The Association between Lipoprotein(a) and Coronary Artery Calcification in Elderly Patients with Diabetes: A...

Lipoprotein(a) [Lp(a)] is associated with the development of coronary artery calcification (CAC), yet its exact function is not fully understood. This study sought to assess the relationship between Lp(a) levels and the risk of CAC in elderly diabetic patients.

This cross-sectional study included 486 elderly diabetic patients. The exposure factor was Lp(a) levels, categorized into three groups (T1, T2, T3). The outcome was the presence of CAC. The relationship between Lp(a) levels and CAC was evaluated using several statistical methods, including univariate and multivariate logistic regression, multivariable stratified analysis, receiver operating characteristic (ROC) curve analysis, and restricted cubic spline (RCS) analysis.

The highest Lp(a) group (T3) showed significantly higher prevalence of CAC compared to the T1 and T2 groups. Univariate logistic regression indicated a significant link between Lp(a) and CAC. Furthermore, multivariate logistic regression supported the finding that elevated Lp(a) levels correlated with a heightened risk of CAC in all models. Specifically, each unit rise in Lp(a) was associated with a notable increase in CAC risk, and Log₁₀Lp(a) and each 1 standard deviation increase in Lp(a) also significantly elevated CAC risk. Multivariable stratified analysis demonstrated significant differences in CAC risk across various subgroups, including age ≤70 years, males, females, smokers, hypertensive, non-hypertensive, hyperlipidemic, non-hyperlipidemic, non-stroke, and non-chronic kidney disease patients. ROC curve analysis showed that adding Lp(a) to the baseline model improved the area under the curve from 0.741 to 0.755. RCS analysis indicated a significant, approximately linear association between Log₁₀Lp(a) and CAC risk (p nonlinear = 0.115).

In an elderly diabetic population, elevated levels of Lp(a) were strongly linked to a greater risk of CAC. Integrating Lp(a) measurements with conventional risk factors improves the predictive accuracy for CAC.