Rest-activity rhythms and cardiovascular events in cardiovascular-kidney-metabolic syndrome: evidence from two nationwide cohorts.

Background: Circadian rest-activity rhythm (CRAR) is a modifiable determinant of metabolic and cardiovascular health, yet its role in cardiovascular events and mortality among individuals with cardiovascular-kidney-metabolic (CKM) syndrome remains unclear. Methods: Accelerometer-derived CRAR parameters were analyzed in two nationally representative cohorts. Primary outcomes included cardiovascular incidence among participants with CKM stages 0-3 and all-cause and cardiovascular mortality among those with stages 1-4. Multinomial logistic and Cox proportional hazards models assessed associations of CRAR with CKM progression and subsequent outcomes. Mediation analyses examined inflammatory biomarkers, and improvements in prediction were evaluated using changes in C-statistics. Results: Among 74,777 participants, higher relative amplitude (RA) tertiles were associated with slower CKM progression and lower risks of cardiovascular incidence (T2: HR 0.87, 95% CI 0.82-0.93; T3: HR 0.79, 95% CI 0.73-0.85), all-cause mortality (T2: HR 0.70, 95% CI 0.64-0.77; T3: HR 0.60, 95% CI 0.54-0.67), and cardiovascular mortality (T2: HR 0.70, 95% CI 0.57-0.86; T3: HR 0.45, 95% CI 0.34-0.61). Higher intradaily variability (IV) was associated with increased all-cause mortality (T2: HR 1.12, 95% CI 1.02-1.22; T3: HR 1.19, 95% CI 1.08-1.30). Inflammatory biomarkers modestly mediated these associations (1%-5%). Optimal thresholds were RA = 0.87 for cardiovascular incidence, RA = 0.81 and IV = 0.68 for mortality. Adding CRAR to basic models improved prediction of all-cause and cardiovascular mortality (ΔC-statistic = 0.019 and 0.017). Results were validated in an independent cohort of 6046 participants. Conclusion: Adverse CRAR is associated with CKM progression and elevated risks of cardiovascular events and mortality, highlighting its utility in identifying high-risk individuals and guiding targeted interventions through risk stratification and incremental prediction.