ReviewBiomedical journal2026
Multi-omics insights into uric acid metabolism in cardiometabolic disease: from genetics to metabolomics.
Review in Biomedical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Hyperuricemia, characterized by elevated serum uric acid (SUA) levels, is increasingly recognized as a significant contributor to cardiometabolic diseases including hypertension, type 2 diabetes, chronic kidney disease, metabolic syndrome, and atherosclerotic cardiovascular disease. In the past, uric acid was seen mainly as a byproduct of purine metabolism linked to gout. However, growing evidence suggests that it plays an active role in causing metabolic and vascular dysfunction. Mechanistic studies have shown that higher uric acid levels can induce endothelial dysfunction, oxidative stress, inflammation, insulin resistance, and activation of the renin-angiotensin-aldosterone system, which together can worsen cardiometabolic conditions. Recent advances in high-throughput omics technologies have greatly improved understanding of the molecular mechanisms regulating uric acid metabolism. Genome-wide association studies (GWAS) have identified important urate transporter genes like SLC2A9, ABCG2, and SLC22A12, while epigenomic studies reveal how DNA methylation, histone changes, and non-coding RNAs connect genetic factors to environmental influences. Transcriptomic and single-cell RNA sequencing analyses explain how urate transport and inflammatory signaling are regulated in specific tissues, including the kidneys, liver, adipose, and vascular tissue. In parallel, metabolomic and proteomic studies have linked hyperuricemia to disruptions in purine metabolism, redox balance, lipid remodeling, and inflammatory protein networks. Together, multi-omics approaches that integrate genomics, epigenomics, transcriptomics, proteomics, and metabolomics, along with expression quantitative trait locus (eQTL) mapping, causal modeling, network biology, and AI analysis, now provide powerful tools for biomarker discovery and mechanistic interpretation. This review summarizes current insights into uric acid metabolism from a multi-omics perspective and highlights emerging opportunities for better risk assessment, biomarker discovery, therapeutic targeting, and tailored prevention strategies in cardiometabolic disease.
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