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（由申请人提供）：艰难梭菌感染（CDI）与美国每年近300万例腹泻和结肠炎病例有关。土发委会， 影响老年人，可导致暴发性、危及生命的结肠炎，并导致多次复发。迫切需要基于生物标志物的临床决策工具来预测哪些患者将经历CDI的不良结局。关于来自宿主、微生物组和病原体的哪些已知和有待发现的生物标志物将最好地预测并发症和复发，存在知识差距。我们的长期目标是开发和验证CDI后不良结局的准确风险预测模型，可用于指导治疗。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，并确定哪些生物标志物在调整后的模型中最能预测复杂的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标：1）验证先前显示与复杂CDI相关的宿主生物标志物; 2）发现复杂CDI的新型微生物生物标志物; 3）开发复杂CDI的候选生物标志物预测模型。我们将前瞻性地收集一组老年人的血清和粪便，以确定微生物组的特征、C。difficile， 抗毒素抗体和/或与并发症相关的炎症介质的水平。将使用线性回归和机器学习技术构建多变量模型，并将使用模型诊断来生成最终的便携式基于生物标志物的预测模型，供未来研究使用。