Comparison of different dosing regimens (once weekly vs. twice weekly, and once weekly vs. once every two weeks) with epoetin delta in patients with chronic kidney disease: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Anaemia is a common complication of chronic kidney disease and prevalence increases with declining renal function. Renal anaemia has significant implications for the well-being and quality of life of patients and impacts on morbidity and mortality. Anaemia can be well managed by therapy with erythropoiesis-stimulating agents (ESAs). Previous clinical trials have shown that the only human cell-line-derived ESA, epoetin delta, is well tolerated and effective in maintaining haemoglobin levels in anaemic patients with chronic kidney disease. The half-life of epoetin delta suggests that administration of this agent is feasible once weekly and once every two weeks. We report on the design and rationale of a trial to compare once weekly <it>vs</it>. twice weekly, and once weekly <it>vs</it>. once every two weeks dosing of epoetin delta.</p> <p>Design and methods</p> <p>This is a randomized, open-label, multicentre trial. Patients aged 18 years or above with chronic kidney disease (Stages 3–5) are eligible to enter this trial. Two groups of patients form the trial population, those naïve to ESA therapy and those previously stable on ESA therapy. There are two primary objectives of this trial: 1) to demonstrate non-inferiority between twice weekly and once weekly dosing of epoetin delta in previously naïve patients (assessed by haemoglobin at Week 24); 2) to demonstrate non-inferiority between once weekly and once every two weeks dosing in previously stable patients (assessed by average haemoglobin over Weeks 16–24). Among the secondary analyses will be assessments of haematocrit, number(%) of patients meeting predefined targets for haemoglobin and haematocrit levels, and comparisons of average dose. All patients will receive study medication for 24 weeks and dose will be adjusted according to a predefined algorithm to achieve and maintain haemoglobin ≥ 11 g/dL. All patients completing this trial are eligible to enter a 2-year follow-up study to enable monitoring of emergent adverse events, anti-erythropoietin antibody responses, maintenance of efficacy and changes in diabetic retinopathy status.</p> <p>Discussion</p> <p>To our knowledge, this trial is the first to randomize ESA-naïve patients to different dosing regimens of the same ESA. Data generated will help in guiding the most appropriate dosing frequency for epoetin delta, particularly in those patients new to epoetin delta therapy.</p> <p>Trial registration</p> <p><b>ClinicalTrials.gov: </b>NCT00450333</p

Anemia and chronic kidney disease are associated with poor outcomes in heart failure patients

BACKGROUND: Chronic kidney disease (CKD) has been linked to higher heart failure (HF) risk. Anemia is a common consequence of CKD, and recent evidence suggests that anemia is a risk factor for HF. The purpose of this study was to examine among patients with HF, the association between CKD, anemia and inhospital mortality and early readmission. METHODS: We performed a retrospective cohort study in two Swiss university hospitals. Subjects were selected based the presence of ICD-10 HF codes in 1999. We recorded demographic characteristics and risk factors for HF. CKD was defined as a serum creatinine ≥ 124 956;mol/L for women and ≥ 133 μmol/L for men. The main outcome measures were inhospital mortality and thirty-day readmissions. RESULTS: Among 955 eligible patients hospitalized with heart failure, 23.0% had CKD. Twenty percent and 6.1% of individuals with and without CKD, respectively, died at the hospital (p < 0.0001). Overall, after adjustment for other patient factors, creatinine and hemoglobin were associated with an increased risk of death at the hospital, and hemoglobin was related to early readmission. CONCLUSION: Both CKD and anemia are frequent among older patients with heart failure and are predictors of adverse outcomes, independent of other known risk factors for heart failure

Reconsidering Association Testing Methods Using Single-Variant Test Statistics as Alternatives to Pooling Tests for Sequence Data with Rare Variants

