Does gestational diabetes increase the risk of maternal kidney disease? A Swedish national cohort study
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes (GDM) is associated with increased risk of type 2 diabetes (T2DM) and cardiovascular disease. It is uncertain whether GDM is independently associated with the risk of chronic kidney disease. The aim was to examine the association between GDM and maternal CKD and end-stage kidney disease (ESKD) and to determine whether this depends on progression to overt T2DM. METHODS: A population-based cohort study was designed using Swedish national registry data. Previous GDM diagnosis was the main exposure, and this was stratified according to whether women developed T2DM after pregnancy. Using Cox regression models, we estimated the risk of CKD (stages 3-5), ESKD and different CKD subtypes (tubulointerstitial, glomerular, hypertensive, diabetic, other). FINDINGS: There were 1,121,633 women included, of whom 15,595 (1·4%) were diagnosed with GDM. Overall, GDM-diagnosed women were at increased risk of CKD (aHR 1·81, 95% CI 1·54-2·14) and ESKD (aHR 4·52, 95% CI 2·75-7·44). Associations were strongest for diabetic CKD (aHR 8·81, 95% CI 6·36-12·19) and hypertensive CKD (aHR 2·46, 95% CI 1·06-5·69). These associations were largely explained by post-pregnancy T2DM. Among women who had GDM + subsequent T2DM, strong associations were observed (CKD, aHR 21·70, 95% CI 17·17-27·42; ESKD, aHR 112·37, 95% CI 61·22-206·38). But among those with GDM only, associations were non-significant (CKD, aHR 1·11, 95% CI 0·89-1·38; ESKD, aHR 1·58, 95% CI 0·70-3·60 respectively). CONCLUSION: Women who experience GDM and subsequent T2DM are at increased risk of developing CKD and ESKD. However, GDM-diagnosed women who never develop overt T2DM have similar risk of future CKD/ESKD to those with uncomplicated pregnancies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".