Jiayong Zheng
Xiuling Ma
Bing Zhang
Jinyang Zhang
Wenhuan Wang
Congcong Sun
Yeping Wang
Jianqiong Zheng
Haiying Chen
Jiejing Tao
Hai Wang
Fengyi Zhang
Jinfeng Wang
Hongping Zhang
aWenzhou People’s Hospital/Wenzhou Maternal and Child Health Care Hospital/The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou 325000, China
bComputational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
cUniversity of Chinese Academy of Sciences, Beijing 100049, China
More InformationCorresponding author: E-mail address: wangjf@biols.ac.cn (Jinfeng Wang);E-mail address: zjzhp@126.com (Hongping Zhang)
Publish Date:2021-01-20
Abstract
Abstract
The oral microbiota plays an important role in the development of various diseases, whereas its association with gestational diabetes mellitus (GDM) remains largely unclear. The aim of this study is to identify biomarkers from the oral microbiota of GDM patients by analyzing the microbiome of the saliva and dental plaque samples of 111 pregnant women. We find that the microbiota of both types of oral samples in GDM patients exhibits differences and significantly varies from that of patients with periodontitis or dental caries. Using bacterial biomarkers from the oral microbiota, GDM classification models based on support vector machine and random forest algorithms are constructed. The area under curve (AUC) value of the classification model constructed by combination of Lautropia and Neisseria in dental plaque and Streptococcus in saliva reaches 0.83, and the value achieves a maximum value of 0.89 by adding clinical features. These findings suggest?that certain bacteria in either saliva or dental plaque can effectively distinguish women with GDM from healthy pregnant women, which provides evidence of oral microbiome as an informative source for developing noninvasive biomarkers of GDM.Keywords: Gestational diabetes mellitus,
Oral microbiome,
Saliva,
Dental plaque,
Classification model
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