Maternal gestational diabetes and offspring type 1 diabetes: a familial or a pregnancy-specific association?
Bibliographic record
Abstract
Background: Pediatric type 1 diabetes (T1D) often remains undiagnosed until the patient present to the emergency room with potentially lethal diabetic ketoacidosis. Understanding early indicators of T1D is crucial for timely detection. Previous studies demonstrate that maternal gestational diabetes (GDM) is a risk factor for offspring T1D development. However, it remains unclear whether this association is primarily driven by pregnancy factors such as the effects of in utero hyperglycemia on developing β-cells or by shared familial factors (genetic and/or behavioural) promoting insulin resistance. To address these possibilities, we leveraged GDM during a first pregnancy as an indicator of familial factors and evaluated its association with T1D in a second-born offspring. We hypothesize that if familial factors play an important role, GDM during the first pregnancy would be associated with T1D in the second child. Alternatively, if in utero factors are predominant, we would not expect such an association, as first-pregnancy factors could not directly impact the second born. Methods: Using health administrative and vital statistics databases from the Canadian province of Quebec, we studied 485,220 second-born offspring of families with two consecutive deliveries between 1990 and 2012, with follow-up data to April 2019. We examined offspring from birth to 22 years of age. More than 85% of diabetes in children and youth is T1D in Quebec, termed diabetes hereafter. We calculated descriptive statistics and offspring diabetes incidence rates. We compared GDM in the first, second, and both pregnancies to the absence of diabetes in either pregnancy in terms of diabetes hazards for the second-born. We included preexisting maternal and paternal diabetes in these models and also examined their associations with second-born diabetes. We created separate adjusted models for children (from birth until 12 years) and for youth (12 to 22 years). Findings: A total of 1845 subjects (960 children and 865 youth) developed T1D over an average of 11.7 years. In children, we did not identify associations between GDM at either the first, second, or both pregnancies with T1D in the second-born. Maternal (HR 2.38, 95% CI 1.60-3.53) and paternal (HR 6.60, 95% CI 4.80-9.06) pre-existing diabetes were conclusively associated with second-born diabetes in this age group. Among youth, GDM in the first pregnancy only (HR 1.54, 95% CI 1.05-2.25), GDM in the second pregnancy only (HR 1.62, 95% CI 1.17-2.26) and GDM in both pregnancies (HR 2.59, 95% CI 1.78-3.76) were associated with diabetes in the second born as were maternal pre-existing diabetes (HR 4.88, 95% CI 3.37-7.08), and paternal pre-existing diabetes (HR 4.35, 95% CI 2.71-6.97).Interpretation: In the second-born offspring of women with two consecutive singleton deliveries, GDM in the first pregnancy only and in the second pregnancy only are similarly associated with T1D in youth. This similarity suggests that familial factors are predominant in associations between GDM and diabetes in youth. The highest hazards occur with GDM in both pregnancies. Pre-existing diabetes in either parent is associated with T1D development in both childhood and youth; the association is stronger for paternal diabetes than maternal diabetes in childhood but similar in youth. Our findings underscore the importance of querying GDM, not only during pregnancy with the patient but also during other pregnancies
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".