Maternal and Childhood Diet and Human Type 1 Diabetes Risk
Bibliographic record
Abstract
Background: The incidence of type 1 diabetes is increasing worldwide for reasons which are incompletely understood. The objective of the study was to investigate the potential association of maternal and childhood dietary components to type 1 diabetes in a case-control retrospective study. Methods: Data of diet during pregnancy and during childhood before the diagnosis of type 1 diabetes were gathered using a modified food frequency questionnaire in 88 mothers of type 1 diabetes patients and 88 mothers of controls. Children born on the same day and of the same sex of type 1 diabetes patients were chosen as controls. Results: Consumption of savoury cakes, savoury pies and ice-cream was significantly more frequent in mothers of type 1 diabetic subjects than in mothers of controls in univariate, but not in multivariate, analysis. Children with type 1 diabetes consumed bread and fish more frequently, and chicken less frequently before diagnosis when compared to controls. There were no differences in the frequency of intake of milk, sweet drinks and tea, fruit, dried fruit, vegetables, potatoes, uncooked cereal, rice or pasta, pizza, savoury cakes and pies, biscuits, packaged snacks, cakes, candy or chocolate. Consumption of bread was independently associated with increased risk of type 1 diabetes whilst consumption of chicken was independently associated with decreased risk in multivariate analysis. Conclusion: We support the hypothesis that dietary factors may be implicated in the pathogenesis of type 1 diabetes. J Endocrinol Metab. 2022;12(6):178-187 doi: https://doi.org/10.14740/jem846
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".