The impact of membrane permeability and dialysate purity on cardiovascular outcomes.

The effects of high-flux dialysis and ultrapure dialysate on survival of hemodialysis patients are incompletely understood. We conducted a randomized controlled trial to investigate the effects of both membrane permeability and dialysate purity on cardiovascular outcomes. We randomly assigned 704 patients on three times per week hemodialysis to either high- or low-flux dialyzers and either ultrapure or standard dialysate using a two-by-two factorial design. The primary outcome was a composite of fatal and nonfatal cardiovascular events during a minimum 3 years follow-up. We did not detect statistically significant differences in the primary outcome between high- and low-flux (HR=0.73, 95% CI=0.49 to 1.08, P=0.12) and between ultrapure and standard dialysate (HR=0.90, 95% CI=0.61 to 1.32, P=0.60). Posthoc analyses suggested that cardiovascular event-free survival was significantly better in the high-flux group compared with the low-flux group for the subgroup with arteriovenous fistulas, which constituted 82% of the study population (adjusted HR=0.61, 95% CI=0.38 to 0.97, P=0.03). Furthermore, high-flux dialysis associated with a lower risk for cardiovascular events among diabetic subjects (adjusted HR=0.49, 95% CI=0.25 to 0.94, P=0.03), and ultrapure dialysate associated with a lower risk for cardiovascular events among subjects with more than 3 years of dialysis (adjusted HR=0.55, 95% CI=0.31 to 0.97, P=0.04). In conclusion, this trial did not detect a difference in cardiovascular event-free survival between flux and dialysate groups. Posthoc analyses suggest that high-flux hemodialysis may benefit patients with an arteriovenous fistula and patients with diabetes and that ultrapure dialysate may benefit patients with longer dialysis vintage.

Patient Survival With Extended Home Hemodialysis Compared to In-Center Conventional Hemodialysis.

Introduction: More frequent and/or longer hemodialysis (HD) has been associated with improvements in numerous clinical outcomes in patients on dialysis. Home HD (HHD), which allows more frequent and/or longer dialysis with lower cost and flexibility in treatment planning, is not widely used worldwide. Although, retrospective studies have indicated better survival with HHD, this issue remains controversial. In this multicenter study, we compared thrice-weekly extended HHD with in-center conventional HD (ICHD) in a large patient population with a long-term follow-up. Methods: We matched 349 patients starting HHD between 2010 and 2014 with 1047 concurrent patients on ICHD by using propensity scores. Patients were followed-up with from their respective baseline until September 30, 2018. The primary outcome was overall survival. Secondary outcomes were technique survival; hospitalization; and changes in clinical, laboratory, and medication parameters. Results: The mean duration of dialysis session was 418 ± 54 minutes in HHD and 242 ± 10 minutes in patients on ICHD. All-cause mortality rate was 3.76 and 6.27 per 100 patient-years in the HHD and the ICHD groups, respectively. In the intention-to-treat analysis, HHD was associated with a 40% lower risk for all-cause mortality than ICHD (hazard ratio [HR] = 0.60; 95% confidence interval [CI] 0.45 to 0.80; P < 0.001). In HHD, the 5-year technical survival was 86.5%. HHD treatment provided better phosphate and blood pressure (BP) control, improvements in nutrition and inflammation, and reduction in hospitalization days and medication requirement. Conclusion: These results indicate that extended HHD is associated with higher survival and better outcomes compared to ICHD.

Nephrosclerosis and carotid atherosclerosis: lessons from kidney donor histology.

AIM: Carotid artery intima media thickness (CA-IMT) measurement has been shown to be a safe and reproducible method to assess severity of atherosclerosis. The association between nephrosclerosis and systemic atherosclerosis is not clear. In this study, we investigated the association between CA-IMT and nephrosclerosis in a group of kidney transplant donors. METHODS: Forty seven potential kidney transplant donors were included. CA-IMT was measured by B-Mode ultrasonography. Kidney allograft biopsy samples were obtained during the transplantation operation and chronic glomerular, vascular and tubulointertitial changes were semiquantitatively scored according to the Banff classification. RESULTS: Mean age was 52 ± 12 years and 55% of the cases were younger than 55 years. Mean CA-IMT was 0.74 ± 0.19 mm and 48% had IMT values > 0.75 mm. Chronicty index was ≥5 in 55% of the cases. Chronicity index was higher in cases older than 55 years. Age and CA-IMT were significantly correlated with chronic vascular changes and chronicity index. CA-IMT > 0.75 mm had a 46% sensitivity and 90% specificity to predict nephrosclerosis. Positive and negative predictive values were 85% and 57%, respectively. CONCLUSION: Aging leads to detrimental changes in every part of the vasculature of the human body. CA-IMT is correlated with the level of nephrosclerosis. Measurement of CA-IMT reflects nephrosclerosis especially in older patients.

