Discovery of Plasma Biomarkers of Delayed Cerebral Ischemia after Subarachnoid Hemorrhage

蛛网膜下腔出血后迟发性脑缺血血浆生物标志物的发现

• 批准号: 10589874
• 负责人: Huimahn Alex Choi
• 金额: $ 48.11万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589874
• 关键词: Address; Affect; Area; Binding Proteins; Biological Assay; Biological Markers; Biological Process; Blinded; Blood; Blood Tests; Brain; Brain Aneurysms; Brain hemorrhage; Caring; Cerebral Aneurysm; Cerebral Ischemia; Cerebral hemisphere hemorrhage; Characteristics; Clinical; Clinical Research; Clinical Trials Design; Clinical stratification; Complex; Complication; Data; Detection; Development; Exposure to; Focal Neurologic Deficits; Functional disorder; GDF8 gene; Glycoproteins; Goals; Health; Hemorrhage; Hospitals; Hour; Individual; Intensive Care Units; Interferon Type II; Intervention; Intervention Studies; Investigation; Lead; Leucine; Logistic Regressions; Measures; Morbidity - disease rate; Neurologic Deficit; Patients; Pattern; Performance; Phase; Plasma; Plasma Proteins; Population; Preparation; Process; Prospective cohort; Proteins; Proteomics; Public Health; Reproducibility; Research; Resource Allocation; Resources; Risk; Rupture; Sampling; Sensitivity and Specificity; Series; Specificity; Subarachnoid Hemorrhage; Subarachnoid Space; Technology; Testing; Treatment Side Effects; United States National Institutes of Health; biomarker identification; biomarker signature; biomarker validation; candidate identification; candidate marker; candidate validation; cohort; detection assay; detection method; effective therapy; growth differentiation factor 5; guanylate; high risk; improved; individualized medicine; innovation; machine learning model; member; minimally invasive; neurexophilin; novel; patient stratification; prevent; prognostic; prognostic assays; prognostic performance; prognostic value; prognostication; prospective; protein biomarkers; randomized, clinical trials; response; risk stratification; scavenger receptor; screening; statistical and machine learning; validation studies

Subarachnoid Hemorrhage (SAH) is a debilitating type of hemorrhagic stroke that affects ~50,000 annually in the US. It is caused by a rupture of a cerebral aneurysm and hemorrhage into the subarachnoid space around the brain. In those who survive the initial bleed, ~30% of the patients develop a serious secondary complication called delayed cerebral ischemia (DCI). DCI typically occurs at 4-21 days after SAH and is characterized by focal neurological deficits. Though DCI is a potentially preventable contributor to morbidity after SAH, interventional studies targeting DCI have failed. A major hindrance in SAH research is our inability to prognosticate which SAH patient will develop DCI. This leads to all SAH patients being observed for prolonged periods in the intensive care unit leading to higher rates of in-hospital complications, potential delay in treatment and misappropriated resources. Furthermore, because of the inability to risk-stratify SAH patients, randomized clinical trials require large populations, are expensive and have failed to identify effective treatments. Inclusion of subjects who are less likely to develop DCI, have led to exposing them to unwanted side effects from the treatment with no potential benefits. Identification of patients who will develop DCI is an unmet clinical need. Currently, there are no biomarkers with sufficient sensitivity and specificity to serve as a clinically useful screening test. This proposal therefore addresses a major unmet clinical need: a lack of a biomarker that can prognosticate DCI. Through a series of previous clinical studies, we have demonstrated that SAH leads to increases in systemic pathophysiological responses. We showed that an early elevated pathophysiological response, quantified by measuring plasma protein biomarkers, was associated with the development of DCI. We have expanded this line of investigation and via targeted proteomic profiling in pilot cohorts. First, we identified 6 plasma proteins that are prognostic of DCI within 48 hours of SAH. Second, using multivariate statistical and machine learning approaches, we identified a biomarker signature (a combination of proteins) that improved DCI prognostication. In this study, we propose to identify and confirm additional candidate biomarkers, develop and internally validate protein detection technology and verify the proof of concept in a prospective cohort. Performance characteristics of the candidate markers and the signature will be determined and validated for clinical use. Development of effective, minimally invasive biomarkers for DCI prognostication will lead to significant improvements in SAH management. Public/Health/Relevance: SAH has detrimental effects on individual health and the economy as a whole. The proposed research is relevant to public health as it will improve prognostication of DCI (an avoidable complication after SAH). This would lead to discovery of proteins that prognosticate DCI, improve our understanding of the pathophysiology of DCI and lead to improvements in design of clinical trials targeting DCI.

蛛网膜下腔出血（SAH）是一种使人衰弱的出血性中风，在美国每年影响约50，000人。 美方它是由脑动脉瘤破裂和周围蛛网膜下腔出血引起的。 大脑在初次出血后存活的患者中，约30%的患者发生严重的继发性并发症 称为迟发性脑缺血（DCI）。DCI通常发生在SAH后4-21天，其特征在于局灶性 神经缺陷尽管DCI是SAH后发病率的潜在可预防因素，但介入治疗可降低其发病率。 针对DCI的研究都失败了蛛网膜下腔出血研究中的一个主要障碍是我们不能明确哪种蛛网膜下腔出血 患者将发展为DCI。这导致所有SAH患者在强化治疗中被长时间观察。 护理单位导致住院并发症的发生率更高，可能延误治疗和挪用 资源此外，由于无法对SAH患者进行风险分层，随机临床试验需要 人口众多，费用昂贵，而且未能找到有效的治疗方法。纳入以下受试者： 不太可能发展为DCI，导致他们暴露于治疗中不必要的副作用， 潜在的好处。识别将发生DCI的患者是一项未满足的临床需求。目前有 没有具有足够灵敏度和特异性的生物标志物作为临床有用的筛选试验。这项建议 因此，解决了一个主要的未满足的临床需求：缺乏一个生物标志物，可以预测DCI。通过 一系列先前的临床研究，我们已经证明，SAH导致全身性 病理生理反应。我们发现，早期升高的病理生理反应，量化 测量血浆蛋白生物标志物，与DCI的发展相关。我们已经扩展了这个 研究路线和通过试点队列中的靶向蛋白质组学分析。首先，我们鉴定了6种血浆蛋白， 在蛛网膜下腔出血后48小时内预测DCI。第二，使用多元统计和机器学习 通过这些方法，我们鉴定了改善DCI识别的生物标志物特征（蛋白质的组合）。 在这项研究中，我们建议确定和确认其他候选生物标志物，开发和内部验证 蛋白质检测技术，并在前瞻性队列中验证概念证明。性能特性 将确定候选标记物和特征并验证用于临床使用。发展 有效的、微创的DCI诊断生物标志物将显著改善SAH 管理 公共/健康/相关性：SAH对个人健康和整个经济都有不利影响。拟议的研究与公共卫生有关，因为它将改善DCI（SAH后可避免的并发症）的诊断。这将导致发现抑制DCI的蛋白质，提高我们对DCI的病理生理学的理解，并导致靶向DCI的临床试验设计的改进。