Microorganisms· 2026Q1
Cervical Microbiota Dysbiosis as a Driver of Cervical Incompetence
- 0citations
- Q1SCImago
- 2026year
Short summary
Cervical incompetence (CIC) is linked to dysbiosis in the endocervical microbiota, characterized by increased alpha-diversity and reduced Actinobacteria and Firmicutes, with a machine learning model achieving 83.3% accuracy in diagnosis using 10 microbial biomarkers.
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Key points
- Cervical incompetence (CIC) is associated with increased endocervical microbial alpha-diversity.
- CIC patients show decreased relative abundances of Actinobacteria and Firmicutes compared to healthy controls.
- A machine learning model based on 10 microbial biomarkers achieved 83.3% diagnostic accuracy for CIC.
- Lactobacillus and Gardnerella species were identified as pivotal driver taxa contributing to dysbiosis.
AI-generated from the title and abstract; the full text is not read.
Abstract
Cervical incompetence (CIC) represents a significant cause of preterm birth globally. Clinical symptoms of CIC are typically delayed until mid-to-late pregnancy, thereby imposing challenges in CIC diagnosis. There is evidence that the microbiota contributes pivotal roles in reproductive health. By comparing the microbial structure and assembly between 30 CIC patients and 30 healthy controls, we found a close correlation between endocervical microbiota dysbiosis and CIC pathogenesis. The CIC group exhibited increased ɑ-diversity and significant alterations in microbial community composition, characterized by decreased relative abundances of Actinobacteria and Firmicutes. Functional characterization revealed a broad reduction in microbial metabolic potential in patients with CIC. NetShift network analysis identified 22 pivotal driver taxa, including Lactobacillus and Gardnerella species that triggered the progression to dysbiosis. Notably, a machine learning diagnostic model achieved 83.3% accuracy in clinical diagnosis based on the abundances of 10 biomarkers. These findings provide critical insights into microbial-mediated regulation of the cervical microenvironment by elucidating how specific microbial members and their metabolic potentials contribute to CIC pathogenesis. The diagnosis model offers a new paradigm for diagnosis using microbial biomarkers and reveals potential therapeutic targets via the intervention of driver taxa, thereby establishing innovative strategies to improve pregnancy outcomes.
The authors' abstract, as published at the source. Microorganisms, 2026 · DOI ↗
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MicrobiologyImmunology and Microbiology