Redefining C.difficile patient outcomes through network medicine

项目总结 从无症状携带到危及生命的腹泻和结肠炎，艰难梭菌通过一种 一系列的表型。这种光谱背后的机制还不完全清楚。尽管取得了进展 在对疾病的了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、结肠切除或需要危重护理(重症)迅速作出反应 结果，~3%)仍然很常见。随着以患者为中心的结果(PCOS)在临床研究中占据中心地位， 艰难梭菌感染(CDI)也不例外。这份职业发展提案是基于中央 对新的和现有的免疫、微生物和代谢数据(IMM/D)进行综合分析的假设 通过识别生物标志物和途径，CDI患者和携带者队列可以改变患者 结果(尤其是多囊卵巢综合征)，并将CDI重新归类为殖民连续体(CDI/cc)。哈维尔·维拉弗尔特 Gálvez[JVG]，医学博士，哈佛医学院(HMS)讲师，胃肠病专家 贝丝以色列女执事医疗中心(BIDMC)的免疫介导的消化系统疾病。他已经收获了 在担任博士后和T32研究员期间，他在临床和翻译研究方面拥有丰富的经验 证明了对以患者为中心的研究事业的承诺。 该奖项将为申请者提供发展定量分析技能的机会 利用有指导的研究机会解决预测、分类和 CDI的病理生物学。CDI和肠道炎症方面的专家Ciarán P.Kelly博士将担任导师， 与生物信息学专家刘阳宇博士一起研究人类肠道微生物组的动力学，他将 成为共同导师。咨询委员会由CDI和方法学专家(Garey博士、Pollock博士、 Gerszten和Dubberke)将定期与申请者磋商，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UNMET 免疫-消化障碍方面的需求，CDI是第一个模式。课程的主干将是 HMS生物医学信息学硕士。通过哈佛催化剂提供的写作培训和支持将是 危急时刻。这个项目的第一个目标是在CDI的时候识别粪便免疫缺陷病毒的生物标志物和途径。 诊断与严重后果、复发和疾病轨迹相关。受此消息，我们将招聘 一个新的CDI队列，在收集生物样本的同时测量PCOS(症状，生活质量)，以便识别 预测和潜在多囊卵巢综合征的IMM/d生物标志物和途径。最后，以imm/d为衬底， 机器学习算法对标准诊断失明，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、预测和 同时将多囊卵巢综合征放在最前列，为R级建议生成基础数据。

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.