Plasma and urinary amino acid metabolomic profiling in patients with different levels of kidney function

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.

Citric Acid Metabolism in Resistant Hypertension: Underlying Mechanisms and Metabolic Prediction of Treatment Response

Resistant hypertension (RH) affects 9% to 12% of hypertensive adults. Prolonged exposure to suboptimal blood pressure control results in end-organ damage and cardiovascular risk. Spironolactone is the most effective drug for treatment, but not all patients respond and side effects are not negligible. Little is known on the mechanisms responsible for RH. We aimed to identify metabolic alterations in urine. In addition, a potential capacity of metabolites to predict response to spironolactone was investigated. Urine was collected from 29 patients with RH and from a group of 13 subjects with pseudo-RH. For patients, samples were collected before and after spironolactone administration and were classified in responders (n=19) and nonresponders (n=10). Nuclear magnetic resonance was applied to identify altered metabolites and pathways. Metabolites were confirmed by liquid chromatography-mass spectrometry. Citric acid cycle was the pathway most significantly altered (P<0.0001). Metabolic concentrations were quantified and ranged from ng/mL malate to μg/mL citrate. Citrate and oxaloacetate increased in RH versus pseudoresistant. Together with α-ketoglutarate and malate, they were able to discriminate between responders and nonresponders, being the 4 metabolites increased in nonresponders. Combined as a prediction panel, they showed receiver operating characteristiccurve with area under the curve of 0.96. We show that citric acid cycle and deregulation of reactive oxygen species homeostasis control continue its activation after hypertension was developed. A metabolic panel showing alteration before spironolactone treatment and predicting future response of patients is shown. These molecular indicators will contribute optimizing the rate of control of RH patients with spironolactone.

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.

Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome

In its early stages, symptoms of chronic kidney disease (CKD) are usually not apparent. Significant reduction of the kidney function is the first obvious sign of disease. If diagnosed early (stages 1 to 3), the progression of CKD can be altered and complications reduced. In stages 4 and 5 extensive kidney damage is observed, which usually results in end-stage renal failure. Currently, the diagnosis of CKD is made usually on the levels of blood urea and serum creatinine (sCr), however, sCr has been shown to be lacking high predictive value. Due to the development of genomics, epigenetics, transcriptomics, proteomics, and metabolomics, the introduction of novel techniques will allow for the identification of novel biomarkers in renal diseases. This review presents some new possible biomarkers in the diagnosis of CKD and in the prediction of outcome, including asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), uromodulin, kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), miRNA, ncRNA, and lincRNA biomarkers and proteomic and metabolomic biomarkers. Complicated pathomechanisms of CKD development and progression require not a single marker but their combination in order to mirror all types of alterations occurring in the course of this disease. It seems that in the not so distant future, conventional markers may be exchanged for new ones, however, confirmation of their efficacy, sensitivity and specificity as well as the reduction of analysis costs are required.

Gene and protein expressions and metabolomics exhibit activated redox signaling and wnt/β-catenin pathway are associated with metabolite dysfunction in patients with chronic kidney disease

Changes in plasma concentration of small organic metabolites could be due to their altered production or urinary excretion and changes in their urine concentration may be due to the changes in their filtered load, tubular reabsorption, and/or altered urine volume. Therefore, these factors should be considered in interpretation of the changes observed in plasma or urine of the target metabolite(s). Fasting plasma and urine samples from 180 CKD patients and 120 age-matched healthy controls were determined by UPLC-HDMS-metabolomics and quantitative real-time RT-PCR techniques. Compared with healthy controls, patients with CKD showed activation of NF-κB and up-regulation of pro-inflammatory and pro-oxidant mRNA and protein expression as well as down-regulation of Nrf2-associated anti-oxidant gene mRNA and protein expression, accompanied by activated canonical Wnt/β-catenin signaling. 124 plasma and 128 urine metabolites were identified and 40 metabolites were significantly altered in both plasma and urine. Plasma concentration and urine excretion of 25 metabolites were distinctly different between CKD and controls. They were related to amino acid, methylamine, purine and lipid metabolisms. Logistic regression identified four plasma and five urine metabolites. Parts of them were good correlated with eGFR or serum creatinine. 5-Methoxytryptophan and homocystine and citrulline were good correlated with both eGFR and creatinine. Clinical factors were incorporated to establish predictive models. The enhanced metabolite model showed 5-methoxytryptophan, homocystine and citrulline have satisfactory accuracy, sensitivity and specificity for predictive CKD. The dysregulation of CKD was related to amino acid, methylamine, purine and lipid metabolisms. 5-methoxytryptophan, homocystine and citrulline could be considered as additional GFR-associated biomarker candidates and for indicating advanced renal injury. CKD caused dysregulation of the plasma and urine metabolome, activation of inflammatory/oxidative pathway and Wnt/β-catenin signaling and suppression of antioxidant pathway.

