Abstract P033: Characterizing the relationship between arsenic exposure and breast cancer risk in Canada
Bibliographic record
Abstract
Abstract Background: Although an established carcinogen for cancers of the skin, bladder and lungs, the relationship between arsenic exposure and breast cancer risk is not known. Studies to date have been conducted in populations with endemically high levels of arsenic, have been limited by small sample sizes and methodological concerns. The impact of chronic exposure to low levels of arsenic remains unclear. This project aims to: 1) describe arsenic exposure in Canada and 2) to evaluate the association between urinary arsenic levels and breast cancer risk among Canadian women. Methods: The Canadian Health Measures Survey (CHMS) is a nationally representative cross-sectional survey that collects self-reported and objective health measures, including biomonitoring data, from participants in biennial cycles. To date, CHMS has collected data from approximately 25,000 participants between the ages of 3 – 80 years. This analysis utilized demographic, lifestyle and reproductive data collected from Cycles 1-5 (2007-2017). Urinary arsenic levels (µg/L) were quantified using high-performance liquid chromatography (HPLC) with inductively coupled plasma mass spectrometry (ICP-MS). Incident breast cancers were ascertained through linkage to the Canadian Cancer Registry and the Discharge Abstract Database. Participants were eligible for inclusion if they were female, older than 18 at the time of urine sample collection, had no personal history of any cancer, and had quantified urinary arsenic and creatinine levels. Arsenic exposure was described using weighted percentiles and then categorized by tertiles. Cox proportional hazards regression was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) of arsenic exposure and breast cancer risk. Multivariate models adjusted for relevant confounders. Quantile g-computation will be used to estimate the joint association between multiple metals and breast cancer risk. Results: This analysis included 5,100 women with a mean age of 44.6 years and an average follow-up of 6.6 years. The median total urinary arsenic level was 7.8 µg/g of creatinine (2.70 µg/g – 84.83 µg/g). Approximately 5% of women had elevated urinary arsenic levels above the clinically relevant threshold of 50 ug/L. Women in the highest tertile of urinary arsenic levels (> 13.0 µg/L) had a significant two-fold increased risk of developing breast cancer compared to women in the referent group in age-adjusted models (HR = 2.10; 95%CI 1.06-3.96, P-trend = 0.03). Similar trends were observed in the fully adjusted models (HR= 2.05; 95%CI 1.05-3.94, P-trend = 0.035). Metal mixture analyses are ongoing, and results will be presented. Significance: To our knowledge, this represents the first analysis of arsenic levels and breast cancer risk in a nationally representative, Canadian cohort. Our findings suggest that exposure to arsenic, even at low levels, is associated with an increased risk of breast cancer and are important for informing population-level interventions to reduce the burden of breast cancer in Canada and around the globe. Citation Format: Katherine Pullella, Vicky Chang, Anthony Hanley, Shelley Harris, Jan Lubiński, Steven Narod, Joanne Kotsopoulos. Characterizing the relationship between arsenic exposure and breast cancer risk in Canada. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P033.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".