Metabolic biomarkers for chronic kidney disease

The impact of normothermic and hypothermic preservation methods on kidney lipidome-comparative study using chemical biopsy with microextraction probes

Introduction

Normothermic ex vivo kidney perfusion (NEVKP) is designed to replicate physiological conditions to improve graft outcomes. A comparison of the impact of hypothermic and normothermic preservation techniques on graft quality was performed by lipidomic profiling using solid-phase microextraction (SPME) chemical biopsy as a minimally invasive sampling approach.

Methods

Direct kidney sampling was conducted using SPME probes coated with a mixed-mode extraction phase in a porcine autotransplantation model of the renal donor after cardiac death, comparing three preservation methods: static cold storage (SCS), NEVKP, and hypothermic machine perfusion (HMP). The lipidomic analysis was done using ultra-high-performance liquid chromatography coupled with a Q-Exactive Focus Orbitrap mass spectrometer.

Results

Chemometric analysis showed that the NEVLP group was separated from SCS and HMP groups. Further in-depth analyses indicated significantly (p < 0.05, VIP > 1) higher levels of acylcarnitines, phosphocholines, ether-linked and longer-chain phosphoethanolamines, triacylglycerols and most lysophosphocholines and lysophosphoethanolamines in the hypothermic preservation group. The results showed that the preservation temperature has a more significant impact on the lipidomic profile of the kidney than the preservation method's mechanical characteristics.

Conclusion

Higher levels of lipids detected in the hypothermic preservation group may be related to ischemia-reperfusion injury, mitochondrial dysfunction, pro-inflammatory effect, and oxidative stress. Obtained results suggest the NEVKP method's beneficial effect on graft function and confirm that SPME chemical biopsy enables low-invasive and repeated sampling of the same tissue, allowing tracking alterations in the graft throughout the entire transplantation procedure.

Multitargeted Internal Calibration for the Quantification of Chronic Kidney Disease-Related Endogenous Metabolites Using Liquid Chromatography-Mass Spectrometry

Accurate quantitative analysis in liquid chromatography-mass spectrometry (LC-MS) benefits from calibration curves generated in the same matrix as the study sample. In the case of endogenous compound quantification, as no blank matrix exists, the multitargeted internal calibration (MTIC) is an attractive and straightforward approach to avoid the need for extensive matrix similarity evaluation. Its principle is to take advantage of stable isotope labeled (SIL) standards as internal calibrants to simultaneously quantify authentic analytes using a within sample calibration. An MTIC workflow was developed for the simultaneous quantification of metabolites related to chronic kidney disease (CKD) using a volumetric microsampling device to collect 20 μL of serum or plasma, followed by a single-step extraction with acetonitrile/water and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Since a single concentration of internal calibrant is necessary to calculate the study sample concentration, the instrument response function was investigated to determine the best SIL concentration. After validation, the trueness of 16 endogenous analytes in authentic human serum ranged from 72.2 to 116.0%, the repeatability from 1.9 to 11.3%, and the intermediate precision ranged overall from 2.1 to 15.4%. The proposed approach was applied to plasma samples collected from healthy control participants and two patient groups diagnosed with CKD. Results confirmed substantial concentration differences between groups for several analytes, including indoxyl sulfate and cortisone, as well as metabolite enrichment in the kynurenine and indole pathways. Multitargeted methodologies represent a major step toward rapid and straightforward LC-MS/MS absolute quantification of endogenous biomarkers, which could change the paradigm of MS use in clinical laboratories.

The Effect of DNA Methylation in the Development and Progression of Chronic Kidney Disease in the General Population: An Epigenome-Wide Association Study Using the Korean Genome and Epidemiology Study Database

BACKGROUND

Although knowledge of the genetic factors influencing kidney disease is increasing, epigenetic profiles, which are associated with chronic kidney disease (CKD), have not been fully elucidated. We sought to identify the DNA methylation status of CpG sites associated with reduced kidney function and examine whether the identified CpG sites are associated with CKD development.

METHOD

We analyzed DNA methylation patterns of 440 participants in the Korean Genome and Epidemiology Study (KoGES) with estimated glomerular filtration rates (eGFRs) ≥ 60 mL/min/1.73 m² at baseline. CKD development was defined as a decrease in the eGFR of <60 at any time during an 8-year follow-up period ("CKD prediction" analysis). In addition, among the 440 participants, 49 participants who underwent a second methylation profiling were assessed for an association between a decline in kidney function and changes in the degree of methylation of CpG sites during the 8 years ("kidney function slope" analysis).

