Plasma metabolomic profiles in different stages of CKD

Efficacy and safety of direct oral anticoagulants for preventing venous thromboembolism in hospitalized cancer patients: a national multicenter retrospective cohort study

Introduction

Studies on the use of direct oral anticoagulants (DOACs) for preventing venous thromboembolism (VTE) in hospitalized cancer patients are lacking. Therefore, we conducted a multicenter retrospective cohort study to evaluate the efficacy and safety of DOACs versus low-molecular-weight heparin (LMWH) for the primary prevention of VTE in hospitalized cancer patients.

Methods

Clinical outcomes included thrombosis, VTE, other thrombosis, all bleeding, major bleeding, nonmajor bleeding, and all-cause death. A 1:1 cohort of rivaroxaban and LMWH patients was created by propensity score matching.

Results

A total of 2,385 cancer patients were included in this study. During the 3-month follow-up period, 129 (5.4%) thrombosis events occurred, 63 (2.7%) of which were VTEs and 66 (2.8%) of which were other thrombosis events. All bleeding occurred in 163 (6.8%) patients, 68 (2.9%) had major bleeding, and 95 (4.0%) had nonmajor bleeding. All-cause deaths occurred in 113 (4.7%) patients. After adjusting for various confounders, the incidence of thrombosis and other thromboses was significantly lower in the rivaroxaban group than in the LMWH group [OR 0.543, 95% CI (0.343-0.859), p = 0.009; OR 0.461, 95% CI (0.241-0.883), p = 0.020]. There were no significant differences in incidence of VTE, total bleeding, major bleeding, nonmajor bleeding, or all-cause death.

Conclusion

In oncology patients receiving thromboprophylaxis, rivaroxaban has a lower incidence of thrombosis and other thrombosis and a similar incidence of VTE as LMWH and does not increase the risk of bleeding. Rivaroxaban may be an attractive alternative to LMWH for preventing VTE in hospitalized cancer patients.

Determination of indoxyl sulfate by spectrofluorimetric method in human plasma through extraction with deep eutectic solvent

A rapid and efficient analytical method was established to quantify indoxyl sulfate (IS) in plasma through extraction technique with a deep eutectic solvent (DES) and spectrofluorimetric method. DES (choline chloride: urea) was mixed with plasma samples for the extraction of IS, followed by the addition of dipotassium hydrogen phosphate (K2HPO4) solution to form an aqueous two-phase system. The fluorescence intensity of IS which was first extracted to the DES-rich-phase and then back-extracted into the salt-rich-phase, was measured by spectrofluorimetric method. Some key factors such as pH, centrifugation speed and time, the volume ratio of DES/salt, and salt concentration were optimized. Under the optimized conditions, the suggested method had a dynamic range between 20 and 160 µg/mL with a coefficient of determination (R2) of 0.99. Precision (relative standard deviation) was less than 15% and accuracy (% relative recovery) was ± 15% at the nominal concentration level. In addition, results showed that IS levels in real samples were higher than 40 µg/mL which was compatible with reported IS levels in end-stage renal disease (ESRD) patients. Overall, all the results reflect the fact that the presented analytical method can potentially be used for the determination of IS in real plasma samples.

Metabolic implications of amino acid metabolites in chronic kidney disease progression: a metabolomics analysis using OPLS-DA and MBRole2.0 database

BACKGROUND

As chronic kidney disease (CKD) progresses, metabolites undergo diverse transformations. Nevertheless, the impact of these metabolic changes on the etiology, progression, and prognosis of CKD remains uncertain. Our objective is to conduct a metabolomics analysis to scrutinize metabolites and identify significant metabolic pathways implicated in CKD progression, thereby pinpointing potential therapeutic targets for CKD management.

METHODS

We recruited 145 patients with CKD and determined their mGFR by measuring the plasma iohexol clearance, whereupon we partitioned them into four groups based on their mGFR values. Non-targeted metabolomics analysis was conducted using UPLC-MS/MS assays. Differential metabolites were identified via one-way ANOVA, PCA, PLS-DA, and OPLS-DA analyses employing the MetaboAnalyst 5.0 platform. Ultimately, we performed differential metabolite pathway enrichment analysis, using both the MetaboAnalyst 5.0 platform and the MBRole2.0 database.

RESULTS

According to the findings of the MBRole2.0 and MetaboAnalyst 5.0 enrichment analysis, six amino acid metabolism pathways were discovered to have significant roles in the progression of CKD, with the glycine, serine, and threonine metabolism pathway being the most prominent. The latter enriched 14 differential metabolites, of which six decreased while two increased concomitantly with renal function deterioration.

CONCLUSIONS

The metabolic analysis unveiled that glycine, serine, and threonine metabolism plays a pivotal role in the progression of CKD. Specifically, glycine was found to increase while serine decreased with the deterioration of CKD.

Plasma MCP-1 and TGF-β1 Levels are Associated with Kidney Injury in Children with Congenital Anomalies of the Kidney and Urinary Tract

Congenital anomalies of the kidney and urinary tract (CAKUT) are primarily causal for end-stage renal disease and have significant implications for long-term survival. A total of 39 healthy controls and 94 children with chronic kidney disease (CKD) were enrolled (3-12 years old as children, 13-18 years old as adolescents), who were divided into CAKUT and Non-CAKUT according to the etiology of CKD. CKD group was further classified according to estimating glomerular filtration rate (eGFR). Circulating levels of inflammatory markers such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), monocyte chemokine-1 (MCP-1), and transforming growth factor-β1 (TGF-β1) were analyzed. The relationship between these inflammatory markers with eGFR and the kidney injury parameter (urine protein) was investigated to assess their potential as early markers of disease progression. All circulating levels of these inflammatory cytokines were increased in CKD patients (including CAKUT and Non-CAKUT) compared with healthy subjects. The circulating levels of MCP-1 and TGF-β1 were increased in CAKUT adolescents compared with CAKUT children. In CAKUT children, levels of MCP-1 and TGF-β1 increased as CKD progressed, and MCP-1 and TGF-β1 were negatively and significantly correlated with eGFR and positively with urine protein. MCP-1 and TGF-β1 may contribute to the early detection of CKD and disease stage/progression in CAKUT children.

A systematic review of metabolomic findings in adult and pediatric renal disease

Chronic kidney disease (CKD) affects over 0.5 billion people worldwide across their lifetimes. Despite a growingly ageing world population, an increase in all-age prevalence of kidney disease persists. Adult-onset forms of kidney disease often result from lifestyle-modifiable metabolic illnesses such as type 2 diabetes. Pediatric and adolescent forms of renal disease are primarily caused by morphological abnormalities of the kidney, as well as immunological, infectious and inherited metabolic disorders. Alterations in energy metabolism are observed in CKD of varying causes, albeit the molecular mechanisms underlying pathology are unclear. A systematic indexing of metabolites identified in plasma and urine of patients with kidney disease alongside disease enrichment analysis uncovered inborn errors of metabolism as a framework that links features of adult and pediatric kidney disease. The relationship of genetics and metabolism in kidney disease could be classified into three distinct landscapes: (i) Normal genotypes that develop renal damage because of lifestyle and / or comorbidities; (ii) Heterozygous genetic variants and polymorphisms that result in unique metabotypes that may predispose to the development of kidney disease via synergistic heterozygosity, and (iii) Homozygous genetic variants that cause renal impairment by perturbing metabolism, as found in children with monogenic inborn errors of metabolism. Interest in the identification of early biomarkers of onset and progression of CKD has grown steadily in the last years, though it has not translated into clinical routine yet. This systematic review indexes findings of differential concentration of metabolites and energy pathway dysregulation in kidney disease and appraises their potential use as biomarkers.

Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience

Purpose of review

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) for which many treatments exist that have been shown to prevent CKD progression and kidney failure. However, DKD is a complex and heterogeneous etiology of CKD with a spectrum of phenotypes and disease trajectories. In this narrative review, we discuss precision medicine approaches to DKD, including genomics, metabolomics, proteomics, and their potential role in the management of diabetes mellitus and DKD. A patient and caregivers of patients with lived experience with CKD were involved in this review.

Sources of information

Original research articles were identified from MEDLINE and Google Scholar using the search terms "diabetes," "diabetic kidney disease," "diabetic nephropathy," "chronic kidney disease," "kidney failure," "dialysis," "nephrology," "genomics," "metabolomics," and "proteomics."

Methods

A focused review and critical appraisal of existing literature regarding the precision medicine approaches to the diagnosis, prognosis, and treatment of diabetes and DKD framed by a patient partner's/caregiver's lived experience.

Key findings

Distinguishing diabetic nephropathy from CKD due to other types of DKD and non-DKD is challenging and typically requires a kidney biopsy for a diagnosis. Biomarkers have been identified to assist with the prediction of the onset and progression of DKD, but they have yet to be incorporated and evaluated relative to clinical standard of care CKD and kidney failure risk prediction tools. Genomics has identified multiple causal genetic variants for neonatal diabetes mellitus and monogenic diabetes of the young that can be used for diagnostic purposes and to specify antiglycemic therapy. Genome-wide-associated studies have identified genes implicated in DKD pathophysiology in the setting of type 1 and 2 diabetes but their translational benefits are lagging beyond polygenetic risk scores. Metabolomics and proteomics have been shown to improve diagnostic accuracy in DKD, have been used to identify novel pathways involved in DKD pathogenesis, and can be used to improve the prediction of CKD progression and kidney failure as well as predict response to DKD therapy.

Limitations

There are a limited number of large, high-quality prospective observational studies and no randomized controlled trials that support the use of precision medicine based approaches to improve clinical outcomes in adults with or at risk of diabetes and DKD. It is unclear which patients may benefit from the clinical use of genomics, metabolomics and proteomics along the spectrum of DKD trajectory.

Implications

Additional research is needed to evaluate the role of the use of precision medicine for DKD management, including diagnosis, differentiation of diabetic nephropathy from other etiologies of DKD and CKD, short-term and long-term risk prognostication kidney outcomes, and the prediction of response to and safety of disease-modifying therapies.

Prognostic Significance of Amino Acid and Biogenic Amines Profiling in Chronic Kidney Disease

There is a pressing need for more precise biomarkers of chronic kidney disease (CKD). Plasma samples from 820 subjects [231 with CKD, 325 with end-stage kidney disease (ESKD) and 264 controls] were analyzed by liquid chromatography with tandem mass spectrometry (LC-MS/MS) to determine a metabolic profile of 28 amino acids (AAs) and biogenic amines to test their value as markers of CKD risk and progression. The kynurenine/tryptophan ratio showed the strongest correlation with estimated glomerular filtration rate values (coefficient = -0.731, p < 0.0001). Models created with orthogonal partial least squares-discriminant analysis (OPLS-DA) containing the metabolic signature showed a high goodness of fit and predictability for controls/CKD (R2X:0.73:R2Y:0.92:Q2:0.92, p < 0.0001) and lower values for CKD/ESKD (R2X:0.56:R2Y:0.59:Q2:0.55, p < 0.0001). Based on generated VIP scores, the most relevant markers for segregating samples into control/CKD and CKD/ESKD groups were citrulline (1.63) and tryptophan (1.47), respectively. ROC analysis showed that the addition of the metabolic profile to a model including CKD classic risk factors improved the AUC from 86.7% (83.6-89.9) to 100% (100-100) for CKD risk (p < 0.0001) and from 63.0% (58.2-67.8) to 96.5% (95.3-97.8) for the risk of progression from CKD to ESKD (p < 0.0001). Plasma concentrations of AAs and related amines may be useful as diagnostic biomarkers of kidney disease, both for CKD risk and for progression of CKD patients to ESKD.

Serum and urine metabolomic biomarkers for predicting prognosis in patients with immunoglobulin A nephropathy

BACKGROUND

Immunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification.

METHODS

We performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression.

RESULTS

We observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667-1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667-1.000) showed the highest discriminatory ability to predict IgAN disease progression.

CONCLUSION

This study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.

Metabolomics and Lipidomics Study Unveils the Impact of Tauroursodeoxycholic Acid on Hyperlipidemic Mice

Bear bile powder is an essential, traditional and valuable Chinese herbal medicine that clears heat, calms the liver, and improves eyesight. Early studies have shown that bear bile powder has lipid-lowering activity, but due to the scarcity of natural bear bile powder resources, it has yet to be used on a large scale. Researchers have found that tauroursodeoxycholic acid (TUDCA) is the primary characteristic bioactive substance of bear bile powder. This study aimed to investigate the therapeutic effect of TUDCA on high-fat diet (HFD)-induced hyperlipidemia. A hyperlipidemia model was established by feeding mice high-fat chow, following the intervention of different concentrations of TUDCA (25/50/100 mg/kg) orally, the hallmark biochemical indexes (total cholesterol (TC), total triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)), histopathological examination (hematoxylin-eosin (HE) staining and oil red O (ORO) staining), and metabolomic analysis of serum and liver. The results showed that TUDCA could downregulate total TC, TG, LDL-C, upregulate HDL-C, reduce fat deposition in hepatocytes, reverse hepatocyte steatosis, and exhibit prominent lipid-lowering activity. In addition, it may play a therapeutic role by regulating glycerophospholipid metabolism.

A Metabolomics study of metabolites associated with the glomerular filtration rate

BACKGROUND

Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates.

METHODS

An untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The 145 samples were divided into four groups based on the patient's measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software.

RESULTS

A large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function.

CONCLUSIONS

This global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function.

Novel Metabolites to Improve Glomerular Filtration Rate Estimation

INTRODUCTION

The glomerular filtration rate (GFR) is crucial for chronic kidney disease (CKD) diagnosis and therapy. Various studies have sought to recognize ideal endogenous markers to improve the estimated GFR for clinical practice. To screen out potential novel metabolites related to GFR (mGFR) measurement in CKD patients from the Chinese population, we identified more biomarkers for improving GFR estimation.

