Association between maternal urinary speciated arsenic concentrations and gestational diabetes in a cohort of Canadian women
Bibliographic record
Abstract
BACKGROUND: Epidemiological and toxicological evidence suggests that maternal total arsenic (As) levels are associated with an elevated risk of gestational diabetes (GDM). Uncertainty remains regarding the metabolic toxicity of specific arsenic species, comprised of both organic and inorganic sources of arsenic exposure. OBJECTIVES: We assessed associations between speciated As and GDM using data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study. METHODS: Concentrations of speciated As [(inorganic (trivalent, pentavalent)), methylated arsenic species metabolites (monomethylarsonic acid (MMA), dimethylarsinic acid (DMA)), and organic (arsenobetaine)] were measured in first trimester maternal urine samples. GDM cases were identified in accordance with Canadian guidelines. Multivariable regression models were used to estimate associations between speciated As and GDM, evaluate potential interaction between speciated As exposures, and assess fetal sex-specific findings. RESULTS: Among 1243 women who had a live, singleton birth and no previous history of diabetes, 4% met the diagnostic criteria for GDM. Our analyses focused on DMA and arsenobetaine as these were the subtypes with detectable concentrations in at least 40% of samples. Compared to women in the lowest tertile of DMA (<1.49 μg As/L), women with concentrations exceeding 3.52 μg As/L (3rd tertile) experienced an increased risk of GDM (aOR = 3.86; 95% CI: 1.18, 12.57) (p-value for trend across tertiles = 0.04). When restricted to women carrying male infants, the magnitude of this association increased (aOR 3rd tertile = 4.71; 95% CI: 1.05, 21.10). CONCLUSIONS: These results suggest a positive relation between DMA and GDM; potential differences in risk by fetal sex requires further investigation.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".