RESULTS

In the CKD prediction analysis, methylation profiles of a total of 403,129 CpG sites were evaluated at baseline in 440 participants, and increased and decreased methylation of 268 and 189 CpG sites, respectively, were significantly correlated with the development of CKD in multivariable logistic regression. During kidney function slope analysis using follow-up methylation profiles of 49 participants, the percent methylation changes in 913 CpG sites showed a linear relationship with the percent change in eGFR during 8 years. During functional enrichment analyses for significant CpG sites found in the CKD prediction and kidney function slope analyses, we found that those CpG sites represented MAPK, PI3K/Akt, and Rap1 pathways. In addition, three CpG sites from three genes, NPHS2, CHCHD4, and AHR, were found to be significant in the CKD prediction analysis and related to a decline in kidney function.

CONCLUSION

It is suggested that DNA methylation on specific genes is associated with the development of CKD and the deterioration of kidney function.

The Effects of Two Kinds of Dietary Interventions on Serum Metabolic Profiles in Haemodialysis Patients

The goal of this study was to evaluate the effects of two kinds of 24-week dietary interventions in haemodialysis patients, a traditional nutritional intervention without a meal before dialysis (HG1) and implementation of a nutritional intervention with a meal served just before dialysis (HG2), in terms of analysing the differences in the serum metabolic profiles and finding biomarkers of dietary efficacy. These studies were performed in two homogenous groups of patients (n = 35 in both groups). Among the metabolites with the highest statistical significance between HG1 and HG2 after the end of the study, 21 substances were putatively annotated, which had potential significance in both of the most relevant metabolic pathways and those related to diet. After the 24 weeks of the dietary intervention, the main differences between the metabolomic profiles in the HG2 vs. HG1 groups were related to the higher signal intensities from amino acid metabolites: indole-3-carboxaldehyde, 5-(hydroxymethyl-2-furoyl)glycine, homocitrulline, 4-(glutamylamino)butanoate, tryptophol, gamma-glutamylthreonine, and isovalerylglycine. These metabolites are intermediates in the metabolic pathways of the necessary amino acids (Trp, Tyr, Phe, Leu, Ile, Val, Liz, and amino acids of the urea cycle) and are also diet-related intermediates (4-guanidinobutanoic acid, indole-3-carboxyaldehyde, homocitrulline, and isovalerylglycine).

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

Insights into the molecular basis of tick-borne encephalitis from multiplatform metabolomics

Background

Tick-borne encephalitis virus (TBEV) is the most prevalent arbovirus, with a tentative estimate of 10,000 to 10,500 infections occurring in Europe and Asia every year. Endemic in Northeast China, tick-borne encephalitis (TBE) is emerging as a major threat to public health, local economies and tourism. The complicated array of host physiological changes has hampered elucidation of the molecular mechanisms underlying the pathogenesis of this disease.

Methodology/Principle findings

System-level characterization of the serum metabolome and lipidome of adult TBEV patients and a healthy control group was performed using liquid chromatography tandem mass spectrometry. By tracking metabolic and lipid changes during disease progression, crucial physiological changes that coincided with disease stages could be identified. Twenty-eight metabolites were significantly altered in the sera of TBE patients in our metabolomic analysis, and 14 lipids were significantly altered in our lipidomics study. Among these metabolites, alpha-linolenic acid, azelaic acid, D-glutamine, glucose-1-phosphate, L-glutamic acid, and mannose-6-phosphate were altered compared to the control group, and PC(38:7), PC(28:3;1), TAG(52:6), etc. were altered based on lipidomics. Major perturbed metabolic pathways included amino acid metabolism, lipid and oxidative stress metabolism (lipoprotein biosynthesis, arachidonic acid biosynthesis, leukotriene biosynthesis and sphingolipid metabolism), phospholipid metabolism and triglyceride metabolism. These metabolites were significantly perturbed during disease progression, implying their latent utility as prognostic markers.

Conclusions/Significance

TBEV infection causes distinct temporal changes in the serum metabolome and lipidome, and many metabolites are potentially involved in the acute inflammatory response and immune regulation. Our global analysis revealed anti- and pro-inflammatory processes in the host and changes to the entire metabolic profile. Relationships between metabolites and pathologies were established. This study provides important insight into the pathology of TBE, including its pathology, and lays the foundation for further research into putative markers of TBE disease.

