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Association of lipid and inflammatory markers with C-reactive protein in cardiovascular risk assessment for primary prevention.

内科学 纤维蛋白原 结合珠蛋白 C反应蛋白 载脂蛋白B 血脂异常 血清淀粉样蛋白A 医学 优势比 内分泌学 载脂蛋白A1 胃肠病学 胆固醇 炎症 疾病
作者
Snežana Jovičić,Svetlana Ignjatović,Marijana Dajak,R Kangrga,Nada Majkić-Singh
出处
期刊:PubMed 卷期号:55 (11-12): 411-9 被引量:3
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摘要

High-sensitivity C-reactive protein (hsCRP) has been recognized as an independent marker of cardiovascular risk. Since atherosclerosis is a multifactorial disease, the aim of this study was to determine association between hsCRP and other markers of inflammation and dyslipidemia.In 242 healthy volunteers, total cholesterol (TC), HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), nonHDL-C, triglycerides (TG) and hsCRP were measured using Olympus AU2700. Apolipoprotein A-I (apoAI), apolipoprotein B (apo B), lipoprotein (a) (Lp(a)), haptoglobin, alpha1-acid glycoprotein (A1AGP), C3 and C4 complement components were determined on Architect c8000, and serum amyloid A (SAA) and fibrinogen on BN II nephelometer and ACL 7000, respectively.Significant (P < 0.05) partial Pearson's correlation coefficients were found between hsCRP and TC (r = 0.172), nonHDL-C (r = 0.182), LDL-C (r = 0.154), apoB (r = 0.167), fibrinogen (r = 0.411), SAA (r = 0.493), A1AGP (r = 0.462), haptoglobin (r = 0.310), C3 (r = 0.349) and C4 (r = 0.371). In multiple regression analysis, BMI, SAA, A1AGP, fibrinogen and nonHDL-C showed independent correlation with hsCRP. Multinomial logistic regression analysis demonstrated that BMI, nonHDL, fibrinogen and SAA were strong predictors of hsCRP concentration. Odds ratios for intermediate and high risk categories compared with the low risk category were 1.177 (1.033-1.341) and 1.289 (1.091-1.523), 1.515 (1.021-2.249) and 2.062 (1.246-3.411), 2.241 (1.268-3.959) and 7.123 (3.259-15.568), and 1.387 (1.179-1.632) and 1.691 (1.397-2.047), for BMI, nonHDL-C, fibrinogen and SAA, respectively.The prediction of risk for future cardiac events based on hsCRP concentration, which is the recommended parameter for improving cardiovascular risk stratification, might be complemented with the information about BMI, nonHDL-C, fibrinogen and SAA.

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