Circulating autophagy regulator Rubicon is linked to increased myocardial infarction risk

Abstract

Background: The identification of new biomarkers that improve existing cardiovascular risk prediction models for acute coronary syndrome is essential for accurately identifying high-risk patients and refining treatment strategies. Autophagy, a vital cellular degradation mechanism, is important for maintaining cardiac health. Dysregulation of autophagy has been described in cardiovascular conditions such as myocardial ischemia-reperfusion injury, a key factor in myocardial infarction (MI). Recently, Rubicon (Run domain Beclin-1-interacting and cysteine-rich domain-containing protein), a key negative regulator of autophagy, has been identified in the modulation of cardiac stress response. Objectives: This study aimed to explore the relationship between circulating Rubicon levels and MI, and to evaluate the incremental predictive value of Rubicon when integrated into a clinical risk prediction model for MI. Results: We analyzed plasma Rubicon concentrations in 177 participants, comprising type I MI patients and high-risk control subjects. Our results revealed significantly elevated plasma Rubicon levels in MI patients compared to the control group (126.5 pg/mL vs. 53 pg/mL, p < 0.001). Furthermore, Rubicon levels showed a positive correlation with cardiovascular risk factors such as total cholesterol and LDL cholesterol. Multivariate analysis confirmed that Rubicon levels were independently associated with an increased risk of MI. The inclusion of Rubicon in traditional cardiovascular risk models notably enhanced predictive accuracy for MI, with the area under the curve (AUC) rising from 0.868 to 0.905 (p < 0.001). Conclusions: These findings suggest that Rubicon is a valuable biomarker associated with MI risk, providing additional predictive value beyond standard cardiovascular risk factors. This highlights the importance of Rubicon's critical role in the pathophysiology of CVD.

Keywords

• Rubicon
• Autophagy
• Biomarker
• Myocardial infarction
• Risk prediction