Metabolomic Prediction of Pneumonia Severity

肺炎严重程度的代谢组学预测

• 批准号: 10396094
• 负责人: Lilliam Ambroggio
• 金额: $ 19.44万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-21 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10396094
• 关键词: Accident and Emergency department; Admission activity; Amino Acids; Bile Acids; Biological Assay; Biological Markers; Blood; Blood specimen; C-reactive protein; Caring; Carnitine; Cessation of life; Chest; Child; Childhood; Clinical; Clinical Data; Cohort Studies; Complex; Data; Development; Diagnosis; Diagnostic tests; Disease; Drainage procedure; Empyema; Environment; Etiology; Financial Hardship; Functional disorder; Guidelines; Health Care Visit; Heterogeneity; Histidine; Hospitalization; Hospitals; Immune response; Infection; Inflammation; Intervention; Lipids; Measures; Metabolic; Mitochondria; Modeling; Nuclear Magnetic Resonance; Ohio; Organ; Outcome; Pediatric Hospitals; Pediatric Intensive Care Units; Pediatric cohort; Performance; Physiological; Pneumonia; Prospective cohort study; Provider; Regulation; Research; Research Personnel; Sampling; Sepsis; Severities; Severity of illness; Specificity; Specimen; Spectrometry; Time; Urine; base; clinically relevant; cohort; community acquired pneumonia; cost; data repository; improved; indexing; liquid chromatography mass spectrometry; metabolomics; novel; novel marker; pediatric sepsis; predictive marker; predictive modeling; predictive test; prevent; procalcitonin; prognostic; prospective; respiratory; risk stratification; small molecule; tool; urinary

PROJECT SUMMARY Community-acquired pneumonia (CAP) is one of the most prevalent infections in children resulting in 1.8 million healthcare visits annually. Validated tools to predict severe clinical outcomes in children with CAP do not exist. The addition of biomarkers to clinical prediction rules may improve severity prediction, however conventional biomarkers (e.g. procalcitonin) have limited ability to predict severity in children. We propose identifying novel biomarkers for pediatric CAP using metabolomics, the study of small molecules. We hypothesize that metabolites may be better predictors of illness severity as they directly represent the complex physiological interaction between the environment (e.g. infection) and the host in a single sample. Preliminary data of urine samples assayed by Nuclear Magnetic Resonance (NMR) spectrometry, a more specific analytical platform for metabolomics, suggests amino acids, carnitine and bile acid molecules are important predictors of CAP severity. As a complimentary approach we will further investigate these classes of metabolites and specific lipid molecules (i.e. oxylipins) in blood samples using liquid chromatography mass spectrometry (LC-MS), a more sensitive analytical platform. Information from urine and blood specimens will generate a comprehensive set of prognostic metabolomic biomarkers as the strengths of both analytical platforms are being leveraged for this proposal. The targeted approach we propose in addition to the use of two separate pediatric CAP cohorts, will result in a clinical and metabolomic biomarker prediction rule to predict severity in children presenting to the emergency department with CAP.

项目摘要 社区获得的肺炎（帽）是最普遍的感染之一 儿童每年导致180万次医疗访问。经过验证的工具预测严重 不存在帽子儿童的临床结果。将生物标志物添加到临床上 预测规则可能会提高严重性预测，但是传统的生物标志物（例如 procalcitin）预测儿童严重程度的能力有限。我们建议识别小说 使用代谢组学，小分子的研究，用于小儿帽的生物标志物。我们 假设代谢物可能是疾病严重程度的更好预测指标 代表环境之间的复杂生理相互作用（例如感染）和 单个样本中的主机。尿液测定的尿液样品的初步数据 共振（NMR）光谱法是代谢组学的更具体的分析平台，建议 氨基酸，肉碱和胆汁酸分子是帽严重程度的重要预测指标。作为 免费方法我们将进一步研究这些代谢物和特定类别的类别 使用液相色谱质谱法，血液样品中的脂质分子（即阿氧蛋白） （LC-MS），一个更敏感的分析平台。来自尿液和血液标本的信息将 将一组全面的预后代谢组生物标志物作为两者的优势 该提案正在利用分析平台。我们提出的目标方法 除了使用两个单独的儿科帽同类群，将导致临床和 代谢组生物标志物预测规则，以预测出现在 急诊室。