2008 Inter-laboratory Comparison Study of a Reference Material for Nutrients in Seawater

Autoclaved natural seawater collected in the North Pacific Ocean was used as a reference material for nutrients in seawater (RMNS) during an inter-laboratory comparison (I/C) study conducted in 2008. This study was a follow-up to previous studies conducted in 2003 and 2006. A set of six samples was distributed to each of 58 laboratories in 15 countries around the globe, and results were returned by 54 of those laboratories (15 countries). The homogeneities of samples used in the 2008 I/C study, based on analyses for three determinants, were improved compared to those of samples used in the 2003 and 2006 I/C studies. Results of these I/C studies indicate that most of the participating laboratories have an analytical technique for nutrients that is sufficient to provide data of high comparability. The differences between reported concentrations from the same laboratories in the 2006 and 2008 I/C studies for the same batch of RMNS indicate that most of the laboratories have been maintaining internal comparability for two years. Thus, with the current high level of performance in the participating laboratories, the use of a common reference material and the adaptation of an internationally accepted nutrient scale system would increase comparability among laboratories worldwide, and the use of a certified reference material would establish traceability. In the 2008 I/C study we observed a problem of non-linearity of the instruments of the participating laboratories similar to that observed among the laboratories in the 2006 I/C study. This problem of non-linearity should be investigated and discussed to improve comparability for the full range of nutrient concentrations. For silicate comparability in particular, we see relatively larger consensus standard deviations than those for nitrate and phosphate

Impact of Selection Bias on Estimation of Subsequent Event Risk

Background— Studies of recurrent or subsequent disease events may be susceptible to bias caused by selection of subjects who both experience and survive the primary indexing event. Currently, the magnitude of any selection bias, particularly for subsequent time-to-event analysis in genetic association studies, is unknown. Methods and Results— We used empirically inspired simulation studies to explore the impact of selection bias on the marginal hazard ratio for risk of subsequent events among those with established coronary heart disease. The extent of selection bias was determined by the magnitudes of genetic and nongenetic effects on the indexing (first) coronary heart disease event. Unless the genetic hazard ratio was unrealistically large (&gt;1.6 per allele) and assuming the sum of all nongenetic hazard ratios was &lt;10, bias was usually &lt;10% (downward toward the null). Despite the low bias, the probability that a confidence interval included the true effect decreased (undercoverage) with increasing sample size because of increasing precision. Importantly, false-positive rates were not affected by selection bias. Conclusions— In most empirical settings, selection bias is expected to have a limited impact on genetic effect estimates of subsequent event risk. Nevertheless, because of undercoverage increasing with sample size, most confidence intervals will be over precise (not wide enough). When there is no effect modification by history of coronary heart disease, the false-positive rates of association tests will be close to nominal. </jats:sec

Fluid balance and outcome in critically ill patients with traumatic brain injury (CENTER-TBI and OzENTER-TBI): a prospective, multicentre, comparative effectiveness study

International audienc

Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations

Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes. To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events. Methods: Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27). Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes. The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits