Identify the heterogeneity and commonality of chronic overlapping pain conditions (COPCs) through phenotypic and genomic perspectives

通过表型和基因组观点识别慢性重叠疼痛病症 (COPC) 的异质性和共性

• 批准号: 10665773
• 负责人: Jungwei Wilfred Fan
• 金额: $ 19.37万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10665773
• 关键词: Adult; Algorithms; All of Us Research Program; American; Anatomy; Bioinformatics; Biological; Biometry; Catalogs; Characteristics; Chronic; Classification Scheme; Clinical; Clinical Data; Clinical Informatics; Code; Cohort Studies; Controlled Study; Craniofacial Pain; Data; Data Science; Data Set; Development; Diagnosis; Diagnostic; Disease; Elements; Epidemiology; Etiology; Event; Functional disorder; Future; Genes; Genetic; Genome; Genomics; Genotype; Genotype-Tissue Expression Project; Goals; Graph; Heterogeneity; Informatics; Irritable Bowel Syndrome; Knowledge; Measurement; Medicine; Methods; Mining; National Human Genome Research Institute; Natural Language Processing; Neuroimmune; Neurosecretory Systems; Noise; Observational Study; Ontology; Outcome; Pain; Pain management; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Population; Positioning Attribute; Prevalence; Protocols documentation; Public Health; Quantitative Trait Loci; Records; Reproducibility; Research; Resolution; Resources; Risk; Sample Size; Semantics; Subgroup; Surface; Taxonomy; Temporomandibular Joint Disorders; Testing; Therapeutic; Translational Research; Untranslated RNA; Work; biobank; chronic pain; clinical data warehouse; comorbidity; cost; craniofacial; design; drug repurposing; effective therapy; functional genomics; gastrointestinal; genome wide association study; genome-wide; genomic data; health record; innovation; insight; interdisciplinary approach; knowledge base; multiple data sources; novel; orofacial; patient subsets; phenome; population based; precision medicine; statistics; stressor; success; symptomatology; treatment strategy

PROJECT SUMMARY/ABSTRACT With an estimated prevalence of 20% in American adults and annual costs above $500 million, chronic pains pose a high toll to public health. Many patients developed chronic overlapping pain conditions (COPCs), where craniofacial pains like temporomandibular disorder (TMD) represent a unique component that co-occurs frequently with other chronic pains including irritable bowel syndrome (IBS). Not only do the comorbidities complicate pain management, but the etiology of COPCs remains unclear. Heterogeneity exists within and across COPCs, so mechanism-based classification schemes are needed to identify safe and effective therapies for distinct subgroups of patients. However, existing research surrounding COPC taxonomy has not fully integrated phenotypic and genotypic data at the population level. Reliance on disease-specific study cohorts seriously limited the sample size and diversity. On the other hand, informatics and data science have advanced secondary use of biomedical data, presenting a strong alternative to hypothesis-driven, controlled studies. We propose to elicit COPC subgroups by mining three distinct population-based clinical datasets and imputing the biological underpinnings of co-occurring COPCs by using functional genomic knowledge bases. Our approach consists of two aims: 1) Identify COPC subgroups and other commonly associated phenotypes from rich longitudinal clinical data and notes, including over one million patients in the Rochester Epidemiology Project. The clinical datasets will be computationally screened for COPCs and other co-occurring phenotypes based on diagnosis codes and natural language processing. We will identify statistically significant COPC comorbidities and progression trajectories using novel and tailored statistics. The discovered trajectories will be clustered into subgroups using cutting-edge graph clustering algorithms. The patients will be assigned to the best matched subgroups, for which additional phenotypic characteristics of each group will be determined by least absolute shrinkage and selection operator. 2) Impute biological underpinnings for comorbid COPCs by integrating phenotypic, genetic, and genomic data in biobanks and biorepositories. We will conduct genome-wide and phenome-wide association studies based on the diagnoses and genotypes from the UK Biobank and All of Us Research Program, leading to identification of additional genotypes that are associated with the COPCs and beyond those in the NHGRI-EBI GWAS catalog. We will apply our information-theoretic framework to impute the functional similarity and shared biological mechanisms across COPCs by using GTEx expression quantitative trait loci data and gene ontology annotations. The findings of shared mechanisms among COPCs will provide novel insight into the genetic factors, particularly in noncoding regions, and functional linkages that are pivotal to developing applications such as drug repurposing for COPCs. The two aims corroborate each other across the genome-phenome boundary to unveil interpretable subgroups that will advance precision medicine for COPCs.

项目摘要/摘要 据估计，慢性疼痛在美国成年人中的患病率为20%，每年的费用超过5亿美元。 对公众健康造成了巨大的损失。许多患者出现慢性重叠疼痛状况(COPC)，其中 像颞下颌关节紊乱症(TMD)这样的颅面疼痛是一种共同发生的独特成分 经常伴有其他慢性疼痛，包括肠易激综合征(IBS)。不仅是合并症 疼痛处理复杂，但COPC的病因尚不清楚。和内部存在异质性 因此，需要基于机制的分类方案来确定安全有效的治疗方法 适用于不同的患者亚群。然而，围绕COPC分类的现有研究还没有完全 综合种群一级的表型和遗传型数据。对疾病特异性研究队列的依赖 严重限制了样本量和多样性。另一方面，信息学和数据科学也取得了进步 生物医学数据的二次利用，为假说驱动的对照研究提供了一个强有力的替代方案。我们 建议通过挖掘三个不同的基于总体的临床数据集并将 利用功能基因组知识库研究共生COPC的生物学基础。我们的方法 包括两个目标：1)从RICH中识别COPC亚群和其他共同相关的表型 纵向临床数据和笔记，包括罗切斯特流行病学项目中的100多万名患者。 临床数据集将根据以下条件对COPC和其他共生表型进行计算筛选 诊断代码和自然语言处理。我们将确定有统计学意义的COPC并存 以及使用新颖和量身定制的统计数据的进度轨迹。已发现的轨迹将被聚集到 使用尖端图聚类算法的子组。病人将被分配给最匹配的人 子组，其中每个组的附加表型特征将由最小绝对值法确定 收缩和选择操作符。2)通过整合确定共病COPC的生物学基础 生物库和生物仓库中的表型、遗传和基因组数据。我们将进行全基因组和 基于英国生物库和我们所有人的诊断和基因型别的全组关联研究 研究计划，导致识别与COPC和COPC相关的其他基因类型 超出NHGRI-EBI Gwas目录中的那些。我们将应用我们的信息论框架来归因于 用GTEx表达定量研究COPC之间的功能相似性和共同的生物学机制 性状基因座数据和基因本体论注释。COPC之间共享机制的调查结果将提供 对遗传因素的新见解，特别是在非编码区，以及对 开发用于COPC的药物再利用等应用。这两个目标是相互印证的 基因组-表型组边界揭示可解释的亚群，这将促进COPC的精确医学。