An analysis of the association between genetic variations of the inflammation biomarker C-reactive protein (CRP) with coronary heart disease failed to support a causal association, according to a study in the July 1 issue of JAMA.

Coronary heart disease (CHD) is the leading cause of death worldwide. Inflammation plays a key role in the development of CHD at every stage, from initiation to progression and rupture of plaque. CRP is currently the most widely used biomarker of inflammation, according to background information in the article. "There is considerable interest in establishing whether CRP has a causal role in CHD or whether CRP is merely a marker of underlying atherosclerosis," the authors write.

Paul Elliott, F.R.C.P., of Imperial College London, and colleagues conducted a genetic association study to identify common genetic loci that influence CRP levels and used the concept of Mendelian randomization to examine the possible causal relationship of CRP levels with CHD. First a genome-wide association (n = 17,967) and replication study (n = 13,615) were conducted to identify genetic loci associated with plasma CRP concentrations. Data collection took place between 1989 and 2008 and genotyping between 2003 and 2008. The researchers then carried out a mendelian randomization study of the most closely associated single-nucleotide polymorphism (SNP) in the CRP locus and published data on other CRP variants involving a total of 28,112 cases and 100,823 controls, to investigate the association of CRP variants with coronary heart disease. These findings were compared with findings predicted from meta-analysis of observational studies of CRP levels and risk of coronary heart disease.

The researchers found: "The present genome-wide association study confirms the associations of common genetic variants in the LEPR, IL6R, CRP, and HNF1A loci and APOE-CI-CII cluster with CRP levels. However, the minor allele of SNP rs7553007 and other variants in the CRP locus included in our Mendelian randomization study were not associated with CHD risk."

The authors write that the variants included in their mendelian randomization study are associated with approximately 20 percent lower CRP levels, corresponding to a 6 percent reduction in CHD risk predicted by the meta-analysis of observational studies of CHD risk. "The lack of association with CHD of genetic variants in the CRP locus suggests that the observational data linking CRP levels and CHD may be confounded by association with other CHD risk factors, or reflect a secondary inflammatory response associated with atherosclerosis (reverse causation), rather than indicate a causal relationship."

"In summary, our mendelian randomization study of more than 28,000 cases and 100,000 controls found no association of variants in the CRP locus and CHD, arguing against a causal role for CRP in atherosclerosis. Moreover, this study suggests that development of therapeutic strategies targeting specific reductions in plasma levels of CRP are unlikely to be fruitful," the researchers conclude.
In an accompanying editorial, Svati H. Shah, M.D., M.H.S., of Duke University Medical Center, Durham, N.C., and James A. de Lemos, M.D., of the University of Texas Southwestern Medical Center, Dallas, comment on the two studies in this week's JAMA that examine the use of biomarkers for predicting cardiovascular disease.

"What are the implications of these 2 important studies? Ideally, biomarkers would also be risk factors and could be used for both risk assessment and to individualize specific therapies. Large collaborative investigations incorporating genome-wide association study and mendelian randomization as highlighted by Elliott et al offer a blueprint for definitive evaluation of the causal role of intermediate traits such as biomarkers. Similarly, studies such as that by Melander et al exemplify the necessity of comprehensive appraisal of the value of novel biomarkers, including CRP, beyond standard risk factors in specific populations. Studies such as these will help determine which biomarkers are likely to be useful as specific drug targets but also whether they have a potential role in risk assessment or even therapeutic selection. In the future, better biomarkers and more creative strategies for combining them will be needed, along with comprehensive statistical and functional evaluation of causality, to fulfill the promise of biomarkers for personalized medicine."