Abstract 5895: Examining the association between arsenic exposure and cancer risk among Canadian adults
Bibliographic record
Abstract
Abstract Background: Arsenic is a highly prevalent environmental toxin and an established carcinogen for cancers of the skin, bladder, and lung. Emerging evidence also suggests that arsenic exposure may be a risk factor for breast and other cancers. Previous studies have been conducted in populations with endemically high levels of arsenic; however, the impact of chronic exposure to low levels is not known. Thus, the purpose of this study was to describe the arsenic exposure in Canadian adults and to evaluate the association between arsenic status and cancer risk. Methods: The Canadian Health Measures Survey (CHMS) is an ongoing, nationally representative survey that collects self-reported and objective health measures, including biomonitoring data, from participants in biennial cycles. This analysis utilized total, inorganic and organic urinary arsenic biomarkers (µg/L) from Cycles 1-5 (2007-2017) of the CHMS. Incident cancers were ascertained through a linkage to the Canadian Cancer Registry. Arsenic exposure was described using weighted percentiles, and Cox proportional hazards regression models were used to estimate the association between arsenic exposure and cancer risk (overall and site-specific). Results: This analysis included 10,000 individuals, aged 18-80. The median total urinary arsenic level was 7.5 ug/g of creatinine. Preliminary models showed that levels of inorganic arsenic exposure above the geometric mean (> 4.5 µg/L) were associated with a 3-fold increased risk of developing any cancer (HR = 3.58; 95%CI 1.63-7.85), compared to those with lower levels. Site-specific risk modelling is ongoing and preliminary results will be presented. Significance: This study found inorganic arsenic exposure may be associated with an increased risk of developing cancer. To our knowledge, this study is the first to ascertain national estimates of arsenic exposure and provide insight on the relationship between arsenic exposure and cancer risk in Canadian adults. These findings are critical for developing population-level interventions to reduce the Canadian, and global, cancer burden. Citation Format: Katherine Pullella, Vicky C. Chang, Shelley A. Harris, Anthony J. Hanley, John R. McLaughlin, Jan Lubinski, Steven A. Narod, Joanne Kotsopoulos. Examining the association between arsenic exposure and cancer risk among Canadian adults [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5895.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".