METHODS

Fifty-three CKD participants were recruited from the Third Affiliated Hospital of Sun Yat-sen University in 2020. For each participant, mGFR was evaluated by utilizing the plasma clearance of iohexol and collecting serum samples for untargeted metabolomics analyses by ultrahigh-performance liquid chromatography-tandem mass spectroscopy. All participants were divided into four groups according to mGFR. The metabolite peak area data were uploaded to MetaboAnalyst 5.0 for one-way analysis of variance, principal component analysis, and partial least squares-discriminant analysis and confirmed the metabolites whose levels increased or decreased with mGFR and variable importance in projection (VIP) values >1. Metabolites were ranked by correlation with the original values of mGFR, and metabolites with a correlation coefficient >0.8 and VIP >2 were identified.

RESULTS

We screened out 198 metabolites that increased or decreased with mGFR decline. After ranking by correlation with mGFR, the top 50 metabolites were confirmed. Further studies confirmed the 10 most highly correlated metabolites.

CONCLUSION

We screened out the metabolites that increased or decreased with mGFR decline in CKD patients from the Chinese population, and 10 of them were highly correlated. They are potential novel metabolites to improve GFR estimation.

Crosstalk between COVID-19 Infection and Kidney Diseases: A Review on the Metabolomic Approaches

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a respiratory disorder. Various organ injuries have been reported in response to this virus, including kidney injury and, in particular, kidney tubular injury. It has been discovered that infection with the virus does not only cause new kidney disease but also increases treatment difficulty and mortality rates in people with kidney diseases. In individuals hospitalized with COVID-19, urinary metabolites from several metabolic pathways are used to distinguish between patients with acute kidney injury (AKI) and those without. This review summarizes the pathogenesis, pathophysiology, treatment strategies, and role of metabolomics in relation to AKI in COVID-19 patients. Metabolomics is likely to play a greater role in predicting outcomes for patients with kidney disease and COVID-19 with varying levels of severity in the near future as data on metabolic profiles expand rapidly. Here, we also discuss the correlation between COVID-19 and kidney diseases and the available metabolomics approaches.

Metabolic profiling identifies the significance of caffeine metabolism in CKD

Background: With the development of chronic kidney disease (CKD), there are various changes in metabolites. However, the effect of these metabolites on the etiology, progression and prognosis of CKD remains unclear. Objective: We aimed to identify significant metabolic pathways in CKD progression by screening metabolites through metabolic profiling, thus identifying potential targets for CKD treatment. Methods: Clinical data were collected from 145 CKD participants. GFR (mGFR) was measured by the iohexol method and participants were divided into four groups according to their mGFR. Untargeted metabolomics analysis was performed via UPLC-MS/MSUPLC-MSMS/MS assays. Metabolomic data were analyzed by MetaboAnalyst 5.0, one-way ANOVA, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) to identify differential metabolites for further analysis. The open database sources of MBRole2.0, including KEGG and HMDB, were used to identify significant metabolic pathways in CKD progression. Results: Four metabolic pathways were classified as important in CKD progression, among which the most significant was caffeine metabolism. A total of 12 differential metabolites were enriched in caffeine metabolism, four of which decreased with the deterioration of the CKD stage, and two of which increased with the deterioration of the CKD stage. Of the four decreased metabolites, the most important was caffeine. Conclusion: Caffeine metabolism appears to be the most important pathway in the progression of CKD as identified by metabolic profiling. Caffeine is the most important metabolite that decreases with the deterioration of the CKD stage.

Associations of serum amino acids related to urea cycle with risk of chronic kidney disease in Chinese with type 2 diabetes

OBJECTIVE

Serum levels of amino acids related to urea cycle are associated with risk of type 2 diabetes mellitus (T2DM). Our study aimed to explore whether serum levels of amino acids related to urea cycle, i.e., arginine, citrulline, and ornithine, are also associated with increased risk of chronic kidney disease (CKD) in T2DM.

METHODS

We extracted medical records of 1032 consecutive patients with T2DM from the Electronic Administrative System of Liaoning Medical University First Affiliated Hospital (LMUFAH) system from May 2015 to August 2016. Of them, 855 patients with completed data available were used in the analysis. CKD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m². Serum amino acids were measured by mass spectrometry (MS) technology. Binary logistic regression was performed to obtain odds ratios (ORs) and their 95% confidence intervals (CIs).

RESULTS

52.3% of the 855 T2DM patients were male, and 143 had CKD. In univariable analysis, high serum citrulline, high ratio of arginine to ornithine, and low ratio of ornithine to citrulline were associated with markedly increased risk of CKD (OR of top vs. bottom tertile: 2.87, 95%CI, 1.79-4.62 & 1.98, 95%CI,1.25-3.14 & 2.56, 95%CI, 1.61-4.07, respectively). In multivariable analysis, the ORs of citrulline and ornithine/citrulline ratio for CKD remained significant (OR of top vs. bottom tertile: 2.22, 95%CI, 1.29-3.82 & 2.24, 1.29-3.87, respectively).

CONCLUSIONS

In Chinese patients with T2DM, high citrulline and low ornithine/citrulline ratio were associated with increased risk of CKD.

Association of Uremic Solutes With Cardiovascular Death in Diabetic Kidney Disease

RATIONALE & OBJECTIVE

Cardiovascular disease (CVD) is a major cause of mortality among people with diabetic kidney disease (DKD). The pathophysiology is inadequately explained by traditional CVD risk factors. The uremic solutes trimethylamine-N-oxide (TMAO) and asymmetric and symmetric dimethylarginine (ADMA, SDMA) have been linked to CVD in kidney failure with replacement therapy (KFRT), but data are limited in populations with diabetes and less severe kidney disease.

STUDY DESIGN

Observational cohort.

SETTINGS & PARTICIPANTS

Random subcohort of 555 REGARDS (Reasons for Geographic and Racial Differences in Stroke) study participants with diabetes and estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 at study entry.

EXPOSURE

ADMA, SDMA, and TMAO assayed by liquid chromatography-mass spectrometry in plasma and urine.

OUTCOME

Cardiovascular mortality (primary outcome); all-cause mortality and incident KFRT (secondary outcomes).

ANALYTICAL APPROACH

Plasma concentrations and ratios of urine to plasma concentrations of ADMA, SDMA, and TMAO were tested for association with outcomes. Adjusted Cox regression models were fitted and hazard ratios of outcomes calculated per standard deviation and per doubling, and as interquartile comparisons.

RESULTS

The mean baseline eGFR was 44 mL/min/1.73 m2. Cardiovascular death, overall mortality, and KFRT occurred in 120, 285, and 89 participants, respectively, during a mean 6.2 years of follow-up. Higher plasma ADMA and SDMA (HRs of 1.20 and 1.28 per 1-SD greater concentration), and lower ratios of urine to plasma concentrations of ADMA, SDMA, and TMAO (HRs per halving of 1.53, 1.69, and 1.38) were associated with cardiovascular mortality. Higher plasma concentrations of ADMA, SDMA, and TMAO (HRs of 1.31, 1.42, and 1.13 per 1-SD greater concentration) and lower urine to plasma ratios of ADMA, SDMA, and TMAO (HRs per halving of 1.34, 1.37, and 1.26) were associated with all-cause mortality. Higher plasma ADMA and SDMA were associated with incident KFRT by categorical comparisons (HRs of 2.75 and 2.96, comparing quartile 4 to quartile 1), but not in continuous analyses.

