Structural, Biochemical and Functional Connectivity in Osteoarthritis using Quantitative Magnetic Resonance Imaging and Skeletal Biomechanics

利用定量磁共振成像和骨骼生物力学研究骨关节炎的结构、生化和功能连接

• 批准号: 10317687
• 负责人: Sharmila Majumdar
• 金额: $ 70.94万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-14 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10317687
• 关键词: 3-Dimensional; Adult; Affect; Age; American; Biochemical; Biochemistry; Biomechanics; Cartilage; Cartilage Matrix; Collagen; Complex; Contralateral; Data; Data Analyses; Data Set; Degenerative Disorder; Degenerative polyarthritis; Development; Disease; Disease Progression; Early Diagnosis; Elderly; Etiology; Exhibits; Financial Hardship; Functional disorder; Gait; Goals; Head; Health; Hip Joint; Hip Osteoarthritis; Hip region structure; Image; Individual; Injury; Intervention; Ipsilateral; Joints; Kinetics; Knee; Knee Osteoarthritis; Knee joint; Lateral; Lead; Lesion; Limb structure; Link; Longevity; Longitudinal Studies; Magnetic Resonance Imaging; Maps; Measures; Mediating; Mediation; Mediator of activation protein; Methods; Modeling; Morphology; Motivation; Multivariate Analysis; Operative Surgical Procedures; Outcome Measure; Pain; Patient Outcomes Assessments; Patients; Pattern; Phenotype; Prevalence; Preventive; Preventive treatment; Principal Component Analysis; Procedures; Progress Reports; Proteoglycan; Relaxation; Replacement Arthroplasty; Reporting; Role; Rotation; Severities; Shapes; Structure; Subgroup; Techniques; Time; Tissues; Water; Work; automated segmentation; base; bone; cartilage degradation; cohort; demographics; disability; disorder risk; follow-up; functional outcomes; gait examination; high risk; hip replacement arthroplasty; innovation; insight; joint destruction; kinematics; knee replacement arthroplasty; machine learning algorithm; multidimensional data; novel; obese person; quantitative imaging; rate of change; recruit; skeletal

Osteoarthritis (OA), a multifactorial disease that causes joint degeneration, affects 27 million U.S. adults, and often leads to severe disability. The prevalence of OA is 33.6% in adults older than 65 years. Despite the fact that OA is a widespread and debilitating disease, treatment options are currently extremely limited, and established disease-modifying therapies do not exist. The solution generally offered to treat late stage, symptomatic knee OA is joint replacement (total knee/hip arthroplasty, TKA/THA), and while this surgically invasive and expensive remedy offers temporary relief, replacements often fail after 10-15 years, with shorter life spans in obese individuals. In order to reduce the number of TKA/THA procedures, preventive efforts and interventions targeting early stage OA are essential – the first step would be identifying subjects at high risk for disease development, at a stage when tissue is not yet lost, and cartilage matrix abnormalities are potentially reversible. Understanding the complex pathophysiology of joint degeneration, knee and hip joint interactions, impact of gait biomechanics, are all critical to determine the mechanistic basis of hip OA. Hip OA progression marked by changes in cartilage biochemistry has complex multi-joint interactions with several mechanistic factors, including morphological features, gait biomechanics and demographics. However, studies to date have mostly focused on the relationship between single mechanistic factors and semi-quantitative/ quantitative imaging measures, gait biomechanics, or non-objective symptomatic evidence of hip OA. The overall goal of this competitive renewal is to extend our longitudinal work to determine the structure-function-connectivity and mechanistic factors that mediate biochemical degeneration of hip cartilage associated with OA progression over the mid-term (6-8-year) period. In this proposal, subjects will be recruited for a longitudinal study (covering 5-8 year follow up) from our existing cohort (n=184 hips, 92 subjects), with the availability of existing 3-year follow-up data including bi- lateral hip radiographs and MR images, gait biomechanics and patient reported outcomes. We will use novel, fully automated and translational methods to measure MRI-based T1ρ and T2 relaxation time on a voxel basis, which will provide precise and localized information on cartilage proteoglycan and collagen integrity and water content. Automatic segmentation techniques combined with machine learning algorithms will be used to analyze cartilage T1ρ/T2 in a well-characterized dataset with quantifiable T1ρ/T2 values on a voxel-level, and to automatically detect the patterns of focal T1ρ/T2 elevations that lead to later stage OA and drive progression of degenerative disease (over 8 years). Detailed analysis of gait biomechanics using functional principal component analysis will utilize the rich skeletal biomechanics data and imaging (over 8 years). Imaging the knee and hip longitudinally will provide a multi-joint analysis of OA progression (over 4 years) not done previously and will provide unique insights in the mechanisms in hip OA, connections with knee changes.

骨关节炎（OA）是一种多因素疾病，可导致关节退化，影响2700万美国人。 成年人，往往会导致严重的残疾。在65岁以上的成人中，OA的患病率为33.6%。 尽管OA是一种广泛的和使人衰弱的疾病，但治疗选择目前是极其困难的。 不存在有限的和已建立的疾病改善疗法。解决办法一般提供治疗晚 阶段，有症状的膝关节OA是关节置换术（全膝关节/髋关节置换术，TKA/THA），而这 手术侵入性和昂贵的补救措施提供了暂时的缓解，更换通常在10-15年后失败， 肥胖者的寿命较短。为了减少TKA/THA手术的数量， 针对早期OA的努力和干预是必不可少的-第一步是确定受试者， 在组织尚未丧失的阶段， 可能是可逆的了解膝关节和髋关节退行性变的复杂病理生理学 相互作用，步态生物力学的影响，都是至关重要的，以确定髋关节骨性关节炎的机械基础。 以软骨生物化学变化为标志的髋关节OA进展具有复杂的多关节相互作用 与几个机械因素，包括形态特征，步态生物力学和人口统计学。 然而，迄今为止的研究大多集中在单一的机械因素和 半定量/定量成像测量、步态生物力学或非客观症状证据 髋关节OA本次竞争性续约的总体目标是扩展我们的纵向工作，以确定 髋关节软骨结构-功能-连接性及其生化变性机制研究 与中期（6-8年）OA进展相关。 在本提案中，将从我们的研究中心招募受试者进行纵向研究（涵盖5-8年随访）。 现有队列（n=184例髋关节，92例受试者），现有3年随访数据可用，包括2- 髋关节侧位X线片和MR图像、步态生物力学和患者报告的结局。我们将使用小说， 全自动和平移方法以体素为基础测量基于MRI的T1ρ和T2弛豫时间， 其将提供关于软骨蛋白聚糖和胶原完整性和水的精确和局部信息 内容自动分割技术与机器学习算法相结合将用于 在具有体素水平上的可量化T1 ρ/T2值的良好表征的数据集中分析软骨T1 ρ/T2，以及 自动检测导致晚期OA的局灶性T1ρ/T2升高的模式，并驱动OA的进展。 退行性疾病（8年以上）。基于功能原理的步态生物力学分析 成分分析将利用丰富的骨骼生物力学数据和成像（超过8年）。成像 膝关节和髋关节纵向将提供OA进展（超过4年）的多关节分析，未进行 之前，并将为髋关节OA的机制以及与膝关节变化的联系提供独特的见解。