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

痛风是一种影响900万美国成年人的炎症性关节炎，其特征是疼痛的发作，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， 其心脏代谢后遗症，但这些协会的性质仍然存在争议。目前还不清楚 调节从长期高尿酸血症（HU）到临床痛风的进展， 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）可以促进对痛风疾病的理解 通过系统流行病学的方法。为了全面调查 代谢综合征，慢性炎症和痛风，我建议通过利用丰富的资源， 英国生物库，护士健康研究（NHS），卫生专业人员随访研究（HPFS）和基因型- 组织表达项目（GTEx）。在目标1 [K99]中，我将整合来自英国的遗传关联（GWAS）数据 生物库、NHS/HPFS和全球联盟（包括新的痛风GWAS），以及GTEx中的转录组数据， 检查MetS组分、全身性炎症标志物和痛风之间共享的遗传结构， 以及对MetS和慢性炎症的多基因易感性是否赋予痛风风险。在目标2 [R 00]中，我将 整合现有的饮食和代谢组学数据，以检查介导相关性的代谢组学特征 在NHS/HPFS中，饮食性高胰岛素血症和炎症潜能与HU和痛风风险之间的关系。在Aim中 3 [R00] 我将在NHS/HPFS的巢式病例对照研究中进行血浆蛋白质组学分析，以确定 炎症蛋白网络与痛风风险的关系，并作为次要目的，整合目的1- 3探讨痛风相关通路在多个生物学维度上的协同调节作用。这一创新项目 应该产生新的机制的见解代谢和炎症途径的基础HU和 痛风风险，这可以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。我将 由麻省总医院和哈佛的跨学科团队指导/建议，包括Hyon Choi博士 (gout流行病学家），梁黎明博士（统计学方法学专家），托尼梅里曼博士（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）。与这些领域的主要领导人一起提供出色和多样化的培训机会 这些领域将为我提供先进的知识和技能，使我能够成功，独立的职业生涯 将系统生物学和综合组学方法应用于痛风和其他复杂特征的研究。这 该项目与NIAMS的科学目标密切相关，即开发机器学习方法，结合层 的组学数据，以产生新的机制假说痛风和其他全身性风湿性疾病。

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.