Subtypes of Gestational Diabetes Mellitus Are Differentially Associated With Newborn and Childhood Metabolic Outcomes
Bibliographic record
Abstract
OBJECTIVE: Subtypes of gestational diabetes mellitus (GDM) based on insulin sensitivity and secretion have been described. We addressed the hypothesis that GDM subtypes are differentially associated with newborn and child anthropometric and glycemic outcomes. RESEARCH DESIGN AND METHODS: Newborn and child (age 11-14 years) outcomes were examined in 7,970 and 4,160 mother-offspring dyads, respectively, who participated in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study and HAPO Follow-Up Study. GDM was classified as insulin-deficient GDM (insulin secretion <25th percentile with preserved insulin sensitivity), insulin-resistant GDM (insulin sensitivity <25th percentile with preserved insulin secretion), or mixed-defect GDM (both <25th percentile). Regression models for newborn and child outcomes included adjustment for field center, maternal BMI, and other pregnancy covariates. Child models also included adjustment for child age, sex, and family history of diabetes. RESULTS: Compared with mothers with normal glucose tolerance, all three GDM subtypes were associated with birth weight and sum of skinfolds >90th percentile. Insulin-resistant and mixed-defect GDM were associated with higher risk of cord C-peptide levels >90th percentile. Insulin-resistant GDM was associated with higher risk of neonatal hypoglycemia. Insulin-resistant GDM was associated with higher risk of neonatal hypoglycemia and childhood obesity (odds ratio [OR] 1.53, 95% CI 1.127-2.08). The risk of childhood impaired glucose tolerance was higher with insulin-resistant GDM (OR 2.21, 95% CI 1.50-3.25) and mixed-defect GDM (OR 3.01, 95% CI 1.47-6.19). CONCLUSIONS: GDM subtypes are differentially associated with newborn and childhood outcomes. Better characterizing individuals with GDM could help identify at-risk offspring to offer targeted, preventative interventions early in life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".