LIMITATIONS

Single cohort, restricted to patients with diabetes and eGFR < 60 mL/min/1.73 m2, potential residual confounding by GFR, no dietary information.

CONCLUSIONS

Higher plasma concentrations and lower ratios of urine to plasma concentrations of uremic solutes were independently associated with cardiovascular and all-cause mortality in DKD. Associations of ratios of urine to plasma concentrations with mortality suggest a connection between renal uremic solute clearance and CVD pathogenesis.

Blood Levels of Endocannabinoids, Oxylipins, and Metabolites Are Altered in Hemodialysis Patients

Hemodialysis patients (HDPs) have higher blood pressure, higher levels of inflammation, a higher risk of cardiovascular disease, and unusually low plasma n-3 polyunsaturated fatty acid (PUFA) levels compared to healthy subjects. The objective of our investigation was to examine the levels of endocannabinoids (eCBs) and oxylipins (OxLs) in female HDPs compared to healthy matched female controls, with the underlying hypothesis that differences in specific PUFA levels in hemodialysis patients would result in changes in eCBs and OxLs. Plasma phospholipid fatty acids were analyzed by gas chromatography. Plasma was extracted and analyzed using ultra-performance liquid chromatography followed by electrospray ionization and tandem MS for eCBs and OxLs. The global untargeted metabolite profiling of plasma was performed by GCTOF MS. Compared to the controls, HDPs showed lower levels of plasma EPA and the associated OxL metabolites 5- and 12-HEPE, 14,15-DiHETE, as well as DHA derived 19(20)-EpDPE. Meanwhile, no changes in arachidonylethanolamide or 2-arachidonylglycerol in the open circulation were detected. Higher levels of multiple N-acylethanolamides, monoacylglycerols, biomarkers of progressive kidney disease, the nitric oxide metabolism-linked citrulline, and the uremic toxins kynurenine and creatine were observed in HDP. These metabolic differences in cCBs and OxLs help explain the severe inflammatory and cardiovascular disease manifested by HDPs, and they should be explored in future studies.

Identification of Metabolite Markers Associated with Kidney Function

Background

Chronic kidney disease (CKD) is a global public health problem. Identifying new biomarkers that can be used to calculate the glomerular filtration rate (GFR) would greatly improve the diagnosis and understanding of CKD at the molecular level. A metabolomics study of blood samples derived from patients with widely divergent glomerular filtration rates could potentially discover small molecule metabolites associated with varying kidney function.

Methods

Using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), serum was analyzed from 53 participants with a spectrum of measured GFR (by iohexol plasma clearance) ranging from normal to severe renal insufficiency. An untargeted metabolomics assay (N ¼ 214) was conducted at the Calibra-Metabolon Joint Laboratory.

Results

From a large number of metabolomics-derived metabolites, the top 30 metabolites correlated to increasing renal insufficiency according to mGFR were selected by the random forest method. Significant differences in metabolite profiles with increasing stages of CKD were observed. Combining candidate lists from six other unique statistical analyses, six novel, potential metabolites that were reproducibly strongly associated with mGFR were selected, including erythronate, gulonate, C-glycosyltryptophan, N-acetylserine, N6-carbamoylthreonyladenosine, and pseudouridine. In addition, hydroxyasparagine were strongly associated with mGFR and CKD, which were unique to this study.

Conclusions

Global metabolite profiling of serum yielded potentially valuable biomarkers of different stages of CKD. Additionally, these potential biomarkers might provide insight into the underlying pathophysiologic processes that contribute to the progression of CKD as well as improve GFR estimation.

Healthy and Chronic Kidney Disease (CKD) Dogs Have Differences in Serum Metabolomics and Renal Diet May Have Slowed Disease Progression

Chronic kidney disease (CKD) is highly prevalent in dogs, and metabolomics investigation has been recently introduced for a better understanding of the role of diet in CKD. This study aimed to compare the serum metabolomic profile of healthy dogs (CG) and dogs with CKD (CKD-T0 and CKD-T6) to evaluate whether the diet would affect metabolites. Six dogs (5 females; 1 male; 7.47 ± 2.31 years old) with CKD stage 3 or 4 (IRIS) were included. CG consisted of 10 healthy female dogs (5.89 ± 2.57 years old) fed a maintenance diet. Serum metabolites were analyzed by ¹H nuclear magnetic resonance (¹H NMR) spectra. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed to assess differences in metabolomic profiles between groups and before (CKD-T0) and after renal diet (CKD-T6). Data analysis was performed on SIMCA-P software. Dogs with CKD showed an altered metabolic profile with increased urea, creatinine, creatine, citrate, and lipids. Lactate, branched-chain amino acids (BCAAs), and glutamine were decreased in the CKD group. However, after 6 months of diet, the metabolite profiles of CKD-T0 and CKD-T6 were similar. Metabolomics profile may be useful to evaluate and recognize metabolic dysfunction and progression of CKD, and the diet may have helped maintain and retard the progression of CKD.

Metabolomic Approaches to Investigate the Effect of Metformin: An Overview

Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.

Metabolite Biomarkers of CKD Progression in Children

BACKGROUND AND OBJECTIVES

Metabolomics facilitates the discovery of biomarkers and potential therapeutic targets for CKD progression.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We evaluated an untargeted metabolomics quantification of stored plasma samples from 645 Chronic Kidney Disease in Children (CKiD) participants. Metabolites were standardized and logarithmically transformed. Cox proportional hazards regression examined the association between 825 nondrug metabolites and progression to the composite outcome of KRT or 50% reduction of eGFR, adjusting for age, sex, race, body mass index, hypertension, glomerular versus nonglomerular diagnosis, proteinuria, and baseline eGFR. Stratified analyses were performed within subgroups of glomerular/nonglomerular diagnosis and baseline eGFR.

RESULTS

Baseline characteristics were 391 (61%) male; median age 12 years; median eGFR 54 ml/min per 1.73 m²; 448 (69%) nonglomerular diagnosis. Over a median follow-up of 4.8 years, 209 (32%) participants developed the composite outcome. Unique association signals were identified in subgroups of baseline eGFR. Among participants with baseline eGFR ≥60 ml/min per 1.73 m², two-fold higher levels of seven metabolites were significantly associated with higher hazards of KRT/halving of eGFR events: three involved in purine and pyrimidine metabolism (N6-carbamoylthreonyladenosine, hazard ratio, 16; 95% confidence interval, 4 to 60; 5,6-dihydrouridine, hazard ratio, 17; 95% confidence interval, 5 to 55; pseudouridine, hazard ratio, 39; 95% confidence interval, 8 to 200); two amino acids, C-glycosyltryptophan, hazard ratio, 24; 95% confidence interval 6 to 95 and lanthionine, hazard ratio, 3; 95% confidence interval, 2 to 5; the tricarboxylic acid cycle intermediate 2-methylcitrate/homocitrate, hazard ratio, 4; 95% confidence interval, 2 to 7; and gulonate, hazard ratio, 10; 95% confidence interval, 3 to 29. Among those with baseline eGFR <60 ml/min per 1.73 m², a higher level of tetrahydrocortisol sulfate was associated with lower risk of progression (hazard ratio, 0.8; 95% confidence interval, 0.7 to 0.9).

