Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies.

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

Role of stress myocardial perfusion scan in pre-operative evaluation of cancer patients undergoing non-cardiac surgery

Background. Cardiac risk assessment in cancer patients has not extensively been studied. We evaluated the role of stress myocardial perfusion imaging (MPI) in predicting cardiovascular outcomes in cancer patients undergoing non-cardiac surgery. ^ Methods. A retrospective chart review was performed on 507 patients who had a MPI from 01/2002 - 03/2003 and underwent non-cardiac surgery. Median follow-up duration was 1.5 years. Cox proportional hazard model was used to determine the time-to-first event. End points included total cardiac events (cardiac death, myocardial infarction (MI) and coronary revascularization), cardiac death, and all cause mortality. ^ Results. Of all 507 MPI studies 146 (29%) were abnormal. There were significant differences in risk factors between normal and abnormal MPI groups. Mean age was 66±11 years, with 60% males and a median follow-up duration of 1.8 years (25th quartile=0.8 years, 75th quartile=2.2 years). The majority of patients had an adenosine stress study (53%), with fewer exercise (28%) and dobutamine stress (16%) studies. In the total group there were 39 total cardiac events, 31 cardiac deaths, and 223 all cause mortality events during the study. Univariate predictors of total cardiac events included CAD (p=0.005), previous MI (p=0.005), use of beta blockers (p=0.002), and not receiving chemotherapy (p=0.012). Similarly, the univariate predictors of cardiac death included previous MI (p=0.019) and use of beta blockers (p=0.003). In the multivariate model for total cardiac events, age at surgery (HR 1.04, p=0.030), use of beta blockers (HR 2.46; p=0.011), dobutamine MPI (HR 3.08; p=0.018) and low EF (HR 0.97; p=0.02) were significant predictors of worse outcomes. In the multivariate model for predictors of cardiac death, beta blocker use (HR=2.74; p=0.017) and low EF (HR=0.95; p<0.003) were predictors of cardiac death. The only univariate MPI predictor of total cardiac events was scar severity (p=0.005). While MPI predictors of cardiac death were scar severity (p= 0.001) and ischemia severity (p=0.02). ^ Conclusions. Stress MPI is a useful tool in predicting long term outcomes in cancer patients undergoing surgery. Ejection fraction and severity of myocardial scar are important factors determining long term outcomes in this group.^