Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study
Abstract Intra-amniotic infection (IAI) is a major cause of preterm birth with a poor perinatal prognosis. We aimed to determine whether analyzing vaginal microbiota can evaluate the risk of chorioamnionitis (CAM) in preterm labor cases. Vaginal discharge samples were collected from 83 pregnant wome...
Guardado en:
| Autores principales: | , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/9a6f2a12ca934d23ac11f2a66fd638f7 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:9a6f2a12ca934d23ac11f2a66fd638f7 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:9a6f2a12ca934d23ac11f2a66fd638f72021-12-02T18:14:40ZVaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study10.1038/s41598-021-98587-42045-2322https://doaj.org/article/9a6f2a12ca934d23ac11f2a66fd638f72021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98587-4https://doaj.org/toc/2045-2322Abstract Intra-amniotic infection (IAI) is a major cause of preterm birth with a poor perinatal prognosis. We aimed to determine whether analyzing vaginal microbiota can evaluate the risk of chorioamnionitis (CAM) in preterm labor cases. Vaginal discharge samples were collected from 83 pregnant women admitted for preterm labor. Based on Blanc’s classification, the participants were divided into CAM (stage ≥ II; n = 46) and non-CAM (stage ≤ I; n = 37) groups. The 16S rDNA amplicons (V1–V2) from vaginal samples were sequenced and analyzed. Using a random forest algorithm, the bacterial species associated with CAM were identified, and a predictive CAM (PCAM) scoring method was developed. The α diversity was significantly higher in the CAM than in the non-CAM group (P < 0.001). The area under the curve was 0.849 (95% confidence interval 0.765–0.934) using the PCAM score. Among patients at < 35 weeks of gestation, the PCAM group (n = 22) had a significantly shorter extended gestational period than the non-PCAM group (n = 25; P = 0.022). Multivariate analysis revealed a significant difference in the frequency of developmental disorders in 3-year-old infants (PCAM, 28%, non-PCAM, 4%; P = 0.022). Analyzing vaginal microbiota can evaluate the risk of IAI. Future studies should establish appropriate interventions for IAI high-risk patients to improve perinatal prognosis.Daichi UrushiyamaEriko OhnishiWataru SudaMasamitsu KurakazuChihiro KiyoshimaToyofumi HirakawaKohei MiyataFusanori YotsumotoKazuki NabeshimaTakashi SetoueShinichiro NagamitsuMasahira HattoriKenichiro HataShingo MiyamotoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Daichi Urushiyama Eriko Ohnishi Wataru Suda Masamitsu Kurakazu Chihiro Kiyoshima Toyofumi Hirakawa Kohei Miyata Fusanori Yotsumoto Kazuki Nabeshima Takashi Setoue Shinichiro Nagamitsu Masahira Hattori Kenichiro Hata Shingo Miyamoto Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study |
| description |
Abstract Intra-amniotic infection (IAI) is a major cause of preterm birth with a poor perinatal prognosis. We aimed to determine whether analyzing vaginal microbiota can evaluate the risk of chorioamnionitis (CAM) in preterm labor cases. Vaginal discharge samples were collected from 83 pregnant women admitted for preterm labor. Based on Blanc’s classification, the participants were divided into CAM (stage ≥ II; n = 46) and non-CAM (stage ≤ I; n = 37) groups. The 16S rDNA amplicons (V1–V2) from vaginal samples were sequenced and analyzed. Using a random forest algorithm, the bacterial species associated with CAM were identified, and a predictive CAM (PCAM) scoring method was developed. The α diversity was significantly higher in the CAM than in the non-CAM group (P < 0.001). The area under the curve was 0.849 (95% confidence interval 0.765–0.934) using the PCAM score. Among patients at < 35 weeks of gestation, the PCAM group (n = 22) had a significantly shorter extended gestational period than the non-PCAM group (n = 25; P = 0.022). Multivariate analysis revealed a significant difference in the frequency of developmental disorders in 3-year-old infants (PCAM, 28%, non-PCAM, 4%; P = 0.022). Analyzing vaginal microbiota can evaluate the risk of IAI. Future studies should establish appropriate interventions for IAI high-risk patients to improve perinatal prognosis. |
| format |
article |
| author |
Daichi Urushiyama Eriko Ohnishi Wataru Suda Masamitsu Kurakazu Chihiro Kiyoshima Toyofumi Hirakawa Kohei Miyata Fusanori Yotsumoto Kazuki Nabeshima Takashi Setoue Shinichiro Nagamitsu Masahira Hattori Kenichiro Hata Shingo Miyamoto |
| author_facet |
Daichi Urushiyama Eriko Ohnishi Wataru Suda Masamitsu Kurakazu Chihiro Kiyoshima Toyofumi Hirakawa Kohei Miyata Fusanori Yotsumoto Kazuki Nabeshima Takashi Setoue Shinichiro Nagamitsu Masahira Hattori Kenichiro Hata Shingo Miyamoto |
| author_sort |
Daichi Urushiyama |
| title |
Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study |
| title_short |
Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study |
| title_full |
Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study |
| title_fullStr |
Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study |
| title_full_unstemmed |
Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study |
| title_sort |
vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/9a6f2a12ca934d23ac11f2a66fd638f7 |
| work_keys_str_mv |
AT daichiurushiyama vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT erikoohnishi vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT watarusuda vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT masamitsukurakazu vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT chihirokiyoshima vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT toyofumihirakawa vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT koheimiyata vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT fusanoriyotsumoto vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT kazukinabeshima vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT takashisetoue vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT shinichironagamitsu vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT masahirahattori vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT kenichirohata vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy AT shingomiyamoto vaginalmicrobiomeasatoolforpredictionofchorioamnionitisinpretermlaborapilotstudy |
| _version_ |
1718378409649766400 |