Exploring the impact of individual components of the Life's Essential 8 on the relationship between atherogenic index of plasma and adverse cardiovascular events: a population-based cohort study in China.

Background: The American Heart Association (AHA) recently emphasized the significance of the "Life's Essential 8" in promoting cardiovascular health. The Atherogenic Index of Plasma (AIP) is increasingly recognized as a valuable alternative biomarker for cardiovascular diseases (CVD) and insulin resistance-related metabolic diseases. However, the impact of the individual components of the "Life's Essential 8" on the association between AIP and CVD has not been adequately investigated.

Methods: We conducted an analysis of data from 8,246 participants enrolled in the China Health and Retirement Longitudinal Study. Lifestyle behaviors and health factors were classified into binary or tertiary categories according to risk levels. We employed multivariate logistic regression and smooth curve fitting techniques to investigate the association between AIP and CVD across varying groups of health behaviors and factors. Additionally, Receiver Operating Characteristic (ROC) curve analysis was utilized to assess the predictive value of combining healthy behaviors, factors, and AIP in forecasting the incidence of CVD.

Results: Upon adjusting for established cardiovascular risk factors, elevated AIP levels correlated with a heightened CVD risk (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.29-1.43). Significant interactions between AIP and CVD risk were observed across subgroups differentiated by blood glucose levels, low-density lipoprotein cholesterol (LDL-C), and sleep duration (P for interaction <0.05). Notably, individuals with blood glucose levels ≥6.1 mmol/L (OR, 1.44; 95% CI, 1.33-1.56) or LDL-C ≥3.12 mmol/L (OR, 1.50; 95% CI, 1.37-1.65) exhibited a more pronounced association between AIP and CVD. Furthermore, the inclusion of AIP in the model alongside traditional risk factors notably enhanced the predictive accuracy for CVD events, as evidenced by an increase in the area under the curve (AUC) from 0.651 to 0.671.

Conclusion: Health behaviors (sleep duration), and health factors, including glucose and LDL-cholesterol levels, may modulate the posstive relationship between the AIP and CVD events in middle-aged and elderly individuals. AIP may offer enhanced predictive value for CVD in patients suffering from diabetes or dyslipidemia.