Integrated cell-free DNA and omics genetic scores for early detection of gestational diabetes: evidence from a nationwide multicenter study

Clinical Snapshot

Key Findings

• P Value: Not reported for primary diagnostic accuracy measures

• Effect Size: AUC 86.82-87.19 for integrated multi-modal score

• Primary Outcome: Early detection of gestational diabetes mellitus at 10-12 weeks gestation

• Nnt Or Sensitivity: Sensitivity 70%, Specificity 89%, identifies 78% of future GDM cases with 18% false-positive rate

• Confidence Interval: Not reported in abstract

Clinical Application

Requires specialized laboratory capabilities for cfDNA analysis and genomic imputation, may be resource-intensive Could complement existing Australian GDM screening protocols, though validation in Australian population needed. May require TGA approval for clinical implementation and PBS consideration for cost-effectiveness Pregnant women at 9-12 weeks gestation, particularly those at high risk for gestational diabetes

Abstract

BACKGROUND: Gestational diabetes mellitus (GDM) affects 15.6% of pregnancies globally, with Vietnam exhibiting one of the highest prevalences at 21%. Current diagnostic approaches at 24-28 weeks limit early intervention opportunities. We developed a multi-modal machine learning framework integrating cell-free DNA (cfDNA) structural features and genetic information for early GDM prediction at 10-12 weeks of gestation in Vietnamese women. METHODS: We analyzed blood samples from 1,086 pregnant women (435 GDM cases, 651 controls) collected at 9-12 weeks. Two parallel analytical pathways were employed: cfDNA profiling extracting cfDNA-specific features (fragment length, end motifs, GC content, nucleosome patterns), and whole-genome imputation generating predictions for ∼19,000 omics traits. Component scores were developed using TabPFN classifier and integrated via logistic regression into a unified master score. RESULTS: Genome-wide analysis identified five omics traits with significant GDM associations: HSD11B1, NEK7, COMMD10, KLRC4, and OCEL1. Component score optimization revealed distinct patterns-cfDNA scores peaked at 200 features (AUC = 71.53), while genetics-based scores improved with up to 2,000 omics traits (AUC = 77.21). The final master score, integrating three components (gbSC2000, gbSCBH, cfSC200), achieved AUCs of 86.82-87.19 across validation cohorts with 70% sensitivity and 89% specificity. Addition-deletion analysis confirmed that both cfDNA and genetic components provided essential, non-redundant contributions. CONCLUSIONS: This multi-modal framework demonstrates superior performance compared to single-biomarker approaches, enabling risk stratification from very low (4% GDM prevalence) to very high risk (90% prevalence). At the cutoff 0.4, the model identifies 78% of future GDM cases at 10-12 weeks while maintaining an 18% false-positive rate, potentially enabling early interventions to prevent GDM development and associated complications.

References

1. 1.Dao, V. N., Tran, N. T., Vo, T. S., Le, H. T., Nguyen, T. H., Nguyen, Q. H. V., ... Giang, H. (2026). Integrated cell-free DNA and omics genetic scores for early detection of gestational diabetes: evidence from a nationwide multicenter study. The Journal of Maternal-Fetal & Neonatal Medicine, 1-12. https://doi.org/10.1080/14767058.2026.2670860