| Title: | Uncovering core diseases in temporal trajectories of cardiovascular-kidney-metabolic (CKM) syndrome in UK Biobank: network-based study |
| Journal: | Scientific Reports |
| Published: | 21 Apr 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42014907/ |
| DOI: | https://doi.org/10.1038/s41598-026-49572-2 |
Publication 17906
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Abstract
Cardiovascular-kidney-metabolic (CKM) syndrome is a progressive, multisystem disorder whose real-world clinical trajectory remains incompletely characterized. This study aimed to characterize the temporal progression of CKM syndrome and its associated disease spectrum using large-scale, population-based diagnostic data. Utilizing first-occurrence diagnosis data from the UK Biobank, we implemented a sequential analytical pipeline to: (i) quantify pairwise disease associations; (ii) construct longitudinal progression maps for cardiovascular, renal, and metabolic conditions; (iii) identify pivotal diagnoses via unsupervised clustering and network centrality; and (iv) validate findings through a nested case-control study. Trajectory-defined subpopulations were analyzed to assess comorbidity-specific progression patterns. From 1,408,155 diagnostic records, 3351 unique disease trajectories were reconstructed. Essential hypertension was the most frequent initiating diagnosis (44.1% of trajectories). Chronic kidney disease (CKD) and type 2 diabetes emerged as frequently co-occurring conditions within the core diagnosis sets. In case-control analysis, CKD was associated with a significantly increased risk of transition to subsequent diagnoses (RR = 3.597; 95% CI 3.325-3.892). Among individuals with hypertension, those with coexisting type 2 diabetes (E11) had a significantly higher risk of developing CKD (N18) compared to those with atrial fibrillation (I48) or chronic ischaemic heart disease (I25) (HR = 2.944; 95% CI 2.760-3.139; P < 0.001). Our findings provide a detailed trajectory map of CKM syndrome based on UK Biobank data, describing hypertension-initiated, CKD-associated multimorbidity patterns. These findings offer a data-driven framework that may inform future mechanistic and interventional research targeting early stages of CKM progression.</p>
4 Authors
- Xinyang Long
- Mingli Li
- Dantong Li
- Zengnan Mo
1 Application
| Application ID | Title |
|---|---|
| 88365 | Construction of multimodal symbiosis medical database |
Enabling scientific discoveries that improve human health