Plasma metabolomics profiling of maintenance hemodialysis based on capillary electrophoresis - time of flight mass spectrometry

Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately. Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD. Conclusion: The retained molecules and metabolite-protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

Serum Orotidine: A Novel Biomarker of Increased CVD Risk in Type 2 Diabetes Discovered Through Metabolomics Studies

OBJECTIVE

To identify novel biomarkers of cardiovascular disease (CVD) risk in type 2 diabetes (T2D) via a hypothesis-free global metabolomics study, while taking into account renal function, an important confounder often overlooked in previous metabolomics studies of CVD.

RESEARCH DESIGN AND METHODS

We conducted a global serum metabolomics analysis using the Metabolon platform in a discovery set from the Joslin Kidney Study having a nested case-control design comprising 409 individuals with T2D. Logistic regression was applied to evaluate the association between incident CVD events and each of the 671 metabolites detected by the Metabolon platform, before and after adjustment for renal function and other CVD risk factors. Significant metabolites were followed up with absolute quantification assays in a validation set from the Joslin Heart Study including 599 individuals with T2D with and without clinical evidence of significant coronary heart disease (CHD).

RESULTS

In the discovery set, serum orotidine and 2-piperidinone were significantly associated with increased odds of incident CVD after adjustment for glomerular filtration rate (GFR) (odds ratio [OR] per SD increment 1.94 [95% CI 1.39-2.72], P = 0.0001, and 1.62 [1.26-2.08], P = 0.0001, respectively). Orotidine was also associated with increased odds of CHD in the validation set (OR 1.39 [1.11-1.75]), while 2-piperidinone did not replicate. Furthermore, orotidine, being inversely associated with GFR, mediated 60% of the effects of declining renal function on CVD risk. Addition of orotidine to established clinical predictors improved (P < 0.05) C statistics and discrimination indices for CVD risk (ΔAUC 0.053, rIDI 0.48, NRI 0.42) compared with the clinical predictors alone.

CONCLUSIONS

Through a robust metabolomics approach, with independent validation, we have discovered serum orotidine as a novel biomarker of increased odds of CVD in T2D, independent of renal function. Additionally, orotidine may be a biological mediator of the increased CVD risk associated with poor kidney function and may help improve CVD risk prediction in T2D.

A Novel Method for Constructing Classification Models by Combining Different Biomarker Patterns

Different biomarker patterns, such as those of molecular biomarkers and ratio biomarkers, have their own merits in clinical applications. In this study, a novel machine learning method used in biomedical data analysis for constructing classification models by combining different biomarker patterns (CDBP)is proposed. CDBP uses relative expression reversals to measure the discriminative ability of different biomarker patterns, and selects the pattern with the higher score for classifier construction. The decision boundary of CDBP can be characterized in simple and biologically meaningful manners. The CDBP method was compared with eight state-of-the-art methods on eight gene expression datasets to test its performance. CDBP, with fewer features or ratio features, had the highest classification performance. Subsequently, CDBP was employed to extract crucial diagnostic information from a rat hepatocarcinogenesis metabolomics dataset. The potential biomarkers selected by CDBP provided better classification of hepatocellular carcinoma (HCC)and non-HCC stages than previous works in the animal model. The statistical analyses of these potential biomarkers in an independent human dataset confirmed their discriminative abilities of different liver diseases. These experimental results highlight the potential of CDBP for biomarker identification from high-dimensional biomedical datasets and demonstrate that it can be a useful tool for disease classification.

Comparative analysis of therapeutic effects between medium cut-off and high flux dialyzers using metabolomics and proteomics: exploratory, prospective study in hemodialysis