Tick-borne encephalitis virus (TBEV) with extreme contagiousness is a key danger to public health systems in Europe and Asia. To date, little information is obtained about the molecular mechanism underlying infection, and although commercial vaccines against TBEV exist, there is no specific treatment for the disease. Metabolomics and lipidomics offer multiple-visions of metabolome and lipidome sights and help elucidating metabolic to disease phenotype. Serum metabolism and lipidome analysis were performed based on mass spectrometer (MS) platform on a cohort of TBEV patients. About 400 metabolites performed crucial shifts in TBEV patients compared with healthy subjects. This study revealed that in the stage of infection, the host metabolome is tightly regulated, with anti-inflammatory processes modulating pro-inflammatory processes implying the self-limiting phenotype of TBEV and the inherent regulation in humans. The crucial perturbed metabolic pathways contained amino acid metabolism, fatty acid metabolism and phospholipid metabolism. This study provides a powerful and new approach to decipher the interactions between host and virus. These potential metabolites provide high sensitivity and specificity and have the capacity to function as biomarkers for disease surveillance and estimation of therapeutic interventions.

Potential for Novel Biomarkers in Diabetes-Associated Chronic Kidney Disease: Epigenome, Metabolome, and Gut Microbiome

Diabetes-associated chronic kidney disease is a pandemic issue. Despite the global increase in the number of individuals with this chronic condition together with increasing morbidity and mortality, there are currently only limited therapeutic options to slow disease progression. One of the reasons for this is that the current-day "gold standard" biomarkers lack adequate sensitivity and specificity to detect early diabetic chronic kidney disease (CKD). This review focuses on the rapidly evolving areas of epigenetics, metabolomics, and the gut microbiome as potential sources of novel biomarkers in diabetes-associated CKD and discusses their relevance to clinical practice. However, it also highlights the problems associated with many studies within these three areas-namely, the lack of adequately powered longitudinal studies, and the lack of reproducibility of results which impede biomarker development and clinical validation in this complex and susceptible population.

The relationship between blood metabolites of the tryptophan pathway and kidney function: a bidirectional Mendelian randomization analysis

Blood metabolites of the tryptophan pathway were found to be associated with kidney function and disease in observational studies. In order to evaluate causal relationship and direction, we designed a study using a bidirectional Mendelian randomization approach. The analyses were based on published summary statistics with study sizes ranging from 1,960 to 133,413. After correction for multiple testing, results provided no evidence of an effect of metabolites of the tryptophan pathway on estimated glomerular filtration rate (eGFR). Conversely, lower eGFR was related to higher levels of four metabolites: C-glycosyltryptophan (effect estimate = − 0.16, 95% confidence interval [CI] (− 0.22; − 0.1); p = 9.2e−08), kynurenine (effect estimate = − 0.18, 95% CI (− 0.25; − 0.11); p = 1.1e−06), 3-indoxyl sulfate (effect estimate = − 0.25, 95% CI (− 0.4; − 0.11); p = 6.3e−04) and indole-3-lactate (effect estimate = − 0.26, 95% CI (− 0.38; − 0.13); p = 5.4e−05). Our study supports that lower eGFR causes higher blood metabolite levels of the tryptophan pathway including kynurenine, C-glycosyltryptophan, 3-indoxyl sulfate, and indole-3-lactate. These findings aid the notion that metabolites of the tryptophan pathway are a consequence rather than a cause of reduced eGFR. Further research is needed to specifically examine relationships with respect to chronic kidney disease (CKD) progression among patients with existing CKD.

