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

• 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
• 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

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.

项目摘要 骨关节炎（OA）影响美国超过5000万人，最终导致慢性疼痛和 残疾。目前，尚无由FDA批准的OA的生物标志物，限制了临床检测早期的能力 - 舞台疾病。 OA的早期检测是迈向改善临床护理的限制性步骤 研究表明，早期干预可以限制或逆转OA症状。但是，OA的早期发现不是 目前可能。 代谢组分析是一种创新的方法，用于表征滑膜关节等生物学系统。 虽然OA是通过关节软骨的变性来传统描述的，但疾病的病理生理学 涉及细胞应激，炎症活性和异常组织代谢。由于滑液包含 关节关节多种细胞类型产生的许多分子（例如软骨细胞，滑膜细胞，滑膜细胞， 和成骨细胞），滑液的代谢分析可以为活动疾病提供​​独特的窗口 由OA影响关节的过程以及滑液的代谢概况可以促进早期检测 OA。 虽然滑液是OA的“犯罪现场”，但血浆在临床上更容易获得。因为 代谢产物小于1000 dalton，等离子体代谢产物谱也可能反映OA病理生理学， 该提案的目的是将全球代谢物谱作为OA等级的临床生物标志物。我们的 视觉是确定一组代谢物生物标志物，该标志物有助于在症状发作之前进行检测。 在AIM 1中，滑液的代谢组分布将用于统计学习以发展代谢物 可以预测射线照相（例如KL得分）和症状（例如疼痛）OA的生物标志物。最大 可用的人类滑液和血浆的临床队列（n = 1850，牛津大学）将用于 代谢物生物标志物开发，并随机进行模型训练，测试和验证 独立子集。由于OA中存在很大的异质性，因此分子内型（即分子 OA概​​况将从这些代谢组概况中开发出来，这些轮廓可能会产生有关OA的重要信息 病理生理学。该队列还包含配对的等离子体，AIM 2的研究将评估 滑液和血浆之间的每种代谢产物。因为血浆更容易获得，所以这些相关性 将定义可以在电路室中评估哪些关节代谢物。 该项目的预期结果是（1）一组经过验证的滑液代谢物生物标志物， 预测OA等级和疼痛水平（2）鉴定OA代谢组内型，以及（3）评估 滑液和血浆中代谢物水平之间的相关性。这将为两个临床医生提供 以及提供改进信息的基础科学家，以用于诊断和治疗衰弱的骨关节炎。