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Quantitative Analysis of Cellular Composition in Advanced Atherosclerotic Lesions of Smooth Muscle Cell Lineage-Tracing Mice
Published on: February 20, 2019
Determinants of Progression and Regression of Subclinical Atherosclerosis: A Temporal Perspective
Rimsha Ahmad1, William H Frishman1, Wilbert S Aronow2
1From the Department of Internal Medicine, Westchester Medical Center and New York Medical College, Valhalla, NY.
Abstract:
Subclinical atherosclerosis is a preclinical stage of atherosclerotic cardiovascular disease marked by structural arterial changes in the absence of symptoms or ischemic events. Its early identification and characterization are central to targeted prevention. This review synthesizes evidence from large cohorts, randomized trials, and meta-analyses on clinical, biochemical, lifestyle, and imaging determinants influencing its progression or regression. Traditional risk factors, including age, male sex, hypertension, dyslipidemia, diabetes, and smoking, remain the strongest predictors of plaque progression. Emerging contributors such as elevated lipoprotein(a), chronic inflammation, metabolic syndrome, genetic susceptibility, and psychosocial stress are increasingly recognized as independent modulators of risk. Imaging tools such as coronary artery calcium scoring, carotid intima-media thickness, and coronary computed tomography angiography allow quantification of plaque burden and composition. Novel markers, including perivascular fat attenuation and radiomic features, provide additional prognostic insights. Although regression is less frequent than progression, it is achievable through intensive lipid-lowering, strict blood pressure control, lifestyle modification, and anti-inflammatory therapies. Despite these advances, uncertainties persist regarding optimal monitoring intervals, individualized treatment thresholds, and the prognostic utility of emerging imaging biomarkers. Future priorities include longitudinal, multiethnic studies with standardized imaging protocols and incorporation of artificial intelligence-driven analytics to enhance predictive models and guide personalized therapy. Understanding the multifactorial drivers of subclinical atherosclerosis remains critical for advancing primary prevention and reducing the global burden of atherosclerotic cardiovascular disease.
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