PATHWAYS TO NEW BIOMARKERS IN RECURRENT ABDOMINAL PAIN IN CHILDREN

项目总结/摘要 腹痛相关功能性胃肠疾病（FGID;以前称为复发性腹部疾病） 疼痛）影响着全世界10-20%的儿童和成人，对他们的经济、社会和情感造成了巨大的影响。 负担高达66%的儿童会出现与成人相似的症状。在儿童中，两种最常见的 FGID是功能性腹痛（FAP）和肠易激综合征（IBS -本质上是FAP， 在大便模式中）。管理和治疗受到缺乏生物标志物的阻碍， 从病理生理学上理解什么是表型和任意定义的条件。以前的研究 我评估了IBS，而不是FAP，主要依赖于回顾性症状评估和使用的方法 (e.g., 16 S测序），其限制了扰动的深度询问（例如，肠道菌群失调） 成人IBS此外，“组学”（代谢组学、脂质组学、元蛋白组学）数据在很大程度上缺失， 我们的初步数据表明，它可能提供关键的机制， 深入了解腹痛症状和肠道生态失调、屏障的病理生物学改变之间的联系 功能障碍和神经免疫功能障碍，我们和其他人已经在FGID中描述过。我们的初步数据 支持这样的假设，即这些改变可以在病理生物学上区分FGID和健康对照， 并确定FGIDs中疼痛的疾病机制。我们建议建立在我们以前的工作和使用 先前收集的前瞻性腹痛和排便日记以及从大型 FGID组（IBS组133例，FAP组47例）和健康对照组（HC组112例）。我们 假设是微生物群落表征和“组学分析"将提供生物标志物， 区分FGID（IBS和FAP）与HC，并深入了解疼痛症状（和排便）的发生 IBS的特征）。我们的具体目标是用途：1）全球无偏全基因组鸟枪测序， 代谢组学、脂质组学和元蛋白质组学（“组学”），以区分FGID儿童与 使用分类器模型的HC;子目标-探索FAP与IBS和HC之间的潜在差异;以及2） 专有的德克萨斯儿童医院微生物组中心参考数据库和最先进的生物信息学 方法（例如，监督学习，二分，贝叶斯模型），以识别疾病特异性生物标志物， FGID儿童的治疗目标：子目标2a -描述“组学”和“治疗”之间的关系， 腹痛症状（和IBS中的排便症状）。子目标2b -描述关系 “组学”与异常生理学（屏障功能受损、神经免疫功能障碍）之间的关系。预计 这项创新的多学科研究将更好地为患者管理提供信息，并提供独特的治疗方法。 基于FGID生物标志物提供的新见解的策略。该应用程序的目标符合NINR PA- 18-140，最近出版的NINR症状科学模型，以及NIH共同基金和精确度 医学倡议。

PROJECT SUMMARY/ABSTRACT Abdominal pain-related functional gastrointestinal disorders (FGIDs; previously called Recurrent Abdominal Pain) affect 10-20% of children and adults worldwide exerting a tremendous economic, social, and emotional burden. Up to 66% of children go on to have similar symptoms as adults. In children, the two most common FGIDs are functional abdominal pain (FAP) and irritable bowel syndrome (IBS - essentially FAP with changes in stooling pattern). Management and treatment are hampered by lack of biomarkers to characterize and understand pathophysiologically what are phenotypically and arbitrarily defined conditions. Previous studies have evaluated IBS, not FAP, and relied primarily on retrospective symptom evaluation and utilized methodology (e.g., 16S sequencing) that limits in depth interrogation of perturbations (e.g., gut dysbiosis) reported in children and adults with IBS. Further, `omics (metabolomics, lipidomics, metaproteomics) data is largely missing from studies of FGIDs and is urgently required - our preliminary data show that it likely provides critical mechanistic insight into the links between abdominal pain symptoms and the pathobiologic alterations of gut dysbiosis, barrier dysfunction, and neuroimmune dysfunction which we and others have described in FGIDs. Our preliminary data support the hypothesis that these alterations can pathobiologically discriminate FGIDs from healthy controls as well as identify disease mechanisms of pain in FGIDs. We propose to build on our previous work and use previously collected prospective abdominal pain and stooling diaries and stool samples collected from a large and well-vetted group of children with FGIDs (IBS n=133, FAP n=47) and healthy controls (HC, n=112). Our Hypothesis is that microbial community characterization and `omics profiling will provide biomarkers to differentiate FGIDs (IBS and FAP) from HC and generate insight into the genesis of pain symptoms (and stooling characteristics in IBS). Our Specific Aims are to use: 1) Global unbiased whole genome shotgun sequencing, metabolomics, lipidomics and metaproteomics (`omics) on stool samples to differentiate children with FGIDs vs HC using classifier models; Sub-Aim – explore potential differences between FAP and IBS and HC; and 2) Proprietary Texas Children's Hospital Microbiome Center reference databases and state-of-the art bioinformatics approaches (e.g., supervised learning, bipartite, Bayesian models) to identify disorder-specific biomarkers and therapeutic targets within children with FGIDs: Sub-Aim 2a - Characterize the relationships between `omics and abdominal pain symptoms (and stooling symptoms in IBS). Sub-Aim 2b – Characterize the relationships between `omics and abnormal physiology (impaired barrier function, neuroimmune dysfunction). It is anticipated that this innovative, multidisciplinary study will better inform patient management and offer unique therapeutic strategies based on novel insights provided by FGID biomarkers. The goals of this application fit with NINR PA- 18-140, the recently published NINR Symptom Science Model, and the NIH Common Fund and Precision Medicine Initiatives.