CONCLUSIONS

Untargeted plasma metabolomic profiling facilitated discovery of novel metabolite associations with CKD progression in children that were independent of established clinical predictors and highlight the role of select biologic pathways.

The synergic renoprotective effect of Rehmanniae Radix Preparata and Corni Fructus on adenine-induced chronic kidney disease rats based on integrated plasma metabolomics and network pharmacology approach

AIMS

Rehmanniae Radix Preparata (RR) and Corni Fructus (CF) are commonly used together for the treatment of chronic kidney disease (CKD) in the clinical practices for thousands of years. However, little information on their synergy mechanism is available.

MAIN METHODS

In this study, an integrated approach combining ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS)-based metabonomics and network pharmacology was adopted to elucidate the cooperation mechanism of RR and CF on the amelioration of CKD. Furthermore, the targets from network pharmacology and metabolism pathways were jointly analyzed. Finally, the activities of key metabolic enzymes were experimentally validated by ELISA.

KEY FINDINGS

Metabolic profiling indicated that the metabolic disturbance in plasma was markedly alleviated after treatment. Nine putative biomarkers mainly involving in phenylalanine, tyrosine and tryptophan biosynthesis and tyrosine metabolism were identified. Moreover, the compound-target-pathway network of RR and CF for CKD treatment was constructed by network pharmacology, which was related to tyrosine metabolism and arginine and proline metabolism. The results were partly consistent with the findings of plasma metabolomics.

SIGNIFICANCE

In conclusion, this study solidly supported and enhanced current understanding of the synergy effects of RR and CF on CKD. Meanwhile, it also confirmed the feasibility of combining metabolomics and network pharmacology to identify active components and elucidate the pharmacological effects of traditional Chinese medicines (TCMs).

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.

Exploring blood alterations in chronic kidney disease and haemodialysis using metabolomics

Chronic kidney disease (CKD) is characterized by retention of uremic solutes. Compared to patients with non-dialysis dependent CKD, those requiring haemodialysis (HD) have increased morbidity and mortality. We wished to characterise metabolic patterns in CKD compared to HD patients using metabolomics. Prevalent non-HD CKD KDIGO stage 3b-4 and stage 5 HD outpatients were screened at a single tertiary hospital. Various liquid chromatography approaches hyphenated with mass spectrometry were used to identify 278 metabolites. Unsupervised and supervised data analyses were conducted to characterize metabolic patterns. 69 patients were included in the CKD group and 35 in the HD group. Unsupervised data analysis showed clear clustering of CKD, pre-dialysis (preHD) and post-dialysis (postHD) patients. Supervised data analysis revealed qualitative as well as quantitative differences in individual metabolites profiles between CKD, preHD and postHD states. An original metabolomics framework could discriminate between CKD stages and highlight HD effect based on 278 identified metabolites. Significant differences in metabolic patterns between CKD and HD patients were found overall as well as for specific metabolites. Those findings could explain clinical discrepancies between patients requiring HD and those with earlier stage of CKD.

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.

Influence of baseline kidney dysfunction on perioperative renal outcomes after endovascular aneurysm repair with suprarenal fixation

BACKGROUND

Conflicting evidence exists regarding the comparative effects of endovascular aneurysm repair (EVAR) with and without suprarenal fixation. We compare outcomes in patients treated by EVAR with baseline normal kidney function and moderate and severe chronic kidney disease.

METHODS

Patients with normal kidney function (glomerular filtration rate [GFR] ≥60 mL/min/1.73 m²) or moderate (GFR = 30-59 mL/min/1.73 m²) or severe (GFR <30 mL/min/1.73 m²) kidney disease who underwent EVAR (N = 5534) were identified from the American College of Surgeons National Surgical Quality Improvement Program targeted database (2011-2015). Groups were determined by the presence (Cook Zenith [Cook Medical, Bloomington, Ind] or Medtronic Endurant [Medtronic, Minneapolis, Minn]) or absence (Gore Excluder [W. L. Gore & Associates, Flagstaff, Ariz]) of a suprarenal fixation system. Postoperative renal complications, defined as rise in creatinine concentration of >2 mg/dL without dialysis or new dialysis requirements, were analyzed within the first 30 days with results stratified by degree of kidney disease.

RESULTS

A total of 5534 patients underwent EVAR, with 3225 (58.3%) receiving a device using a suprarenal fixation system. Suprarenal fixation systems were less commonly used for symptomatic patients (11.0% vs 13.7%; P = .002) and patients with ruptured abdominal aortic aneurysm (4.5% vs 6.3%; P = .01). There was no difference in baseline kidney function between groups. EVAR with suprarenal fixation was associated with more renal complications (1.40% vs 0.65%; P = .008). In subgroup analysis, patients with moderate kidney dysfunction (n = 1780) had more renal complications (2.2% vs 0.8%; P = .02) with suprarenal fixation systems. No differences were seen in patients with normal kidney function (0.4% vs 0.2%; P = .32; n = 3597) or severe kidney dysfunction (14.3% vs 10.2%; P = .45; n = 157). This difference was driven mostly by postoperative elevation of creatinine concentration (0.6% vs 0.2%; P = .03) without requirements for new dialysis (0.8% vs 0.4%; P = .08). After adjustments with multivariate logistic regression models, EVAR with suprarenal fixation was associated with more renal complications (odds ratio, 2.65; 95% confidence interval, 1.32-5.34).

CONCLUSIONS

In our study, EVAR with suprarenal fixation devices was associated with more perioperative renal complications in patients with moderate kidney dysfunction. Long-term evaluation of these patients undergoing EVAR should be considered.

Serum metabolites associate with lipid phenotypes among Bogalusa Heart Study participants

BACKGROUND AND AIMS

Dyslipidemia has been identified as a major risk factor for cardiovascular disease. We aimed to identify metabolites and metabolite modules showing novel association with lipids among Bogalusa Heart Study (BHS) participants using untargeted metabolomics.

METHODS AND RESULTS

Untargeted ultrahigh performance liquid chromatography-tandem mass spectroscopy was used to quantify serum metabolites of 1 243 BHS participants (816 whites and 427 African-Americans). The association of single metabolites with lipids was assessed using multiple linear regression models to adjust for covariables. Weighted correlation network analysis was utilized to identify modules of co-abundant metabolites and examine their covariable adjusted correlations with lipids. All analyses were conducted according to race and using Bonferroni-corrected α-thresholds to determine statistical significance. Thirteen metabolites with known biochemical identities showing novel association achieved Bonferroni-significance, p < 1.04 × 10^-5, and showed consistent effect directions in both whites and African-Americans. Twelve were from lipid sub-pathways including fatty acid metabolism (arachidonoylcholine, dihomo-linolenoyl-choline, docosahexaenoylcholine, linoleoylcholine, oleoylcholine, palmitoylcholine, and stearoylcholine), monohydroxy fatty acids (2-hydroxybehenate, 2-hydroxypalmitate, and 2-hydroxystearate), and lysoplasmalogens [1-(1-enyl-oleoyl)-GPE (P-18:1) and 1-(1-enyl-stearoyl)-GPE (P-18:0)]. The gamma-glutamylglutamine, peptide from the gamma-glutamyl amino acid sub-pathway, were also identified. In addition, four metabolite modules achieved Bonferroni-significance, p < 1.39 × 10^-3, in both whites and African-Americans. These four modules were largely comprised of metabolites from lipid sub-pathways, with one module comprised of metabolites which were not identified in the single metabolite analyses.

