ReviewFrontiers in cardiovascular medicine2026
Clinical utility of lipoprotein(a) and the ApoB/ApoA-I ratio in the risk assessment of acute coronary syndrome: from single biomarkers to integrated risk stratification.
Review in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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6 authors.
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Abstract
Acute coronary syndrome (ACS) is a critical clinical emergency resulting from the rupture or erosion of coronary atherosclerotic plaques and subsequent thrombosis. Its pathogenesis is driven by a complex interplay of dysregulated lipid metabolism, inflammatory responses, thrombotic processes, and genetic susceptibility. Although traditional lipid parameters are widely utilized in ACS risk assessment, they remain limited in their ability to account for residual cardiovascular risk, inherited lipid abnormalities, and the overall atherosclerotic burden. Lipoprotein(a) [Lp(a)], an independent, genetically determined cardiovascular risk factor, exhibits distinctive pro-atherogenic, pro-inflammatory, and pro-thrombotic properties. In parallel, the apolipoprotein B/apolipoprotein A-I (ApoB/ApoA-I) ratio reflects the equilibrium between atherogenic lipoprotein particles and anti-atherogenic lipoprotein functions, providing a more comprehensive measure of lipid-related metabolic risk. Recent evidence has highlighted the value of both biomarkers in assessing coronary lesion severity, plaque instability, and prognosis in ACS. This review synthesizes current findings regarding the pathophysiological basis, clinical utility, and potential for combined assessment of Lp(a) and the ApoB/ApoA-I ratio, while critically discussing the limitations of existing research. Future investigations should shift from univariate biomarker analysis toward multi-marker integration, risk reclassification, genetic stratification, and validation of targeted interventions to advance precision risk assessment and individualized management of ACS.
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