An explained variance‐based genetic risk score associated with gestational diabetes antecedent and with progression to pre‐diabetes and type 2 diabetes: a cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine whether an explained-variance genetic risk score (GRS), with 36 single nucleotide polymorphisms (SNPs) previously associated with type 2 diabetes (T2D), is also associated with gestational diabetes mellitus (GDM), and with the progression to pre-diabetes and T2D among women with prior GDM. DESIGN: A cohort study. SETTING: Clinical investigation unit of Laval University, Quebec, Canada. POPULATION: A cohort of 214 women with prior GDM and 82 controls recruited between 2009 and 2012. METHODS: Associations between the GRS and GDM. MAIN OUTCOMES MEASURES: GDM and prevalence of pre-diabetes and T2D. RESULTS: Women with prior GDM had a higher GRS compared with controls (38.6 ± 3.9, 95% CI 38.1-39.1, versus 37.4 ± 3.2, 95% CI 36.7-38.1; P < 0.0001). In women with prior GDM, the explained-variance GRS was higher for pre-diabetic women compared with women who remained normoglucotolerant at testing (1.21 ± 0.18, 95% CI 1.18-1.23, versus 1.17 ± 0.15, 95% CI 1.13-1.20; P < 0.0001). Similarly, women with T2D had a higher explained-variance GRS compared with women with prior GDM who remained normoglucotolerant (1.20 ± 0.18, 95% CI 1.14-1.25, versus 1.17 ± 0.17, 95% CI 1.13-1.20; P < 0.0001). The predictive effects of the explained-variance GRS, age, and body mass index (BMI), or the additive effects of the three variables, were tested for pre-diabetes and T2D. We observed an area under the curve of 0.6269 (95% CI 0.5638-0.6901) for age and BMI, and adding the explained-variance GRS into the model increased the area to 0.6672 (95% CI 0.6064-0.7281) for the prediction of pre-diabetes. CONCLUSIONS: An explained-variance GRS is associated with both GDM and progression to pre-diabetes and T2D in women with prior 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".