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

项目摘要 骨关节炎(OA)在美国影响着5000多万人，最终导致慢性疼痛和 残疾。目前，还没有FDA批准的OA生物标志物存在，限制了临床医生早期发现- 阶段性疾病。早期发现骨性关节炎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的症状。然而，早期发现骨性关节炎并不是 目前是可能的。 代谢组学是表征滑膜关节等生物系统的一种创新方法。 虽然骨性关节炎的经典描述是关节软骨退变，但该病的病理生理学 涉及细胞应激、炎症活动和组织代谢异常。因为滑液中含有 关节多种细胞类型产生的许多分子(如软骨细胞，滑膜细胞， 和成骨细胞)，滑液的代谢特征可以为活动性疾病提供一个独特的窗口 关节病变关节的过程和滑液中的代谢谱有助于早期发现 对于办公自动化来说。 虽然滑液是骨性关节炎的“犯罪现场”，但临床上更容易获得血浆。因为 代谢物小于1000道尔顿，血浆代谢物谱也可反映骨关节炎的病理生理， 这项建议的目标是推动全球代谢物图谱作为OA级别的临床生物标记物。我们的 VISION是识别一组代谢物生物标记物，在症状出现之前帮助检测OA。 在目标1中，将使用滑液的代谢组谱和统计学习来开发代谢物。 预测放射学(如KL评分)和症状性(如疼痛)骨性关节炎的生物标记物。最大的 可用的人类滑液和血浆临床队列(牛津大学n=1850)将用于 代谢物生物标记物开发，随机进行模型训练、测试和验证 独立子集。由于骨性关节炎有很大的异质性，分子内型(即分子 将从这些代谢组图谱中开发出可能产生有关OA的重要信息的 病理生理学。该队列还包含成对的血浆，目标2的研究将评估 滑液和血浆之间的每种代谢物。因为等离子体更容易获得，所以这些关联 将确定哪些联合代谢物可以在循环室中进行评估。 该项目的预期结果是(1)一套经过验证的滑液代谢物生物标记物 预测骨性关节炎的分级和疼痛程度(2)确定骨性关节炎的代谢内型，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 以及基础科学家，为诊断和治疗衰弱的骨关节炎提供改进的信息。

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.