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BackgroundThe reason(s) for the apparently paradoxical 'reverse' association in end-stage renal disease (ESRD) patients in whom a low, rather than a high, total plasma total homocysteine (tHcy) level is an indicator of poor outcome remains unclear. The aim of this study was to examine whether the inverse association maintains, mitigates or reverses after comprehensive multivariate adjustment for the presence of wasting and inflammation as well as other potential confounders.MethodsWe studied 317 ESRD patients starting dialysis therapy. Fasting blood samples were taken for the analyses of tHcy, serum albumin, C-reactive protein (CRP), serum creatinine and plasma folate. Nutritional status was assessed by subjective global assessment (SGA). Survival was followed for up to 66 months; 105 patients died.ResultsUsing Kaplan-Meier analysis, a low tHcy concentration (< or =30 micromol/l) was associated with higher all-cause and cardiovascular (CV) mortality (P < 0.05). Using Cox proportional analysis adjusting for age, gender, glomerular filtration rate = GFR, cardiovascular disease = CVD, plasma folate, total cholesterol and diabetes mellitus, the all-cause and CV mortality still tended to be high for patients with low tHcy. Adding nutritional and inflammation markers (Body mass index = BMI, SGA, serum creatinine, serum albumin and CRP), a low tHcy level was no longer associated with higher mortality but a trend for high tHcy was observed.ConclusionsThe link between wasting inflammation and a low tHcy appears to be responsible for the reverse association between plasma tHcy and clinical outcome in ESRD patients. After adjustment for confounders including nutritional and inflammation markers, a trend towards increased death risk for high, rather than low, tHcy levels was apparent after adjustment.

Recently, Thomadakis et al. quantified potential sources of bias that can occur when shared parameter (SP) models are used to jointly model longitudinal trends of a biomarker over time (e.g., a slope) and time‐to‐dropout in an effort to address concerns over possible informative censoring. Although SP models induce no bias under a missingness completely at random dropout mechanism, the authors demonstrate that bias can occur under a missingness at random (MAR) dropout mechanism wherein dropout depends on the observed biomarker data. To address this, the authors propose including the most recent observed marker value within the hazard function for the time‐to‐dropout portion of an SP model. They demonstrate via a limited simulation that the proposed model minimizes bias under a specific MAR dropout mechanism and a specific missingness not‐at‐random dropout mechanism. In the present article, we compare and contrast their work with that of previous authors by illustrating via simulation and an example the degree of bias or lack thereof that can occur when applying SP models, particularly, in the presence of competing dropout mechanisms. We propose the use of a competing risk SP model as a means to minimize bias whenever competing dropout mechanisms are suspected assuming the competing mechanisms result from distinct observable causes of dropout. [ABSTRACT FROM AUTHOR]

Background/Aims: We derived a novel equation for calculating weekly urea standard Kt/V (stdKt/V) during hemodialysis (HD) based on urea mass removed, comparable to the approach during peritoneal dialysis. Methods: Theoretical consideration of urea mass balance during HD led to the following equation for stdKt/V, namely, stdKt/V = N × (URR + UFV/V), where N is the number of treatments per week, URR is urea reduction ratio per treatment, UFV is ultrafiltration volume per treatment, and V is postdialysis urea distribution volume. URR required corrections for postdialysis rebound and intradialytic urea generation. We compared the accuracy of this approach with previous equations for stdKt/V by numerical simulations using a 2-compartment model of urea kinetics for thrice-weekly and more frequent HD prescriptions. Results: The proposed equation based on urea mass removed predicted values of stdKt/V that are equivalent to those calculated by previous equations for stdKt/V. Conclusion: This work provides a novel approach for calculating stdKt/V during HD and strengthens the theoretical understanding of stdKt/V. [ABSTRACT FROM AUTHOR]

A general method is proposed by which nonnormally distributed data can be transformed to achieve approximate normality. The method uses an empirical nonlinear data-fitting approach and can be applied to a broad class of transformations including the Box-Cox, arcsine, generalized logit, and Weibull-type transformations. It is easy to implement using standard statistical software packages. Several examples are provided.