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基于唾液中炎症介质和微生物谱筛查牙周炎的诊断模型

Diagnostic Models for Screening of Periodontitis with Inflammatory Mediators and Microbial Profiles in Saliva.

作者信息

Lee Jungwon, Lee Jun-Beom, Song Hyun-Young, Son Min Jung, Li Ling, Rhyu In-Chul, Lee Yong-Moo, Koo Ki-Tae, An Jung-Sub, Kim Jin Sup, Kim Eunkyung

机构信息

One-Stop Specialty Center, Seoul National University Dental Hospital, Seoul 03080, Korea.

Department of Periodontology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul 03080, Korea.

出版信息

Diagnostics (Basel). 2020 Oct 14;10(10):820. doi: 10.3390/diagnostics10100820.

Abstract

This study aims to investigate and assess salivary biomarkers and microbial profiles as a means of diagnosing periodontitis. A total of 121 subjects were included: 28 periodontally healthy subjects, 24 with Stage I periodontitis, 24 with Stage II, 23 with Stage III, and 22 with Stage IV. Salivary proteins (including active matrix metalloproteinase-8 (MMP-8), pro-MMP-8, total MMP-8, C-reactive protein, secretory immunoglobulin A) and planktonic bacteria (including , , , , , , , , , , , , , , and ) were measured from salivary samples. The performance of the diagnostic models was assessed by receiver operating characteristics (ROCs) and area under the ROC curve (AUC) analysis. The diagnostic models were constructed based on the subjects' proteins and/or microbial profiles, resulting in two potential diagnosis models that achieved better diagnostic powers, with an AUC value > 0.750 for the diagnosis of Stages II, III, and IV periodontitis (Model PA-I; AUC: 0.796, sensitivity: 0.754, specificity: 0.712) and for the diagnosis of Stages III and IV periodontitis (Model PA-II; AUC: 0.796, sensitivity: 0.756, specificity: 0.868). This study can contribute to screening for periodontitis based on salivary biomarkers.

摘要

本研究旨在调查和评估唾液生物标志物及微生物谱,作为诊断牙周炎的一种方法。共纳入121名受试者:28名牙周健康受试者、24名患有I期牙周炎者、24名患有II期牙周炎者、23名患有III期牙周炎者以及22名患有IV期牙周炎者。从唾液样本中检测唾液蛋白(包括活性基质金属蛋白酶-8(MMP-8)、前MMP-8、总MMP-8、C反应蛋白、分泌型免疫球蛋白A)和浮游细菌(包括 , , , , , , , , , , , , , 以及 )。通过受试者工作特征(ROC)和ROC曲线下面积(AUC)分析评估诊断模型的性能。基于受试者的蛋白质和/或微生物谱构建诊断模型,产生了两个具有更好诊断能力的潜在诊断模型,对于II、III和IV期牙周炎的诊断,AUC值>0.750(模型PA-I;AUC:0.796,敏感性:0.754,特异性:0.712),对于III和IV期牙周炎的诊断(模型PA-II;AUC:0.796,敏感性:0.756,特异性:0.868)。本研究有助于基于唾液生物标志物筛查牙周炎。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/775f/7602207/b4579b4bd870/diagnostics-10-00820-g001.jpg

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