Association of the atherogenic index of plasma with cardiovascular risk beyond the traditional risk factors: a nationwide population-based cohort study

Abstract

Abstract Background The atherogenic index of plasma (AIP) is composed of triglycerides and high-density lipoprotein cholesterol and is a novel marker for assessing the risk of atherogenicity and cardiometabolic health. An association between AIP and greater frequency of major adverse cardiovascular events (MACEs) in patients with type 2 diabetes mellitus and high cardiovascular (CV) disease risk has been reported. However, only few studies have examined the correlation between AIP and CV risk in general populations. We thus aimed to evaluate the relationship between AIP and CV diseases using a large-scale population dataset from the Korean National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS). Methods A total of 514,866 participants were enrolled from the NHIS-HEALS and classified according to the AIP quartiles. We performed univariate and multivariate Cox proportional hazards regression analyses to determine the association between AIP and MACEs, CV events, and CV mortality. Results During follow-up, we documented 12,133, 11,055, and 1942 cases of MACEs, CV events, and CV mortality, respectively. The multivariate-adjusted hazard ratios [HRs; 95% confidence interval (CI)] for MACEs gradually and significantly increased with the AIP quartiles [1.113 (1.054–1.175) in Q2, 1.175 (1.113–1.240) in Q3, and 1.278 (1.209–1.350) in Q4], following an adjustment for the conventional CV risk factors, including age, sex, body mass index, smoking, alcohol drinking, physical activities, household income, fasting glucose, systolic blood pressure, low-density lipoprotein cholesterol, and estimated glomerular filtration rate. In subgroup analyses, the association of AIP with MACEs and CV events was particularly outstanding in patients with diabetes. Conclusions AIP was significantly associated with CV risks after adjusting for the traditional risk factors. Therefore, it may be used as an effective mass screening method to identify patients at a high risk of CV events.