ArticleAJOG global reports2026
First-trimester non-invasive prediction of preterm birth using cell-free DNA fragmentomics.
Article in AJOG global reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Spontaneous preterm birth (PTB) remains a leading cause of neonatal morbidity and mortality, yet accurate prediction in asymptomatic women during early gestation remains a critical unmet clinical need. Cell-free DNA (cfDNA) obtained through the same maternal blood collection used for routine first-trimester non-invasive prenatal testing (NIPT) represents an underutilized substrate for early PTB risk identification. Objective: To develop and validate a cell-free DNA (cfDNA) fragmentomic classifier for the early prediction of spontaneous preterm birth (PTB) using routine first-trimester non-invasive prenatal testing (NIPT) data. Study design: A case-control study was conducted within a multicenter Vietnamese cohort comprising 286 pregnancies, including 82 spontaneous PTB cases and 204 term controls. Maternal plasma cfDNA collected during routine first-trimester NIPT (median gestational age, 12 weeks) was sequenced to a depth of approximately 20 million reads per sample. Five fragmentomic feature categories including copy number alterations, end-motif composition, nucleosome distance, fragment length, and joint fragment-length × end-motif were evaluated for PTB prediction. Machine learning classifiers were developed in a training cohort (n=228, 65 PTB vs 163 TB) and tested in an internal held-out validation cohort (n=58, 17 PTB vs 41 TB). Results: Among the 5 fragmentomic feature classes evaluated, 4-mer end-motif (EM) profiles exhibited the most pronounced differences between PTB and term control samples. Consistent with these findings, the EM-based classifier demonstrated the highest discriminative performance in the internal validation cohort, achieving an AUC of 0.970 (95% CI, 0.912-1.000). At a specificity >90%, the model achieved a sensitivity of 94% (95% CI, 78%-100%). Conclusion: These findings demonstrate that cfDNA EM signatures derived from routine first-trimester NIPT can accurately identify pregnancies at risk of spontaneous preterm birth, using only a routine maternal blood sample, supporting the potential to extend the clinical utility of existing prenatal screening infrastructure pending prospective validation at production sequencing depths.
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