Development of a Preterm Birth Risk Prediction Model Based on CCL28 Biomarker Selection and Multidimensional Data Integration

ABSTRACT Aim This study aimed to identify potential biomarkers for preterm birth (PTB) and construct a multidimensional risk prediction model integrating clinical and proteomic data, with a focus on CCL28. Materials and Methods Pregnant women were enrolled and categorized into PTB and term birth (TB) groups. Plasma and placental samples were analyzed using OLINK proteomics, ELISA, and immunohistochemistry. Logistic regression and nomogram modeling were employed to assess predictive markers, while ROC curve analysis was used to evaluate model performance. Results Proteomic analysis revealed significantly lower CCL28 levels in PTB compared to TB ( p < 0.01), which was validated by ELISA ( p < 0.0001). Immunohistochemistry confirmed reduced CCL28 expression in PTB placental tissue. Multivariate logistic regression identified CCL28 as an independent predictor of PTB (OR = 0.150, p = 0.01). The developed nomogram, incorporating CCL28 levels and clinical variables, demonstrated strong predictive ability (AUC = 0.841). Conclusion CCL28 was identified as a key biomarker for PTB prediction. The integrated prediction model enhanced early risk assessment, providing a valuable tool for targeted clinical interventions in high‐risk pregnancies.