The combined predictive power of the atherogenic index of plasma and serum glycated albumin for cardiovascular events in postmenopausal patients with acute coronary syndrome after percutaneous coronary intervention - Xunxun Feng; Yang Liu; Jiaqi Yang; Shiwei Yang; Zhiming Zhou; Yujie Zhou; Qianyun Guo (Lipids in Health and Disease)
Abstract Background Glycated Albumin (GA) and atherogenic index of plasma (AIP) are two important biomarkers that respectively reflect lipid and glucose levels. Previous research has revealed their roles in cardiovascular diseases (CVD) and diabetes. However, their combined predictive ability in forecasting cardiovascular events (CVE) after percutaneous coronary intervention (PCI) among postmenopausal acute coronary syndrome (ACS) patients remains insufficiently studied. Methods Based on the levels of AIP (AIP-L and AIP-H) and GA (GA-L and GA-H), four groups were used to categorize the patients. The CVE assessed included cardiac death, nonfatal myocardial infarction (MI) and nonfatal stroke. To evaluate the relationship between AIP, GA, and CVE, multivariate Cox regression analyses were performed. Results 98 patients (7.5%) experienced CVE during follow-up. AIP and GA were revealed as strong independent predictors of CVE through multivariate analysis (AIP: HR 3.324, 95%CI 1.732–6.365, P = 0.004; GA: HR 1.098, 95% CI 1.023–1.177, P = 0.009). In comparison to those in the initial group (AIP-L and GA-L), the fourth group (AIP-H and GA-H) of patients exhibited the greatest CVE risk (HR 2.929, 95% CI 1.206–5.117, P = 0.018). Derived from the model of baseline risk, the combination of AIP + GA significantly enhanced the AUC, meanwhile combining AIP and GA levels maximized prognostic accuracy in the baseline risk model. Conclusions This study found that the combined measurement of AIP and GA significantly enhanced the predictive capability for CVE following PCI in postmenopausal ACS patients. By integrating these two biomarkers, it became possible to more accurately identify high-risk individuals and provided clinicians with new predictive tools for postmenopausal ACS patients in risk assessment and management.