Integrated Multi-Omics Mapping of the Causal Landscape of Gout across the Circulating-Tissue Axis

Meng Li^1*Liang Huang²Jiani Liu³Xiaohui Zheng²Kai Zhang⁴Yixin Chen⁵Xiaoling Chen⁵Siqi Zhang⁵Shanshan Cai^6*Li Cai^7*Yanyan Guo^8*

• ¹Department of Rheumatology and Immunology, Nanfang Hospital, Southern Medical University, Guangzhou, China
• ²The Second Affliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan, China
• ³Department of Traditional Chinese Internal Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
• ⁴Nanjing Hospital of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
• ⁵The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
• ⁶Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YG, UK Lancashire, United Kingdom
• ⁷Department of Nephrology and Rheumatology, The Affiliated Traditional Chinese Medicine Hospital, Guangzhou Medical University, Guangzhou, China
• ⁸Department of Radiology, Taishan People's Hospital, Jiangmen, Guangdong, China

Background: Gout is a prevalent inflammatory arthropathy driven by monosodium urate crystal deposition, yet the causal relationships between circulating biomarkers and disease susceptibility remain incompletely characterized. Establishing robust causal associations and mapping them to specific effector genes and tissues is essential for identifying mechanistically informed therapeutic targets. Methods: We conducted a comprehensive multi-omics Mendelian randomization study integrating a meta-analysis of three large-scale gout genome-wide association studies (N=1,538,494) with genome-wide data for 233 metabolites, 179 lipid species, and 926 plasma proteins. Findings were replicated in an independent cohort (N=327,457). Summary-data-based Mendelian randomization and Bayesian colocalization (HyPrColoc) were applied to map causal biomarkers to tissue-specific effector genes using expression quantitative trait loci data from kidney, liver, and whole blood. Candidate genes were experimentally validated in monosodium urate-stimulated THP-1 macrophages. Results: We identified 32 metabolites, one lipid species (TAG 54:3), and two protective plasma proteins (ISLR2, ITIH3) with replicated causal associations with gout. Triglyceride-rich very-low-density lipoprotein particles and circulating isoleucine emerged as prominent risk factors. Multi-tissue transcriptomic mapping prioritised PRELID1 (kidney), NIPAL1 (liver), LMAN2 (whole blood), and CAD as high-confidence effector genes with strong colocalization evidence (posterior probability >0.70). Functional validation confirmed concordant transcriptional and translational dysregulation of these genes following inflammatory stimulation. Conclusion: This integrative analysis establishes a causal framework linking specific lipoprotein subfractions, amino acid metabolism, and novel effector genes to gout pathogenesis, elucidating the systemic metabolic architecture of the disease and identifying potential therapeutic candidates warranting further preclinical investigation before clinical translation.