Metabolic syndrome, chronic inflammation, and gout: a multi-omics approach

代谢综合征、慢性炎症和痛风：多组学方法

• 批准号: 10351601
• 负责人: Natalie McCormick
• 金额: $ 9.4万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10351601
• 关键词: Adult; Affect; Area; Arthritis; Award; Bioinformatics; Biological; Branched-Chain Amino Acids; C-Peptide; C-reactive protein; Candidate Disease Gene; Chinese; Chronic; Clinical; Complex; Crystallization; Data; Data Analytics; Development; Dietary Assessment; Dimensions; Disease; Epidemiologist; Epidemiology; Flare; Follow-Up Studies; Food; Foundations; Frequencies; Future; General Hospitals; Genes; Genetic; Genetic Markers; Genetic Predisposition to Disease; Genomics; Genotype-Tissue Expression Project; Gout; Health Professional; Hyperinsulinism; Hyperuricemia; Inflammasome; Inflammation; Inflammatory; Inflammatory Arthritis; Insulin Resistance; Intake; Interleukin-6; Japanese; Knowledge; Leukocytes; Limes; Mediating; Mediterranean Diet; Mentors; Meta-Analysis; Metabolic; Metabolic syndrome; Methodology; Methods; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Nature; Nested Case-Control Study; Nonesterified Fatty Acids; Nurses' Health Study; Obesity; Oxidative Stress; Pain; Pathogenesis; Pathway interactions; Pattern; Pilot Projects; Plasma; Positioning Attribute; Precipitation; Predisposition; Premature Mortality; Prevention; Prospective cohort; Proteins; Proteome; Proteomics; Regulator Genes; Regulatory Element; Research; Resources; Rheumatism; Risk; Risk Factors; Role; Syndrome; System; Systems Biology; TNFRSF1A gene; Training; Trans-Omics for Precision Medicine; Urate; Work; adiponectin; base; biobank; cardiometabolism; career; case control; circulating biomarkers; clinical care; comorbidity; cost; dietary; follow-up; genetic architecture; genetic association; genetic variant; genome wide association study; genomic data; high dimensionality; improved; indexing; inflammatory marker; innovation; insight; joint injury; machine learning method; machine learning model; metabolome; metabolomics; modifiable risk; multidimensional data; multidisciplinary; multiple omics; novel; population health; prevent; prospective; response; skills; systemic inflammatory response; training opportunity; trait; transcriptome; transcriptomics; whole genome

Gout, an inflammatory arthritis affecting 9 million US adults, is characterised by painful flares, joint damage, and premature mortality. Gout frequently coexists with the metabolic (insulin resistance) syndrome (MetS) and its cardiometabolic sequalae, but the nature of these associations remains controversial. It is also unclear what modulates the progression from prolonged hyperuricemia (HU) to clinical gout, though emerging ‘omics data have implicated genes and metabolites associated with inflammation. As such, multi-omics integration (genomics, transcriptomics, metabolomics, and proteomics) could advance understanding of gout disease mechanisms via a systems epidemiology approach. To comprehensively investigate the relationships between MetS, chronic inflammation, and gout, I propose to examine 3 Specific Aims by leveraging the rich resources in UK Biobank, Nurses Health Studies (NHS), Health Professionals Follow-Up Study (HPFS), and Genotype- Tissue Expression project (GTEx). In Aim 1 [K99] I will integrate genetic association (GWAS) data from UK Biobank, NHS/HPFS, and global consortia (including a new gout GWAS), and transcriptomic data in GTEx, to examine shared genetic architectures between MetS components, systemic inflammatory markers, and gout, and whether polygenic susceptibility to MetS and chronic inflammation confers gout risk. In Aim 2 [R00], I will integrate existing dietary and metabolomic data to examine metabolomic profiles mediating the associations between dietary hyperinsulinemic and inflammatory potentials, and HU and gout risk in the NHS/HPFS. In Aim 3 [R00] I will conduct plasma proteomic profiling in a nested case-control study within NHS/HPFS to identify inflammatory protein networks in relation to gout risk, and as a Secondary Aim, integrate findings from Aims 1- 3 to explore gout-related pathways co-regulating at multiple biological dimensions. This innovative project should generate novel mechanistic insights into the metabolic and inflammatory pathways underlying HU and gout risk, which could inform prevention and treatment; for example, whether improving metabolic syndrome would reduce gout risk. Simultaneously, I will receive extensive training in gout systems biology and cutting- edge, high-dimensional data analytics and bioinformatics, including machine-learning methods. I will be mentored/advised by an interdisciplinary team at Mass General Hospital and Harvard including Dr. Hyon Choi (gout epidemiologist), Dr. Liming Liang (expert in statistical ‘omics methodologies), Dr. Tony Merriman (gout geneticist), Dr. Jessica Lasky-Su (expert in metabolomics and multi-omics integration), and Dr. Robert Gerszten (proteomics expert). The outstanding and diverse training opportunities with key leaders in these areas will provide me with advanced knowledge and skills, positioning me for a successful, independent career applying systems biology and integrated ‘omics approaches to the study of gout and other complex traits. This project aligns closely with NIAMS’ scientific objective to develop machine learning methods, combining layers of ‘omics data, to generate new mechanistic hypothesises for gout and other systemic rheumatic diseases.

