The impact of family history of diabetes on risk factors for gestational diabetes
Bibliographic record
Abstract
OBJECTIVE: Familial history of type 2 diabetes (FHD) represents a pathophysiologically unique risk factor for gestational diabetes (GDM), insofar as it encompasses both inherited and lifestyle elements. We thus hypothesized that the risk factors for gestational hyperglycaemia in women with FHD may differ from those in women without FHD. DESIGN/PATIENTS/MEASUREMENTS: GDM risk factors were evaluated in 90 women with FHD and in 83 women without FHD, at the time of oral glucose tolerance testing in late pregnancy. RESULTS: There were no significant differences between the two groups in ethnicity, prepregnancy BMI, the insulin-sensitizing protein adiponectin, glucose tolerance status and area-under-the-glucose-curve (AUC(gluc)). In women with FHD, a multiple linear regression model of established GDM risk factors reconciled 35% of the variance in AUC(gluc), with (i) previous GDM (t = 3.74, P = 0.0003) identified as a positive independent determinant and (ii) log adiponectin (t = -3.48, P = 0.0008) and, unexpectedly, parity (t = -3.19, P = 0.0021) emerging as negative independent covariates of AUC(gluc). In contrast, in women without FHD, the same multivariate model reconciled only 15% of the variance in AUC(gluc), with no significant variables identified. Interestingly, in the entire population (n = 173), parity significantly modified the relationship between FHD and AUC(gluc) (FHD-parity interaction: t = -2.29, P = 0.0235). Indeed, FHD was an independent determinant of AUC(gluc) in nulliparous women (n = 91), but not in parous women (n = 82). CONCLUSION: Established risk factors for GDM are relevant in women with FHD but may not be the principal determinants of gestational hyperglycaemia in women without FHD. Moreover, FHD may be more relevant to risk of GDM in nulliparous women than in parous subjects. These findings highlight the complex relationship between FHD and gestational hyperglycaemia, and may hold implications for selective screening for GDM.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".