Amino Acid Metabolites Associated with Chronic Kidney Disease: An Eight-Year Follow-Up Korean Epidemiology Study

The discovery of metabolomics-based biomarkers has been a focus of recent kidney dysfunction research. In the present study, we aimed to identify metabolites associated with chronic kidney disease (CKD) in the general population using a cross-sectional study design. At baseline, 6.5% of subjects had CKD. Pearson correlation analysis showed that 28 metabolites were significantly associated with estimated glomerular filtration rate (eGFR) after Bonferroni correction. Among these metabolites, 4 acylcarnitines, 12 amino acids, 4 biogenic amines, 1 phosphatidylcholine, and 1 sphingolipid were associated with CKD (p < 0.05). After eight years, 13.5% of subjects had CKD. Three amino acid metabolites were positively associated with new-onset CKD: citrulline [odds ratio (OR): 2.41, 95% confidence interval (CI): 1.26–4.59], kynurenine (OR: 1.98, 95% CI: 1.05–3.73), and phenylalanine (OR: 2.68, 95% CI: 1.00–7.16). The kynurenine:tryptophan ratio was also associated with CKD (OR: 3.20; 95% CI: 1.57–6.51). The addition of multiple metabolites significantly improved the CKD prediction by C statistics (0.756–0.85, p < 0.0001), and the net reclassification improvement was 0.84 (95% CI: 0.72–0.96). Elevated hs-C reactive protein (CRP) was associated with new-onset CKD (OR: 1.045, 95% CI: 1.005–1.086); however, this association disappeared following adjustment with the kynurenine:tryptophan ratio. The levels of citrulline and kynurenine and their ratio to tryptophan in CKD patients with proteinuria were worse than those with one or neither characteristic. Together, the results of this study demonstrate that amino acid metabolites are associated with CKD eight years after initial metabolite assessment. These results could improve the identification of subjects at high risk of CKD who have modified amino acid metabolism.

Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study

RATIONALE & OBJECTIVE

Biomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified 13 urine metabolites that had levels reduced in DKD compared with healthy controls. We evaluated associations of these 13 metabolites with future DKD progression.

STUDY DESIGN

Prospective cohort.

SETTING & PARTICIPANTS

1,001 Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes with estimated glomerular filtration rates (eGFRs) between 20 and 70mL/min/1.73m² were followed up prospectively for a median of 8 (range, 2-10) years.

PREDICTORS

13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A_1c level, blood pressure, urinary albumin, and eGFR.

OUTCOMES

Annual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant).

ANALYTICAL APPROACH

Several clinical metabolite models were developed for eGFR slope as the outcome using stepwise selection and penalized regression, and further tested on the time-to-KFRT outcome. A best cross-validated (final) prognostic model was selected based on high prediction accuracy for eGFR slope and high concordance statistic for incident KFRT.

RESULTS

During follow-up, mean eGFR slope was-1.83±1.92 (SD) mL/min/1.73m² per year; 359 (36%) participants experienced KFRT. Median time to KFRT was 7.45 years from the time of entry to the CRIC Study. In our final model, after adjusting for clinical variables, levels of metabolites 3-hydroxyisobutyrate (3-HIBA) and 3-methylcrotonyglycine had a significant negative association with eGFR slope, whereas citric and aconitic acid were positively associated. Further, 3-HIBA and aconitic acid levels were associated with higher and lower risk for KFRT, respectively (HRs of 2.34 [95% CI, 1.51-3.62] and 0.70 [95% CI, 0.51-0.95]).

LIMITATIONS

Subgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches.

CONCLUSIONS

Urine metabolites may offer insights into DKD progression. If replicated in future studies, aconitic acid and 3-HIBA could identify individuals with diabetes at high risk for GFR decline, potentially leading to improved clinical care and targeted therapies.

Urinary proteome and metabolome in dogs (Canis lupus familiaris): The effect of chronic kidney disease

Chronic kidney disease (CKD) is a progressive and irreversible disease. Although urine is an ideal biological sample for proteomics and metabolomics studies, sensitive and specific biomarkers are currently lacking in dogs. This study characterised dog urine proteome and metabolome aiming to identify and possibly quantify putative biomarkers of CKD in dogs. Twenty-two healthy dogs and 28 dogs with spontaneous CKD were selected and urine samples were collected. Urinary proteome was separated by SDS-PAGE and analysed by mass spectrometry, while urinary metabolome was analysed in protein-depleted samples by 1D ¹H NMR spectra. The most abundant proteins in urine samples from healthy dogs were uromodulin, albumin and, in entire male dogs, arginine esterase. In urine samples from CKD dogs, the concentrations of uromodulin and albumin were significantly lower and higher, respectively, than in healthy dogs. In addition, these samples were characterised by a more complex protein pattern indicating mixed glomerular (protein bands ≥65 kDa) and tubular (protein bands <65 kDa) proteinuria. Urine spectra acquired by NMR allowed the identification of 86 metabolites in healthy dogs, belonging to 49 different pathways mainly involved in amino acid metabolism, purine and aminoacyl-tRNA biosynthesis or tricarboxylic acid cycle. Seventeen metabolites showed significantly different concentrations when comparing healthy and CKD dogs. In particular, carnosine, trigonelline, and cis-aconitate, might be suggested as putative biomarkers of CKD in dogs. SIGNIFICANCE: Urine is an ideal biological sample, however few proteomics and metabolomics studies investigated this fluid in dogs and in the context of CKD (chronic kidney disease). In this research, applying a multi-omics approach, new insights were gained regarding the molecular changes triggered by this disease in canine urinary proteome and metabolome. In particular, the involvement of the tubular component was highlighted, suggesting uromodulin, trigonelline and carnosine as possible biomarkers of CKD in dogs.

