Evidence map›Paper›PMID 39604824›Full record

ArticleBMC microbiology2024

Analysis of vaginal microbiota during postpartum and postmenopausal periods based on metagenomics.

Xiaocui Li, Zhan Zhang, Huihui Bai, Zhaohui Liu

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Article in BMC microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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0cells of the map it votes in
7citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xiaocui LiBeijing Obstetrics and Gynecology Hospital, Beijing Maternal and Child Health Care Hospital, Capital Medical University, Beijing, 100026, China.
Zhan ZhangBeijing Obstetrics and Gynecology Hospital, Beijing Maternal and Child Health Care Hospital, Capital Medical University, Beijing, 100026, China.
Huihui BaiBeijing Obstetrics and Gynecology Hospital, Beijing Maternal and Child Health Care Hospital, Capital Medical University, Beijing, 100026, China.
Zhaohui LiuBeijing Obstetrics and Gynecology Hospital, Beijing Maternal and Child Health Care Hospital, Capital Medical University, Beijing, 100026, China. liuzhaohui@ccmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPostmenopausal and the postpartum periods are essential physiological phases that result in low estrogen levels in women; however, they are important to female reproductive health. Traditional as well as new detection methods (such as 16 S RNA sequencing) have limitations in detecting the composition of vaginal microbiota. Therefore, in this study, we used metagenomic detection technology to study the composition of vaginal microbiota in postmenopausal and postpartum women. Six women were randomly selected from each group (healthy women of childbearing age, postmenopausal group, and postpartum) for vaginal microecology, composition, α-diversity, linear discriminant analysis effect size (LEfSe), and Comprehensive Antibiotic Resistance Database (CARD) analyses.

resultsWe discovered that Lactobacillus dominance disappeared in postpartum and postmenopausal group women and that diversity increased. However, the proportions of Atopobium vaginae, Escherichia coli, and Streptococcus agalactiae significantly increased. Diversity was the highest in the postpartum period, with a significant increase in the proportions of A. vaginae, Gardnerella vaginalis, Prevotella, and occasionally, Chlamydia trachomatis. Linear discriminant analysis effect size analysis revealed that Lactobacillus crispatus and L. iners enrichment in the postpartum and menopausal periods was much lower than that in the childbearing age group. CARD analysis revealed that ABC-F ATP-binding cassette ribosomal protection protein subfamily gene abundance was significantly lower in the menopausal than in the childbearing age group, whereas the gimA family macrolide glycosyltransferase gene abundance was significantly higher.

conclusionsThe dominance of vaginal Lactobacillus in postpartum and menopausal women disappeared, while their diversity increased. In addition, the reproductive tract of postpartum women was susceptible to invasion by pathogenic microorganisms, which deserves clinical attention. When menopausal women receive treatment for vaginal infections, the likelihood that certain bacterial communities develop antibiotic resistance through ribosomal protection mechanisms is lower than that of women in the childbearing age, while the possibility of developing resistance to macrolides through glycosylation may increase. This, however, requires further research.

Indexed as

BacteriaMetagenomicsMicrobiotaPostmenopausePostpartum PeriodVaginaAdultFemaleHumansMiddle AgedRNA, Ribosomal, 16SRNA, Ribosomal, 16SCARD analysisLEfSe analysisMetagenomicsPostmenopausalPostpartumVaginal microbiota

Identifiers

PMID39604824
PMCPMC11600617

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.