Oral Glucose Tolerance Test Results in Pregnancy Can Be Used to Individualize the Risk of Future Maternal Type 2 Diabetes Mellitus in Women With Gestational Diabetes Mellitus
Bibliographic record
Abstract
OBJECTIVE: We aimed to quantify the risk of future maternal type 2 diabetes mellitus (T2DM) in women with gestational diabetes mellitus (GDM) based on the type and number of abnormal 75-g oral glucose tolerance test (OGTT) values and the diagnostic criteria used for the diagnosis of GDM. RESEARCH DESIGN AND METHODS: We conducted a population-based retrospective cohort study of all nulliparous women with a live singleton birth who underwent testing for GDM using a 75-g OGTT in Ontario, Canada (2007-2017). We estimated the incidence rate (per 1,000 person-years), overall risk (expressed as adjusted hazard ratio [aHR]), and risk at 5 years after the index pregnancy of future maternal T2DM. Estimates were stratified by the type and number of abnormal OGTT values, as well as by the diagnostic criteria for GDM (Diabetes Canada [DC] vs. International Association of the Diabetes and Pregnancy Study Groups [IADPSG] criteria). RESULTS: A total of 55,361 women met the study criteria. The median duration of follow-up was 4.4 (interquartile range 2.8-6.3; maximum 10.3) years. Using women without GDM as reference (incidence rate 2.18 per 1,000 person-years), women with GDM were at an increased risk of future T2DM; this risk was greater when using the DC compared with the IADPSG criteria (incidence rate 18.74 [95% CI 17.58-19.90] vs. 14.07 [95% CI 13.24-14.91] per 1,000 person-years, respectively). The risk of future maternal T2DM increased with the number of abnormal OGTT values and was highest for women with three abnormal values (incidence rate 49.93 per 1,000 person-years; aHR 24.57 [95% CI 21.26-28.39]). The risk of future T2DM was also affected by the type of OGTT abnormality: women with an abnormal fasting value had the greatest risk, whereas women with an abnormal 2-h value had the lowest risk (aHR 14.09 [95% CI 12.46-15.93] vs. 9.22 [95% CI 8.19-10.37], respectively). Similar findings to those described above were observed when the risk of T2DM at a fixed time point of 5 years after the index pregnancy was considered as the outcome of interest. CONCLUSIONS: In women with GDM, individualized information regarding the future risk of T2DM can be provided based on the type and number of abnormal OGTT values, as well as the diagnostic criteria used for the diagnosis of 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".