CONCLUSION

The current study identified 13 metabolites and 4 metabolite modules showing novel association with lipids, providing new insights into the physiological mechanisms regulating lipid levels.

Urinary NMR Profiling in Pediatric Acute Kidney Injury-A Pilot Study

Acute kidney injury (AKI) in critically ill children and adults is associated with significant short- and long-term morbidity and mortality. As serum creatinine- and urine output-based definitions of AKI have relevant limitations, there is a persistent need for better diagnostics of AKI. Nuclear magnetic resonance (NMR) spectroscopy allows for analysis of metabolic profiles without extensive sample manipulations. In the study reported here, we examined the diagnostic accuracy of NMR urine metabolite patterns for the diagnosis of neonatal and pediatric AKI according to the Kidney Disease: Improving Global Outcomes (KDIGO) definition. A cohort of 65 neonatal and pediatric patients (0–18 years) with established AKI of heterogeneous etiology was compared to both a group of apparently healthy children (n = 53) and a group of critically ill children without AKI (n = 31). Multivariate analysis identified a panel of four metabolites that allowed diagnosis of AKI with an area under the receiver operating characteristics curve (AUC-ROC) of 0.95 (95% confidence interval 0.86–1.00). Especially urinary citrate levels were significantly reduced whereas leucine and valine levels were elevated. Metabolomic differentiation of AKI causes appeared promising but these results need to be validated in larger studies. In conclusion, this study shows that NMR spectroscopy yields high diagnostic accuracy for AKI in pediatric patients.

Identification of Potential Serum Metabolic Biomarkers of Diabetic Kidney Disease: A Widely Targeted Metabolomics Study

Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility.

RESULT

A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis.

CONCLUSION

Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.

Silybin ameliorates hepatic lipid accumulation and modulates global metabolism in an NAFLD mouse model

Silybin shows good effects against obesity and metabolic syndrome, but the systemic modulation effect of silybin has not been fully revealed. This study aims to investigate the metabolic regulation by silybin of nonalcoholic fatty liver disease (NAFLD). C57BL/6 J mice were fed a high-fat/high-cholesterol diet for 8 weeks and treated with silybin (50 or 100 mg/kg/day) and sodium tauroursodeoxycholate (TUDCA, 50 mg/kg/day) by gavage for the last 4 weeks. Blood biochemical indexes and hepatic lipid measurement as well as Oil red O staining of the liver were conducted to evaluate the model and the lipid-lowering effect of silybin and TUDCA. Furthermore, serum and liver samples were detected by a metabolomic platform based on gas chromatography-mass spectrometry (GC/MS). Multivariate/univariate data analysis and pathway analysis were used to investigate differential metabolites and metabolic pathways. The results showed that the mouse NAFLD model was established successfully and that silybin and TUDCA significantly lowered both serum and hepatic lipid accumulation. Metabolomic analysis of serum and liver showed that a high-fat/high-cholesterol diet caused abnormal metabolism of metabolites involved in lipid metabolism, polyol metabolism, amino acid metabolism, the urea cycle and the TCA cycle. Silybin and TUDCA treatment both reversed metabolic disorders caused by HFD feeding. In conclusion, a high-fat/high-cholesterol diet caused metabolic abnormalities in the serum and liver of mice, and silybin treatment improved hepatic lipid accumulation and modulated global metabolic pathways, which provided a possible explanation of its multiple target mechanism.

Comprehensive and semi-quantitative analysis of carboxyl-containing metabolites related to gut microbiota on chronic kidney disease using 2-picolylamine isotopic labeling LC-MS/MS

Carboxyl-containing metabolites, such as bile acids and fatty acids, have many important functions and microbiota is involved in the production of them. In the previous study, we found that the chronic kidney disease (CKD) model mice raised under germ-free conditions provided more severe renal damage than the mice with commensal microbiota. However, the precise influence by the microbiome and carboxyl-containing metabolites to the renal functions is unknown. In this study, we aimed to develop a novel chemical isotope labeling-LC-MS/MS method using the 2-picolylamine and its isotopologue and applied the analysis of effects of microbiome and CKD pathophysiology. The developed semi-quantitative method provided the high accuracy not inferior to the absolute quantification. By comparing of four groups of mice, we found that both microbiota and renal function can alter the composition and level of these metabolites in both plasma and intestine. In particular, the intestinal level of indole-3-acetic acid, short-chain fatty acids and n-3 type of polyunsaturated fatty acid, which play important roles in the endothelial barrier function, were significantly lower in germ-free conditions mice with renal failure. Accordingly, it is suggested these metabolites might have a renoprotective effect on CKD by suppressing epithelial barrier disruption.

Novel associations between blood metabolites and kidney function among Bogalusa Heart Study and Multi-Ethnic Study of Atherosclerosis participants

INTRODUCTION

Chronic kidney disease (CKD) is a major public health challenge given its high global prevalence and associated risks of cardiovascular disease and progression to end stage renal disease. Although it is known that numerous metabolic changes occur in CKD patients, identifying novel metabolite associations with kidney function may enhance our understanding of the physiologic pathways relating to CKD.

OBJECTIVES

The objective of this study was to elucidate novel metabolite associations with kidney function among participants of two community-based cohorts with carefully ascertained metabolomics, kidney function, and covariate data.

METHODS

Untargeted ultrahigh-performance liquid chromatography-tandem mass spectrometry was used to detect and quantify blood metabolites. We used multivariate adjusted linear regression to examine associations between single metabolites and creatinine-based estimated glomerular filtration rate (eGFRcr) among 1243 Bogalusa Heart Study (BHS) participants (median eGFRcr: 94.4, 5th-95th percentile: 66.0-119.6 mL/min/1.73 m²). Replication, determined by statistical significance and consistent effect direction, was tested using gold standard measured glomerular filtration rate (mGFR) among 260 Multi-Ethnic Study of Atherosclerosis (MESA) participants (median mGFR: 72.0, 5th-95th percentile: 43.5-105.0 mL/min/1.73 m²). All analyses used Bonferroni-corrected alpha thresholds.

RESULTS

Fifty-one novel metabolite associations with kidney function were identified, including 12 from previously unrelated sub-pathways: N6-carboxymethyllysine, gulonate, quinolinate, gamma-CEHC-glucuronide, retinol, methylmalonate, 3-hydroxy-3-methylglutarate, 3-aminoisobutyrate, N-methylpipecolate, hydroquinone sulfate, and glycine conjugates of C₁₀H₁₂O₂ and C₁₀H₁₄O₂(1). Significant metabolites were generally inversely associated with kidney function and smaller in mass-to-charge ratio than non-significant metabolites.

