ArticleBMC cardiovascular disorders2026
Longitudinal cohort study on the dynamic changes of atherogenic index of plasma and the risk of cardiometabolic multimorbidity in middle - aged and elderly people.
Article in BMC cardiovascular disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
backgroundThe association between long-term dynamic changes in the Atherogenic Index of Plasma (AIP) and the risk of cardiometabolic multimorbidity (CMM) has not been fully elucidated. This study was designed to investigate this association in a middle-aged and elderly Chinese population.
methodsBased on the China Health and Retirement Longitudinal Study (CHARLS), we included 5,401 participants without CMM at baseline (2015). Cumulative AIP (cumAIP) was calculated based on data from 2011 to 2015. Group-based trajectory modeling (GBTM) was used to identify distinct AIP change patterns over the follow-up period. Multivariable Cox proportional hazards models were used to assess the associations between cumAIP, AIP trajectories, and incident CMM.
resultsDuring follow-up, compared to the lowest quartile of cumAIP (Q1), participants in the highest quartile (Q4) had a significantly increased risk of CMM (adjusted HR: 2.27, 95% CI: 1.21–4.26). Similarly, compared to the persistently low AIP trajectory group, those with persistently moderate-level (HR = 1.76, 95% CI: 1.38–2.26) and high-level (HR = 1.84, 95% CI: 1.42–2.37) trajectories faced elevated risks. The risk was most pronounced for the triple CMM combination (diabetes, heart disease, and stroke). A nonlinear dose-response relationship was observed between cumAIP and CMM risk. Subgroup and sensitivity analyses confirmed the robustness of these findings.
conclusionThis longitudinal study demonstrates, for the first time, that long-term elevation in AIP and persistently high AIP trajectories are independent risk factors for CMM in Chinese middle-aged and elderly adults. Monitoring the dynamic changes of AIP is crucial for the early identification of high-risk individuals to guide targeted interventions.
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