Characterizing the serum metabolome in multiple sclerosis

描述多发性硬化症的血清代谢组特征

• 批准号: 10197636
• 负责人: Farren B. S. Briggs
• 金额: $ 51.9万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10197636
• 关键词: Antibodies; Antigen Presentation; Autoimmune Diseases; Binding; Biochemical; Biological; Biological Markers; Biological Process; Chronic; Classification; Clinic; Complement; Demyelinating Diseases; Development; Diagnosis; Diagnostic; Diagnostic Sensitivity; Diagnostic Specificity; Discrimination; Disease; Disease remission; Early treatment; Evaluation; FDA approved; Financial cost; Free Will; Genes; Genetic; Goals; Guidelines; HLA-DRB1; Health; Inflammatory; Medical Care Costs; Metabolic; Metabolite Interaction; Migraine; Modeling; Molecular; Monitor; Multiple Sclerosis; Nerve Degeneration; Neuraxis; Neurologic Symptoms; Neuromyelitis Optica; Outcome; Patients; Pattern; Peptides; Persons; Pharmaceutical Preparations; Phase; Process; Prognostic Marker; Relapse; Research; Resources; Risk; Risk Factors; Sampling; Serum; Serum Markers; Signal Transduction; Sjogren' s Syndrome; Specificity; Suggestion; Supervision; Systemic Lupus Erythematosus; Testing; Time; Treatment outcome; Variant; autoreactive T cell; clinical Diagnosis; cohort; cost; diagnostic biomarker; disability; disorder control; immunomodulatory therapies; improved; improved outcome; medical specialties; metabolome; metabolomics; multiple sclerosis patient; novel; novel therapeutics; oligodendrocyte-myelin glycoprotein; side effect; supervised learning; targeted therapy trials; trait

PROJECT SUMMARY AND ABSTRACT Within the last decade, we have made great strides in our understanding of the mechanisms underlying multiple sclerosis (MS) risk and progression, however much of the variation remains unexplained. We have achieved significant reductions in the time to diagnosis and we have improved diagnostic sensitivity, however specificity is not ideal. Further, most of the FDA-approved MS-specific immunomodulatory therapies (IMTs) focus on the inflammatory disease component in the relapsing phase and have little effect on improving outcomes once a patient enters the progressive phase. The challenge for drug trials is the lack biomarkers to detect and monitor MS progression. The objectives of the current application are: 1. To identify and characterize biomarkers that discriminate MS and from other central nervous system inflammatory demyelinating diseases (CNSIDDs) and non-CNSIDD controls, and 2. To identify biomarkers of disease activity and biomarkers that distinguish relapsing from progressive forms of MS. We propose a multi-stage analysis of pre-existing and well- defined biological samples from two resources. Aim 1. Identify biochemical traits that discriminate MS from other CNSIDDs and healthy controls. Supervised machine learning and classification models will identify a metabolic signature discriminating MS from other CNSIDDs and healthy controls (HCs) in two cohorts. In the 1st cohort, MS patients who are early in their diagnosis (≤ 2 years) and IMT naïve/free will be compared to HCs and other CNSIDD cases. Discriminating metabolites will be tested for replication in a 2nd cohort comparing similarly defined MS patients to HCs and other CNSIDDs, and other autoimmune disease patients. We will determine the direction of the replicating MS- metabolite associations using bidirectional genetic instrumental variable analyses. Aim 2. Identify biochemical features of MS disease activity. We will identify metabolic variation corresponding to disease activity by comparing IMT naïve/free patients within 2 years of diagnosis and with a recent relapse to those who have been in remission for ≥3 months and to HCs using supervised classification in a discovery cohort followed by replication analyses in a 2nd cohort. Aim 3. Identify biochemical traits that discriminate progressive from relapsing MS. Supervised machine learning and classification models will identify metabolic patterns associated with MS progression by comparing IMT naïve/free patients with relapsing forms of MS to progressive MS from at a single academic specialty clinic. Aim 4. Identify metabolites that interact with HLA-DRB1*15:01 to increase MS risk. In this exploratory aim we will identify gene-metabolite (GxM) interactions involving the primary MS risk factor, HLA-DRB1*15:01. The encoded peptide is involved in antigen presentation and effectively binds to many endogenous metabolites, suggesting a mechanism through which autoreactive T cells may be activated. We will conduct GxM analyses in MS-HC matched pairs to identify metabolites associated with MS risk in the context of HLA-DRB1. At the completion of the proposed research, our expected outcomes are to have identified and characterized a serum-derived metabolomic signature that discriminates MS from other CNSIDDs and non- CNSIDD controls. We also expect to have identified novel serum markers of MS disease activity and progression, as well as putative metabolites that interact with HLA-DRB1*15:01 to modify risk. These results will have an important positive impact by identifying serum-derived biochemical traits that could be used to improve diagnostic specificity in MS. There is also the promise of discerning novel molecular processes underlying MS, which will provide new opportunities for the development and evaluation of novel therapies.

