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Clinical Chemistry 53: 8-16, 2007. First published November 27, 2006; 10.1373/clinchem.2006.074591
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(Clinical Chemistry. 2007;53:8-16.)
© 2007 American Association for Clinical Chemistry, Inc.

Molecular Diagnostics and Genetics

Candidate Gene Genotypes, Along with Conventional Risk Factor Assessment, Improve Estimation of Coronary Heart Disease Risk in Healthy UK Men

Steve E. Humphries1,a, Jackie A. Cooper1, Philippa J. Talmud1 and George J. Miller2

1 Centre for Cardiovascular Genetics, Department of Medicine, British Heart Foundation Laboratories, Royal Free and University College Medical School, London, United Kingdom.
2 Medical Research Council Cardiovascular Group, Department of Environmental and Preventive Medicine, Wolfson Institute of Preventive Medicine, London, United Kingdom.

aAddress correspondence to this author at: Centre for Cardiovascular Genetics, Department of Medicine, British Heart Foundation Laboratories, Rayne Bldg., Royal Free and University College Medical School, 5 University St., London WC1E 6JF, United Kingdom. Fax 0107-679-6212; e-mail rmhaseh{at}ucl.ac.uk.

Background: One of the aims of cardiovascular genetics is to test the efficacy of the use of genetic information to predict cardiovascular risk. We therefore investigated whether inclusion of a set of common variants in candidate genes along with conventional risk factor (CRF) assessment enhanced coronary heart disease (CHD)-risk algorithms.

Methods: We followed middle-aged men in the prospective Northwick Park Heart Study II (NPHSII) for 10.8 years and analyzed complete trait and genotype information available on 2057 men (183 CHD events).

Results: Of the 12 genes previously associated with CHD risk, in stepwise multivariate risk analysis, uncoupling protein 2 (UCP2; P = 0.0001), apolipoprotein E (APOE; P = 0.0003), lipoprotein lipase (LPL; P = 0.007), and apolipoprotein AIV (APOA4; P = 0.04) remained in the model. Their combined area under the ROC curve (AROC) was 0.62 (0.58–0.66) [12.6% detection rate for a 5% false positive rate (DR5)]. The AROC for the CRFs age, triglyceride, cholesterol, systolic blood pressure, and smoking was 0.66 (0.61–0.70) (DR5 = 14.2%). Combining CRFs and genotypes significantly improved discrimination (P = 0.001). Inclusion of previously demonstrated interactions of smoking with LPL, interleukin-6 (IL6), and platelet/endothelial cell adhesion molecule (PECAM1) genotypes increased the AROC to 0.72 (0.68–0.76) for a DR5 of 19.1% (P = 0.01 vs CRF combined with genotypes).

Conclusions: For a modest panel of selected genotypes, CHD-risk estimates incorporating CRFs and genotype–risk factor interactions were more effective than risk estimates that used CRFs alone.




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