Machine learning and the role of the vaginal and fecal microbiome in miscarriage: a matched case-control study

Abstract Miscarriage occurs in approximately 15% of all pregnancies, and recent studies have suggested a potential role of the microbiome. A nested case-control study from the Swedish Maternal Microbiome cohort was conducted, including 34 participants who sent at least one vaginal or fecal microbiome sample and questionnaire data before miscarrying ( n = 34), and matched controls ( n = 105 for regression models, n = 27 for machine learning models). Non-vaccine type HPV (aOR 3.95, 95%CI 1.04–15.06) and vaginal microbiome with community state type (CST) II (aOR 6.52, 95%CI 1.58–26.98) or CST-IVB (aOR 4.18, 95%CI 1.08–16.18) in early pregnancy were associated with an increased risk of miscarriage. Furthermore, we explored six machine learning algorithms using 70% of the cohort for training and 30% for testing, for the prediction of miscarriage using vaginal (AUROC 85%), fecal (AUROC 81%) and questionnaire (AUROC 82%) data separately and combined (AUROC 82%). Our results highlight the urgency of HPV screening and vaccine development for women’s reproductive health. Despite limitations, including a small number of miscarriage cases, our results indicate the potential for both vaginal and fecal microbiomes in the prediction of miscarriage.