Telomere length and cardiovascular mortality: a multistate competing risk analysis
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Background
Cardiovascular 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.
Methods
We 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.
Results
The 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). Gray’s 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).
Conclusion
Our 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.
