Propensity score-matched cohort study investigates cffDNA levels and their associations with PTB and LBW risks, highlighting implications for pregnancy outcomes.
Objective To investigate the association between the concentration of cell-free fetal DNA (cffDNA) in the first trimester and the risks of preterm birth (PTB) and low birth weight (LBW) in a large cohort. Methods This study employed a propensity score matching (PSM) analysis to balance baseline characteristics between case and control groups. Univariate and multivariate logistic regression models were used to assess the association between cffDNA (analyzed in quartiles and as a binary variable) and the outcomes. Hierarchical regression models were built with progressive adjustment for confounders. Subgroup and threshold effect analyses were conducted to evaluate the consistency and nature of the associations. Results Before PSM, significant baseline differences were observed across multiple covariates. After 1:3 ratio PSM, 682 PTB cases were matched with 1,983 controls and 533 LBW cases with 1,579 controls, achieving statistical balance. For PTB, a consistent, significant inverse association was observed across all cffDNA quartiles. Higher cffDNA levels were associated with a reduced risk of PTB in all models, both before and after matching. For LBW, a significantinverse association was confined to the highest cffDNA quartile (Q4), indicating a threshold effect. Subgroup analyses confirmed the robustness of these associations across most demographic strata. Threshold analysis identified a significant inflection point for PTB risk both before and after PSM. Conclusion Elevated cffDNA levels are significantly associated with a reduced risk of adverse pregnancy outcomes, but the relationship patterns differ. For preterm birth, the protection is graded across all cffDNA levels, while for low birth weight, it is a threshold effect evident only at the highest concentrations. These findings suggest cffDNA may serve as a biomarker of placental health and a useful tool for risk stratification.
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Wang et al. (2026) studied this question.
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