项目概要和摘要 在过去的十年中，我们在理解其背后的机制方面取得了长足的进步 多发性硬化症（MS）的风险和进展，但我们仍然无法解释其中的大部分变化。 然而，我们显着缩短了诊断时间，并且提高了诊断灵敏度 此外，FDA 批准的大多数 MS 特异性免疫调节疗法 (IMT) 的特异性并不理想。 对复发阶段的炎症性疾病成分有影响，对改善结果影响不大 一旦患者进入进展阶段，药物试验面临的挑战是缺乏可检测和检测的生物标志物。 监测 MS 进展当前应用的目标是： 1. 识别和表征 区分多发性硬化症和其他中枢神经系统炎症性脱髓鞘疾病的生物标志物 (CNSIDD) 和非 CNSIDD 对照，以及 2. 识别疾病活动的生物标志物和 区分复发性多发性硬化症和进行性多发性硬化症 我们建议对已有的和良好的多发性硬化症进行多阶段分析。 来自两种资源的定义的生物样本。 目标 1. 确定将 MS 与其他 CNSIDD 和健康对照区分开来的生化特征。 监督机器学习和分类模型将识别区分 MS 和 MS 的代谢特征 两个队列中的其他 CNSIDD 和健康对照 (HC) 在第一个队列中，是早期 MS 患者。 诊断（≤ 2 年）和未接受 IMT 的病例将与 HC 和其他 CNSIDD 病例进行比较。 将在第二组中测试代谢物的复制情况，将类似定义的 MS 患者与 HC 和其他患者进行比较 CNSIDD 和其他自身免疫性疾病患者，我们将确定 MS 复制的方向。 使用双向遗传工具变量分析代谢物关联 目标 2. 识别生化指标。 我们将通过以下方式识别与疾病活动相对应的代谢变化。 将诊断后 2 年内未接受过 IMT 且近期复发的患者与接受过 IMT 治疗的患者进行比较 缓解≥3个月，并在发现队列中使用监督分类进行HCs，然后 第二组中的复制分析 目标 3. 识别区分进展型和进展型的生化特征。 复发性多发性硬化症的监督机器学习和分类模型将识别相关的代谢模式。 通过比较患有复发性 MS 的 IMT 未接受/未接受 IMT 的患者与进行性 MS 的进展情况 目标 4. 识别与 HLA-DRB1*15:01 相互作用的代谢物 在此探索性目标中，我们将确定涉及以下因素的基因-代谢物 (GxM) 相互作用。 主要 MS 危险因素，HLA-DRB1*15:01 编码的肽参与抗原呈递和 有效地与许多内源性代谢物结合，表明自身反应性 T 细胞通过这种机制 我们将在 MS-HC 匹配对中进行 GxM 分析，以识别相关的代谢物。 HLA-DRB1 背景下具有 MS 风险。 在完成拟议的研究后，我们的预期结果是确定并 表征了血清衍生的代谢组学特征，该特征将 MS 与其他 CNSIDD 和非 我们还希望能够鉴定出 MS 疾病活动的新血清标志物和 CNSIDD 对照。 进展，以及与 HLA-DRB1*15:01 相互作用以改变风险的假定代谢物。 通过识别可用于改善血清来源的生化特征，产生重要的积极影响 多发性硬化症的诊断特异性还有望辨别多发性硬化症背后的新分子过程， 这将为新疗法的开发和评估提供新的机会。