Predicting spontaneous preterm birth: Integrating maternal vaginal and gut microbiome profiles with individual risk factors

Abstract Preterm birth remains the leading cause of neonatal morbidity and mortality globally, with rates staying unchanged despite advances in obstetric and neonatal care. Inflammation and infection are key mechanisms implicated in preterm birth, with the maternal microbiome emerging as a potential predictor and therapeutic target. This study aimed to predict spontaneous preterm birth by analysing vaginal and faecal microbiomes (collected at <20 weeks and 28–30 weeks of gestation) together with extensive questionnaire data. Leveraging the largest microbiome cohort for preterm birth to date (collected in Sweden, 2017–2021, with 132 cases of spontaneous preterm birth), we developed a machine learning-based prediction model, achieving an AUROC of 0.89. This work underscores the significant potential of early prediction for preterm birth, highlighting that accurate prediction relies on the integration of lifestyle, health status, and microbiome composition. Our results provide a pathway for developing targeted prevention strategies.