Identification of Clinically Relevant TMD Subtypes Using Cluster Analysis

描述(申请人提供)：在美国人群中，颞下颌关节紊乱病(TMD)是仅次于头痛的最有可能导致头面部疼痛和功能障碍的临床疾病。然而，在临床实践中，TMD可以说是最不了解和最不有效管理的头面部疼痛和功能障碍形式，许多患者的治疗仅基于对症护理。痛苦的范围和缺乏有效的循证护理之间的差异可以部分归因于TMD是一种高度异质性的疾病。许多不同的生物机制可能导致口面部疼痛，而最有效的治疗可能取决于引起疼痛的机制。此外，不符合TMD临床标准的患者仍可能出现由同样的生物机制引起的亚临床症状。这类患者可能会增加发生首发TMD的风险，通过为他们提供适当的预防性治疗，有可能防止这些患者发生TMD。因此，在具有相似症状的TMD患者和无TMD的对照组中识别出更多同质性的亚群是可取的。幸运的是，这个目标可以在不需要昂贵的大规模研究的情况下实现。我们在口腔面部疼痛：前瞻性评估和风险评估(OPPERA；U01-DE017018-08)研究过程中产生了大量数据集，这是一项旨在确定导致TMD发生和持续的心理、生理和遗传因素的大型前瞻性研究。我们建议的研究是使用聚类分析和其他机器学习方法重新分析OPPERA收集的数据，以确定TMD和TMD样症状的临床相关亚型。为了实现这些目标，我们将重点关注以下具体目标：1.使用聚类分析，我们将识别和验证潜在的疼痛症状结构，这些结构定义了OPPERA参与者的亚组。我们假设我们将确定在临床和病因学上有意义的不同的TMD患者的新亚组。我们进一步假设，没有TMD的对照可以归类为类似的集群。2.利用判别分析、Logistic回归和更先进的机器学习方法，制定TMD患者亚组的分类规则。我们假设我们将为我们在特定目标1中识别的TMD的不同亚组制定简单的分类标准。3.使用遗传关联分析，我们将识别与TMD亚型相关的SNP。我们假设我们将确定与TMD每一亚型相关的SNP，为不同形式TMD的潜在机制提供更多的生物学见解。好了！

DESCRIPTION (provided by applicant): Temporomandibular disorder (TMD) ranks second only to headache as the clinical condition most likely to cause craniofacial pain and dysfunction in the U.S. population. Yet in clinical practice, TMD arguably is the least understood and least effectively managed form of craniofacial pain and dysfunction, with treatment for many patients based on little more than symptomatic care. This discrepancy between the scope of suffering and paucity of effective, evidence-based care can be attributed in part to the fact that TMD is a highly heterogeneous disorder. Numerous different biological mechanisms may contribute to orofacial pain, and the most efficacious treatment is likely to depend on the mechanism that is causing the pain. Moreover, patients who do not meet the clinical criteria for TMD may nevertheless experience subclinical symptoms caused by these same biological mechanisms. Such patients may have elevated risk of developing first-onset TMD, and it may be possible to prevent the development of TMD in these patients by providing them with appropriate preventative therapy. Thus, it would be desirable to identify more homogeneous subgroups among TMD patients and TMD-free controls with comparable symptoms. Fortuitously, this goal can be accomplished without an expensive large-scale study. We have generated a large data set during the course of the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA; U01-DE017018-08) study, which is a large-scale prospective study designed to identify psychological, physiological, and genetic factors contributing to the onset and persistence of TMD. Our proposed study is to reanalyze the data collected in OPPERA using cluster analysis and other machine learning methods to identify clinically relevant subtypes of TMD and TMD-like symptoms. To achieve these goals, we will focus on the following specific aims: 1. Using cluster analysis, we will identify and validate latent constructs underlying pain symptoms that define subgroups of OPPERA participants. We hypothesize that we will identify novel subgroups of patients with TMD that differ in clinically and etiologically meaningful ways. We further hypothesize that TMD-free controls can be grouped into similar clusters. 2. Using discriminant analysis, logistic regression, and more advanced machine learning methods, we will develop rules for classifying TMD patients into subgroups. We hypothesize that we will develop simple classification criteria for the different subgroups of TMD that we identify in Specific Aim 1. 3. Using genetic association analysis, we will identify SNP's that are associated with subtypes of TMD. We hypothesize that we will identify SNP's associated with each subtype of TMD, offering additional biological insight into the mechanisms underlying different forms of TMD. !