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.

项目概要 骨关节炎 (OA) 影响美国超过 5000 万人，最终导致慢性疼痛和 目前，尚无 FDA 批准的 OA 生物标志物，限制了临床医生早期检测的能力。 早期发现 OA 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，但早期发现 OA 却并非如此。 目前可能。 代谢组学分析是一种表征滑膜关节等生物系统特征的创新方法。 虽然 OA 通常被描述为关节软骨退化，但该疾病的病理生理学 由于滑液含有细胞应激、炎症活动和异常组织代谢。 关节的多种细胞类型（例如软骨细胞、滑膜细胞、 和成骨细胞），滑液的代谢分析可以提供了解活动性疾病的独特窗口 受 OA 影响的关节中的过程和滑液的代谢组学特征可以促进早期检测 的 O.A. 虽然滑液是 OA 的“犯罪现场”，但血浆在临床上更容易获得。 代谢物小于 1000 道尔顿，血浆代谢物谱也可能反映 OA 病理生理学， 该提案的目标是推进全球代谢谱作为 OA 级的临床生物标志物。 愿景是识别一组代谢物生物标志物，帮助在症状出现之前检测 OA。 在目标 1 中，滑液的代谢组学特征将与统计学习一起使用来开发代谢物 预测放射学（例如 KL 评分）和症状（例如疼痛）OA 的生物标志物。 可用的人类滑液和血浆临床队列（n = 1850，牛津大学）将用于 代谢生物标志物开发，随机进行模型训练、测试和验证 因为 OA 存在显着的异质性，分子内型（即 OA 谱）将从这些代谢组学谱中开发出来，这可能会产生有关 OA 的重要信息 该队列还包含配对血浆，Aim 2 的研究将评估两者之间的相关性。 由于血浆更容易获得，因此滑液和血浆之间的每种代谢物之间存在相关性。 将定义可以在循环室中评估哪些关节代谢物。 该项目的预期成果是 (1) 一组经过验证的滑液代谢生物标志物， 预测 OA 等级和疼痛水平 (2) 鉴定 OA 的代谢组内型，以及 (3) 评估 滑液和血浆中代谢物水平之间的相关性。 以及为诊断和治疗衰弱性骨关节炎提供改进信息的基础科学家。