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验证和确认用于口腔健康的预测性唾液生物标志物。

Validation and verification of predictive salivary biomarkers for oral health.

机构信息

Section of Periodontology and Dental Prevention, Division of Oral Diseases, Department of Dental Medicine, Karolinska Institutet, Alfred Nobels Allé 8, 14104, Huddinge, Stockholm, Sweden.

Hahn-Schickard, Georges-Koehler-Allee 103, 79110, Freiburg, Germany.

出版信息

Sci Rep. 2021 Mar 19;11(1):6406. doi: 10.1038/s41598-021-85120-w.

Abstract

Oral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.

摘要

口腔健康不仅与口腔疾病有关,还与全身健康直接相关,因此,早期、准确地描述口腔健康状况至关重要。有几种唾液生物标志物可作为牙龈炎和牙周炎的候选标志物,这两种疾病都是主要的口腔健康威胁,会影响牙龈。在将其转化为椅旁诊断和个性化监测之前,需要对其进行验证和确认,以评估其作为健康、牙龈炎和牙周炎状态的区分标志物的潜力。我们旨在使用高灵敏度 ELISA 测量 10 种候选标志物,这些候选标志物来自三个组的 127 名个体:牙周炎组(60 名)、牙龈炎组(31 名)和健康组(36 名)。统计方法包括单变量统计检验、具有相应曲线下面积 (AUC) 的接收者操作特征曲线 (ROC) 和分类和回归树 (CART) 分析。主要结果是,多种生物标志物检测的组合而不是单一生物标志物的使用,可以为牙龈炎与健康组提供预测准确率>90%;牙周炎与健康组和牙周炎与牙龈炎组的预测准确率为 100%。此外,与单一生物标志物相比,MMP-8、MMP-9 和 TIMP-1 的生物标志物比值也被证明是强大的区分值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd7f/7979790/1fad1e4d58cc/41598_2021_85120_Fig1a_HTML.jpg

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