Genetic loci on chromosome 5 are associated with circulating levels of interleukin-5 and eosinophil count in a European population with high risk for cardiovascular disease

IL-5 is a Th2 cytokine which activates eosinophils and is suggested to have an atheroprotective role. Genetic variants in the IL5 locus have been associated with increased risk of CAD and ischemic stroke. In this study we aimed to identify genetic variants associated with IL-5 concentrations and apply a Mendelian randomisation approach to assess IL-5 levels for causal effect on intima-media thickness in a European population at high risk of coronary artery disease. We analysed SNPs within robustly associated candidate loci for immune, inflammatory, metabolic and cardiovascular traits. We identified 2 genetic loci for IL-5 levels (chromosome 5, rs56183820, BETA = 0.11, P = 6.73E−5 and chromosome 14, rs4902762, BETA = 0.12, P = 5.76E−6) and one for eosinophil count (rs72797327, BETA = −0.10, P = 1.41E−6). Both chromosome 5 loci were in the vicinity of the IL5 gene, however the association with IL-5 levels failed to replicate in a meta-analysis of 2 independent cohorts (rs56183820, BETA = 0.04, P = 0.2763, I2 = 24, I2 − P = 0.2516). No significant associations were observed between SNPs associated with IL-5 levels or eosinophil count and IMT measures. Expression quantitative trait analyses indicate effects of the IL-5 and eosinophil-associated SNPs on RAD50 mRNA expression levels (rs12652920 (r2 = 0.93 with rs56183820) BETA = −0.10, P = 8.64E−6 and rs11739623 (r2 = 0.96 with rs72797327) BETA = −0.23, P = 1.74E−29, respectively). Our data do not support a role for IL-5 levels and eosinophil count in intima-media thickness, however SNPs associated with IL-5 and eosinophils might influence stability of the atherosclerotic plaque via modulation of RAD50 levels

A practical comparison of blinded methods for sample size reviews in survival data clinical trials.

This paper presents practical approaches to the problem of sample size re-estimation in the case of clinical trials with survival data when proportional hazards can be assumed. When data are readily available at the time of the review, on a full range of survival experiences across the recruited patients, it is shown that, as expected, performing a blinded re-estimation procedure is straightforward and can help to maintain the trial's pre-specified error rates. Two alternative methods for dealing with the situation where limited survival experiences are available at the time of the sample size review are then presented and compared. In this instance, extrapolation is required in order to undertake the sample size re-estimation. Worked examples, together with results from a simulation study are described. It is concluded that, as in the standard case, use of either extrapolation approach successfully protects the trial error rates. Copyright © 2012 John Wiley & Sons, Ltd

Proteomic profiling identifies novel independent relationships between inflammatory proteins and myocardial infarction

Background Inflammation has been implicated in the pathogenesis of coronary heart disease, but the relevance and independence of individual inflammatory proteins is uncertain. Objective To examine the relationships between a spectrum of inflammatory proteins and myocardial infarction (MI). Methods and results A panel of 92 inflammatory proteins was assessed using an OLINK multiplex immunoassay among 432 MI cases (diagnosed < 66 years) and 323 controls. Logistic regression was used to estimate associations between individual proteins and MI, after adjustment for established cardiovascular risk factors and medication use, and stepwise regression to identify proteins with independent effects. Machine learning techniques (Boruta analysis and LASSO regression) and bioinformatic resources were used to examine the concordance of results with those obtained by conventional methods and explore the underlying biological processes to inform the validity of the associations. Among the 92 proteins studied, 62 (67%) had plasma concentrations above the lower limit of detection in at least 50% of samples. Of these, 15 individual proteins were significantly associated with MI after covariate adjustment and correction for multiple testing. Five of these 15 proteins (CDCP1, CD6, IL1–8R1, IL-6, and CXCL1) were independently associated with MI, with up to three-fold higher risks of MI per doubling in plasma concentrations. Findings were further validated using machine learning techniques and biologically focused analyses. Conclusions This study, demonstrating independent relationships between five inflammatory proteins and MI, provides important novel insights into the inflammatory hypothesis of MI and the potential utility of proteomic analyses in precision medicine

Data from: Relative effects of LDL-C on ischaemic stroke & coronary disease: a Mendelian randomization study

Objective: To examine the causal relevance of lifelong differences in LDL-C for ischaemic stroke (IS) relative to that for coronary heart disease (CHD) using a Mendelian randomization approach. Methods: We undertook a two-sample Mendelian randomization, based on summary data, to estimate the causal relevance of LDL-C for risk of IS and CHD. Information from 62 independent genetic variants with genome-wide significant effects on LDL-C levels was used to estimate the causal effects of LDL-C for IS and IS subtypes (based on 12,389 IS cases from METASTROKE) and for CHD (based on 60,801 cases from CARDIoGRAMplusC4D). We then assessed the effects of LDL-C on IS and CHD for heterogeneity. Results: A 1 mmol/L higher genetically-determined LDL-C was associated with a 50% higher risk of CHD (OR: 1.49, 95%CI: 1.32-1.68, p=1.1x10-8). By contrast, the causal effect of LDL-C was much weaker for IS (OR: 1.12, 95%CI: 0.96-1.30, p=0.14; p for heterogeneity=2.6x10-3) and, in particular, for cardioembolic stroke (OR: 1.06, 95%CI: 0.84-1.33, p=0.64; p for heterogeneity=8.6x10-3) when compared with that for CHD. Conclusions: In contrast with the consistent effects of LDL-C lowering therapies on IS and CHD, genetic variants that confer lifelong LDL-C differences show a weaker effect on IS than on CHD. The relevance of aetiologically distinct IS subtypes may contribute to the differences observed

An R package for implementing simulations for seamless phase II/III clinical trials using early outcomes for treatment selection

Adaptive seamless phase II/III clinical trial designs allowing treatment selection at an interim analysis have gained much attention because of their potential benefits compared to more conventional drug development programmes with separate trials for individual phases. A scenario of particular interest is that in which the final outcome in the trial is based on long-term follow-up, but the interim analysis can only realistically be based on early (short-term) outcomes. A new software package (asd) for the statistical software R implements simulations for designs of this type, in addition to the simpler scenario where treatment selection is based on the definitive (final) outcome. The methodology is briefly described and two examples of proposed trial designs in progressive multiple sclerosis are provided, with R code to illustrate application of the methodology

A novel adaptive design strategy increases the efficiency of clinical trials in secondary progressive multiple sclerosis

Background:Adaptive seamless designs (ASDs) have been proposed to test multiple candidate compounds using an interim decision point which allows potentially effective therapies to be taken into the next design stage and to be assessed using a phase III outcome. Objective:To determine whether ASDs are feasible in secondary progressive multiple sclerosis (SPMS) and to compare them with conventional trial designs. Methods:We develop an innovative adaptive trial design for SPMS, which builds on recent developments in statistical methodology. A literature search and individual clinical datasets were used to inform a framework to run simulations to evaluate the proposed design. Results:ASDs are feasible in SPMS with MRI informing an interim decision point and Expanded Disability Status Scale (EDSS) as the final disability endpoint. Furthermore ASDs are more efficient than conventional designs with sample size savings of up to 40%. Sample sizes of 1000–1250 patients are sufficient to test up to four experimental treatments. Controlled recruitment is important to realize the full benefits of ASDs. Conclusions:Although more complex in design, ASDs have the potential to be more efficient and more powerful than conventional designs

Data Supplement

Supplementary Tables (e-1, e-2, e-3), Supplementary Figures (e-1, e-2, e-3), METASTROKE Acknowledgment