Association between atherogenic index of plasma and future risk of cardiovascular disease in individuals with cardiovascular-kidney-metabolic syndrome stages 0–3: a nationwide prospective cohort study - Gaoshu Zheng; Jijie Jin; Fei Wang; Qianrong Zheng; Jiaxin Shao; Jiangnan Yao; Pan Huang; Hao Zhou; Jianghua Zhou (Cardiovascular Diabetology)
Abstract Background As an emerging concept, Cardiovascular-kidney-metabolic syndrome (CKM) elucidates the intricate interconnection between metabolic disorders(Mets), cardiovascular disease(CVD), and chronic kidney disease(CKD). Within this context, while numerous studies have demonstrated a correlation between the Atherogenic Index of Plasma (AIP) and CVD, the precise relationship between long-term fluctuations in the AIP and the incidence of CVD in patients with CKM syndrome remains unclear. Method The CKM stages 0–3 population was obtained from the China Health and Retirement Longitudinal Study (CHARLS). The outcome CVD was defined as self-reported heart disease and/or stroke. AIP control level was classified using k-mean cluster analysis. Logistic regression was used to analyse the effect of cumulative AIP (cumAIP) on the incidence of CVD. Restricted cubic spline models (RCS) were used to explore the potential non-linear relationship between cumulative AIP and CVD risk at different CKM syndrome stages. Results Of the 3429 CKM stages 0–3 participants, 620 patients developed CVD during the 3-year follow-up period. After adjusting for various confounders, the odds ratio (OR) for the well-controlled class 2 compared with the best AIP control class 1 were 1.37 (1.04, 1.81), the OR for the moderately-controlled class 3 were 1.54 (95% CI, 1.04–2.26), the poorly-controlled class 4 were 1.65 (95% CI, 1.13–2.41), and the worst-controlled class 5 were 2.14 (95% CI, 1.15–3.97). In restricted cubic spline regression analyses, changes in AIP were linearly associated with the occurrence of CVD events. Further weighted quartiles and regression analyses indicated that triglyceride(TG) was a key variable for AIP in predicting CVD events in the CKM stages 0–3 population. Conclusions Poor control level of AIP are associated with an increased risk of CVD events in the population of CKM stages 0–3. Long-term dynamic monitoring of changes in AIP may help in the early identification of patients at high risk of developing CVD in the individuals with CKM stages 0–3.