The Salivary Microbiota Diagnostic Model for Laryngopharyngeal Reflux Based on Microbiome and Machine Learning
10.3969/j.issn.1006-7299.2024.03.002
- VernacularTitle:基于微生物组学和机器学习构建咽喉反流唾液菌群诊断模型
- Author:
Linxin ZHOU
1
,
2
,
3
;
Longlong YIN
;
Xiaohuan CUI
;
Xinxin BI
;
Yanping ZHANG
;
Xingwang JIANG
;
Lina LI
Author Information
1. 河北北方学院(张家口 075031)
2. 中国人民解放军总医院第六医学中心耳鼻咽喉头颈外科医学部派驻解放军总医院第八医学中心耳鼻咽喉头颈外科
3. 国家耳鼻咽喉疾病临床医学中心
- Keywords:
Laryngopharyngeal reflux(LPR);
Salivary microbiota;
16S rRNA sequencing;
Disease di-agnosis model
- From:
Journal of Audiology and Speech Pathology
2024;32(3):200-205
- CountryChina
- Language:Chinese
-
Abstract:
Objective To study the possibility of salivary microbiota model to diagnose laryngopharyngeal re-flux(LPR).Methods A case-control study was applied to enroll 34 patients as case group who showed significant efficacy after 8 weeks of proton pump inhibitor treatment from February 2022 to November 2022.And 47 healthy volunteers matched by age,gender and body mass index with the case group were enrolled as the control group.Their salivary samples were collected before medication,and the salivary microbiota was detected by 16S rDNA se-quencing.Bioinformatics analysis was conducted on the sequencing results to compare species differences at the ge-nus level.A total of 24 patients and 33 cases in the control group were selected as train set and the rest as test set.Random forest method was used to classify data and ten fold cross validation was applied to select the optimal bacte-rial genus combination to construct a diagnostic model.The probability of disease(POD)index was calculated and receiver operating characteristic curve(ROC)was used to evaluate the diagnostic model in diagnosis of LPR.SPSS 18.0 software was utilized for statistical analysis.Results Compared with the control group,there was a statistical difference in the relative abundance of 22 genera in saliva between the case group and the control group(P＜0.05).A diagnostic model consisting of 6 genera was constructed,namely Lactobacillus,Novosphingobium,Bacillus,Pseudoalteromonas,Ralstonia and Phocaeicola.The area under the ROC curve of the test set was 0.843,the sensi-tivity of the diagnostic model was 60.0％,the specificity was 87.71％,and the Kappa value was 0.470.Conclusion The bacterial combination diagnostic model constructed from saliva microbiota based on microbiome and machine learning can effectively distinguish LPR patients from healthy individuals,which has potential clinical application value.
