Exposure to Gestational Diabetes Mellitus: Impact on the Development of Early-Onset Type 2 Diabetes in Canadian First Nations and Non–First Nations Offspring
Bibliographic record
Abstract
OBJECTIVE: Type 2 diabetes is increasing in children worldwide, with Canadian First Nations (FN) children disproportionally affected. The prevalence of gestational diabetes mellitus (GDM) also is increasing. The objective of this study was to evaluate the impact of GDM exposure in utero and FN status on the subsequent risk of type 2 diabetes in offspring in the first 30 years of life. RESEARCH DESIGN AND METHODS: In this population-based historical prospective cohort study, we used administrative databases linked to a clinical database to explore the independent association and interaction between GDM and FN status on the subsequent development of type 2 diabetes in offspring. RESULTS: Among 321,008 births with a median follow-up of 15.1 years, both maternal GDM and FN status were independently associated with subsequent risk of type 2 diabetes in offspring in the first 30 years of life (hazard ratio 3.03 [95% CI 2.44-3.76; P < 0.0001] vs. 4.86 [95% CI 4.08-5.79; P < 0.0001], respectively). No interaction between GDM and FN status on type 2 diabetes risk was observed. FN status had a stronger impact on the development of type 2 diabetes in offspring than GDM. CONCLUSIONS: GDM is an important modifiable risk factor for type 2 diabetes, and its prevention may reduce the prevalence of subsequent type 2 diabetes in offspring. This study adds unique and rigorous evidence to the global public health debate about the impact of GDM on the long-term health of offspring.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".