In this single-center prospective study of 20 patients receiving maintenance hemodialysis (HD), we compared the therapeutic effects of medium cut-off (MCO) and high flux (HF) dialyzers using metabolomics and proteomics. A consecutive dialyzer membrane was used for 15-week study periods: 1st HF dialyzer, MCO dialyzer, 2nd HF dialyzer, for 5 weeks respectively. ¹H-nuclear magnetic resonance was used to identify the metabolites and liquid chromatography-tandem mass spectrometry (LC–MS/MS) analysis was used to identify proteins. To compare the effects of the HF and MCO dialyzers, orthogonal projection to latent structure discriminant analysis (OPLS-DA) was performed. OPLS-DA showed that metabolite characteristics could be significantly classified by 1st HF and MCO dialyzers. The Pre-HD metabolites with variable importance in projection scores ≥ 1.0 in both 1st HF versus MCO and MCO versus 2nd HF were succinate, glutamate, and histidine. The pre-HD levels of succinate and histidine were significantly lower, while those of glutamate were significantly higher in MCO period than in the HF period. OPLS-DA of the proteome also substantially separated 1st HF and MCO periods. Plasma pre-HD levels of fibronectin 1 were significantly higher, and those of complement component 4B and retinol-binding protein 4 were significantly lower in MCO than in the 1st HF period. Interestingly, as per Ingenuity Pathway Analysis, an increase in epithelial cell proliferation and a decrease in endothelial cell apoptosis occurred during the MCO period. Overall, our results suggest that the use of MCO dialyzers results in characteristic metabolomics and proteomics profiles during HD compared with HF dialyzers, which might be related to oxidative stress, insulin resistance, complement-coagulation axis, inflammation, and nutrition.

CE-MS-Based Identification of Uremic Solutes Specific to Hemodialysis Patients

Uremic toxins are suggested to be involved in the pathophysiology of hemodialysis (HD) patients. However, the profile of uremic solutes in HD patients has not been fully elucidated. In this study using capillary electrophoresis mass spectrometry (CE-MS), we comprehensively quantified the serum concentrations of 122 ionic solutes before and after HD in 11 patients. In addition, we compared the results with those in non-HD patients with chronic kidney disease (CKD) to identify HD patient-specific solutes. We identified 38 solutes whose concentrations were higher in pre-HD than in CKD stage G5. Ten solutes among them did not significantly accumulate in non-HD CKD patients, suggesting that these solutes accumulate specifically in HD patients. We also identified 23 solutes whose concentrations were lower in both pre- and post-HD than in CKD stage G5. The serum levels of 14 solutes among them were not affected by renal function in non-HD patients, suggesting that these solutes tend to be lost specifically in HD patients. Our data demonstrate that HD patients have a markedly different profile of serum uremic solute levels compared to that in non-HD CKD patients. The solutes identified in our study may contribute to the pathophysiology of HD patients.

Metabolomics Evaluation of Patients With Stage 5 Chronic Kidney Disease Before Dialysis, Maintenance Hemodialysis, and Peritoneal Dialysis

Objective

Current treatment options for patients with stage 5 chronic kidney disease before dialysis (predialysis CKD-5) are determined by individual circumstances, economic factors, and the doctor’s advice. This study aimed to explore the plasma metabolic traits of patients with predialysis CKD-5 compared with maintenance hemodialysis (HD) and peritoneal dialysis (PD) patients, to learn more about the impact of the dialysis process on the blood environment.

Methods

Our study enrolled 31 predialysis CKD-5 patients, 31 HD patients, and 30 PD patients. Metabolite profiling was performed using a targeted metabolomics platform by applying an ultra-high-performance liquid chromatography-tandem mass spectrometry method, and the subsequent comparisons among all three groups were made to explore metabolic alterations.

Results

Cysteine metabolism was significantly altered between predialysis CKD-5 patients and both groups of dialysis patients. A disturbance in purine metabolism was the most extensively changed pathway identified between the HD and PD groups. A total of 20 discriminating metabolites with large fluctuations in plasma concentrations were screened from the group comparisons, including 2-keto-D-gluconic acid, kynurenic acid, s-adenosylhomocysteine, L-glutamine, adenosine, and nicotinamide.

Conclusion

Our study provided a comprehensive metabolomics evaluation among predialysis CKD-5, HD, and PD patients, which described the disturbance of metabolic pathways, discriminating metabolites and their possible biological significances. The identification of specific metabolites related to dialysis therapy might provide insights for the management of advanced CKD stages and inform shared decision-making.