CONCLUSION

The 51 novel metabolites identified may serve as early, clinically relevant, kidney function biomarkers.

Chronic kidney disease: Biomarker diagnosis to therapeutic targets

Chronic kidney disease (CKD), characterized as renal dysfunction, is recognized as a major public health problem with high morbidity and mortality worldwide. Unfortunately, there are no obvious clinical symptoms in early stage disease until severe damage has occurred. Further complicating early diagnosis and treatment is the lack of sensitive and specific biomarkers. As such, novel biomarkers are urgently needed. Metabolomics has shown an increasing potential for identifying underlying disease mechanisms, facilitating clinical diagnosis and developing pharmaceutical treatments for CKD. Recent advances in metabolomics revealed that CKD was closely associated with the dysregulation of numerous metabolites, such as amino acids, lipids, nucleotides and glycoses, that might be exploited as potential biomarkers. In this review, we summarize recent metabolomic applications based on animal model studies and in patients with CKD and highlight several biomarkers that may play important roles in diagnosis, intervention and development of new therapeutic strategies.

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.

Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease

Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.

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.

A Systems-Level View of Renal Metabolomics

The measurement of select circulating metabolites such as creatinine, glucose, and cholesterol are integral to clinical medicine, with implications for diagnosis, prognosis, and treatment. Metabolomics studies in nephrology research seek to build on this paradigm, with the goal to identify novel markers and causal participants in the pathogenesis of kidney disease and its complications. This article reviews three themes pertinent to this goal. Each is rooted in long-established principles of human physiology, with recent updates enabled by metabolomics and other tools. First, the kidney has a broad and heterogeneous impact on circulating metabolites, with progressive loss of kidney function resulting in a multitude of small molecule alterations. Second, an increasing number of circulating metabolites have been shown to possess functional roles, in some cases acting as ligands for specific G-protein-coupled receptors. Third, circulating metabolites traffic through varied, and sometimes complex, interorgan circuits. Taken together, these themes emphasize the importance of viewing renal metabolomics at the systems level, recognizing the diverse origins and physiologic effects of blood metabolites. However, how to synthesize these themes and how to establish clinical relevance remain uncertain and will require 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.

Optimization of kidney dysfunction prediction in diabetic kidney disease using targeted metabolomics

AIMS

Metabolomics have been used to evaluate the role of small molecules in human disease. However, the cost and complexity of the methodology and interpretation of findings have limited the transference of knowledge to clinical practice. Here, we apply a targeted metabolomics approach using samples blotted in filter paper to develop clinical-metabolomics models to detect kidney dysfunction in diabetic kidney disease (DKD).

METHODS

We included healthy controls and subjects with type 2 diabetes (T2D) with and without DKD and investigated the association between metabolite concentrations in blood and urine with eGFR and albuminuria. We also evaluated performance of clinical, biochemical and metabolomic models to improve kidney dysfunction prediction in DKD.

RESULTS

Using clinical-metabolomics models, we identified associations of decreased eGFR with body mass index (BMI), uric acid and C10:2 levels; albuminuria was associated to years of T2D duration, A1C, uric acid, creatinine, protein intake and serum C0, C10:2 and urinary C12:1 levels. DKD was associated with age, A1C, uric acid, BMI, serum C0, C10:2, C8:1 and urinary C12:1. Inclusion of metabolomics increased the predictive and informative capacity of models composed of clinical variables by decreasing Akaike's information criterion, and was replicated both in training and validation datasets.

CONCLUSIONS

Targeted metabolomics using blotted samples in filter paper is a simple, low-cost approach to identify outcomes associated with DKD; the inclusion of metabolomics improves predictive capacity of clinical models to identify kidney dysfunction and DKD-related outcomes.

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.

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.

Associations of Plasma Amino Acid and Acylcarnitine Profiles with Incident Reduced Glomerular Filtration Rate

BACKGROUND AND OBJECTIVES

Metabolomics is instrumental in identifying novel biomarkers of kidney function to aid in the prevention and management of CKD. However, data linking the metabolome to incident eGFR are sparse, particularly in Asian populations with different genetic backgrounds and environmental exposures. Therefore, we aimed to investigate the associations of amino acid and acylcarnitine profiles with change in eGFR in a Chinese cohort.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

This study included 1765 community-living Chinese adults aged 50-70 years with baseline eGFR≥60 ml/min per 1.73 m2. At baseline, 22 amino acids and 34 acylcarnitines in plasma were quantified by gas or liquid chromatography coupled with mass spectrometry. Annual rate of change in eGFR was calculated, and incident eGFR decline was defined as eGFR<60 ml/min per 1.73 m2 by the end of 6 years of follow-up.

RESULTS

The mean (SD) unadjusted annual change in eGFR was 2.2±2.0 ml/min per 1.73 m2 and the incidence of reduced eGFR was 16%. After Bonferroni correction, 13 of 56 metabolites were significantly associated with annual eGFR change. After multivariable adjustment of baseline covariates, including baseline eGFR, seven of the 13 metabolites, including cysteine, long-chain acylcarnitines (C14:1OH, C18, C18:2, and C20:4), and other acylcarnitines (C3DC and C10), were significantly associated with incident reduced eGFR (relative risks ranged from 1.16 to 1.25 per SD increment of metabolites; P<3.8E-03 after Bonferroni correction of multiple testing of the 13 metabolites). Moreover, principal component analysis identified two factors, consisting of cysteine and long-chain acylcarnitines, respectively, that were associated with incident reduced eGFR.

CONCLUSIONS

Elevated plasma levels of cysteine and a panel of acylcarnitines were associated with a higher incidence of reduced eGFR in Chinese adults, independent of baseline eGFR and other conventional risk factors.

1,5-Anhydroglucitol predicts CKD progression in macroalbuminuric diabetic kidney disease: results from non-targeted metabolomics

INTRODUCTION

Metabolomics allows exploration of novel biomarkers and provides insights on metabolic pathways associated with disease. To date, metabolomics studies on CKD have been largely limited to Caucasian populations and have mostly examined surrogate end points.

OBJECTIVE

In this study, we evaluated the role of metabolites in predicting a primary outcome defined as dialysis need, doubling of serum creatinine or death in Brazilian macroalbuminuric DKD patients.

METHODS

Non-targeted metabolomics was performed on plasma from 56 DKD patients. Technical triplicates were done. Metabolites were identified using Agilent Fiehn GC/MS Metabolomics and NIST libraries (Agilent MassHunter Work-station Quantitative Analysis, version B.06.00). After data cleaning, 186 metabolites were left for analyses.

