Early Pregnancy Biomarker Meta-Analysis for Gestational Diabetes Prediction

Wenhui Li's 2026 meta-analysis on figshare synthesizes data from 12 studies with sample sizes ranging from 75 to 41,786 participants. It evaluates the diagnostic accuracy of early pregnancy biomarkers like PAPP-A and acylcarnitines for predicting gestational diabetes mellitus, reporting pooled sensitivity of 81.6% and specificity of 93.4%.

Use Cases

• Analyze the pooled sensitivity and specificity metrics across studies to benchmark biomarker performance for GDM prediction.
• Compare the diagnostic odds ratio (DOR) and area under the curve (AUROC) values for different biomarker categories like inflammatory markers or metabolomic markers.
• Assess the reported standardized mean difference (SMD) for Pregnancy-associated plasma protein-A (PAPP-A) levels between women who developed GDM and those who did not.
• Investigate the substantial heterogeneity (I²=94-97%) observed across the included studies in the meta-analysis.

Strengths

• Meta-analysis synthesizes data from 12 distinct studies.
• Reports specific pooled accuracy metrics: sensitivity 81.6%, specificity 93.4%, DOR 73.84, and AUROC 0.881.
• Includes studies with large sample sizes, up to 41,786 participants.

Limitations

• High statistical heterogeneity across studies (I²=94-97%) indicates inconsistent results.
• Limited data on integrated multi-marker panels, as noted in the conclusions.
• The dataset is a 19.6 KB document (DOCX), containing summary tables rather than raw, analyzable data rows.

Provenance

Source

figshare, authored by Wenhui Li.

Collection Method

Systematic review and meta-analysis of studies from PubMed, Web of Science, Embase, and Cochrane Library.

Time Range

Studies from 1980 to 2024 were included in the review.

Freshness

Literature search covered studies from inception up to December 31, 2024; dataset last updated March 26, 2026.

Geography

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