Association of glycolipid metabolism 7 factors (GLM7) with new-onset cardiovascular disease and its subtypes in Chinese population

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Abstract

Background

The glycolipid metabolism 7 factors (GLM7) was recently developed to improve cardiovascular disease (CVD) risk stratification. However, its predictive performance for incident CVD—particularly myocardial infarction (MI) and stroke— has not been validated in independent cohorts. This study aims to: (1) evaluate associations between baseline GLM7 levels and incident total CVD, MI, and stroke; and (2) compare GLM7’s predictive accuracy with established glycolipid indices, including estimated glucose disposal rate (eGDR), triglyceride-glucose index (TyG) and others.

Methods

This study included 8176 participants from the China Health and Nutrition Survey (CHNS) 2009. Data on MI and stroke onset were collected during two waves of follow-up surveys in 2011 and 2015. A binary logistic regression and a restricted cubic spline (RCS) model was used to examine the associations between GLM7 and outcomes. The area under the receiver operating characteristic (ROC) curve (AUC) with 95% confidence interval (CI) was calculated to evaluate and compare the predictive performance.

Results

In total, 218 CVD events, including 94 MI and 132 stroke events, were documented during the follow-up period. In the fully-adjusted model, compared with participants in the lowest quartile of GLM7, those in the highest quartile had adjusted odds ratios (ORs) of 2.56 (95% CI: 1.47-4.46) for CVD, 3.29 (95% CI: 1.31-8.28) for MI, and 2.06 (95% CI: 1.06-4.02) for stroke. A linear dose-response relationship exists between GLM7 and CVD as well as stroke. The AUC values of GLM7 for predicting CVD, MI, and stroke are 0.769 (95% CI: 0.744-0.794), 0.750 (95% CI: 0.711-0.790), and 0.780 (95% CI: 0.749-0.811), respectively. Among all six glycolipid indices, eGDR exhibits the best predictive performance.

Conclusion

GLM7 was positively associated with incident CVD, MI, and stroke in Chinese population. Its predictive performance was comparable to that of most established glycolipid indices, while inferior to that of eGDR.

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