Interventions designed to reduce gestational weight gain can reduce the incidence of gestational diabetes: a systematic review and meta-analysis
Bibliographic record
Abstract
Excessive gestational weight gain (GWG) increases the risk of gestational diabetes mellitus (GDM). Many interventions have been designed to reduced GWG. However, the effect on GDM is still unknown. This systematic review (SLR) aimed to (i) evaluate the impact of interventions designed to prevent excessive GWG on the incidence of GDM, and (ii) examine if effects differ by geographical region and body mass index (BMI). A SLR of randomised controlled trials (RCTs) was conducted without date limits using seven international databases and three Chinese databases. RCTs that reported a primary/secondary aim to reduce excessive GWG and the incidence of GDM were considered. Two authors independently identified and assessed the included studies. Meta-analysis data are reported as risk ratio (RR) for GDM incidence with interventions covering diet, physical activity (PA) and lifestyle (diet plus PA).Of 20,517 manuscripts screened, 45 were included and 37 were included in the meta-analysis (n=12,942). Diet interventions reduced the risk of GDM by 44% (RR: 0.56, 95% CI: 0.36-0.87, p=0.009), while PA interventions reduced the risk by 38% (RR: 0.62, 95% CI: 0.50-0.78). Both lifestyle interventions and BMI did not significantly alter the risk. PA interventions from Southern Europe reduced GDM risk by 37% (RR: 0.63, 95% CI: 0.50, 0.80). Both diet and lifestyle interventions conducted in Asia resulted in a 62% (RR: 0.38, 95% CI: 0.24, 0.59) and 32% (RR: 0.68, 95% CI: 0.54, 0.86) reduction in GDM, respectively. Interventions designed to prevent excessive GWG can reduce the risk of GDM. Regional differences indicate that other factors possibly physiological and/or behavioural responses to intervention type must be taken into consideration when planning GDM prevention strategies, certainly, the one size fits all approach is not supported.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 teacher head, 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".