Department of Periodontology, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Department of Dental Ecology, School of Dentistry, University of North Carolina at Chapel Hill.
J Periodontol. 2017 Feb;88(2):153-165. doi: 10.1902/jop.2016.160379. Epub 2016 Sep 13.
The goal of this study is to use bioinformatics tools to explore identification and definition of distinct periodontal and tooth profile classes (PPCs/TPCs) among a cohort of individuals by using detailed clinical measures at the tooth level, including both periodontal measurements and tooth loss.
Full-mouth clinical periodontal measurements (seven clinical parameters) from 6,793 individuals from the Dental Atherosclerosis Risk in Communities Study (DARIC) were used to identify PPC. A custom latent class analysis (LCA) procedure was developed to identify clinically distinct PPCs and TPCs. Three validation cohorts were used: NHANES (2009 to 2010 and 2011 to 2012) and the Piedmont Study population (7,785 individuals).
The LCA method identified seven distinct periodontal profile classes (PPCs A to G) and seven distinct tooth profile classes (TPCs A to G) ranging from health to severe periodontal disease status. The method enabled identification of classes with common clinical manifestations that are hidden under the current periodontal classification schemas. Class assignment was robust with small misclassification error in the presence of missing data. The PPC algorithm was applied and confirmed in three distinct cohorts.
The findings suggest PPC and TPC using LCA can provide robust periodontal clinical definitions that reflect disease patterns in the population at an individual and tooth level. These classifications can potentially be used for patient stratification and thus provide tools for integrating multiple datasets to assess risk for periodontitis progression and tooth loss in dental patients.
本研究旨在利用生物信息学工具，通过使用详细的牙齿水平临床测量值（包括牙周测量值和牙齿缺失），在个体队列中探索和定义不同的牙周和牙齿轮廓类别（PPC/TPC）。
利用来自社区动脉粥样硬化风险研究（DARIC）的 6793 名个体的全口临床牙周测量值（七个临床参数）来识别 PPC。开发了一种定制的潜在类别分析（LCA）程序来识别临床上不同的 PPC 和 TPC。使用了三个验证队列：NHANES（2009 年至 2010 年和 2011 年至 2012 年）和皮埃蒙特研究人群（7785 名个体）。
LCA 方法确定了七个不同的牙周轮廓类别（PPCs A 至 G）和七个不同的牙齿轮廓类别（TPCs A 至 G），范围从健康到严重牙周病状态。该方法能够识别具有共同临床表现的类别，这些类别隐藏在当前的牙周分类方案下。在存在数据缺失的情况下，分类算法具有较小的错误分类误差，因此具有稳健的分类能力。该 PPC 算法已在三个不同的队列中进行了应用和验证。
研究结果表明，使用 LCA 的 PPC 和 TPC 可以提供反映人群中个体和牙齿水平疾病模式的稳健牙周临床定义。这些分类法可用于患者分层，从而为整合多个数据集以评估牙周炎进展和牙科患者牙齿丧失的风险提供工具。