Effect of changing treatment to high-flux hemodialysis (HFHD) on mortality in patients with long-term low flux hemodialysis (LFHD): a propensity score matched cohort study

Background

The purpose of this study was to explore the effect of changing treatment to high-flux hemodialysis (HFHD) on mortality rate in patients with long-term low flux hemodialysis (LFHD).

Methods

The patients with end-stage renal disease (ESRD) who underwent LFHD with dialysis age more than 36 months and stable condition in our hospital before December 31, 2014 were included in this study. They were divided into control group and observation group. Propensity score matched method was used to select patients in the control group. The hemodialysis was performed 3 times a week for 4 h. The deadline for follow-up is December 31, 2018. End-point event is all-cause death. The survival rates of the two groups were compared and multivariate Cox regression analysis was carried out.

Results

K-M survival analysis showed that the 1-year, 2-year, 3-year and 4-year survival rates of HFHD group were 98, 96, 96 and 96%, respectively. The 1-year, 2-year, 3-year and 4-year survival rates of LFHD group were 95, 85, 80 and 78%, respectively. Log-rank test showed that the survival rate of HFHD group was significantly higher than that of LFHD group (x²= 7.278, P = 0.007). Multivariate Cox regression analysis showed that male, age, hemoglobin and low-throughput dialysis were independent predictors of death (P < 0.05). Compared with LFHD, HFHD can significantly reduce the mortality risk ratio of patients, as high as 86%.

Conclusion

The prognosis of patients with ESRD who performed long-term LFHD can be significantly improved after changing to HFHD.

Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation

Systemic metabolic changes after renal transplantation reflect the key processes that are related to graft accommodation. In order to describe and better understand these changes, the ¹HNMR based metabolomics approach was used. The changes of 47 metabolites in the serum samples of 19 individuals were interpreted over time with respect to their levels prior to transplantation. Considering the specific repeated measures design of the experiments, data analysis was mainly focused on the multiple analyses of variance (ANOVA) methods such as ANOVA simultaneous component analysis and ANOVA-target projection. We also propose here the combined use of ANOVA and classification and regression trees (ANOVA-CART) under the assumption that a small set of metabolites the binary splits on which may better describe the graft accommodation processes over time. This assumption is very important for developing a medical protocol for evaluating a patient's health state. The results showed that besides creatinine, which is routinely used to monitor renal activity, the changes in levels of hippurate, mannitol and alanine may be associated with the changes in renal function during the post-transplantation recovery period. Specifically, the level of hippurate (or histidine) is more sensitive to any short-term changes in renal activity than creatinine.

Metabolite profiling paradoxically reveals favorable levels of lipids, markers of oxidative stress and unsaturated fatty acids in a diabetes susceptible group of Middle Eastern immigrants

AIMS

The population of immigrants from the Middle East in Sweden show a higher prevalence of type 2 diabetes (T2D) compared to native Swedes. The exact reason for this is unknown. Here, we have performed metabolite profiling to investigate these differences.

METHODS

Metabolite profiling was conducted in Iraqi immigrants (n = 93) and native Swedes (n = 77) using two complementary mass spectrometry-based platforms. Differences in metabolite levels were compared after adjustment for confounding anthropometric, diet and clinical variables.

RESULTS

The Iraqi immigrant population were more obese (44.1 vs 24.7%, p < 0.05), but had a lower prevalence of hypertension (32.3 vs 54.8%, p < 0.01) than the native Swedish population. We detected 140 metabolites, 26 of which showed different levels between populations (q < 0.05,) after adjustment for age, sex, BMI, T2D and use of metformin. Twenty-two metabolites remained significant after further adjustment for HOMA-IR, HOMA-beta or insulin sensitivity index. Levels of polyunsaturated acylcarnitines (14:2 and 18:2) and fatty acid (18:2) were higher, whereas those of saturated and monounsaturated acylcarnitines (14:0, 18:1, and 8:1), fatty acids (12:0, 14:0, 16:0, and 18:1), uremic solutes (urate and quinate) and ketone bodies (beta-hydroxybutyrate) were lower in Iraqi immigrants. Further, levels of phospholipids were generally lower in the Iraqi immigrant population.

