#AI reads Urine# Urinary Multiomics Signatures of Preterm Premature Rupture of Membranes

Published 16 December, 2025

This study focused on patients with preterm premature rupture of membranes (PPROM) and healthy pregnant women. By collecting urine samples from both groups, it analyzed the urinary microbial community using 16S rRNA gene sequencing and examined urinary metabolites via nuclear magnetic resonance technology, aiming to explore PPROM-related characteristics for understanding its pathogenesis and developing diagnostic methods. The research found that the urine of healthy pregnant women was dominated by Lactobacillus, while in PPROM patients, the level of Lactobacillus decreased, and bacteria prone to causing infections (such as Hoylesella and Escherichia coli) increased with a more complex microbial community. In terms of metabolites, PPROM patients had elevated levels of substances like valine and isoleucine in their urine, and reduced levels of substances including hippurate and formate; most of these differential metabolites were associated with glucose metabolism and amino acid metabolism. Additionally, there was a correlation between specific pathogenic bacteria and metabolites in patients, and their interaction influenced the occurrence of PPROM. In terms of diagnostic value, the accuracy of diagnosing PPROM by combining microbial and metabolite data reached 97.8%, which was much higher than using either type of data alone. This study fills the research gap regarding the relationship between urinary microbes/metabolites and PPROM, confirms that PPROM is associated with disorders in multiple aspects such as microbes, metabolism, and inflammation (rather than being a simple infection), and provides a direction for the subsequent development of non-invasive diagnostic methods and prevention/treatment strategies.

 

J Proteome Res. 2025 Nov 18. doi: 10.1021/acs.jproteome.5c00727.

 

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

 

For earlier AI Reads Urine articles:

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/

 

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