An update of urine and blood metabolomics in chronic kidney disease

Chronic kidney disease is considered as a serious obstacle in global health, with increasing incidence and prevalence. In spite of numerous attempts by using recent omics technologies, specially metabolomics, for understanding pathophysiology, molecular mechanism and identification reliable consensus biomarkers for diagnosis and prognosis of this complex disease, the current biomarkers are still insensitive and many questions about its pathomechanism are still to be unanswered. This review is focused on recent findings about urine and serum/plasma metabolite biomarkers and changes in the pathways that occurs in the disease conditions. The urine and blood metabolome content in the normal and disease state is investigated based on the current metabolomics studies and well known metabolite candidate biomarkers for chronic kidney disease are discussed.

Meprin β metalloproteases associated with differential metabolite profiles in the plasma and urine of mice with type 1 diabetes and diabetic nephropathy

Background

Meprin metalloproteases are abundantly expressed in the brush border membranes of kidney proximal tubules and small intestines. Meprins are also expressed in podocytes and leukocytes (monocytes and macrophages). Meprins are implicated in the pathophysiology of diabetic nephropathy (DN) but underlying mechanisms are not fully understood. Single nucleotide polymophisms (SNPs) in the meprin β gene were associated with DKD in human subjects. Furthermore, meprin α and β double deficiency resulted in more severe kidney injury and higher mortality rates in mice with Streptozotocin (STZ)-induced type 1 diabetes. Identification of meprin substrates has provided insights on how meprins could modulate kidney injury. Meprin targets in the kidney include extracellular matrix (ECM) proteins, modulators of inflammation, and proteins involved in the protein kinase A (PKA) and PKC signaling pathways. The current study used a global metabolomics approach to determine how meprin β expression impacts the metabolite milieu in diabetes and DKD.

Methods

Low dose STZ was used to induce type 1 diabetes in 8-week old wild-type (WT) and meprin β knockout (βKO) mice. Blood and urine samples were obtained at 4 and 8 weeks post-STZ injection. Assays for albumin, creatinine, neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule − 1 (KIM-1), and cystatin C were used for biochemical assessment of kidney injury. Data for biomarkers of kidney injury utilized two-way ANOVA. Metabolomics data analysis utilized UPLC-QTOF MS and multivariate statistics.

Results

The number of metabolites with diabetes-associated changes in levels were significantly higher in the WT mice when compared to meprin βKO counterparts. Annotated meprin β expression-associated metabolites with strong variable importance in projection (VIP) scores play roles in lipid metabolism (LysoPC(16:1(9Z)), taurocholic acid), amino acid metabolism (indoxyl sulfate, hippuric acid), and neurotransmitter/stress hormone synthesis (cortisol, 3-methoxy-4-hydroxyphenylethylene glycolsulfate, homovanillic acid sulfate). Metabolites that associated with meprin β deficiency include; 3,5-dihydroxy-3′,4′-dimethoxy-6,7-methylenedioxyflavone 3-glucuronide, pantothenic acid, and indoxyl glucuronide (all decreased in plasma).

Conclusion

Taken together, the annotated metabolites suggest that meprin β impacts complications of diabetes such as DKD by altering distinct metabolite profiles.

Electronic supplementary material

The online version of this article (10.1186/s12882-019-1313-2) contains supplementary material, which is available to authorized users.

Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases

Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidney disease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidney disease and a control. These metabolites are likely to be robust indicators of kidney disease processes, and therefore potential biomarkers, warranting further investigation.

