Elucidating the Relationship between Arsenic Exposure and Cancer Risk in Canada
Bibliographic record
Abstract
Background: Arsenic, an established carcinogen for skin, bladder, and lung cancers, is an environmental toxin to which all Canadians are exposed. Evidence suggests arsenic may also be a risk factor for breast and other cancers; however, previous studies have focused on high-exposure populations, and the impact of chronic low-level exposure is unknown. Objectives: To (1) describe arsenic exposure and determine dietary and lifestyle predictors of arsenic status, (2) evaluate the association between arsenic exposure and cancer risk among Canadian adults, and (3) characterize the cumulative association of metal mixtures on breast cancer risk in Canada. Methods: Using data from the Canadian Health Measures Survey (CHMS), demographic, lifestyle information, and urinary arsenic biomarkers (µg/L) were analyzed. Multivariable regression models were used to determine dietary and lifestyle predictors of arsenic status. Incident cancers were ascertained through linkage to the Canadian Cancer Registry (CCR) and Discharge Abstract Database (DAD) for a sub-population of CHMS participants from 2007 - 2017. Cox regression models were used to estimate the association between arsenic exposure and cancer risk (overall and site-specific). Bayesian Kernel Machine Regression, with a probit extension (BKMR-P), was used to model the cumulative association between total urine arsenic, serum cadmium, lead, and mercury exposure on breast cancer risk. Results: This analysis represented over 14,500,000 Canadians with a median total urinary arsenic level of 11.48 µg/L (95% CI 10.42-12.64). Weekly total fish consumption significantly predicted urinary arsenic levels (Percent Change Geometric Mean = 40.66, P < 0.001), in addition to age, sex, and region of Canada. We observed that women in the highest quartile of total urinary arsenic exposure (≥21.8.0 µg/L) had a three-fold increased risk of developing breast cancer compared to women with lower levels (< 21.8 µg/L) (Hazard Ratio [HR] = 3.06, 95% CI 1.10-8.49, P = 0.03). BKMR-P confirmed that cumulative exposure to arsenic, cadmium, mercury, and lead exposure was related to increasing breast cancer risk. Conclusion: This work ascertains national arsenic exposure estimates and explores the relationship between arsenic exposure and cancer risk in Canadian adults. These findings are critical for developing population-level interventions to reduce cancer burdens in Canada and globally.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".