Abstract

Chorioamnionitis (CAM), an inflammation of the foetal membranes due to infection, is associated with preterm birth and poor perinatal prognosis. The present study aimed to determine whether CAM can be diagnosed prior to delivery based on the bacterial composition of the amniotic fluid (AF). AF samples from 79 patients were classified according to placental inflammation: Stage III (n = 32), CAM; Stage II (n = 27), chorionitis; Stage 0-I (n = 20), sub-chorionitis or no neutrophil infiltration; and normal AF in early pregnancy (n = 18). Absolute quantification and sequencing of 16S rDNA showed that in Stage III, the 16S rDNA copy number was significantly higher and the α-diversity index lower than those in the other groups. In principal coordinate analysis, Stage III formed a separate cluster from Stage 0-I, normal AF, and blank. Forty samples were classified as positive for microbiomic CAM (miCAM) defined by the presence of 11 bacterial species that were found to be significantly associated with CAM and some parameters of perinatal prognosis. The diagnostic accuracy for CAM according to miCAM was: sensitivity, approximately 94%, and specificity, 79–87%. Our findings indicate the possibility of predicting CAM prior to delivery based on the AF microbiome profile.

Details

Title
Microbiome profile of the amniotic fluid as a predictive biomarker of perinatal outcome
Author
Urushiyama, Daichi 1 ; Suda, Wataru 2 ; Ohnishi, Eriko 3 ; Araki, Ryota 4 ; Kiyoshima, Chihiro 4 ; Kurakazu, Masamitsu 4 ; Sanui, Ayako 5 ; Yotsumoto, Fusanori 4 ; Murata, Masaharu 5 ; Nabeshima, Kazuki 6 ; Shin’ichiro Yasunaga 7 ; Saito, Shigeru 8 ; Nomiyama, Makoto 9 ; Hattori, Masahira 10 ; Miyamoto, Shingo 4 ; Hata, Kenichiro 3 

 Department of Maternal-Fetal Biology, National Research Institute for Child Health and Development, Tokyo, Japan; Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, Fukuoka, Japan 
 Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan; Department of Microbiology and Immunology, Keio University School of Medicine, Tokyo, Japan 
 Department of Maternal-Fetal Biology, National Research Institute for Child Health and Development, Tokyo, Japan 
 Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, Fukuoka, Japan 
 Center for Maternal, Fetal and Neonatal Medicine, Fukuoka University Hospital, Fukuoka, Japan 
 Department of Pathology, Fukuoka University School of Medicine and Hospital, Fukuoka, Japan 
 Department of Biochemistry, Faculty of Medicine, Fukuoka University, Fukuoka, Japan 
 Department of Obstetrics and Gynecology, University of Toyama, Toyama, Japan 
 Department of Obstetrics and Gynecology, National Hospital Organization Saga Hospital, Saga, Japan 
10  Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan; Cooperative Major in Advanced Health Science, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan 
Pages
1-10
Publication year
2017
Publication date
Sep 2017
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1955052205
Copyright
© 2017. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.