Glucose Intolerance in Pregnancy and Future Risk of Pre-Diabetes or Diabetes
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to test the hypothesis that any degree of abnormal glucose homeostasis detected on antepartum screening for gestational diabetes mellitus (GDM) should be associated with an increased risk of postpartum pre-diabetes or diabetes. RESEARCH DESIGN AND METHODS: In this prospective cohort study, 487 women underwent 1) antepartum GDM screening by a glucose challenge test (GCT) and a diagnostic oral glucose tolerance test (OGTT) and 2) postpartum metabolic characterization by OGTT at 3 months after delivery. Four baseline glucose tolerance groups were defined on the basis of the antepartum GCT/OGTT: 1) GDM (n = 137); 2) gestational impaired glucose tolerance (GIGT) (n = 91); 3) abnormal GCT with normal glucose tolerance on an OGTT (abnormal GCT NGT) (n = 166); and 4) normal GCT with NGT on an OGTT (normal GCT NGT) (n = 93). RESULTS: The prevalence of postpartum glucose intolerance (pre-diabetes or diabetes) increased across the groups from normal GCT NGT (3.2%) to abnormal GCT NGT (10.2%) to GIGT (16.5%) to GDM (32.8%) (P(trend) < 0.0001). On logistic regression analysis, all three categories of abnormal glucose homeostasis in pregnancy independently predicted postpartum glucose intolerance: abnormal GCT NGT odds ratio (OR) 3.6 (95% CI 1.01-12.9); GIGT OR 5.7 (1.6-21.1); and GDM OR 14.3 (4.2-49.1). Furthermore, both in pregnancy and at 3 months postpartum, insulin sensitivity (IS(OGTT)) and pancreatic beta-cell function (insulinogenic index/homeostasis model assessment of insulin resistance) progressively decreased across the groups from normal GCT NGT to abnormal GCT NGT to GIGT to GDM (all P(trend) < 0.0001). CONCLUSIONS: Any degree of abnormal glucose homeostasis in pregnancy independently predicts an increased risk of glucose intolerance postpartum.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".