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: 10378057
• 负责人: GABRIELLE JOSEPH
• 金额: $ 69.17万
• 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/10378057
• 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; 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.

项目总结