Targeted Metabolomic Profiling of Peritoneal Dialysis Effluents Shows Anti-oxidative Capacity of Alanyl-Glutamine

Readily available peritoneal dialysis (PD) effluents from PD patients in the course of renal replacement therapy are a potentially rich source for molecular markers for predicting clinical outcome, monitoring the therapy, and therapeutic interventions. The complex clinical phenotype of PD patients might be reflected in the PD effluent metabolome. Metabolomic analysis of PD effluent might allow quantitative detection and assessment of candidate PD biomarkers for prognostication and therapeutic monitoring. We therefore subjected peritoneal equilibration test effluents from 20 stable PD patients, obtained in a randomized controlled trial (RCT) to evaluate cytoprotective effects of standard PD solution (3.86% glucose) supplemented with 8 mM alanyl-glutamine (AlaGln) to targeted metabolomics analysis. One hundred eighty eight pre-defined metabolites, including free amino acids, acylcarnitines, and glycerophospholipids, as well as custom metabolic indicators calculated from these metabolites were surveyed in a high-throughput assay requiring only 10 μl of PD effluent. Metabolite profiles of effluents from the cross-over trial were analyzed with respect to AlaGln status and clinical parameters such as duration of PD therapy and history of previous episodes of peritonitis. This targeted approach detected and quantified 184 small molecules in PD effluent, a larger number of detected metabolites than in all previous metabolomic studies in PD effluent combined. Metabolites were clustered within substance classes regarding concentrations after a 4-h dwell. PD effluent metabolic profiles were differentiated according to PD patient sub-populations, revealing novel changes in small molecule abundance during PD therapy. AlaGln supplementation of PD fluid altered levels of specific metabolites, including increases in alanine and glutamine but not glutamate, and reduced levels of small molecule indicators of oxidative stress, such as methionine sulfoxide. Our study represents the first application of targeted metabolomics to PD effluents. The observed metabolomic changes in PD effluent associated with AlaGln-supplementation during therapy suggested an anti-oxidant effect, and were consistent with the restoration of important stress and immune processes previously noted in the RCT. High-throughput detection of PD effluent metabolomic signatures and their alterations by therapeutic interventions offers new opportunities for metabolome-clinical correlation in PD and for prescription of personalized PD therapy.

Plasma acylcarnitines could predict prognosis and evaluate treatment of IgA nephropathy

Background

Effective evaluation or prediction of therapy response could be helpful for treatment of chronic kidney disease (CKD), which may rely on accurate biomarkers. Acylcarnitines are involved with lipid metabolism and mitochondrial function. The relation of acylcarnitines with treatment response in patients with CKD is unknown. The purpose of this study is to investigate the association of plasma acylcarnitines with renal function and its alteration by intervention in patients with IgA nephropathy (IgAN).

Methods

A retrospective study was performed in 81 IgAN patients with treatment by traditional Chinese medicine (TCM). Multivariate linear regression analyses were performed to identify the association of acylcarnitines with baseline estimated glomerular filtration rate (eGFR) and eGFR changes after treatment.

Results

Twenty-seven acylcarnitines were measured at baseline and after 1-year TCM intervention. Certain short-chain and median-chain acylcarnitines were independently associated with baseline eGFR and eGFR alterations after 1 year treatment. Particularly, patients with high C5:1(β = − 0.42), C8:1(β = − 0.49), C3DC(β = − 0.5), C10:1(β = − 0.36) and C5DC(β = − 0.64)at baseline would have worse prognosis and treatment response. Moreover, certain acylcarnitines could be changed along with the eGFR alteration after 1-year TCM treatment.

Conclusions

The findings indicate a significant association between plasma acylcarnitines with prognosis and treatment responses in patients with IgAN, which suggest its role as a potential penal of biomarker for IgAN.

Electronic supplementary material

The online version of this article (10.1186/s12986-018-0328-1) contains supplementary material, which is available to authorized users.