CONCLUSIONS

Our result suggests an overall beneficial lipid profile in Iraqi immigrants, despite a higher risk to develop T2D. Higher levels of polyunsaturated fatty acids may suggest differences in dietary pattern, which in turn may reduce the risk of hypertension.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Toward a better understanding of chronic kidney disease with complementary chromatographic methods hyphenated with mass spectrometry for improved polar metabolome coverage

The prevalence of chronic kidney disease (CKD) is increasing worldwide. New technical approaches are needed to improve early diagnosis, disease understanding and patient monitoring, and to evaluate new therapies. Metabolomics, as a prime candidate in the field of CKD research, aims to comprehensively analyze the metabolic complexity of biological systems. An extensive analysis of the metabolites contained in biofluids is therefore needed, and the combination of data obtained from multiple analytical platforms constitutes a promising methodological approach. This study presents an original workflow based on complementary chromatographic conditions, reversed-phase and hydrophilic interaction chromatography hyphenated to mass spectrometry to improve the polar metabolome coverage coupled with a univocal metabolite annotation strategy enabling a rapid access to the biological interpretation. This multiplatform workflow was applied in a CKD cohort study to assess plasma metabolic profile modifications related to renal disease. Multivariate analysis of 278 endogenous annotated metabolites enabled patient stratification with respect to CKD stages and helped to generate new biological insights, while also confirming the relevance of tryptophan metabolism pathway in this condition.

Recent advances in capillary electrophoresis-mass spectrometry: Instrumentation, methodology and applications

Capillary electrophoresis (CE) offers fast and high-resolution separation of charged analytes from small injection volumes. Coupled to mass spectrometry (MS), it represents a powerful analytical technique providing (exact) mass information and enables molecular characterization based on fragmentation. Although hyphenation of CE and MS is not straightforward, much emphasis has been placed on enabling efficient ionization and user-friendly coupling. Though several interfaces are now commercially available, research on more efficient and robust interfacing with nano-electrospray ionization (ESI), matrix-assisted laser desorption/ionization (MALDI) and inductively coupled plasma mass spectrometry (ICP) continues with considerable results. At the same time, CE-MS has been used in many fields, predominantly for the analysis of proteins, peptides and metabolites. This review belongs to a series of regularly published articles, summarizing 248 articles covering the time between June 2016 and May 2018. Latest developments on hyphenation of CE with MS as well as instrumental developments such as two-dimensional separation systems with MS detection are mentioned. Furthermore, applications of various CE-modes including capillary zone electrophoresis (CZE), nonaqueous capillary electrophoresis (NACE), capillary gel electrophoresis (CGE) and capillary isoelectric focusing (CIEF) coupled to MS in biological, pharmaceutical and environmental research are summarized.

CE-MS for metabolomics: Developments and applications in the period 2016-2018

In the field of metabolomics, CE-MS is now recognized as a strong analytical technique for the analysis of (highly) polar and charged metabolites in a wide range of biological samples. Over the past few years, significant attention has been paid to the design and improvement of CE-MS approaches for (large-scale) metabolic profiling studies and for establishing protocols in order to further expand the role of CE-MS in metabolomics. In this paper, which is a follow-up of a previous review paper covering the years 2014-2016 (Electrophoresis 2017, 38, 190-202), main advances in CE-MS approaches for metabolomics studies are outlined covering the literature from July 2016 to June 2018. Aspects like developments in interfacing designs and data analysis tools for improving the performance of CE-MS for metabolomics are discussed. Representative examples highlight the utility of CE-MS in the fields of biomedical, clinical, microbial, and plant metabolomics. A complete overview of recent CE-MS-based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings and MS detection mode. Finally, some general conclusions and perspectives are given.

The metabolomic quest for a biomarker in chronic kidney disease

Chronic kidney disease (CKD) is a growing burden on people and on healthcare for which the diagnostics are niether disease-specific nor indicative of progression. Biomarkers are sought to enable clinicians to offer more appropriate patient-centred treatments, which could come to fruition by using a metabolomics approach. This mini-review highlights the current literature of metabolomics and CKD, and suggests additional factors that need to be considered in this quest for a biomarker, namely the diet and the gut microbiome, for more meaningful advances to be made.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.