Joint analysis of atherogenic index of plasma clusters and a body shape index trajectories in incident stroke risk

Li et al. Lipids in Health and Disease (2025) 24:358 https://doi.org/10.1186/s12944-025-02771-8 Lipids in Health and Disease Open Access RESEARCH Joint analysis of atherogenic index of plasma clusters and a body shape index trajectories in incident stroke risk Shuang Li1†, Hongxuan Fan2†, Tianjiao Li1†, Shanyi Zhou3†, Zhuolin Huang4, Lei Liu1, Yafen Yang4, Jiahui Li4, Zhaoyu Ren4, Yanyan Lu5, Weihao Meng6, Boda Zhou2* and Hongqiang Ren1* Abstract Objective As emerging biomarkers for stroke risk, the clinical value of the atherogenic index of plasma and a body shape index has gained increasing attention. However, current research on their combined use for stroke risk stratification remains limited. This study aims to analyze the combined effects of Atherogenic Index of Plasma (AIP) and A Body Shape Index (ABSI) trajectories to explore their potential contribution to improving stroke risk prediction accuracy. Methods The study data were derived from the China Health and Retirement Longitudinal Study conducted between 2011 and 2018, ultimately including 4,942 participants with two AIP measurements and three ABSI measurements collected for each participant. AIP was classified using K-means clustering analysis, and cumulative AIP values were calculated. The latent class trajectory model was employed to identify characteristic ABSI trajectory patterns over time. Cox proportional hazards models were used to calculate hazard ratios (HRs) with 95% confidence intervals (95% CIs). Results The median follow-up duration in China Health and Retirement Longitudinal Study (CHARLS) was 3.0 years, during which 395 of 4,942 participants (7.99%) developed stroke. Adjusted multivariable Cox regression models demonstrated that both the high AIP clustering combined with high ABSI trajectory model (HR = 2.256, 95% CI: 1.346–3.781, P = 0.002) and the high cumulative AIP with high ABSI trajectory model (HR = 2.455, 95% CI: 1.514–3.983, P < 0.001) showed significant associations with stroke in their respective groups, with both associations remaining robust in sensitivity analyses. The AIP clustering combined with ABSI trajectory model exhibited the highest diagnostic performance for stroke (area under the receiver operating characteristic curve [AUC]: 0.612). Conclusion The combined prediction of AIP and ABSI enables earlier identification of stroke risk in the general population, demonstrating significant clinical value for stroke prevention and treatment. † Shuang Li, Hongxuan Fan, Tianjiao Li and Shanyi Zhou joint first author. *Correspondence: Boda Zhou Hongqiang Ren Full list of author information is available at the end of the article © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creati vecommons.org/licenses/by-nc-nd/4.0/. Li et al. Lipids in Health and Disease (2025) 24:358 Page 2 of 14 Keywords Atherogenic index of plasma, A body shape index, Stroke, Cluster analysis, Trajectory analysis, AIP cluster, ABSI trajectory, Cumulative AIP, CHARLS Introduction Stroke is a complex and devastating cerebrovascular disease characterized by the sudden disruption of blood flow to the brain, leading to ischemic or hemorrhagic damage. This pathological process can result in severe neurological deficits, long-term disability, and even death, imposing a significant burden on healthcare systems and societies worldwide [1]. As one of the leading causes of mortality and morbidity globally, stroke affects millions of people each year, with its prevalence expected to rise due to aging populations and increasing risk factors such as hypertension, diabetes, and obesity [2, 3]. The sequential time series analysis reveals a significant and substantial decline in stroke mortality rates in the USA from 1975 to 2019, with a more pronounced decrease observed for ischemic strokes compared to hemorrhagic strokes [4]. However, recent data indicate that stroke incidence in China remains a significant public health concern. According to the latest statistics, China experiences over 2.5 million new stroke cases annually, with ischemic stroke accounting for approximately 70% of these cases. Moreover, the age-standardized incidence rate of stroke in China has reached over 336 per 100,000, the highest globally [5]. This high incidence, coupled with the significant disability and mortality rates associated with stroke, underscores the urgent need for enhanced prevention and treatment strategies [6]. The development of stroke is influenced by a multifactorial interplay of genetic, metabolic, and lifestyle factors, with key risk factors including hypertension, dyslipidemia, smoking, obesity, physical inactivity, and a history of cardiovascular disease. Effective prevention and management strategies are crucial for reducing the incidence and impact of stroke, particularly in high-risk populations [7, 8]. These strategies often include lifestyle modifications (such as a healthy diet, regular exercise, and smoking cessation) and pharmacological interventions (such as antihypertensive and anticoagulant therapies) to mitigate risk factors and prevent future events. AIP has emerged as a novel and integrative biomarker that combines key aspects of lipid metabolism and atherogenic risk. It is derived by calculating the logarithm of the triglyceride (TG) to high-density lipoprotein cholesterol (HDL-C) ratio, providing a more comprehensive assessment of an individual’s lipid profile and cardiovascular disease risk [9]. From a biological mechanism perspective, the AIP reflects lipid-/atherosclerosis-related pathological processes, including elevated levels of small, dense low-density lipoprotein (sd-LDL), dysfunctional HDL, as well as inflammatory responses and endothelial injury [10, 11]. These processes constitute the critical pathological basis for stroke occurrence. Consequently, the AIP serves as a significant predictive tool for identifying individuals at high risk for stroke and other vascular diseases. By integrating measures of triglycerides and HDL-C, AIP offers a more holist (...truncated)