Jan Lubinski, Anthony Hanley, Shelley Harris, Steven Narod, Joanne Kotsopoulos. Environmental Metal Exposures and Breast Cancer Risk: A Prospective Study of Nationally Representative Canadian Data [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-09-03.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".