Type 1 Diabetes Mellitus and Components in Drinking Water and Diet: A Population-Based, Case-Control Study in Prince Edward Island, Canada
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between the risk of type 1 diabetes (T1D) and daily intake of drinking water and dietary components, including nitrate, nitrite, and nitrosamines, during the year prior to diagnosis. METHODS: Controls (n = 105) were matched by age at diagnosis and sex to T1D cases (n = 57) newly diagnosed during 2001-2004. Food consumption was assessed using a food frequency questionnaire. Locally available samples of foods were tested for nitrate, nitrite, and nitrosamine concentrations. Water consumption was determined through an additional questionnaire, and water samples were taken from homes and tested for routine chemical components, including nitrate. RESULTS: After controlling for age, age, sex, and daily energy intake, nitrate intake from food sources showed a non-significant positive trend (odds ratios and 95% confidence intervals for quartiles = 1.00, 1.63 (0.58, 4.63), 1.71 (0.54, 5.40), 3.02 (0.78, 11.74); p for trend = 0.13). Nitrite and nitrosamine intake were not related to T1D risk (p for trend = 0.77 and 0.81, respectively). When food and water components were combined, zinc and calcium intakes were marginally and inversely related to T1D risk (p for trend = 0.07 and 0.06, respectively). After further model adjustment of possible confounders and significant risk factors, an increased intake of caffeine marginally increased the risk of T1D (p = 0.07). CONCLUSION: Dietary components from both food and water sources may influence the risk of developing T1D in young persons.
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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) | 0.000 | 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".