{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:33:38Z","timestamp":1782840818817,"version":"3.54.5"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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 prioritized\n                      <jats:italic>PRELID1<\/jats:italic>\n                      (kidney),\n                      <jats:italic>NIPAL1<\/jats:italic>\n                      (liver),\n                      <jats:italic>LMAN2<\/jats:italic>\n                      (whole blood), and\n                      <jats:italic>CAD<\/jats:italic>\n                      as high-confidence effector genes with strong colocalization evidence (posterior probability &amp;gt;0.70). Functional validation confirmed concordant transcriptional and translational dysregulation of these genes following inflammatory stimulation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  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