Mechanisms and consequences of carbamoylation

Protein carbamoylation is a non-enzymatic post-translational modification that binds isocyanic acid, which can be derived from the dissociation of urea or from the myeloperoxidase-mediated catabolism of thiocyanate, to the free amino groups of a multitude of proteins. Although the term 'carbamoylation' is usually replaced by the term "carbamylation" in the literature, carbamylation refers to a different chemical reaction (the reversible interaction of CO₂ with α and ε-amino groups of proteins). Depending on the altered molecule (for example, collagen, erythropoietin, haemoglobin, low-density lipoprotein or high-density lipoprotein), carbamoylation can have different pathophysiological effects. Carbamoylated proteins have been linked to atherosclerosis, lipid metabolism, immune system dysfunction (such as inhibition of the classical complement pathway, inhibition of complement-dependent rituximab cytotoxicity, reduced oxidative neutrophil burst, and the formation of anti-carbamoylated protein antibodies) and renal fibrosis. In this Review, we discuss the carbamoylation process and evaluate the available biomarkers of carbamoylation (for example, homocitrulline, the percentage of carbamoylated albumin, carbamoylated haemoglobin, and carbamoylated low-density lipoprotein). We also discuss the relationship between carbamoylation and the occurrence of cardiovascular events and mortality in patients with chronic kidney disease and assess the effects of strategies to lower the carbamoylation load.

Urinary metabolomics reveals the therapeutic effect of HuangQi Injections in cisplatin-induced nephrotoxic rats

The side effects of cisplatin (CDDP), notably nephrotoxicity, greatly limited its use in clinical chemotherapy. HuangQi Injections (HI), a commonly used preparation of the well-known Chinese herbal medicine Astragali radix, appeared to be promising treatment for nephrotoxicity without compromising the anti-tumor activity of CDDP. In this study, the urinary metabolomics approach using liquid chromatography time of flight mass spectrometry (LC-TOF/MS) was developed to assess the toxicity-attenuation effects and corresponding mechanisms of HI on CDDP-exposed rats. As a result, successive administration of HI significantly recovered the decline of body weight and downregulated the abnormal increase of serum creatinine and urea. HI partly restored the CDDP-induced alteration of metabolic profiling back into normal condition. Totally 43 toxicity-attenuation potential biomarkers were screened and tentatively identified, which were involved in important metabolic pathways such as amino acid metabolism, TCA cycle, fatty acid metabolism, vitamin B6 metabolism and purine metabolism. The results clearly revealed that HI could alleviate CDDP-induced nephrotoxicity and improve the disturbed metabolic balance induced by repeated CDDP exposure. The present study provided reliable evidence for the protective effect of HI on CDDP-induced toxicity with the multi-target pharmacological characteristics.

Nutrients Turned into Toxins: Microbiota Modulation of Nutrient Properties in Chronic Kidney Disease

In chronic kidney disease (CKD), accumulation of uremic toxins is associated with an increased risk of death. Some uremic toxins are ingested with the diet, such as phosphate and star fruit-derived caramboxin. Others result from nutrient processing by gut microbiota, yielding precursors of uremic toxins or uremic toxins themselves. These nutrients include l-carnitine, choline/phosphatidylcholine, tryptophan and tyrosine, which are also sold over-the-counter as nutritional supplements. Physicians and patients alike should be aware that, in CKD patients, the use of these supplements may lead to potentially toxic effects. Unfortunately, most patients with CKD are not aware of their condition. Some of the dietary components may modify the gut microbiota, increasing the number of bacteria that process them to yield uremic toxins, such as trimethylamine N-Oxide (TMAO), p-cresyl sulfate, indoxyl sulfate and indole-3 acetic acid. Circulating levels of nutrient-derived uremic toxins are associated to increased risk of death and cardiovascular disease and there is evidence that this association may be causal. Future developments may include maneuvers to modify gut processing or absorption of these nutrients or derivatives to improve CKD patient outcomes.

