Elevated Metastasis-Associated lung adenocarcinoma transcript 1 (MALAT1) expression predicts coronary artery disease severity as a potential biomarker for risk stratification: a cross-sectional study

转移相关肺腺癌转录本1 (MALAT1) 表达升高可预测冠状动脉疾病严重程度,并可作为风险分层的潜在生物标志物:一项横断面研究

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Abstract

BACKGROUND: Coronary artery disease (CAD) remains a major global health burden with limited precision for early risk assessment and stratification. This study aims to investigate circulating Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1) potentially associated with the presence and severity of CAD as a non-invasive biomarker. METHODS: This cross-sectional study included 179 participants undergoing coronary angiography comprising 84 with angiographically confirmed CAD and 95 without CAD. Coronary lesion severity was assessed using the Gensini score and categorized as non-CAD, non-complex CAD, or complex CAD. Circulating MALAT1 expression was quantified by qPCR. Correlation analyses, hierarchical logistic regression, ordinal logistic regression, and ROC analyses were applied to evaluate the diagnostic performance between MALAT1, CAD risk, and CAD severity. RESULTS: MALAT1 expression was significantly higher in CAD participants than those without CAD, and greatest among participants with complex CAD. MALAT1 positively correlated with BMI, LDL, non-HDL, and triglycerides, while negatively correlated with LVEF, CI, CO, RV diameter, and HDL. In multivariable models, MALAT1 independently associated with CAD alongside age, sex, BMI, CI, hypertension, concomitant cardiovascular disease, and medication therapy. MALAT1 showed high specificity but moderate sensitivity for identifying CAD , and improved diagnostic performance when combining with agePredictive accuracy was strongest in participant aged ≥60 years. After multivariable adjustment, MALAT1 remained independently associated with CAD severity, particularly for complex CAD. CONCLUSION: Circulating MALAT1 is independently associated with both CAD risk and coronary lesion severity. Its enhanced predictive performance in older individuals suggests promising value as a non-invasive biomarker for refined cardiovascular risk stratification.

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