First-Trimester Non-Invasive Prediction of Preterm Birth Using Cell-Free DNA Fragmentomics
Journal:
bioRxiv
Published Date:
Jul 11, 2026
Abstract
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. Methods. A nested case-control study was conducted within a prospective 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 ength end motif were evaluated for PTB prediction. Machine learning classifiers were developed in a training cohort (n = 228, 65 PTB vs 163TB) and tested in a validation cohort (n = 58, 17 PTB vs 41 TB). Results. Among the five 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 validation cohort, achieving an AUC of 0.970 (95% CI, 0.912 to 1.000). At a specificity >90%, the model achieved a sensitivity of 94% (95% CI, 78 to 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, without additional blood collection or sequencing, thereby extending the clinical utility of existing prenatal screening infrastructure.
