Redefining C.difficile patient outcomes through network medicine

项目摘要 从无症状的携带到危及生命的腹泻和结肠炎，艰难梭菌通过一种免疫反应与人类相互作用。 表型的范围。这一光谱的机制还不完全清楚。尽管取得了进展 在对疾病的理解和新疗法的出现中，患者的结局，如未能 对治疗（~20%）、复发（~21%）、死亡、结肠切除术或需要重症监护（重度）的反应迅速 结果，~3%）仍然很常见。随着以患者为中心的结局（PCO）成为临床研究的中心， 艰难梭菌感染（CDI）也不例外。本职业发展建议基于中央 假设来自新的和现有的免疫、微生物和代谢数据（IMM/d）的综合分析 CDI患者和携带者的队列，通过识别生物标志物和途径，可以将患者 结果（特别是PCO），并将CDI重新分类为定植连续体（CDI/cc）。哈维尔·A维拉富尔特 Gálvez [JVG]，MD是哈佛医学院（HMS）的讲师，也是胃肠病学家， Beth Israel Deaconess Medical Center（BIDMC）的一项研究。他已经获得 在博士后和T32奖学金期间，他在临床和转化研究方面拥有丰富的经验， 证明致力于以患者为中心的研究事业。 该奖项将为申请人提供发展定量分析技能的机会，同时充分利用 * 利用辅导研究机会，解决预测、分类和 CDI的病理生物学CDI和肠道炎症专家Ciarán P. Kelly博士将担任导师，沿着 刘扬宇博士，一位研究人类肠道微生物组动态的生物信息学专家， 成为共同导师咨询委员会由土发委会和方法学专家（Garey、Pollock、 Gerszten和Dubberke）将定期与申请人协商，以评估进展情况。 K23奖将帮助JVG建立一个独立的研究计划，将生物信息学工具应用于未满足的 免疫消化系统疾病的需求，CDI作为第一个模型。课程的主干将是 HMS生物医学信息学硕士通过哈佛Catalyst提供写作培训和支持， 很危险本项目的第一个目的是确定粪便IMM/d的生物标志物和途径在CDI的时间 诊断与严重结局、复发和疾病轨迹相关。在此基础上，我们将招募 一个新的CDI队列，在收集生物标本时测量PCO（症状、生活质量），以确定 IMM/d生物标志物和途径预测和潜在的PCO。最后，以IMM/d为衬底， 机器学习算法对标准诊断设盲，我们将在新的CDI/CC中对患者进行重新分类， 功能性疾病分组。这项研究将回答CDI诊断、诊断和诊断中的关键问题。 病理生物学，同时将PCO放在最前沿，为R级提案生成基础数据。

PROJECT SUMMARY From asymptomatic carriage to life-threatening diarrhea and colitis, C.difficile interacts with humans through a range of phenotypes. The mechanisms underlying this spectrum are incompletely understood. Despite progress in the understanding of the disease and the advent of newer therapies, patient outcomes such as failure to respond promptly to therapy (~20%), recurrence (~21%), death, colectomy or need for critical care (severe outcomes, ~3 %) remain common. As patient-centered outcomes (PCOs) take center stage in clinical research, C.difficile Infection (CDI) will not be an exception. This Career Development proposal is based on the central hypothesis that integrated analysis of immune, microbial and metabolic data (IMM/d) from new and existing cohorts of CDI patients and carriers, by identifying biological markers and pathways, can transform patient outcomes (especially PCOs) and re-categorize the CDI to colonization continuum (CDI/cc). Javier A. Villafuerte Gálvez [JVG], MD is an Instructor at Harvard Medical School (HMS) and a gastroenterologist subspecialized in immune-mediated digestive disease at Beth Israel Deaconess Medical Center (BIDMC). He has gained substantial experience in clinical and translational research during his post-doctoral and T32 fellowships while proving commitment to a patient centered research career. This Award will provide the applicant with the opportunity to develop quantitative analysis skill while taking full advantage of mentored research opportunities to address knowledge gaps in prediction, classification and pathobiology of CDI. Dr. Ciarán P. Kelly, an expert in CDI and bowel inflammation will serve as mentor, along with Dr. Yang-Yu Liu, an expert in bioinformatics studying the dynamics of the human gut microbiome who will be co-mentor. The Advisory Committee composed by CDI and methodological experts (Drs. Garey, Pollock, Gerszten and Dubberke) will confer regularly with the applicant to evaluate progress. The K23 award will aid JVG to establish an independent research program applying bioinformatics tools to unmet needs in immune-digestive disorders, with CDI as a first model. The backbone of the curriculum will be the Master in Biomedical Informatics at HMS. Grant writing training and support through Harvard Catalyst will be critical. The first aim of this project is to identify fecal IMM/d biological markers and pathways at the time of CDI diagnosis associated with severe outcomes, recurrence and disease trajectory. Informed by this, we will recruit a new CDI cohort to measure PCOs (symptoms, quality of life) while collecting biospecimens, in order to identify IMM/d biological markers and pathways predicting and underlying PCOs. Finally, with IMM/d as substrate to machine learning algorithms blinded to standard diagnostics, we will re-categorize patients in the CDI/cc in new functional disease groupings. This research will answer critical questions in CDI diagnostics, prognostication and pathobiology while placing PCOs at the forefront, generating foundational data for R-level proposals.