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Background <p>Cardiovascular-kidney-metabolic (CKM) syndrome is increasingly recognized as a distinct disorder with important implications for health outcomes, but its heterogeneity of presentation and genetic underpinning remains poorly understood. We aimed to identify potential CKM subtypes and their genetic basis by analyzing biomarkers and health outcomes in a large biobank.</p> Methods <p>Blood and urine biomarkers from 121,918 participants in the Lifelines cohort were analyzed using topic modelling. Candidate CKM subtypes were operationally defined as blood-urine topics that were simultaneously and positively associated with self-reported kidney disease, type 2 diabetes, and cardiovascular disease. Genome-wide association studies were performed on 52,727 genotyped participants to identify common genetic variants linked to these candidate subtypes.</p> Results <p>Five candidate CKM subtypes were identified, each characterized by high levels of blood glucose, uric acid, urea and inflammation biomarkers, but differing in liver enzyme, cholesterol, and glycaemic profiles. Genetic analyses revealed 57 genome-wide significant variants, with the majority (35) not detected in single-biomarker analyses. Most variants were subtype-specific, suggesting that distinct biological pathways contribute to these candidate CKM subtypes.</p> Conclusions <p>Our analysis suggests distinct genetic architectures underlying different CKM manifestations and demonstrates that combining biomarkers in disease-relevant constellations improves detection of genetic variants.</p>

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Cardiovascular-kidney-metabolic syndrome: candidate subtypes and genetic risk factors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Hylke C. Donker,
  • Vartika Bisht,
  • Om Prakash Dwivedi,
  • Stefanie Mueller,
  • Dorien Neijzen,
  • Zhihao Ding,
  • Gerton Lunter

摘要

Background

Cardiovascular-kidney-metabolic (CKM) syndrome is increasingly recognized as a distinct disorder with important implications for health outcomes, but its heterogeneity of presentation and genetic underpinning remains poorly understood. We aimed to identify potential CKM subtypes and their genetic basis by analyzing biomarkers and health outcomes in a large biobank.

Methods

Blood and urine biomarkers from 121,918 participants in the Lifelines cohort were analyzed using topic modelling. Candidate CKM subtypes were operationally defined as blood-urine topics that were simultaneously and positively associated with self-reported kidney disease, type 2 diabetes, and cardiovascular disease. Genome-wide association studies were performed on 52,727 genotyped participants to identify common genetic variants linked to these candidate subtypes.

Results

Five candidate CKM subtypes were identified, each characterized by high levels of blood glucose, uric acid, urea and inflammation biomarkers, but differing in liver enzyme, cholesterol, and glycaemic profiles. Genetic analyses revealed 57 genome-wide significant variants, with the majority (35) not detected in single-biomarker analyses. Most variants were subtype-specific, suggesting that distinct biological pathways contribute to these candidate CKM subtypes.

Conclusions

Our analysis suggests distinct genetic architectures underlying different CKM manifestations and demonstrates that combining biomarkers in disease-relevant constellations improves detection of genetic variants.