Apelin levels in pregnant women with and without gestational diabetes mellitus: a collaborative systematic review and meta-analysis
Bibliographic record
Abstract
Aims: This systematic review and meta-analysis investigated maternal apelin levels in pregnant women with and without GDM. Secondary outcomes were glucose- and lipid-related results.Methods: Databases including PubMed, Embase, Cochrane Library, LILACS, CNKI, and Wang Fang were searched. The methodological quality of included studies was evaluated with the Newcastle-Ottawa Scale. Mean differences (MDs) or standardized MDs (SMDs) with their 95% confidence intervals (CIs) were evaluated. Random effect model analyses were carried out and heterogeneity with the I2 and Tau2 statistics.Results: Fourteen observational studies (sample size: 1033 women with GDM and 1053 for control women) with a low or moderate risk of bias were included in the analysis. During the second half of pregnancy, maternal apelin estimate was significantly higher in women with GDM (SMD = 0.64; 95% CI: 0.03 to 1.25), as well as insulin (SMD = 1.41% CI: 0.84 to 1.99), glucose (SMD = 1.56; 95% CI 1.20 to 1.91), glycated hemoglobin (SMD = 1.11, 95% CI: 0.69 to 1.54), HOMA-IR (MD = 2.25; 95%CI: 1.51 to 2.98), BMI (MD = 0.80 kg/m2, 95%CI: 0.52 to 1.08), total cholesterol (SMD = 0.42, 0.12 to 0.73), LDL-cholesterol (SMD = 0.63, 95%CI: 0.23 to 1.02), and triglycerides (SMD = 0.40, 95%CI: 0.19 to 0.61) as compared to control women. There was heterogeneity between studies as evidence by high I2 values. Meta-regression analysis indicated statistically significant regression coefficients for age of women, glucose and total cholesterol.Conclusions: GDM was associated with increased circulating apelin, insulin, glucose, glycated hemoglobin, total cholesterol, LDL-cholesterol levels, and HOMA-IR index.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".