Application of metabolomics: Focus on the quantification of organic acids in healthy adults

Metabolomics, a 'budding' discipline, may accurately reflect a specific phenotype which is sensitive to genetic and epigenetic interactions. This rapidly evolving field in science has been proposed as a tool for the evaluation of the effects of epigenetic factors, such as nutrition, environment, drug and lifestyle on phenotype. Urine, being sterile, is easy to obtain and as it contains metabolized or non-metabolized products, is a favored study material in the field of metabolomics. Urine organic acids (OAs) reflect the activity of main metabolic pathways and have been used to assess health status, nutritional status, vitamin deficiencies and response to xenobiotics. To date, a limited number of studies have been performed which actually define reference OA values in a healthy population and as reference range for epigenetic influences, and not as a reference to congenital metabolic diseases. The aim of the present study was thus the determination of reference values (RVs) for urine OA in a healthy adult population. Targeted metabolomics analysis of 22 OAs in the urine of 122 healthy adults by gas chromatography-mass spectrometry, was conducted. Percentile distributions of the OA concentrations in urine, as a base for determining the RVs in the respective population sample, were used. No significant differences were detected between female and male individuals. These findings can facilitate the more sensitive determination of OAs in pathological conditions. Therefore, the findings of this study may contribute or add to the information already available on urine metabolite databases, and may thus promote the use of targeted metabolomics for the evaluation of OAs in a clinical setting and for pathophysiological evaluation. However, further studies with well-defined patients groups exhibiting specific symptoms or diseases are warranted in order to discern between normal and pathological values.

Tryptophan Metabolism in Patients With Chronic Kidney Disease Secondary to Type 2 Diabetes: Relationship to Inflammatory Markers

Objective:

Type 2 diabetes (T2D) is the primary case of chronic kidney disease (CKD). Inflammation is associated with metabolic dysregulation in patients with T2D and CKD. Tryptophan (TRP) metabolism may have relevance to the CKD outcomes and associated symptoms. We investigated the relationships of TRP metabolism with inflammatory markers in patients with T2D and CKD.

Methods:

Data were collected from a well-characterized cohort of type 2 diabetic individuals with all stages of CKD, including patients on hemodialysis. Key TRP metabolites (kynurenine [KYN], kynurenic acid [KYNA], and quinolinic acid [QA]), proinflammatory cytokines (tumor necrosis factor-α [TNF-α] and interleukin-6 [IL-6]), and C-reactive protein were measured in plasma. The KYN/TRP ratio was utilized as a surrogate marker for indoleamine 2,3-dioxygenase 1 (IDO1) enzyme activity.

Results:

There was a significant inverse association between circulating TRP level and stages of CKD (P < 0.0001). Downstream bioactive TRP metabolites KYN, KYNA, and QA were positively and robustly correlated with the severity of kidney disease (P < 0.0001). In multiple linear regression, neither TNF-α nor IL-6 was independently related to KYN/TRP ratio after adjusting for estimated glomerular filtration rate (eGFR). Only TNF-α was independently related to KYN after taking into account the effect of eGFR.

Conclusions:

Chronic kidney disease secondary to T2D may be associated with accumulation of toxic TRP metabolites due to both inflammation and impaired kidney function. Future longitudinal studies to determine whether the accumulation of KYN directly contributes to CKD progression and associated symptoms in patients with T2D are warranted.

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.

Metabolomic Profiling in Individuals with a Failing Kidney Allograft

BACKGROUND

Alteration of certain metabolites may play a role in the pathophysiology of renal allograft disease.

METHODS

To explore metabolomic abnormalities in individuals with a failing kidney allograft, we analyzed by liquid chromatography-mass spectrometry (LC-MS/MS; for ex vivo profiling of serum and urine) and two dimensional correlated spectroscopy (2D COSY; for in vivo study of the kidney graft) 40 subjects with varying degrees of chronic allograft dysfunction stratified by tertiles of glomerular filtration rate (GFR; T1, T2, T3). Ten healthy non-allograft individuals were chosen as controls.

RESULTS

LC-MS/MS analysis revealed a dose-response association between GFR and serum concentration of tryptophan, glutamine, dimethylarginine isomers (asymmetric [A]DMA and symmetric [S]DMA) and short-chain acylcarnitines (C4 and C12), (test for trend: T1-T3 = p<0.05; p = 0.01; p<0.001; p = 0.01; p = 0.01; p<0.05, respectively). The same association was found between GFR and urinary levels of histidine, DOPA, dopamine, carnosine, SDMA and ADMA (test for trend: T1-T3 = p<0.05; p<0.01; p = 0.001; p<0.05; p = 0.001; p<0.001; p<0.01, respectively). In vivo 2D COSY of the kidney allograft revealed significant reduction in the parenchymal content of choline, creatine, taurine and threonine (all: p<0.05) in individuals with lower GFR levels.

CONCLUSIONS

We report an association between renal function and altered metabolomic profile in renal transplant individuals with different degrees of kidney graft function.

