Identification of Clinically Relevant TMD Subtypes Using Cluster Analysis

使用聚类分析识别临床相关 TMD 亚型

• 批准号: 8704425
• 负责人: Eric Bair
• 金额: $ 14.62万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-22 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8704425
• 关键词: Animals; Biological; Blood specimen; Caring; Chronic; Classification; Clinical; Cluster Analysis; Code; Craniofacial Pain; Data; Data Analyses; Data Set; Development; Discriminant Analysis; Disease; Distress; Enrollment; Epidemiologist; Epidemiology; Evaluation; Functional disorder; Genes; Genetic; Genotype; Goals; Headache; Human; Individual; Inflammation; International; Lead; Link; Logistic Regressions; Machine Learning; Mentors; Methods; Modeling; Moods; Orofacial Pain; Pain; Pain Disorder; Parents; Participant; Patients; Phenotype; Physiological; Population; Positioning Attribute; Process; Prospective Studies; Qualifying; Questionnaires; Research; Research Design; Research Personnel; Resources; Risk; Risk Assessment; Risk Factors; Single Nucleotide Polymorphism; Statistical Models; Subgroup; Symptoms; Temporomandibular Joint Disorders; base; chronic pain; clinical practice; clinically relevant; cost; cost effective; disorder subtype; effective therapy; evidence base; experience; genetic association; insight; mechanical pressure; meetings; member; novel; prevent; programs; prospective; psychologic; public health relevance; response

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. !

描述（由申请人提供）：颞下颌关节紊乱病（TMD）在美国人群中仅次于头痛，是最可能导致颅面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的颅面疼痛和功能障碍的形式，许多患者的治疗仅基于对症治疗。这种痛苦的范围和缺乏有效的，基于证据的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。许多不同的生物学机制可能导致口面疼痛，最有效的治疗可能取决于引起疼痛的机制。此外，不符合TMD临床标准的患者可能会出现由这些相同的生物学机制引起的亚临床症状。这类患者可能具有发展为首发TMD的高风险，并且可能通过为这些患者提供适当的预防性治疗来预防TMD的发展。因此，需要在具有可比症状的TMD患者和无TMD对照组中确定更同质的亚组。幸运的是，这一目标可以在不进行昂贵的大规模研究的情况下实现。我们在口面疼痛：前瞻性评价和风险评估（OPPERA; U01-DE017018 - 08）研究过程中生成了大量数据集，该研究是一项大规模前瞻性研究，旨在确定导致TMD发作和持续的心理、生理和遗传因素。我们提出的研究是使用聚类分析和其他机器学习方法重新分析OPPERA中收集的数据，以识别临床相关的TMD亚型和TMD样症状。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结构潜在的疼痛症状，定义亚组的OPPERA参与者。我们假设，我们将确定不同的临床和病因学意义的方式与TMD患者的新亚组。我们进一步假设，无TMD对照组可以分为类似的集群。2.使用判别分析，逻辑回归和更先进的机器学习方法，我们将制定将TMD患者分类为亚组的规则。我们假设，我们将制定简单的分类标准，我们在具体目标1中确定的不同亚组的TMD。3.使用遗传关联分析，我们将确定与TMD亚型相关的SNP。我们假设，我们将确定SNP的与每一个亚型的TMD，提供额外的生物学洞察机制的不同形式的TMD。!