Effectiveness of pre-pregnancy lifestyle in preventing gestational diabetes mellitus—a systematic review and meta-analysis of 257,876 pregnancies
Bibliographic record
Abstract
BACKGROUND: Gestational Diabetes Mellitus (GDM) is hyperglycaemia first detected during pregnancy. Globally, GDM affects around 1 in 6 live births (up to 1 in 4 in low- and middle-income countries- LMICs), thus, urgent measures are needed to prevent this public health threat. OBJECTIVE: To determine the effectiveness of pre-pregnancy lifestyle in preventing GDM. METHODS: We searched MEDLINE, Web of science, Embase and Cochrane central register of controlled trials. Randomized control trials (RCTs), case-control studies, and cohort studies that assessed the effect of pre-pregnancy lifestyle (diet and/or physical activity based) in preventing GDM were included. Random effects model was used to calculate odds ratio (OR) with 95% confidence interval. The Cochrane ROB-2 and the Newcastle-Ottawa Scale were used for assessing the risk of bias. The protocol was registered in PROSPERO (ID: CRD42020189574) RESULTS: Database search identified 7935 studies, of which 30 studies with 257,876 pregnancies were included. Meta-analysis of the RCTs (N = 5; n = 2471) in women who received pre-pregnancy lifestyle intervention showed non-significant reduction of the risk of developing GDM (OR 0.76, 95% CI: 0.50-1.17, p = 0.21). Meta-analysis of cohort studies showed that women who were physically active pre-pregnancy (N = 4; n = 23263), those who followed a low carbohydrate/low sugar diet (N = 4; n = 25739) and those women with higher quality diet scores were 29%, 14% and 28% less likely to develop GDM respectively (OR 0.71, 95% CI: 0.57, 0.88, p = 0.002, OR 0.86, 95% CI: 0.68, 1.09, p = 0.22 and OR 0.72, 95% CI 0.60-0.87, p = 0.0006). CONCLUSION: This study highlights that some components of pre-pregnancy lifestyle interventions/exposures such as diet/physical activity-based preparation/counseling, intake of vegetables, fruits, low carbohydrate/low sugar diet, higher quality diet scores and high physical activity can reduce the risk of developing gestational diabetes. Evidence from RCTs globally and the number of studies in LMICs are limited, highlighting the need for carefully designed RCTs that combine the different aspects of the lifestyle and are personalized to achieve better clinical and cost effectiveness.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".