Gestational Diabetes Mellitus (GDM) Predicts Future Risk of Serious Liver Disease
Bibliographic record
Abstract
Like type 2 diabetes (T2DM), GDM has recently been associated with enhanced hepatic fat deposition. We thus hypothesized that GDM may predict future lifetime risk of serious liver disease, such as cirrhosis, liver failure and transplantation. Using population-based administrative databases, we identified all women in Ontario, Canada, with a live-birth pregnancy between April 1994 and March 2002 (n=698,078). The women were stratified into those who had GDM (n=17,932) and those who did not (n=680,146), and followed for median 17.1 years for the development of serious liver disease (defined by hospitalization for cirrhosis, liver failure or transplantation). Compared to their peers, women with GDM had an elevated risk of this outcome (HR=1.40, 95% CI 1.01-1.94). Since GDM also predicts future risk of T2DM, the women were further stratified based on incident T2DM in the years after delivery (Figure). Women with GDM who developed T2DM (n=8,567) had an elevated risk of serious liver disease (adjusted HR=1.56, 95% CI 1.02-2.39), as did those who did not have GDM but developed T2DM (n=44,148) (adjusted HR=2.48, 95% CI 2.10-2.93). However, there was no increased risk in women with GDM who did not develop T2DM (n=9,365) (adjusted HR=1.15, 95% CI 0.69-1.91). Thus, despite being a diagnosis of young women of childbearing age, GDM predicts future risk of serious liver disease, the development of which may be dependent upon progression to T2DM. Disclosure R. Retnakaran: Research Support; Self; Novo Nordisk Inc.. Consultant; Self; Novo Nordisk Inc.. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Consultant; Self; Sanofi, Eli Lilly and Company. J. Luo: None. B.R. Shah: None.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".