Santamaria Pasquale, Jin Yi, Ghuman Mandeep, Shoaie Saeed, Spratt David, Troiano Giuseppe, Nibali Luigi
Periodontology Unit, Centre for Host Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK.
Microbial Diseases, Eastman Dental Institute, University College London, London, UK.
J Clin Periodontol. 2024 Nov;51(11):1421-1432. doi: 10.1111/jcpe.14034. Epub 2024 Aug 7.
To describe the microbiological composition of subgingival dental plaque and molecular profile of gingival crevicular fluid (GCF) of periodontal furcation-involved defects.
Fifty-seven participants with periodontitis contributed with a degree II-III furcation involvement (FI), a non-furcation (NF) periodontal defect and a periodontally healthy site (HS). Subgingival plaque was analysed by sequencing the V3-V4 region of the 16S rRNA gene, and a multiplex bead immunoassay was carried out to estimate the GCF levels of 18 GCF biomarkers. Aiming to explore inherent patterns and the intrinsic structure of data, an AI-clustering method was also applied.
In total, 171 subgingival plaque and 84 GCF samples were analysed. Four microbiome clusters were identified and associated with FI, NF and HS. A reduced aerobic microbiota (p = .01) was detected in FI compared with NF; IL-6, MMP-3, MMP-8, BMP-2, SOST, EGF and TIMP-1 levels were increased in the GCF of FI compared with NF.
This is the first study to profile periodontal furcation defects from a microbiological and inflammatory standpoint using conventional and AI-based analyses. A reduced aerobic microbial biofilm and an increase of several inflammatory, connective tissue degradation and repair markers were detected compared with other periodontal defects.
描述牙周根分叉病变处龈下牙菌斑的微生物组成及龈沟液(GCF)的分子特征。
57名牙周炎患者提供了II - III度根分叉病变(FI)、非根分叉(NF)牙周缺损及牙周健康部位(HS)的样本。通过对16S rRNA基因的V3 - V4区域进行测序分析龈下菌斑,并采用多重微珠免疫测定法评估18种GCF生物标志物在GCF中的水平。为探索数据的内在模式和结构,还应用了人工智能聚类方法。
共分析了171份龈下菌斑和84份GCF样本。识别出四个微生物群簇,并与FI、NF和HS相关。与NF相比,FI中需氧微生物群减少(p = 0.01);与NF相比,FI的GCF中白细胞介素 - 6、基质金属蛋白酶 - 3、基质金属蛋白酶 - 8、骨形态发生蛋白 - 2、硬化蛋白、表皮生长因子和金属蛋白酶组织抑制因子 - 1水平升高。
这是第一项从微生物学和炎症角度,使用传统分析方法和基于人工智能的分析方法对牙周根分叉病变进行分析的研究。与其他牙周缺损相比,检测到需氧微生物生物膜减少,以及几种炎症、结缔组织降解和修复标志物增加。