Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials.

BACKGROUND: Genetic variants have been associated with the risk of coronary heart disease. In this study, we tested whether or not a composite of these variants could ascertain the risk of both incident and recurrent coronary heart disease events and identify those individuals who derive greater clinical benefit from statin therapy. METHODS: A community-based cohort study (the Malmo Diet and Cancer Study) and four randomised controlled trials of both primary prevention (JUPITER and ASCOT) and secondary prevention (CARE and PROVE IT-TIMI 22) with statin therapy, comprising a total of 48,421 individuals and 3477 events, were included in these analyses. We studied the association of a genetic risk score based on 27 genetic variants with incident or recurrent coronary heart disease, adjusting for traditional clinical risk factors. We then investigated the relative and absolute risk reductions in coronary heart disease events with statin therapy stratified by genetic risk. We combined data from the different studies using a meta-analysis. FINDINGS: When individuals were divided into low (quintile 1), intermediate (quintiles 2-4), and high (quintile 5) genetic risk categories, a significant gradient in risk for incident or recurrent coronary heart disease was shown. Compared with the low genetic risk category, the multivariable-adjusted hazard ratio for coronary heart disease for the intermediate genetic risk category was 1·34 (95% CI 1·22-1·47, p<0·0001) and that for the high genetic risk category was 1·72 (1·55-1·92, p<0·0001). In terms of the benefit of statin therapy in the four randomised trials, we noted a significant gradient (p=0·0277) of increasing relative risk reductions across the low (13%), intermediate (29%), and high (48%) genetic risk categories. Similarly, we noted greater absolute risk reductions in those individuals in higher genetic risk categories (p=0·0101), resulting in a roughly threefold decrease in the number needed to treat to prevent one coronary heart disease event in the primary prevention trials. Specifically, in the primary prevention trials, the number needed to treat to prevent one such event in 10 years was 66 in people at low genetic risk, 42 in those at intermediate genetic risk, and 25 in those at high genetic risk in JUPITER, and 57, 47, and 20, respectively, in ASCOT. INTERPRETATION: A genetic risk score identified individuals at increased risk for both incident and recurrent coronary heart disease events. People with the highest burden of genetic risk derived the largest relative and absolute clinical benefit from statin therapy. FUNDING: National Institutes of Health.

A general approach to single-nucleotide polymorphism discovery.

Single-nucleotide polymorphisms (SNPs) are the most abundant form of human genetic variation and a resource for mapping complex genetic traits. The large volume of data produced by high-throughput sequencing projects is a rich and largely untapped source of SNPs (refs 2, 3, 4, 5). We present here a unified approach to the discovery of variations in genetic sequence data of arbitrary DNA sources. We propose to use the rapidly emerging genomic sequence as a template on which to layer often unmapped, fragmentary sequence data and to use base quality values to discern true allelic variations from sequencing errors. By taking advantage of the genomic sequence we are able to use simpler yet more accurate methods for sequence organization: fragment clustering, paralogue identification and multiple alignment. We analyse these sequences with a novel, Bayesian inference engine, POLYBAYES, to calculate the probability that a given site is polymorphic. Rigorous treatment of base quality permits completely automated evaluation of the full length of all sequences, without limitations on alignment depth. We demonstrate this approach by accurate SNP predictions in human ESTs aligned to finished and working-draft quality genomic sequences, a data set representative of the typical challenges of sequence-based SNP discovery.

Prognostic value of diastolic filling parameters derived using a novel image processing technique in patients > or = 70 years of age with congestive heart failure.

Conventional echocardiographic characterization of diastolic function requires manual analysis of Doppler E-and A-wave amplitudes, deceleration times, isovolumic relaxation times, and pulmonary venous flow patterns. Mathematic modeling of the suction pump activity of the heart permits characterization of diastolic function through model-based image processing, which relies solely on transmitral Doppler images. This automated method uniquely specifies the entire E-wave contour using 3 parameters (x(o), k, and c) that determine E-wave amplitude, width, and rate of decay. Moreover, the index beta = c2 - 4k, reflecting the balance between chamber viscosity and stiffness/recoil, represents a novel parameter for characterizing diastolic function. We analyzed Doppler E waves from 39 patients (mean age 79 years, 61% women, mean ejection fraction 47%) using the model-based image processing technique. A value of beta <-900 was selected as indicative of severe diastolic dysfunction. Of 17 subjects with beta <-900, 8 (47%) were no longer alive at 1 year. Of 22 subjects with beta >-900, all were alive (p = 0.001). The index beta, dichotomized at <-900, had a predictive accuracy of 0.769 (30 of 39), a negative predictive value of 1.0 (22 of 22 alive), and a positive predictive value of 0.471 (8 of 17 deceased) for 1-year vital status. Of 14 subjects with deceleration time < or =160 ms, 5 (36%) were deceased at 1 year, whereas for deceleration time >160 ms, 22 of 25 patients were alive (p = NS). Of 16 subjects with ejection fraction <45%, 6 (38%) were deceased at 1 year. Of 23 subjects with ejection fraction >45%, 21 were alive at 1 year (p = 0.074). On multivariate analysis, beta dichotomized at -900 was the strongest independent predictor of 1-year mortality. We conclude that evaluation of diastolic function using model-based image processing provides valuable prognostic information in elderly patients with heart failure.