Arsenic exposure and type 2 diabetes: results from the 2007–2009 Canadian Health Measures Survey
Bibliographic record
Abstract
INTRODUCTION: Inorganic arsenic and its metabolites are considered dangerous to human health. Although several studies have reported associations between low-level arsenic exposure and diabetes mellitus in the United States and Mexico, this association has not been studied in the Canadian population. We evaluated the association between arsenic exposure, as measured by total arsenic concentration in urine, and the prevalence of type 2 diabetes (T2D) in 3151 adult participants in Cycle 1 (2007-2009) of the Canadian Health Measures Survey (CHMS). METHODS: All participants were tested to determine blood glucose and glycated hemoglobin. Urine analysis was also performed to measure total arsenic. In addition, participants answered a detailed questionnaire about their lifestyle and medical history. We assessed the association between urinary arsenic levels and T2D and prediabetes using multivariate logistic regression while adjusting for potential confounders. RESULTS: Total urinary arsenic concentration was positively associated with the prevalence of T2D and prediabetes: adjusted odds ratios were 1.81 (95% CI: 1.12-2.95) and 2.04 (95% CI: 1.03-4.05), respectively, when comparing the highest (fourth) urinary arsenic concentration quartile with the lowest (first) quartile. Total urinary arsenic was also associated with glycated hemoglobin levels in people with untreated diabetes. CONCLUSION: We found significant associations between arsenic exposure and the prevalence of T2D and prediabetes in the Canadian population. Causal inference is limited due to the cross-sectional design of the study and the absence of long-term exposure assessment.
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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 | 0.006 |
| 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".