Abstract 857: Evaluating the relationship between arsenic exposure and cancer risk in Canada
Bibliographic record
Abstract
Abstract Background: Arsenic is an established carcinogen for cancers of the skin, bladder and lungs; it is also the most prevalent environmental toxin, resulting in all Canadians being exposed to some extent. In a recent prospective study of a low-exposure population, our team found that women in the highest quartile of circulating arsenic had a significant 13-fold increased risk of developing breast cancer, compared to the lowest quartile. This suggests that even at low-doses, arsenic may be an important risk factor for breast, and potentially other cancers not previously recognized. To date, the sources of arsenic in Canada, a low-exposure population, are unclear. Objectives: To describe the status of arsenic exposure, identify the key predictors of arsenic status, and evaluate the relationship between arsenic exposure and cancer risk within the Canadian population. Methods: Using data from Cycles 1-5 (2007-2017) of the Canadian Health Measures Survey (CHMS), exposure data (e.g., diet, water), demographic information, and urinary arsenic biomarkers will be analyzed. The CHMS is an ongoing, nationally representative survey, collecting objective health measures and food intake information from participants in biennial cycles. Data will be described using weighted percentages or means, and arsenic levels (total and speciated urinary arsenic metabolite concentrations) will be reported by percentile. Weighted multivariate regression models will be used to identify predictors of total urinary arsenic. Linkage to the Canadian Cancer Registry will be performed to ascertain incident cancers, and Cox proportional hazards regression used to estimate the relationship between arsenic status and cancer risk (total and site-specific). Results: This analysis included 15,700 individuals, aged 18-80. The median total urinary arsenic level was 7.5 ug/g of creatinine. Significant predictors of total urinary arsenic levels were found to be weekly intake of fish and seafood (β= 0.3, 95%CI 0.27,0.33, P < 0.001), grains (β=-0.02, 95%CI -0.02, -0.01, P< 0.001) and consumption of well and/or cistern water (β = 0.13, 95%CI 0.04, 0.22, P=0.005). Linkage and data analysis are ongoing. Impact: This work is the first to ascertain national exposure estimates to an environmental carcinogen. Fish, seafood, grains, and consumption of well and/or cistern water represent significant predictors of total urinary arsenic levels in Canada. Ultimately, this information is needed to develop critical population-level intervention strategies to reduce the Canadian, and global burden of cancer. Citation Format: Katherine Pullella, Shelley A. Harris, John R. McLaughlin, Jan Lubinski, Steven A. Narod, Joanne Kotsopoulos. Evaluating the relationship between arsenic exposure and cancer risk in Canada [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 857.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".