Systems Biology to Support Nanomaterial Grouping

The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges.

An overview of renal metabolomics

The high-throughput, high-resolution phenotyping enabled by metabolomics has been applied increasingly to a variety of questions in nephrology research. This article provides an overview of current metabolomics methodologies and nomenclature, citing specific considerations in sample preparation, metabolite measurement, and data analysis that investigators should understand when examining the literature or designing a study. Furthermore, we review several notable findings that have emerged in the literature that both highlight some of the limitations of current profiling approaches, as well as outline specific strengths unique to metabolomics. More specifically, we review data on the following: (i) tryptophan metabolites and chronic kidney disease onset, illustrating the interpretation of metabolite data in the context of established biochemical pathways; (ii) trimethylamine-N-oxide and cardiovascular disease in chronic kidney disease, illustrating the integration of exogenous and endogenous inputs to the blood metabolome; and (iii) renal mitochondrial function in diabetic kidney disease and acute kidney injury, illustrating the potential for rapid translation of metabolite data for diagnostic or therapeutic aims. Finally, we review future directions, including the need to better characterize interperson and intraperson variation in the metabolome, pool existing data sets to identify the most robust signals, and capitalize on the discovery potential of emerging nontargeted methods.

Biomarkers in Transplantation--Proteomics and Metabolomics

Modern multianalyte "omics" technologies allow for the identification of molecular signatures that confer significantly more information than measurement of a single parameter as typically used in current medical diagnostics. Proteomics and metabolomics bioanalytical assays capture a large set of proteins and metabolites in body fluids, cells, or tissues and, complementing genomics, assess the phenome. Proteomics and metabolomics contribute to the development of novel predictive clinical biomarkers in transplantation in 2 ways: they can be used to generate a diagnostic fingerprint or they can be used to discover individual proteins and metabolites of diagnostic potential. Much fewer metabolomics than proteomics biomarker studies in transplant patients have been reported, and, in contrast to proteomics discovery studies, new lead metabolite markers have yet to emerge. Most clinical proteomics studies have been discovery studies. Several of these studies have assessed diagnostic sensitivity and specificity. Nevertheless, none of these newly discovered protein biomarkers have yet been implemented in clinical decision making in transplantation. The currently most advanced markers discovered in proteomics studies in transplant patients are the chemokines CXCL-9 and CXCL-10, which have successfully been validated in larger multicenter trials in kidney transplant patients. These chemokines can be measured using standard immunoassay platforms, which should facilitate clinical implementation. Based on the published evidence, it is reasonable to expect that these chemokine markers can help guiding and individualizing immunosuppressive regimens, may be able to predict acute and chronic T-cell-mediated and antibody-mediated rejection, and may be useful tools for risk stratification of kidney transplant patients.

1 H NMR-based metabolomics exploring urinary biomarkers correlated with proteinuria in focal segmental glomerulosclerosis: a pilot study

Focal segmental glomerulosclerosis (FSGS) is a common glomerulonephritis, and its rates of occurrence are increasing worldwide. Proteinuria is a clinical defining feature of FSGS which correlates with the severity of podocyte injury in patients with nephrotic-range protein excretion. Metabolite biomarkers corresponding with the level of proteinuria could be considered as non-invasive complementary prognostic factors to proteinuria. The urine samples of 15 patients (n = 6 women and n = 9 men) with biopsy-proven FSGS were collected and subjected to nuclear magnetic resonance (NMR) analysis for metabolite profiling. Multivariate statistical analyses, including principal component analysis and orthogonal projection to latent structure discriminant analysis, were applied to construct a predictive model based on patients with proteinuria >3000 mg/day and <3000 mg/day. In addition, random forest was performed to predict differential metabolites, and pathway analysis was performed to find the defective pathways responsible for proteinuria. Ten metabolites, significant in both statistical methods (orthogonal projection to latent structure discriminant analysis and random forest), were considered as prognostic biomarkers for FSGS: citrulline, dimethylamine, proline, acetoacetate, alpha-ketoisovaleric acid, valine, isobutyrate, D-Palmitylcarnitine, histidine, and N-methylnicotinamide. Pathway analysis revealed impairment of the branched-chain amino acid degradation pathways in patients with massive proteinuria. This study shows that metabolomics can reveal the molecular changes corresponding with disease progression in patients with FSGS and provide a new insight for pathogenic pathways. Copyright © 2016 John Wiley & Sons, Ltd.

