First-trimester non-invasive prediction of preterm birth using cell-free DNA fragmentomics.

Authors

My-Diem Nguyen Pham, Minh-Tam Thi Phan, Nhat-Thang Tran, Ta-Son Vo, Hong-Thinh Le, Thu-Ha Thi Nguyen, Quoc-Huy Vu Nguyen, Minh-Thi Thi Ha, Tam Minh Le, Diem-Tuyet Thi Hoang, Khanh-Trang Nguyen Huynh, Nhan Viet Nguyen, Chuong Canh Nguyen, Thuong Chi Bui, Xuan Thanh Nguyen, Sa Viet Le, Vinh Dinh Tran, My-Nhi Ba Nguyen, Thong Van Nguyen, Tuyet-Anh Thi Nguyen, Ba Phuoc Hoang, Trong Van Nguyen, Thuy-Ai Thuy Nguyen, Toa Tri Nguyen, Thang Duc Duong, Cuong Huy Pham, Kim-Oanh Thi Luong, Cuong Ngoc Dao, Khanh Van Hoang, Thu-Thanh Thi Huynh, Khuong Manh Nguyen, Son-Tra Thi Tran, Hoanh Trung Tran, Son Canh Nguyen, Thuy Dinh Tran, Phương Thi Lan Nguyen, Thanh Viet Pham, Kong Chi Pham, Minh Doan Thai, Thanh-Thuy Thi Do, Hoa Thi Dao, Yen-Linh Thi Van, Sang Hung Tang, Hoai-Nghia Nguyen, Minh-Duy Phan, Hoa Giang, Cuong Thach Nguyen, Quang Thinh Trac

Published in

AJOG global reports. Volume 6. Issue 4. Pages 100692. Epub Sep 05, 2026.

Abstract

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.
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.
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).
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%).
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.

PMID:
42859453
Bibliographic data and abstract were imported from PubMed on 11 Oct 2026.

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