Host and Microbial Biomarkers Related to the Development of Complicated Clostridium difficile Infection

与复杂艰难梭菌感染发展相关的宿主和微生物生物标志物

• 批准号: 9094678
• 负责人: VINCENT B YOUNG
• 金额: $ 19.38万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9094678
• 关键词: Address; Adult; Affect; Aftercare; Age; Antibiotics; Antibodies; Antitoxins; Bacterial Toxins; Biological Markers; Cessation of life; Characteristics; Clinical; Clinical Data; Clostridium difficile; Colitis; Colon; Data; Development; Diagnosis; Diagnostic; Diarrhea; Disease; Elderly; Feces; Future; Genes; Goals; HGF gene; Health; Infection; Inflammation Mediators; Interleukin-8; Intervention; Knowledge; Lead; Leukocyte L1 Antigen Complex; Life; Linear Models; Linear Regressions; Loop ileostomy; Machine Learning; Measures; Modeling; Monoclonal Antibodies; Morbidity - disease rate; Onset of illness; Outcome; Outcomes Research; Patients; Procedures; Process; Recurrence; Research; Resources; Retrieval; Ribosomal RNA; Risk; Risk Factors; Sequence Analysis; Serum; Techniques; Testing; Therapeutic; Toxin; Transplantation; Work; adverse outcome; aged; base; candidate marker; clinical decision-making; cohort; data reduction; experience; fecal transplantation; high risk; microbial; microbial host; microbiome; microbiota; mortality; novel; novel marker; pathogen; predictive modeling; predictive tools; procalcitonin; prospective; secondary outcome; specific biomarkers; tool; treatment strategy

 DESCRIPTION (provided by applicant): Clostridium difficile infection (CDI) is implicated in nearly 3 million cases of diarrhea and colitis in the U.S. each year. CDI, which disproportionately affects older adults, can result in fulminant, life-threatening colitis and lead to multiple recurrnt episodes. There is an urgent need for validated biomarker-based clinical decision-making tools to predict which patients will experience adverse outcomes from CDI. There is a gap in knowledge regarding which known and yet to-be-discovered biomarkers, derived from the host, microbiome and pathogen, will best predict complications and recurrence. Our long-term goal is to develop and validate accurate risk-prediction models for adverse outcomes following CDI that can be used to guide therapy. The next step in pursuit of that goal, and our overall objective for the proposed research, is to discover new candidate biomarkers and determine which ones together best predict complicated CDI in an adjusted model. Our central hypothesis is that a biomarker-based model will better predict complicated CDI compared to models based on clinical variables alone. To address this hypothesis we propose three specific aims: 1) validate host biomarkers previously shown to associate with complicated CDI; 2) discover novel microbial biomarkers for complicated CDI; and 3) develop a candidate biomarker-based predictive model for complicated CDI. We will collect sera and stool prospectively in a cohort of older adults to determine if characteristics of the microbiome, microbial features of C. difficile, levels of antitoxin antibodies, and/or inflammatory mediators associate with complications. Multivariable models will be constructed using linear regression and machine learning techniques and model diagnostics will be used to generate a final portable, biomarker-based predictive model for use in future studies.

 描述（由适用提供）：每年在美国，艰难梭菌感染（CDI）与近300万例腹泻和结肠炎有关。 CDI，不成比例 影响老年人，可能导致暴发，威胁生命的结肠炎并导致多个复发发作。迫切需要验证的基于生物标记的临床决策工具，以预测哪些患者会遇到CDI的不良结果。知道从宿主，微生物组和病原体衍生的已知且尚未发现的生物标志物的知识差距有差距，可以最好地预测并发症和复发。我们的长期目标是开发和验证可用于指导治疗的CDI的不良结果的准确风险预测模型。追求这一目标的下一步以及我们提出的研究的总体目标是发现新的候选生物标志物，并确定哪些候选生物标志物在调整后的模型中最好地预测复杂的CDI。我们的中心假设是，与仅基于临床变量的模型相比，基于生物标志物的模型将更好地预测复杂的CDI。为了解决这一假设，我们提出了三个具体目的：1）验证先前证明的宿主生物标志物与复杂的CDI相关； 2）发现复杂CDI的新型微生物生物标志物； 3）为复杂的CDI开发基于候选生物标志物的预测模型。我们将在一群老年人中前瞻性地收集血清和粪便，以确定艰难梭菌的微生物体特征是否特征 抗毒素抗体的水平和/或炎症介质与并发症相关。将使用线性回归和机器学习技术构建多变量模型，模型诊断将用于生成最终的便携式，基于生物标志物的预测模型，以在未来的研究中使用。