ArticleNPJ biofilms and microbiomes2026
Machine learning and the role of the vaginal and fecal microbiome in miscarriage: a matched case-control study.
Article in NPJ biofilms and microbiomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Inferred functional microbiome features are associated with clinical trajectories of low-grade cervical lesions.BMC microbiology · 2026Article
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
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Authors and funding
14 authors.
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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.
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