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，其特点是疼痛发作、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， 其与心脏代谢后遗症有关，但这些关联的性质仍然存在争议。也不清楚是什么 尽管新兴的“组学数据”调节从长期高尿酸血症（HU）到临床痛风的进展 涉及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）可以增进对痛风疾病的了解 通过系统流行病学方法的机制。全面考察之间的关系 MetS、慢性炎症和痛风，我建议利用丰富的资源来研究 3 个具体目标 英国生物银行、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 Biobank、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 成分、全身炎症标志物和痛风之间共享的遗传结构， 对 MetS 和慢性炎症的多基因易感性是否会带来痛风风险。在目标 2 [R00] 中，我将 整合现有的饮食和代谢组学数据来检查介导关联的代谢组学概况 NHS/HPFS 中膳食高胰岛素血症和潜在炎症以及 HU 和痛风风险之间的关系。瞄准 3 [R00] 我将在 NHS/HPFS 内的嵌套病例对照研究中进行血浆蛋白质组分析，以确定 炎症蛋白网络与痛风风险相关，作为次要目标，整合目标 1- 的发现 3 探索痛风相关通路在多个生物维度上的共同调节。这个创新项目 应该对 HU 和炎症途径的代谢和炎症途径产生新的机制见解 痛风风险，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（包括 Hyon Choi 博士）提供指导/建议 （痛风流行病学家）、梁黎明博士（统计组学方法专家）、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）。与这些领域的主要领导者一起提供出色且多样化的培训机会 领域将为我提供先进的知识和技能，使我能够获得成功、独立的职业生涯 应用系统生物学和综合组学方法来研究痛风和其他复杂特征。这个 该项目与 NIAMS 开发机器学习方法的科学目标紧密结合，将各层相结合 组学数据，为痛风和其他系统性风湿病产生新的机制假设。

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.