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Objective <p>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.</p> Methods <p>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).</p> Results <p>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, <i>P</i> = 0.002) and the high cumulative AIP with high ABSI trajectory model (HR = 2.455, 95% CI: 1.514–3.983, <i>P</i> &lt; 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).</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月12日

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  • Shuang Li,
  • Hongxuan Fan,
  • Tianjiao Li,
  • Shanyi Zhou,
  • Zhuolin Huang,
  • Lei Liu,
  • Yafen Yang,
  • Jiahui Li,
  • Zhaoyu Ren,
  • Yanyan Lu,
  • Weihao Meng,
  • Boda Zhou,
  • Hongqiang Ren

摘要

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.