Gestational diabetes mellitus and development of intergenerational overall and subtypes of cardiovascular diseases: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: We aimed to summarize the association between gestational diabetes mellitus (GDM) and its intergenerational cardiovascular diseases (CVDs) impacts in both mothers and offspring post-delivery in existing literature. METHODS: PubMed, Embase, Web of Science, and Scopus were utilized for searching publications between January 1980 and June 2024, with data extraction and meta-analysis continuing until 31 July 2024. Based on a predefined PROSPERO protocol, studies published as full-length, English-language journal articles that reported the presence of GDM during pregnancy and its association with any CVD development post-delivery were selected. All studies were evaluated using the Newcastle-Ottawa Scale. Maximally adjusted risk estimates were pooled using random-effects meta-analysis to assess the risk ratio (RR) of GDM, and overall and subtypes of CVDs in both mothers and offspring post-delivery. RESULTS: The meta-analysis was based on 38 studies with a total of 77,678,684 participants. The results showed a 46% increased risk (RR 1.46, 95% CI 1.34-1.59) for mothers and a 23% increased risk (1.23, 1.05-1.45) for offspring of developing overall CVDs after delivery, following a GDM-complicated pregnancy. Our subgroup analysis revealed that mothers with a history of GDM faced various risks (20% to 2-fold) of developing different subtypes of CVDs, including cerebrovascular disease, coronary artery disease, heart failure, and venous thromboembolism. CONCLUSIONS: These findings underscore the heightened risk of developing various CVDs for mothers and offspring affected by GDM, emphasizing the importance of preventive measures even right after birth to mitigate the burden of CVDs in these populations.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".