Analysis of Plasma Albumin, Vitamin D, and Apolipoproteins A and B as Predictive Coronary Risk Biomarkers in the REGICOR Study.

Rev Esp Cardiol (Engl Ed)

Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Barcelona, Spain; Grupo de Epidemiología y Genética Cardiovascular, Grupo del Estudio REGICOR (REgistre GIroní del COR), IMIM (Instituto Hospital del Mar de Investigaciones Médicas), Barcelona, Spain. Electronic address:

Published: November 2018

Introduction And Objectives: New biomarkers could improve the predictive capacity of classic risk functions. The aims of this study were to determine the association between circulating levels of apolipoprotein A1 (apoA1), apolipoprotein B (apoB), albumin, and 25-OH-vitamin D and coronary events and to analyze whether these biomarkers improve the predictive capacity of the Framingham-REGICOR risk function.

Methods: A case-cohort study was designed. From an initial cohort of 5404 individuals aged 35 to 74 years with a 5-year follow-up, all the participants who had a coronary event (n = 117) and a random group of the cohort (subcohort; n = 667) were selected. Finally, 105 cases and 651 individuals representative of the cohort with an available biological sample were included. The events of interest were angina, fatal and nonfatal myocardial infarction and coronary deaths.

Results: Case participants were older, had a higher proportion of men and cardiovascular risk factors, and showed higher levels of apoB and lower levels of apoA1, apoA1/apoB ratio, 25-OH-vitamin D and albumin than the subcohort. In multivariate analyses, plasma albumin concentration was the only biomarker independently associated with coronary events (HR, 0.73; P = .002). The inclusion of albumin in the risk function properly reclassified a significant proportion of individuals, especially in the intermediate risk group (net reclassification improvement, 32.3; P = .048).

Conclusions: Plasma albumin levels are inversely associated with coronary risk and improve the predictive capacity of classic risk functions.

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Source
http://dx.doi.org/10.1016/j.rec.2018.01.027DOI Listing

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