Understanding the mechanisms by which weight change affects progression of knee osteoarthritis in obese and overweight individuals: An analysis of the Osteoarthritis Initiative Dataset

了解体重变化影响肥胖和超重个体膝骨关节炎进展的机制：骨关节炎倡议数据集的分析

• 批准号: 10180705
• 负责人: GABRIELLE JOSEPH
• 金额: $ 71.44万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10180705
• 关键词: Adult; Affect; Aging; Anti-Inflammatory Agents; Area; Automobile Driving; Biochemistry; Biomechanics; Body Composition; Body Weight Changes; Body Weight decreased; Cartilage; Characteristics; Clinical; Clinical Data; Data; Data Set; Degenerative polyarthritis; Development; Disease; Elderly; Epidemic; Fatty acid glycerol esters; Goals; Individual; Infiltration; Inflammation; Joints; Knee; Knee Osteoarthritis; Knee joint; Knowledge; Lead; Machine Learning; Magnetic Resonance Imaging; Measures; Mediating; Mediation; Mediator of activation protein; Morphology; Muscle; Obesity; Operative Surgical Procedures; Outcome; Outcome Measure; Overweight; Pathway Analysis; Pathway interactions; Persons; Population; Preventive measure; Probability; Research; Risk Factors; Role; Stress; Structural defect; Structure; Thick; Time; Tissues; United States; Weight; Weight Gain; base; cost; disability; disorder prevention; improved; individualized medicine; joint destruction; joint inflammation; knee replacement arthroplasty; mechanical load; modifiable risk; muscle form; muscle strength; novel; obese person; personalized risk prediction; predictive modeling; prevent; public health relevance; risk prediction model; sarcopenia; strength training; targeted treatment; treatment optimization; treatment planning

Project Summary Osteoarthritis (OA) is a degenerative joint disease which affects more than 27 million people in the US and is the single most common cause of disability in older adults. The number of people affected with symptomatic OA will increase due to the aging of the population and the growing number of people with obesity, which represents an established risk factor for OA. Obesity has become a US “epidemic,” and projections have suggested that 86.3% of adults in the United States will be overweight or obese by 2030. Obesity is a modifiable risk factor for OA with weight loss offering a potential non-invasive therapy for disease prevention in obese and overweight individuals. Research has shown that weight loss slows OA progression and weight gain exacerbates progression. However, these studies did not specifically assess factors or pathways which would be responsible for improved or worse outcomes, such as associated inflammation, local body composition and sarcopenia. In the current proposal, we will comprehensibly examine the mechanisms associated with mechanical loading (weight loss and gain) that are responsible for driving knee joint degenerative changes including cartilage loss, namely concurrent changes in inflammation, local body composition (periarticular fat), and muscle morphology and strength. Identifying which mechanism(s) are most beneficial for slowing OA progression during weight loss will lead to targeted therapies for effective and optimized treatment of OA at early stages of disease during which progression may be prevented. Through pathway analysis, mediation analysis, and machine learning, we will identify potential preventive measures (such as muscle strengthening and anti-inflammatory measures) that could amplify the positive effects of reduced mechanical loading on OA. Thus, the clinical impact of our project is development of a subject-specific prediction model for clinicians to individually-tailor treatment plans that slow joint breakdown, and decrease probability for invasive and costly surgeries such as total knee arthroplasty. Three specific aims are proposed: Specific Aim 1: We will characterize the associations between changes in weight with changes in knee joint inflammation, local body composition, muscle cross-sectional area (CSA), fat infiltration and muscle strength, and investigate the associations between these parameters and progression of knee degenerative changes. Specific Aim 2: Using a path analysis we will explore the mechanisms by which weight change impacts OA progression, and quantify the degree to which these factors mediate this relationship. Specific Aim 3: Finally, we will develop a prediction model using machine learning to determine which demographic, clinical, and MRI features (including changes in local body composition, inflammation, muscle) can predict progression of OA over 8 years.

项目摘要 骨关节炎（OA）是一种退化性关节疾病，影响美国超过2700万人，是 老年人最常见的残疾最常见原因。受症状的OA影响的人数 由于人口的老龄化以及肥胖的人数越来越多，这将增加 OA的既定风险因素。肥胖已成为美国的“流行病”，项目表明 到2030年，美国有86.3％的成年人将超重或肥胖。肥胖是可修改的风险因素 体重减轻的OA提供潜在的肥胖和超重疾病预防疾病的非侵入性疗法 个人。研究表明，减肥会减慢OA的进展和体重增加加剧 进展。但是，这些研究并未专门评估将负责的因素或途径 为了改善或更糟糕的结果，例如相关感染，局部身体组成和肌肉减少症。 在当前的建议中，我们将全面检查与机械相关的机制 负载（减肥和增益）负责驱动膝盖关节退行性变化，包括 软骨损失，即炎症的并发变化，局部身体组成（周围脂肪）， 以及肌肉形态和力量。确定哪种机制对于放慢最有益 体重减轻期间的OA进展将导致有效和优化治疗的靶向疗法 OA在疾病的早期阶段可以预防进展。通过途径分析， 调解分析和机器学习，我们将确定潜在的预防措施（例如肌肉 加强和抗炎措施）可以扩大机械减少的积极作用 在OA上加载。这是我们项目的临床影响是开发特定于主题的预测模型 临床医生到单独的尾部治疗计划，这些计划缓慢关节崩溃，并降低了侵入性的概率 以及昂贵的手术，例如全膝关节置换术。 提出了三个具体目标： 特定目标1：我们将表征重量变化与膝关节变化之间的关联 炎症，局部身体成分，肌肉横断面区域（CSA），脂肪浸润和肌肉力量， 并研究这些参数之间的关联与膝盖退行性变化的进展。 特定目的2：使用路径分析，我们将探索重量变化影响OA的机制 进展，并量化这些因素介导这种关系的程度。 特定目标3：最后，我们将使用机器学习制定一个预测模型，以确定哪个 人口统计，临床和MRI特征（包括局部身体成分，注射，肌肉的变化） 可以在8年内预测OA的进展。