Novel glomerular filtration markers

Chronic kidney disease is currently assessed by estimated glomerular filtration rate, a mathematical construct based on creatinine or creatinine and cystatin concentration. Creatinine-based equations have improved with standardization efforts and the Modification of Diet in Renal Disease Study (MDRD) and CKD-Epidemiology Collaboration Study (CKD-EPI). Because the measurement of creatinine is subject to interference from non-GFR determinants, alternative markers have long been sought. These have included cystatin C and low molecular weight proteins like β2-microglobulin and beta trace protein. Tubular disease often occurs before glomerular filtration is impaired and investigators have investigated the excretion of other low molecular weight proteins such as Neutrophil Gelatinase-Associated Lipocalin (NGAL) and Kidney Injury Molecule-1 and N-acetyl-β-d-glucosaminidase. While preliminary, there is some evidence linking these analytes with GFR, disease stage and mortality. Although asymmetrical dimethyl arginine, an inhibitor of nitric oxide, has been shown to be associated with progression of renal disease, symmetric dimethyl arginine may be a better marker. Recent work has also explored the potential of microRNA (miRNA) analysis and metabolomics studies to further elucidate this complex pathophysiologic disease process. Investigators hope to improve our ability to detect CKD by the use of test panels, i.e., various marker combinations thereof. Unfortunately, most of these markers lack standardization unlike traditional measures that rely on creatinine and cystatin C measurement.

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.

Non-invasive staging of chronic kidney allograft damage using urine metabolomic profiling

Chronic kidney allograft damage is characterized by IFTA and GS. We sought to identify urinary metabolite signatures associated with severity of IFTA and GS in pediatric kidney transplant recipients. Urine samples (n = 396) from 60 pediatric transplant recipients were obtained at the time of kidney biopsy and assayed for 133 metabolites by mass spectrometry. Metabolite profiles were quantified via PLS-DA. We used mixed-effects regression to identify laboratory and clinical predictors of histopathology. Urine samples (n = 174) without rejection or AKI were divided into training/validation sets (75:25%). Metabolite classifiers trained on IFTA severity and %GS showed strong statistical correlation (r = .73, P < .001 and r = .72; P < .001, respectively) and remained significant on the validation sets. Regression analysis identified additional clinical features that improved prediction: months post-transplant (GS, IFTA); and proteinuria, GFR, and age (GS only). Addition of clinical variables improved performance of the %GS classifier (AUC = 0.9; 95% CI = 0.85-0.96) but not for IFTA (AUC = 0.82; 95% CI = 0.71-0.92). Despite the presence of potentially confounding phenotypes, these findings were further validated in samples withheld for rejection or AKI. We identify urine metabolite classifiers for IFTA and GS, which may prove useful for non-invasive assessment of histopathological damage.

Association Between Newborn Metabolic Profiles and Pediatric Kidney Disease

INTRODUCTION

Metabolomics offers considerable promise in early disease detection. We set out to test the hypothesis that routine newborn metabolic profiles at birth, obtained through screening for inborn errors of metabolism, would be associated with kidney disease and add incremental information to known clinical risk factors.

METHODS

We conducted a population-level cohort study in Ontario, Canada, using metabolic profiles from 1,288,905 newborns from 2006 to 2015. The primary outcome was chronic kidney disease (CKD) or dialysis. Individual metabolites and their ratio combinations were examined by logistic regression after adjustment for established risk factors for kidney disease and incremental risk prediction measured.

RESULTS

CKD occurred in 2086 (0.16%, median time 612 days) and dialysis in 641 (0.05%, median time 99 days) infants and children. Individual metabolites consisted of amino acids, acylcarnitines, markers of fatty acid oxidation, and others. Base models incorporating clinical risk factors only provided c-statistics of 0.61 for CKD and 0.70 for dialysis. The addition of identified metabolites to risk prediciton models resulted in significant incremental improvement in the performance of both models (CKD model: c-statistic 0.66 NRI 0.36 IDI 0.04, dialysis model: c-statistic 0.77 NRI 0.57 IDI 0.09). This was consistent after internal validation using bootstrapping and a sensitivity analysis excluding outcomes within the first 30 days.

CONCLUSION

Routinely collected screening metabolites at birth are associated with CKD and the need for dialytic therapies in infants and children, and add incremental information to traditional clinical risk factors.