Association tests that pool minor alleles into a measure of burden at a locus have been proposed for case-control studies using sequence data containing rare variants. However, such pooling tests are not robust to the inclusion of neutral and protective variants, which can mask the association signal from risk variants. Early studies proposing pooling tests dismissed methods for locus-wide inference using nonnegative single-variant test statistics based on unrealistic comparisons. However, such methods are robust to the inclusion of neutral and protective variants and therefore may be more useful than previously appreciated. In fact, some recently proposed methods derived within different frameworks are equivalent to performing inference on weighted sums of squared single-variant score statistics. In this study, we compared two existing methods for locus-wide inference using nonnegative single-variant test statistics to two widely cited pooling tests under more realistic conditions. We established analytic results for a simple model with one rare risk and one rare neutral variant, which demonstrated that pooling tests were less powerful than even Bonferroni-corrected single-variant tests in most realistic situations. We also performed simulations using variants with realistic minor allele frequency and linkage disequilibrium spectra, disease models with multiple rare risk variants and extensive neutral variation, and varying rates of missing genotypes. In all scenarios considered, existing methods using nonnegative single-variant test statistics had power comparable to or greater than two widely cited pooling tests. Moreover, in disease models with only rare risk variants, an existing method based on the maximum single-variant Cochran-Armitage trend chi-square statistic in the locus had power comparable to or greater than another existing method closely related to some recently proposed methods. We conclude that efficient locus-wide inference using single-variant test statistics should be reconsidered as a useful framework for devising powerful association tests in sequence data with rare variants

Disease-associated alleles in genome-wide association studies are enriched for derived low frequency alleles relative to HapMap and neutral expectations

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies give insight into the genetic basis of common diseases. An open question is whether the allele frequency distributions and ancestral vs. derived states of disease-associated alleles differ from the rest of the genome. Characteristics of disease-associated alleles can be used to increase the yield of future studies.</p> <p>Methods</p> <p>The set of all common disease-associated alleles found in genome-wide association studies prior to January 2010 was analyzed and compared with HapMap and theoretical null expectations. In addition, allele frequency distributions of different disease classes were assessed. Ages of HapMap and disease-associated alleles were also estimated.</p> <p>Results</p> <p>The allele frequency distribution of HapMap alleles was qualitatively similar to neutral expectations. However, disease-associated alleles were more likely to be low frequency derived alleles relative to null expectations. 43.7% of disease-associated alleles were ancestral alleles. The mean frequency of disease-associated alleles was less than randomly chosen CEU HapMap alleles (0.394 vs. 0.610, after accounting for probability of detection). Similar patterns were observed for the subset of disease-associated alleles that have been verified in multiple studies. SNPs implicated in genome-wide association studies were enriched for young SNPs compared to randomly selected HapMap loci. Odds ratios of disease-associated alleles tended to be less than 1.5 and varied by frequency, confirming previous studies.</p> <p>Conclusions</p> <p>Alleles associated with genetic disease differ from randomly selected HapMap alleles and neutral expectations. The evolutionary history of alleles (frequency and ancestral vs. derived state) influences whether they are implicated in genome-wide assocation studies.</p

Meta-analysis indicates that common variants at the DISC1 locus are not associated with schizophrenia

Several polymorphisms in the Disrupted-in-Schizophrenia-1 (DISC1) gene are reported to be associated with schizophrenia. However, to date, there has been little effort to evaluate the evidence for association systematically. We carried out an imputation-driven meta-analysis, the most comprehensive to date, using data collected from 10 candidate gene studies and three genome-wide association studies containing a total of 11 626 cases and 15 237 controls. We tested 1241 single-nucleotide polymorphisms in total, and estimated that our power to detect an effect from a variant with minor allele frequency >5% was 99% for an odds ratio of 1.5 and 51% for an odds ratio of 1.1. We find no evidence that common variants at the DISC1 locus are associated with schizophrenia

Genetic linkage analysis in the age of whole-genome sequencing

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data

Comparative Linkage Meta-Analysis Reveals Regionally-Distinct, Disparate Genetic Architectures: Application to Bipolar Disorder and Schizophrenia

New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for ”missing heritability.” However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1–5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods—GSMA and MSP—applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era