| Title: | Cardiovascular-kidney-metabolic syndrome: candidate subtypes and genetic risk factors |
| Journal: | BMC Medical Genomics |
| Published: | 31 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41620767/ |
| DOI: | https://doi.org/10.1186/s12920-026-02315-8 |
Publication 18405
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Abstract
BackgroundCardiovascular-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.MethodsBlood 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.ResultsFive 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.ConclusionsOur analysis suggests distinct genetic architectures underlying different CKM manifestations and demonstrates that combining biomarkers in disease-relevant constellations improves detection of genetic variants.</p>
7 Authors
- Hylke C. Donker
- Vartika Bisht
- Om Prakash Dwivedi
- Stefanie Mueller
- Dorien Neijzen
- Zhihao Ding
- Gerton Lunter
1 Application
| Application ID | Title |
|---|---|
| 206855 | Using Machine Learning to uncover disease patterns in large-scale cohort data |
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