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Background <p>A newer and novel index, the Cholesterol, High-Density Lipoprotein, and Glucose (CHG) index, has been proposed as a potential index for metabolic disorders. However, research on the relationship between CHG changes and cardiovascular disease (CVD) is limited. Our research aims to investigate the association between cumulative exposure and dynamic trajectories of CHG and cardiovascular disease risk.</p> Methods <p>Participants aged 45 and older were recruited from the China Health and Retirement Longitudinal Study (CHARLS). CVD was defined as self-reported description. K-means clustering analysis was used to classify dynamic CHG changes, and cumulative CHG (cuCHG) was calculated as follows: cuCHG=(CHG<sub>2012</sub> + CHG<sub>2015</sub>)/time interval (2012–2015). Cox proportional hazards regression and restricted cubic spline (RCS) regression models were conducted to evaluate the association between cumulative exposure and dynamic trajectories of CHG and CVD risk.</p> Results <p>A total of 6,171 participants were included in the study, among whom 1,136 (18.4%) experienced incident of CVD. The risk of CVD increased with higher levels of cuCHG. K-means clustering indicated three distinct trajectories CHG variation. Compared to the stable reference group (Cluster 3), participants in the high-risk slowly increasing trajectory (Cluster 2) had a significantly higher risk of CVD (HR = 1.28, 95%CI: 1.10–1.49, <i>P</i> = 0.002). However, the moderate-decreasing trajectory (Cluster 1) was not significantly associated with CVD risk (HR = 1.09, 95%CI: 0.98–1.21, <i>P</i> = 0.126). In the Cox regression analysis, compared with the lowest quartile (Q1), participants in the highest quartile (Q4) had a significantly increased risk of CVD by 22% (HR = 1.22, 95% CI: 1.06–1.40, <i>P</i> = 0.005, adjusted <i>P</i> = 0.007). Furthermore, CVD risk increased progressively across ascending cuCHG quartiles (<i>P</i> for trend &lt; 0.05). RCS analysis demonstrated a linear association between cuCHG and CVD risk (for overall, <i>P</i> &lt; 0.001).</p> Conclusion <p>Our research indicates that both cuCHG and CHG changes are associated with CVD risk in middle-aged and older adults, particularly for those with consistently high-risk CHG levels, which are linked to a significantly increased CVD risk. In clinical practice, monitoring long-term CHG changes and maintaining relatively stable levels may help prevent CVD in this population.</p> Graphical Abstract <p></p>

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Associations of cumulative exposure and dynamic trajectories of cholesterol-HDL-glucose (CHG) index with cardiovascular disease in middle-aged and older Chinese adults: a longitudinal analysis

  • 期刊论文

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  • 发表日期：

    2026年3月15日

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  • Yuting Zhang,
  • Bei Liu,
  • Yuan Zhu,
  • Yaofei Xie,
  • Yanling Du,
  • Peng Xiong,
  • Qiyuan Lyu

摘要

Background

A newer and novel index, the Cholesterol, High-Density Lipoprotein, and Glucose (CHG) index, has been proposed as a potential index for metabolic disorders. However, research on the relationship between CHG changes and cardiovascular disease (CVD) is limited. Our research aims to investigate the association between cumulative exposure and dynamic trajectories of CHG and cardiovascular disease risk.

Methods

Participants aged 45 and older were recruited from the China Health and Retirement Longitudinal Study (CHARLS). CVD was defined as self-reported description. K-means clustering analysis was used to classify dynamic CHG changes, and cumulative CHG (cuCHG) was calculated as follows: cuCHG=(CHG2012 + CHG2015)/time interval (2012–2015). Cox proportional hazards regression and restricted cubic spline (RCS) regression models were conducted to evaluate the association between cumulative exposure and dynamic trajectories of CHG and CVD risk.

Results

A total of 6,171 participants were included in the study, among whom 1,136 (18.4%) experienced incident of CVD. The risk of CVD increased with higher levels of cuCHG. K-means clustering indicated three distinct trajectories CHG variation. Compared to the stable reference group (Cluster 3), participants in the high-risk slowly increasing trajectory (Cluster 2) had a significantly higher risk of CVD (HR = 1.28, 95%CI: 1.10–1.49, P = 0.002). However, the moderate-decreasing trajectory (Cluster 1) was not significantly associated with CVD risk (HR = 1.09, 95%CI: 0.98–1.21, P = 0.126). In the Cox regression analysis, compared with the lowest quartile (Q1), participants in the highest quartile (Q4) had a significantly increased risk of CVD by 22% (HR = 1.22, 95% CI: 1.06–1.40, P = 0.005, adjusted P = 0.007). Furthermore, CVD risk increased progressively across ascending cuCHG quartiles (P for trend < 0.05). RCS analysis demonstrated a linear association between cuCHG and CVD risk (for overall, P < 0.001).

Conclusion

Our research indicates that both cuCHG and CHG changes are associated with CVD risk in middle-aged and older adults, particularly for those with consistently high-risk CHG levels, which are linked to a significantly increased CVD risk. In clinical practice, monitoring long-term CHG changes and maintaining relatively stable levels may help prevent CVD in this population.

Graphical Abstract