Assessment of ultrasound features of knee osteoarthritis in a population-based community cohort

项目总结 我们的长期目标是展示超声波在OA评估中的效用，使其采购标准化 和评分，并促进在临床、研究和试验环境中增加对US的吸收。膝 骨关节炎(KOA)非常普遍，经常使人虚弱。潜在治疗方法的开发已经 受制于这种常见慢性疾病的异质性，其特征是 具有不同潜在病理生理机制的亚群或表型的数量。成像， 遗传学、生化生物标记物和其他特征可以用来表征表型，但 数据类型可能会使协调定义变得困难。虽然X光照相术被广泛应用于KOA成像，但它是 在评估早期疾病(干预最有可能成功的时候)的能力有限，并且对 变化。超声(US)是一种可广泛使用、省时且经济实惠的成像方式，可以 提供有关所有关节组织(如软骨、半月板、滑膜、骨)的详细和可靠的信息；以及 因此可以告知膝关节骨性关节炎的表型(例如，滑膜炎、积液、软骨损伤、钙 晶状体沉积，以及月国窝囊肿。US的使用目前受到缺乏系统执行的限制 在特征良好的非临床人群中进行的研究。为了弥补这一差距并进一步利用这一点 对于膝关节骨性关节炎，我们将在人群中获得标准化的US和X线片- 基于约翰斯顿县健康研究(JoCoHS)，约翰斯顿县新招生阶段25年 OA项目，包括35-70岁的白人、非裔美国人和西班牙裔男性和女性，以实现 三个目标。在目标1中，我们将确定膝部超声特征的人群患病率(n~3000)，包括 软骨和半月板损伤、滑膜炎/积液、钙晶体沉积、月国窝囊肿和骨赘 总体上和按年龄、性别、种族/民族和症状状况划分的关键亚组。目标2将允许量化 这些超声征象与放射学结果和症状评分之间的关系 小组(例如，按性别、按种族/族裔划分的有或无放射检查的膝骨性关节炎患者)。对于目标3，我们将申请 新颖的机器学习方法(例如，方向-投影-置换[DiProPerm]假设检验， 联合和个体差异[Jive]和距离加权判别[DWD])到a)开发总体 美国对症状性膝关节骨性关节炎的评分和b)确定美国变量对与膝关节骨性关节炎相关的表型的贡献 基于一般健康、体力活动和功能评估。这项研究是建立 美国作为膝关节骨性关节炎临床使用、研究和临床试验评估工具的基金会，提供 关于美国效用的独特的基于人口的横截面数据，并构成未来的基础 纵向工作，评估其与突发事件和进步性KOA相关的价值和绩效特征。

Project summary Our long-term goal is to demonstrate the utility of ultrasound for OA assessment, standardize its acquisition and scoring, and promote increased uptake of US for use in clinical, research, and trial settings. Knee osteoarthritis (KOA) is highly prevalent and frequently debilitating. Development of potential treatments has been hampered by the heterogenous nature of this common chronic condition, which is characterized by a number of subgroups, or phenotypes, with different underlying pathophysiological mechanisms. Imaging, genetics, biochemical biomarkers, and other features can be used to characterize phenotypes, but variations in data types can make it difficult to harmonize definitions. While radiography is widely used in KOA imaging, it is limited in its ability to assess early disease (when interventions are most likely to succeed) and is insensitive to change. Ultrasound (US) is a widely accessible, time-efficient and cost-effective imaging modality that can provide detailed and reliable information about all joint tissues (e.g., cartilage, meniscus, synovium, bone), and could therefore inform phenotypes in KOA (e.g., by presence of synovitis, effusion, cartilage damage, calcium crystal deposition, and popliteal cysts). Use of US is currently limited by the lack of systematically performed studies in well-characterized non-clinical populations. To address this gap and further the use of this advantageous imaging modality for KOA, we will obtain standardized US and radiography in the population- based Johnston County Health Study (JoCoHS), the new enrollment phase of the 25+ year Johnston County OA Project which includes white, African American, and Hispanic men and women aged 35-70, to achieve three aims. In Aim 1, we will determine the population prevalence (n~3000) of knee US features including cartilage and meniscal damage, synovitis/effusion, calcium crystal deposition, popliteal cysts and osteophytes overall and in key subgroups by age, sex, race/ethnicity, and symptom status. Aim 2 will allow quantification of the associations between these US features and radiographic findings and symptom scores overall and in key subgroups (e.g., those with and without radiographic KOA, by sex, by race/ethnicity). For Aim 3, we will apply novel machine learning methodologies (e.g., Direction-projection-permutation [DiProPerm] hypothesis testing, Joint and Individual Variation [JIVE], and Distance-Weighted Discrimination [DWD]) to a) develop an overall US score for symptomatic KOA and b) identify the contribution of US variables to phenotypes relevant to KOA based on general health, physical activity, and functional assessments. This study is a crucial step to establish the foundation for US as an assessment tool for clinical use, research, and clinical trials in KOA, providing unique population-based cross-sectional data regarding the utility of US and forming the basis for future longitudinal work evaluating its value and performance characteristics related to incident and progressive KOA.