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

项目摘要 骨关节炎（OA）影响着美国超过5000万人，最终导致慢性疼痛， 残疾。目前，不存在FDA批准的OA生物标志物，限制了临床医生早期检测OA的能力。 阶段性疾病OA的早期检测是改善临床护理的限速步骤，因为最近 研究表明，早期干预可以限制或逆转OA症状。然而，OA的早期发现并不是 目前可能。 代谢组学分析是一种创新的方法来表征生物系统，如滑膜关节。 虽然OA通常被描述为关节软骨的退化，但该疾病的病理生理学 涉及细胞应激、炎症活动和异常组织代谢。由于滑液中含有 由关节的多种细胞类型（例如软骨细胞，滑膜细胞， 和成骨细胞），滑液的代谢谱可以提供一个独特的窗口到活动性疾病 OA影响关节的过程，滑液的代谢组学特征有助于早期检测 的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 代谢物小于1000道尔顿，血浆代谢物谱也可反映OA病理生理学， 该提案的目标是推进全球代谢物谱作为OA级别的临床生物标志物。我们 愿景是确定一组代谢物生物标志物，有助于在症状发作前检测OA。 在目标1中，滑液的代谢组学特征将与统计学习一起用于开发代谢物 预测放射学（例如KL评分）和症状性（例如疼痛）OA的生物标志物。最大的 可用的人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，随机进行模型训练，测试和验证 独立子集。因为OA中存在大量异质性，所以分子内型（即，分子内型）可能与OA的分子内型相关。 OA特征）将从这些代谢组学特征中发展出来，这些代谢组学特征可能会产生关于OA的重要信息 病理生理学队列还包含配对血浆，Aim 2的研究将评估以下各项的相关性： 滑液和血浆之间的每种代谢物。由于等离子体更容易获得，这些相关性 将定义可以在循环室中评估哪些关节代谢物。 该项目的预期结果是（1）一组经验证的滑液代谢物生物标志物， 预测OA等级和疼痛水平（2）识别OA的代谢组学内型，以及（3）评估 滑液和血浆中代谢物水平之间的相关性。这将为临床医生提供 和基础科学家，为诊断和治疗衰弱性骨关节炎提供更好的信息。

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.