Phylogenetic analysis of modularity in protein interaction networks.

BACKGROUND: In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity. RESULTS: In this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology. CONCLUSION: These results establish modularity and network proximity as useful features in comparative network analysis and motivate detailed studies of the evolutionary histories of network modules.

Volume control associated with better cardiac function in long-term peritoneal dialysis patients.

BACKGROUND: This study was undertaken to investigate the effect of long-term blood pressure (BP) reduction, achieved with salt restriction and strict volume control, on frequency and regression of left ventricular hypertrophy (LVH) in long-term peritoneal dialysis (PD) patients. METHODS: 56 patients who had been treated for more than 2 years under our care were enrolled. After echocardiographic (Echo) evaluation, 46 patients were included in the follow-up study. In our unit, we aim to keep patients' BP below 130/85 mmHg and cardiothoracic index below 0.50. To reach these targets, moderate salt restriction is advised, and if necessary, hypertonic PD solutions are used. Echo was performed at the beginning of the study (after a mean period of 36 months on PD) and at the end of the prospective follow-up period (24 months later). RESULTS: At the time of the first Echo, LVH was detected in only 8 (21%) patients. Residual urine volume was significantly decreased compared to data taken when they first started PD (658 +/- 795 vs 236 +/- 307 mL/day). Mean left ventricular mass index (LVMI) was 107 +/- 26.5 g/m2. LVMI was significantly decreased at the end of the follow-up in patients who had LVH at baseline. No LVH developed in patients who had normal LVMI at baseline. CONCLUSION: Our results indicate that control of hypertension is possible when extracellular fluid volume is kept under control using hypertonic PD solutions in case of recruitment in addition to salt restriction in long-term PD patients. Sustained normovolemia is associated with low incidence and regression of LVH.

MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring.

Network-based analyses are commonly used as powerful tools to interpret the findings of genome-wide association studies (GWAS) in a functional context. In particular, identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association, are shown to be promising in uncovering the functional relationships among genes and proteins associated with diseases. An important issue in this regard is the scoring of subnetworks by integrating two quantities: disease association of individual gene products and network connectivity among proteins. Current scoring schemes either disregard the level of connectivity and focus on the aggregate disease association of connected proteins or use a linear combination of these two quantities. However, such scoring schemes may produce arbitrarily large subnetworks which are often not statistically significant or require tuning of parameters that are used to weigh the contributions of network connectivity and disease association. Here, we propose a parameter-free scoring scheme that aims to score subnetworks by assessing the disease association of interactions between pairs of gene products. We also incorporate the statistical significance of network connectivity and disease association into the scoring function. We test the proposed scoring scheme on a GWAS dataset for two complex diseases type II diabetes (T2D) and psoriasis (PS). Our results suggest that subnetworks identified by commonly used methods may fail tests of statistical significance after correction for multiple hypothesis testing. In contrast, the proposed scoring scheme yields highly significant subnetworks, which contain biologically relevant proteins that cannot be identified by analysis of genome-wide association data alone. We also show that the proposed scoring scheme identifies subnetworks that are reproducible across different cohorts, and it can robustly recover relevant subnetworks at lower sampling rates.

Identifying stage-specific protein subnetworks for colorectal cancer.

BACKGROUND: In recent years, many algorithms have been developed for network-based analysis of differential gene expression in complex diseases. These algorithms use protein-protein interaction (PPI) networks as an integrative framework and identify subnetworks that are coordinately dysregulated in the phenotype of interest. MOTIVATION: While such dysregulated subnetworks have demonstrated significant improvement over individual gene markers for classifying phenotype, the current state-of-the-art in dysregulated subnetwork discovery is almost exclusively limited to binary phenotype classes. However, many clinical applications require identification of molecular markers for multiple classes. APPROACH: We consider the problem of discovering groups of genes whose expression signatures can discriminate multiple phenotype classes. We consider two alternate formulations of this problem (i) an all-vs-all approach that aims to discover subnetworks distinguishing all classes, (ii) a one-vs-all approach that aims to discover subnetworks distinguishing each class from the rest of the classes. For the one-vs-all formulation, we develop a set-cover based algorithm, which aims to identify groups of genes such that at least one gene in the group exhibits differential expression in the target class. RESULTS: We test the proposed algorithms in the context of predicting stages of colorectal cancer. Our results show that the set-cover based algorithm identifying "stage-specific" subnetworks outperforms the all-vs-all approaches in classification. We also investigate the merits of utilizing PPI networks in the search for multiple markers, and show that, with correct parameter settings, network-guided search improves performance. Furthermore, we show that assessing statistical significance when selecting features greatly improves classification performance.

Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks.

Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow?based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the "topological profile" of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.

DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization.

BACKGROUND: High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths. RESULTS: We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes. CONCLUSIONS: These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/.