痛风是一种影响900万美国成年人的炎症性关节炎，其特征是疼痛发作，关节损伤， 和过早死亡。痛风经常与代谢(胰岛素抵抗)综合征(METS)共存，并 它的心脏代谢后遗症，但这些联系的性质仍然存在争议。目前也不清楚是什么 调节从长期高尿酸血症(HU)到临床痛风的进展，尽管新兴的组学数据 有与炎症相关的基因和代谢物。因此，多组学整合 (基因组学、转录组学、代谢组学和蛋白质组学)可以促进对痛风病的理解 通过系统流行病学方法的机制。全面调查两国之间的关系 METS、慢性炎症和痛风，我建议通过利用 英国生物库、护士健康研究(NHS)、卫生专业人员后续研究(HPFS)和基因分型- 组织表达计划(GTEx)。在目标1[K99]中，我将整合来自英国的遗传关联(Gwas)数据 Biobank、NHS/HPFS和全球联盟(包括新的痛风GWAS)，以及GTEx中的转录数据，以 检查蛋氨酸组分、全身炎症标志物和痛风之间的共同遗传结构， 以及对Met和慢性炎症的多基因易感性是否会增加痛风风险。在Aim 2[R00]中，我将 整合现有的饮食和代谢组学数据以检查调节这种关联的代谢组学特征 NHS/HPFS中饮食高胰岛素和炎症潜能与HU和痛风风险之间的关系。在AIM 3[R00] 我将在NHS/HPFS的嵌套病例对照研究中进行血浆蛋白质组学分析，以确定 炎性蛋白网络与痛风风险的关系，并作为次要目标，整合AIMS 1- 3从多个生物学维度探讨痛风相关通路的协同调控。这个创新的项目 应该产生对HU和HU背后的代谢和炎症途径的新的机械性见解 痛风风险，可为预防和治疗提供信息；例如，是否改善代谢综合征 会降低痛风的风险。同时，我将接受痛风系统生物学和切割方面的广泛培训- 边缘、高维数据分析和生物信息学，包括机器学习方法。我会的 由麻省总医院和哈佛大学的跨学科团队指导/建议，其中包括蔡贤博士 (痛风流行病学家)，梁黎明博士(统计组学方法论专家)，Tony Merriman博士(痛风 遗传学家)、Jessica Lasky-Su博士(代谢组学和多组学整合专家)和Robert博士 Gerszten(蛋白质组学专家)。优秀和多样化的培训机会，与这些领域的主要领导者 这些领域将为我提供先进的知识和技能，为我成功、独立的职业生涯做好准备 应用系统生物学和集成的组学方法研究痛风和其他复杂的特征。这 该项目与NIAMS的科学目标密切一致，即开发结合层次的机器学习方法 以产生痛风和其他系统性风湿病的新机制假说。