Telomere length and cardiovascular mortality among US adults aged 25 years or older: a multistate competing risk analysis

BackgroundCardiovascular disease (CVD) is the leading cause of mortality in the United States, with substantial economic and health impacts. While traditional risk factors are well studied, non-traditional factors like telomere length (TL), have garnered interest due to mixed findings on their association with CVD-specific mortality. This study investigates the association between TL and CVD-specific mortality, accounting for non-CVD-specific mortality as a competing risk, using a multistate framework. MethodsWe conducted a retrospective cohort study using data from the National Health and Nutrition Examination Survey 1999-2002 and 2019 Linked Mortality Files. This study included 6,516 non-institutionalized adults aged 25 years or older. TL was measured using quantitative PCR and analyzed continuously and categorically. We employed a multistate model to evaluate transitions from an event-free state to CVD-specific and non-CVD-specific mortality, estimating cause-specific hazard ratios (HRs) adjusted for sociodemographic and health risk factors. ResultsThe cumulative incidence function for CVD-specific mortality was significantly higher in the lowest TL quartile than in the highest quartile (Gray test, p < 0.01). Grays test was used to compare CIFs across quartiles without applying a Fine and Gray model. We found that a shorter TL was associated with a higher risk of CVD-specific mortality. In the adjusted model, each unit decrease in TL was associated with a 57% higher rate of CVD-specific mortality (HR: 1.57, 95% CI: 1.24-1.98). Adults in the shortest TL quartile had an 88% higher rate of CVD-specific mortality compared with those in the longest TL quartile (HR: 1.88, 95% CI: 1.29-2.72). ConclusionOur findings suggest a significant inverse association between TL and CVD-specific mortality, highlighting TL as a potential biomarker for CVD risk. The use of a multistate framework provides a comprehensive understanding of competing risks and enhances the robustness of our results. Further studies are needed to validate these findings and explore the underlying mechanisms.