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Metabolomic Profiling to Identify Candidate Biomarker Profiles and Molecular Endotypes for Osteoarthritis

通过代谢组学分析来鉴定骨关节炎的候选生物标志物谱和分子内型

基本信息

批准号：
10737184
负责人：
Ronald Kent June
金额：
$ 46.7万
依托单位：
MONTANA STATE UNIVERSITY - BOZEMAN
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-10 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10737184
关键词：
Accounting Affect American Arthralgia Biochemical Reaction Biological Factors Biological Markers Biology Blood Chemical Analysis Cartilage Cellular Stress Chondrocytes Circulation Clinical Clinical Trials Cluster Analysis Consumption Crime Data Degenerative polyarthritis Degradation Pathway Detection Deterioration Development Diagnosis Disease Early Diagnosis Early Intervention Early identification Evaluation Extracellular Matrix Degradation FDA approved Foundations Functional disorder Goals Health Heterogeneity Human Inflammation Inflammatory Inflammatory Response Injury Joints Knowledge Lead Mass Spectrum Analysis Measures Medical Metabolic Metabolic Pathway Metabolism Methods Mission Modeling Molecular Osteoblasts Outcome Pain Pathogenesis Pathway interactions Patients Pattern Persons Physiology Plasma Process Research Rheumatoid Arthritis Sampling Scientist Severities Statistical Models Structure Symptoms Synovial Fluid Synovial joint Techniques Testing Therapeutic Intervention Time Tissues Training United States National Institutes of Health Universities Validation Vision articular cartilage biological systems biomarker development bone candidate identification candidate marker cell type chronic pain clinical biomarkers clinical care cohort dalton diagnostic tool disability improved innovation insight metabolomics novel predictive marker predictive modeling prospective radiological imaging random forest repaired small molecule statistical learning tool transcriptomics

项目摘要

Project Summary Osteoarthritis (OA) affects more than 50 million people in the US, eventually leading to chronic pain and disability. Currently, no FDA-approved biomarkers for OA exist, limiting a clinician’s ability to detect early- stage disease. Early detection of OA is a rate-limiting step toward improved clinical care because recent studies show that early intervention can limit or reverse OA symptoms. However, early detection of OA is not currently possible. Metabolomic profiling is an innovative approach to characterize biological systems like synovial joints. While OA is classically described by degeneration of the articular cartilage, the pathophysiology of the disease involves cell stress, inflammatory activity, and abnormal tissue metabolism. Since the synovial fluid contains many of the molecules produced by the articular joint’s multiple cell types (eg chondrocytes, synoviocytes, and osteoblasts), metabolic profiling of the synovial fluid could provide a unique window into active disease processes in the OA-affected joint, and metabolomic profiles from synovial fluid could facilitate early detection of OA. While synovial fluid is “the scene of the crime” for OA, plasma is easier to obtain clinically. Because metabolites are smaller than 1000 Daltons, plasma metabolite profiles may also reflect OA pathophysiology, and the goal of this proposal is to advance global metabolite profiles as clinical biomarkers of OA grade. Our vision is to identify a panel of metabolite biomarkers that aid in the detection OA before the onset of symptoms. In Aim 1, metabolomic profiles of synovial fluid will be used with statistical learning to develop metabolite biomarkers that predict both radiographic (e.g. KL-score) and symptomatic (e.g. pain) OA. The largest available clinical cohort of human synovial fluid and plasma (n=1850, University of Oxford) will be used for metabolite biomarker development, with model training, testing, and validation performed on a random independent subsets. Because there is substantial heterogeneity in OA, molecular endotypes (i.e. molecular OA profiles) will be developed from these metabolomic profiles that may yield important information on OA pathophysiology. The cohort also contains paired plasma, and studies of Aim 2 will assess the correlations of each metabolite between synovial fluid and plasma. Because plasma is easier to obtain, these correlations will define which joint metabolites can be assessed in the circulatory compartment. The expected outcomes of this project are (1) a validated set of synovial fluid metabolite biomarkers that predict OA grade and pain levels (2) identification of metabolomic endotypes of OA, and (3) assessment of correlations between metabolite levels in the synovial fluid and the plasma. This will provide both clinicians and basic scientists with improved information for diagnosing and treating debilitating osteoarthritis.

项目总结