Gestational diabetes and parental type 2 diabetes as risk indicators for type 1 diabetes in offspring: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Type 1 diabetes can be accelerated by insulin resistance, an increasingly prevalent condition. Some studies report an association between offspring type 1 diabetes development and insulin resistant parental diabetes, namely type 2 and gestational diabetes. We aimed to evaluate and synthesize this evidence, to delineate risk indicators for early detection and, potentially, for prevention. Methods We conducted a systematic review and meta-analyses for studies assessing associations between gestational diabetes, maternal type 2 diabetes, and/or paternal type 2 diabetes, with offspring type 1 diabetes. We searched Embase, PubMed/Medline, Cochrane and Scopus from inception to April 30, 2025, without language restrictions. We assessed study quality, extracted summary data from published reports, and calculated pooled estimates using random effects models. Study Registration: ResearchRegistry (reviewregistry1606; April 27, 2023). Findings Across 3205 studies screened, 18 were relevant, 11 of which we rated as moderate-to-high quality. Fifteen studies examined gestational diabetes, eight maternal type 2 diabetes, and seven paternal type 2 diabetes. Compared to no maternal diabetes, gestational diabetes was associated with a 94% risk increase for offspring type 1 diabetes (pooled OR 1·94, 95% CI 1·51–2·49; I 2 = 86·9%). Compared to no paternal diabetes, paternal type 2 diabetes was associated with a 77% risk increase (pooled OR 1·77, 95% CI 1·17–2·69; I 2 = 55·2%). The point estimate for maternal type 2 diabetes suggested the possibility of association with offspring type 1 diabetes but was inconclusive (pooled estimate 1·87, 95% CI 0·94–3·75; I 2 = 71·9%). Interpretation Gestational diabetes and paternal type 2 diabetes are offspring type 1 diabetes risk indicators. Including these conditions in family history assessments for patients with compatible symptoms may facilitate earlier diagnosis and reduce complications such as diabetic ketoacidosis. Funding None.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".