New perspectives on CKD-induced dyslipidemia

INTRODUCTION

Chronic kidney disease (CKD) is a world-wide health concern associated with a significantly higher cardiovascular morbidity and mortality. One of the principal cardiovascular risk factors is the lipid profile. CKD patients have a more frequent and progressive atheromatous disease that cannot be explained by the classical lipid parameters used in the daily clinical practice. Areas covered: The current review summarizes prevailing knowledge on the role of lipids in atheromathosis in CKD patients, including an overview of lipoprotein metabolism highlighting the CKD-induced alterations. Moreover, to obtain information beyond traditional lipid parameters, new state-of-the-art technologies such as lipoprotein subfraction profiling and lipidomics are also reviewed. Finally, we analyse the potential of new lipoprotein subclasses as therapeutic targets in CKD. Expert opinion: The CKD-induced lipid profile has specific features distinct from the general population. Besides quantitative alterations, renal patients have a plethora of qualitative lipid alterations that cannot be detected by routine determinations and are responsible for the excess of cardiovascular risk. New parameters, such as lipoprotein particle number and size, together with new biomarkers obtained by lipidomics will personalize the management of these patients. Therefore, nephrologists need to be aware of new insights into lipoprotein metabolism to improve cardiovascular risk assessment.

Absolute quantification of cholesteryl esters using liquid chromatography-tandem mass spectrometry uncovers novel diagnostic potential of urinary sediment

BACKGROUND

Urine has been utilized as a source of biomarkers in renal disease. However, urinary lipids have not attracted much attention so far. Here we studied urinary cholesteryl ester (CE) and its relevance in renal disease.

METHODS

Quantitative analysis of CE molecular species in serum, urinary supernatant, and urinary sediment from patients with renal disease (N=64) and non-renal disease (N=23) was carried out using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and deuterated CEs as internal standards.

RESULTS

Validation study showed good precision and accuracy of LC-MS/MS. Many CE species were detected in the urinary sediment and supernatant in the renal disease group, whereas only a few CE species were detected in the other group. In the renal disease group, the sum of the concentrations of all CE species showed a significant correlation between the sediment and the supernatant from urinary samples (r=0.876, p<0.001); however, the composition of CEs was significantly different between them. Further, the composition of CEs of the supernatant was similar to that of the serum.

CONCLUSIONS

Our LC-MS/MS analysis uncovered a distinct CE profile in urinary sediment from patients with renal disease, suggesting a possible contribution of CEs in urothelial cells to the development of renal disease.

Metabolomics for clinical use and research in chronic kidney disease

Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.

Identification of biomarkers for development of end-stage kidney disease in chronic kidney disease by metabolomic profiling

A critical issue in the management of chronic kidney disease (CKD) is to prevent patients from the progression to end-stage kidney disease (ESKD), however, there is only limited number of biomarkers for the discrimination of the high-risk CKD patients. We aimed to identify the metabolites which possess the ability to predict the earlier kidney deterioration. We performed capillary electrophoresis and liquid chromatography mass spectrometry (CE-MS)-based metabolic profiling in a prospective cohort, which consisted of referred 112 CKD patients with median follow-up period of 4.4 years. The association between the levels of candidate metabolites and the outcomes (progression to ESKD alone or in combination with death before ESKD) were assessed by multivariate Cox proportional hazard models after adjusting for the baseline covariates. A total of 218 metabolites were detected in the plasma of CKD patients. We identified 16 metabolites which have predictive values for the composite outcome: The risk for composite outcome was elevated from 2.0- to 8.0-fold in those with higher levels of 16 plasma metabolites. Our results suggest that the measurement of these metabolites may facilitate CKD management by predicting the risk of progression to ESKD.

Proteomic Biomarkers Panel: New Insights in Chronic Kidney Disease

Chronic kidney disease, despite being a "silent epidemic" disease, represents one of the main causes of mortality in general population, along with cardiovascular disease, which is the leading cause of poor prognosis for these patients. The specific objective of our study was to characterize the relationship between the inflammatory status, the bone disorders markers, and kidney failure in chronic kidney disease patient stages 2-4, in order to design a novel biomarker panel that improves early disease diagnosis and therapeutic response, thus being further integrated into clinical applications. A panel of proteomic biomarkers, assessed by xMAP array, which includes mediators of inflammation (IL-6, TNF-α) and mineral and bone disorder biomarkers (OPG, OPN, OCN, FGF-23, and Fetuin-A), was found to be more relevant than a single biomarker to detect early CKD stages. The association between inflammatory cytokines and bone disorders markers, IL-6, TNF-α, OPN, OPG, and FGF-23, reflects the severity of vascular changes in CKD and predicts disease progression. Proteomic xMAP analyses shed light on a new approach to clinical evaluation for CKD staging and prognosis.