RESULTS

During a median follow-up time of 2.5 years, the PO occurred in 17 patients (30.3%). In non-parametric testing, 13 metabolites were associated with the PO. In univariate Cox regression, only 1,5-anhydroglucitol (HR 0.10; 95% CI 0.01-0.63, p = .01), norvaline and L-aspartic acid were associated with the PO. After adjustment for baseline renal function, 1,5-anhydroglucitol (HR 0.10; 95% CI 0.02-0.63, p = .01), norvaline (HR 0.01; 95% CI 0.001-0.4, p = .01) and aspartic acid (HR 0.12; 95% CI 0.02-0.64, p = .01) remained significantly and inversely associated with the PO.

CONCLUSION

Our results show that lower levels of 1,5-anhydroglucitol, norvaline and L-aspartic acid are associated with progression of macroalbuminuric DKD. While norvaline and L-aspartic acid point to interesting metabolic pathways, 1,5-anhydroglucitol is of particular interest since it has been previously shown to be associated with incident CKD. This inverse biomarker of hyperglycemia should be further explored as a new tool in DKD.

Untargeted metabolomics for plasma biomarker discovery for early chronic kidney disease diagnosis in pediatric patients using LC-QTOF-MS

Pediatric chronic kidney disease (CKD) is a clinical syndrome characterized by renal hypofunction occurring due to gradual and irreversible kidney damage that can further progress over time.

Metabolomics and Gene Expression Analysis Reveal Down-regulation of the Citric Acid (TCA) Cycle in Non-diabetic CKD Patients

Chronic kidney disease (CKD) is a public health problem with very high prevalence and mortality. Yet, there is a paucity of effective treatment options, partly due to insufficient knowledge of underlying pathophysiology. We combined metabolomics (GCMS) with kidney gene expression studies to identify metabolic pathways that are altered in adults with non-diabetic stage 3-4 CKD versus healthy adults. Urinary excretion rate of 27 metabolites and plasma concentration of 33 metabolites differed significantly in CKD patients versus controls (estimate range-68% to +113%). Pathway analysis revealed that the citric acid cycle was the most significantly affected, with urinary excretion of citrate, cis-aconitate, isocitrate, 2-oxoglutarate and succinate reduced by 40-68%. Reduction of the citric acid cycle metabolites in urine was replicated in an independent cohort. Expression of genes regulating aconitate, isocitrate, 2-oxoglutarate and succinate were significantly reduced in kidney biopsies. We observed increased urine citrate excretion (+74%, p=0.00009) and plasma 2-oxoglutarate concentrations (+12%, p=0.002) in CKD patients during treatment with a vitamin-D receptor agonist in a randomized trial. In conclusion, urinary excretion of citric acid cycle metabolites and renal expression of genes regulating these metabolites were reduced in non-diabetic CKD. This supports the emerging view of CKD as a state of mitochondrial dysfunction.

Simultaneous quantitative analysis of uremic toxins by LC-MS/MS with a reversed-phase/cation-exchange/anion-exchange tri-modal mixed-mode column

Column choice is crucial to the development of liquid chromatography/tandem mass spectrometry (LC-MS/MS) methods because analyte selectivity is dependent on the nature of the stationary phase. Recently, mixed-mode chromatography, which employs a combination of two or more stationary phases and solvent systems, has emerged as an alternative to multiple, complementary, single-column systems. This report describes the development and validation of a novel analytical method based on LC-MS/MS employing a reversed-phase/cation-exchange/anion-exchange tri-modal column (Scherzo SS-C18; Imtakt) for the simultaneous quantification of various uremic toxins (UTx), including creatinine, 1-methyladenosine, trimethylamine-N-oxide, indoxyl sulfate, p-cresyl sulfate, phenyl sulfate and 4-ethylphenyl sulfate. Stable isotope-labeled compounds were prepared as internal standards (ISs) for each analyte. Mobile phase optimization and appropriate gradient conditions resulted in satisfactory retention and peak resolution that could not have been attained with a single stationary phase LC system. The essential validation parameters, including intra- and inter-assay precision and accuracy, were adequate. The validated method was applied to measure serum levels of the aforementioned compounds in 19 patients with chronic kidney disease. This is the first report detailing the simultaneous quantification of these analytes using stable isotopes as ISs. Our results suggest that Scherzo SS-C18 columns will be considered breakthrough tools in the development of analytical methods for compounds that are difficult to quantify simultaneously in traditional LC systems.

Inhibiting aerobic glycolysis suppresses renal interstitial fibroblast activation and renal fibrosis

Chronic kidney diseases generally lead to renal fibrosis. Despite great progress having been made in identifying molecular mediators of fibrosis, the mechanism that governs renal fibrosis remains unclear, and so far no effective therapeutic antifibrosis strategy is available. Here we demonstrated that a switch of metabolism from oxidative phosphorylation to aerobic glycolysis (Warburg effect) in renal fibroblasts was the primary feature of fibroblast activation during renal fibrosis and that suppressing renal fibroblast aerobic glycolysis could significantly reduce renal fibrosis. Both gene and protein assay showed that the expression of glycolysis enzymes was upregulated in mouse kidneys with unilateral ureter obstruction (UUO) surgery or in transforming growth factor-β1 (TGF-β1)-treated renal interstitial fibroblasts. Aerobic glycolysis flux, indicated by glucose uptake and lactate production, was increased in mouse kidney with UUO nephropathy or TGF-β1-treated renal interstitial fibroblasts and positively correlated with fibrosis process. In line with this, we found that increasing aerobic glycolysis can remarkably induce myofibroblast activation while aerobic glycolysis inhibitors shikonin and 2-deoxyglucose attenuate UUO-induced mouse renal fibrosis and TGF-β1-stimulated myofibroblast activation. Furthermore, mechanistic study indicated that shikonin inhibits renal aerobic glycolysis via reducing phosphorylation of pyruvate kinase type M2, a rate-limiting glycolytic enzyme associated with cell reliance on aerobic glycolysis. In conclusion, our findings demonstrate the critical role of aerobic glycolysis in renal fibrosis and support treatment with aerobic glycolysis inhibitors as a potential antifibrotic strategy.

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

Uremia has been a rapidly increasing health problem in China. Hemodialysis (HD) is the main renal replacement therapy for uremia. The results of large-scale clinical trials have shown that the HD pattern is crucial for long-term prognosis of maintenance hemodialysis (MHD) in uremic patients. Plasma metabolism is very important for revealing the biological insights linked to the therapeutic effects of the HD pattern on uremia. Alteration of plasma metabolites in uremic patients in response to HD therapy has been reported. However, HD-pattern-dependent changes in plasma metabolites remain poorly understood. To this end, a capillary electrophoresis-time of flight mass spectrometry (CE-TOF/MS)-based metabolomics method was performed to systemically study the differences between HD and high flux hemodialysis (HFD) on plasma metabolite changes in patients. Three hundred and one plasma samples from three independent human cohorts (i.e., healthy controls, patients with pre-HD/post-HD, and patients with pre-HFD/post-HFD) were used in this study. Metabolites significantly changed (p < 0.05) after a single HD or HFD process. However, 11 uremic retention solutes could be more efficiently removed by HFD. Our findings indicate that a CE-TOF/MS-based metabolomics approach is promising for providing novel insights into understanding the effects of different dialysis methods on metabolite alterations of uremia.