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Objective <p>Cardiovascular-Kidney-metabolic (CKM) syndrome is a concept introduced by the American Heart Association, describing systemic diseases resulting from the pathophysiological interaction among obesity, stroke, type 2 diabetes, chronic kidney disease, and cardiovascular disease. Glycoprotein acetyls (GlycA) is a novel biomarker for systemic inflammation and cardiovascular risk. This study aims to uncover the shared genetic architecture between GlycA and CKM, identify common risk loci and key tissues, and clarify the mechanisms linking these conditions.</p> Methods <p>Using summary data from large-scale GWAS studies, we evaluated both global and local genetic correlations between GlycA and various components of CKM. A cross-trait pleiotropic analysis was conducted to identify shared pleiotropic loci and related genes. FUSION was used to find susceptibility genes linked to GlycA and CKM. A comprehensive set of functional annotations and tissue-specific analyses was performed to uncover potential connections between these traits. Furthermore, a multi-trait colocalization method was utilized to explore the underlying mechanisms of the association between GlycA and CKM.</p> Results <p>Our study revealed a close genetic correlation between GlycA and CKM. We identified 260 potential SNPs that were corrected using the Bonferroni method, of which 30 passed the colocalization test (PP.H4 &gt; 0.70). Notably, locus 9q34.2 appeared in both GlycA-T2D and GlycA-HF trait pairs. By integrating post-GWAS methods, we identified 53 potentially drug targets (CMIP, DNAJC5G, FCGRT, etc.). FUSION analysis identified 23 shared susceptibility genes for GlycA and CKM (ZPR1, IL6R, PABPC4, etc.). Analysis of tissue enrichment at the SNP level revealed pleiotropic mechanisms associated with whole blood and liver. Gene-level analysis highlighted important roles of metabolic signaling pathways. Furthermore, multi-trait colocalization analysis suggested that rs1260326 and rs2925979 may play significant roles in the mechanistic link between GlycA and CKM.</p> Conclusion <p>Our study confirmed a significant genetic association and shared genetic structure between GlycA and CKM, revealing potential mechanisms that may be involved.</p> Graphical abstract <p></p>

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GlycA-gene interactions in cardiovascular-kidney-metabolic syndrome: evidence from genetic association

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月25日

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  • Mingyang Yuan,
  • Suwen Cao,
  • Xing Wan,
  • Yuting Yang,
  • Siyu Fan,
  • Jiang Xie,
  • Han Wang

摘要

Objective

Cardiovascular-Kidney-metabolic (CKM) syndrome is a concept introduced by the American Heart Association, describing systemic diseases resulting from the pathophysiological interaction among obesity, stroke, type 2 diabetes, chronic kidney disease, and cardiovascular disease. Glycoprotein acetyls (GlycA) is a novel biomarker for systemic inflammation and cardiovascular risk. This study aims to uncover the shared genetic architecture between GlycA and CKM, identify common risk loci and key tissues, and clarify the mechanisms linking these conditions.

Methods

Using summary data from large-scale GWAS studies, we evaluated both global and local genetic correlations between GlycA and various components of CKM. A cross-trait pleiotropic analysis was conducted to identify shared pleiotropic loci and related genes. FUSION was used to find susceptibility genes linked to GlycA and CKM. A comprehensive set of functional annotations and tissue-specific analyses was performed to uncover potential connections between these traits. Furthermore, a multi-trait colocalization method was utilized to explore the underlying mechanisms of the association between GlycA and CKM.

Results

Our study revealed a close genetic correlation between GlycA and CKM. We identified 260 potential SNPs that were corrected using the Bonferroni method, of which 30 passed the colocalization test (PP.H4 > 0.70). Notably, locus 9q34.2 appeared in both GlycA-T2D and GlycA-HF trait pairs. By integrating post-GWAS methods, we identified 53 potentially drug targets (CMIP, DNAJC5G, FCGRT, etc.). FUSION analysis identified 23 shared susceptibility genes for GlycA and CKM (ZPR1, IL6R, PABPC4, etc.). Analysis of tissue enrichment at the SNP level revealed pleiotropic mechanisms associated with whole blood and liver. Gene-level analysis highlighted important roles of metabolic signaling pathways. Furthermore, multi-trait colocalization analysis suggested that rs1260326 and rs2925979 may play significant roles in the mechanistic link between GlycA and CKM.

Conclusion

Our study confirmed a significant genetic association and shared genetic structure between GlycA and CKM, revealing potential mechanisms that may be involved.

Graphical abstract