Long-term trajectories of apolipoprotein A1 and major adverse cardiovascular events and mortality in a community cohort

社区人群中载脂蛋白A1的长期变化轨迹与主要不良心血管事件和死亡率的关系

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Abstract

BACKGROUND: Apolipoprotein A1 (ApoA1) is a major component of high-density lipoprotein cholesterol and plays a critical role in reverse cholesterol transport. Dynamic changes in ApoA1 levels may be associated with major adverse cardiovascular events. This study aimed to evaluate the impact of ApoA1 trajectories over three early assessments. METHODS: Participants in the Chin-Shan Community Cardiovascular Cohort with dyslipidemia and receiving three early ApoA1 assessments were enrolled. Group-based multivariate trajectory modeling was used to classify participants into distinct trajectories after multivariable adjustment. The follow-up duration was from April 1990 to August 2022, and the long-term outcomes of major adverse cardiovascular events (MACE) and death outcomes were evaluated. RESULTS: A total of 1,080 participants were included (median [interquartile range] age 66.14 [57.93-75.04] years, 43.2% males). Participants were classified into four ApoA1 trajectories: Trajectory 1 (low-level persistence pattern); Trajectory 2 (fall-then-rise pattern); Trajectory 3 (rise-then-fall pattern); and Trajectory 4 (elevated stable pattern). The cumulative incidence of MACE was ranked as Trajectory 4 (7.9%) < Trajectory 2 (9.3%) < Trajectory 3 (9.4%) < Trajectory 1 (12.7%). Comparing to Trajectory 4, both Trajectory 1 and Trajectory 2 had significantly higher risks of MACE (Trajectory 1: hazard ratio [HR] = 2.06, 95% confidence interval [CI] 1.10-3.86; Trajectory 2: HR = 2.38, 95% CI 1.03-5.48). For cardiovascular death, similar results were present. There were no significant differences in composite outcome, all-cause death, non-cardiovascular death across ApoA1 trajectories. CONCLUSION: The trajectory changes of ApoAI levels significantly influences MACE risk during long-term follow-up, particularly in the low-stable and J-shaped trajectories. Dynamic monitoring of ApoAI may serve as a valuable tool for early risk stratification in high-risk populations, facilitating more individualised interventions.

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