Literature DB >> 18609102

Diagnostic performance of plasma high sensitive C-reactive protein in detecting three-vessel coronary artery disease: modification by apolipoprotein E genotype.

Ari Mennander1, Pekka Kuukasjärvi, Jari Laurikka, Kjell Nikus, Pekka J Karhunen, Matti Tarkka, Terho Lehtimäki.   

Abstract

OBJECTIVES: Plasma high sensitive C-reactive protein (hsCRP) concentration is an important clinical test of systemic inflammation and, like apoE epsilon4 allele, an important risk factor of coronary artery disease (CAD). We investigated whether the diagnostic performance of plasma hsCRP in detecting severe 3-vessel CAD may be modified by apoE epsilon4 carrier status.
METHODS: The study population (Angiography and Genes Study) comprised 485 Finnish subjects (336 men and 149 women, mean age 64.0+/-1.0) undergoing coronary angiography. ApoE genotypes were determined by the PCR-based method and by hsCRP using an automatic analyser.
RESULTS: The diagnostic performance of hsCRP concentration in distinguishing 3-vessel CAD from its less widespread forms (non-3-vessel CAD) was assessed by receiver operating characteristic curve (ROC) analysis separately in apoE epsilon4 non-carriers and epsilon4 carriers. ROC analysis showed that hsCRP predicted 3-vessel CAD in apoE epsilon4 non-carriers (AUC 0.646; SE 0.035; p = 0.0001; 95 % CI 0.578-0.714) but not in epsilon4 carriers (AUC 0.518; SE 0.049; p = 0.719; 95 % CI 0.422-0.615). Multinomial logistic regression analysis revealed a significant (p<0.05) apoE epsilon4 group versus hsCRP group (<1.0 mg/L/>or=1.0 mg/L) interaction in relation to incidence of 3-vessel CAD. In apoE epsilon4 non-carriers, high hsCRP (>or=1.0 mg/L) was significantly (OR 2.1; 95 % CI 1.233-3.562; p = 0.006) associated with high incidence of 3-vessel CAD after adjustment for major CAD risk factors.
CONCLUSION: The diagnostic performance of hsCRP in distinguishing 3-vessel CAD from less extensive forms of coronary atherosclerosis is more accurate in a group of subjects without the apoE epsilon4 allele than in patients with it.

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Year:  2008        PMID: 18609102     DOI: 10.1080/00365510802172145

Source DB:  PubMed          Journal:  Scand J Clin Lab Invest        ISSN: 0036-5513            Impact factor:   1.713


  1 in total

1.  Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning.

Authors:  Alena Orlenko; Daniel Kofink; Leo-Pekka Lyytikäinen; Kjell Nikus; Pashupati Mishra; Pekka Kuukasjärvi; Pekka J Karhunen; Mika Kähönen; Jari O Laurikka; Terho Lehtimäki; Folkert W Asselbergs; Jason H Moore
Journal:  Bioinformatics       Date:  2020-03-01       Impact factor: 6.937

  1 in total

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