Myocardial fibrosis and tissue alterations predict cardiovascular outcomes in chronic kidney disease—a prospective virtual twin study design using large-scale population database

Background Myocardial fibrosis is a key feature of chronic kidney disease (CKD) and may contribute to its disproportionate cardiovascular (CV) burden. Cardiovascular magnetic resonance (CMR) T1-mapping quantifies diffuse fibrosis non-invasively, but its prognostic value in CKD remains uncertain Aims To investigate associations between native myocardial T1, mortality and incident CV outcomes, in CKD using a virtual twin-matching framework within the UK Biobank imaging cohort. Methods We conducted a 1:1 virtual twin-matched case-cohort study of CKD cases and phenotypically matched non-CKD controls based on demographics, comorbidities, socioeconomic status, and cardiovascular risk factors. Paired analyses compared myocardial T1 between groups, and stratified Cox models estimated the effect of a 1-SD T1 increase on incident outcomes over a median follow-up of 4.9 years. Results Among 193 matched pairs (median age 68 years; 57.5% women), CKD participants had trend-level higher myocardial T1 values than controls ( p = 0.063), with greater differences among those experiencing adverse outcomes. Over follow-up, CKD participants had higher cumulative incidences of all-cause mortality (11.3 vs. 3.8 events/1,000 person-years; p = 0.049) and heart failure (12.7 vs. 3.9 events/1,000 person-years; p = 0.021). In CKD, each 1-SD increase in T1 was associated with higher risks of cardiovascular death (HR: 3.86; 95% CI: 1.62, 9.18), heart failure (HR: 2.00; 95% CI: 1.27, 3.15), and atrial fibrillation (HR: 2.04; 95% CI: 1.01, 4.12), but not all-cause mortality or myocardial infarction. No significant associations were observed in matched non-CKD controls. Conclusions Elevated native myocardial T1 was independently associated with CV death, heart failure, and atrial fibrillation in CKD. Virtual twin-matching framework improved comparability and internal validity in matched pairs supporting the precision-matched cohort designs for mechanistic inference and risk stratification in multimorbid populations.