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

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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.