Kidney Tissue Targeted Metabolic Profiling of Unilateral Ureteral Obstruction Rats by NMR

Renal interstitial fibrosis is a common pathological process in the progression of kidney disease. A nuclear magnetic resonance (NMR) based metabolomic approach was used to analyze the kidney tissues of rats with renal interstitial fibrosis (RIF), induced by unilateral ureteral obstruction (UUO). The combination of a variety of statistical methods were used to screen out 14 significantly changed potential metabolites, which are related with multiple biochemical processes including amino acid metabolism, adenine metabolism, energy metabolism, osmolyte change and induced oxidative stress. The exploration of the contralateral kidneys enhanced the understanding of the disease, which was also supported by serum biochemistry and kidney histopathology results. In addition, the pathological parameters (clinical chemistry, histological and immunohistochemistry results) were correlated with the significantly changed differential metabolites related with RIF. This study showed that targeted tissue metabolomic analysis can be used as a useful tool to understand the mechanism of the disease and provide a novel insight in the pathogenesis of RIF.

Metabolomics in Diabetic Kidney Disease: Unraveling the Biochemistry of a Silent Killer

The development of new therapies for chronic diseases, such as diabetic kidney disease (DKD), will continue to be hampered by lack of sufficient biomarkers that will provide insights and will be responsive to treatment interventions. The recent application of metabolomic technologies, such as nuclear magnetic resonance and mass spectroscopy, has allowed large-scale analysis of small molecules to be interrogated in a targeted or untargeted manner. Recent advances from both human and animal studies that have arisen from metabolomic analysis have recognized that mitochondrial function and fatty acid oxidation play key roles in the development and progression of DKD. Although many challenges in the technology for clinical chronic kidney disease (CKD) are yet to be validated, there will very likely be ongoing major contributions of metabolomics to develop new biochemical understanding for diabetic and CKD. The clinical application of metabolomics and accompanying bioinformatic tools will likely be a cornerstone of personalized medicine triumphs for CKD.

Quantification of 18 amino acids in human plasma: application in renal transplant patient plasma by targeted UHPLC–MS/MS

Aim: Quantification of amino acids in human plasma has become an important and essential analysis parameter in life science. In this paper, we developed a targeted UHPLC–MS/MS method for 18 amino acids in the renal transplant patients. Methods & results: Plasma in small volume (150 μl) was pretreated by a one-step protein precipitant extraction for analysis. Detection was executed by MS/MS in the MRM mode. Assays were validated according to current bioanalytical guidelines, with good linearity (R > 0.99), intraday and interday precision (CV < 11.6%, RE ≤ ± 14.8%), extraction recovery (between 77.4 and 117.6%), matrix effect (73.3–118.0%) and stability (RE≤ ±14.7%). Conclusion: The method was successfully applicable for amino acid analysis in the renal transplant patient.

Reduction of carbamylated albumin by extended hemodialysis

Introduction Among conventional hemodialysis (CHD) patients, carbamylated serum albumin (C-Alb) correlates with urea and amino acid deficiencies and is associated with mortality. We postulated that reduction of C-Alb by intensive HD may correlate with improvements in protein metabolism and cardiac function. Methods One-year observational study of in-center nocturnal extended hemodialysis (EHD) patients and CHD control subjects. Thirty-three patients receiving 4-hour CHD who converted to 8-hour EHD were enrolled, along with 20 controls on CHD. Serum C-Alb, biochemistries, and cardiac MRI parameters were measured before and after 12 months of EHD. Findings EHD was associated with reduction of C-Alb (average EHD change -3.20 mmol/mol [95% CI -4.23, -2.17] compared to +0.21 [95% CI -1.11, 1.54] change in CHD controls, P < 0.001). EHD was also associated with increases in average essential amino acids (in standardized units) compared to CHD (+0.38 [0.08, 0.68 95%CI]) vs. -0.12 [-0.50, 0.27, 95% CI], P = 0.047). Subjects who reduced C-Alb more than 25% were found to have reduced left ventricular mass, increased urea reduction ratio, and increased serum albumin compared to nonresponders, and % change in C-Alb significantly correlated with % change in left ventricular mass. Discussion EHD was associated with reduction of C-Alb as compared to CHD, and reduction of C-Alb by EHD correlates with reduction of urea. Additional studies are needed to test whether reduction of C-Alb by EHD also correlates with improved clinical outcomes.

Amino acids in a targeted versus a non-targeted metabolomics LC-MS/MS assay. Are the results consistent?

BACKGROUND

The results of plasma amino acid patterns in samples from kidney transplant patients with good and impaired renal function using a targeted LC-MS/MS amino acid assay and a non-targeted metabolomics assay were compared.

METHODS

EDTA plasma samples were prospectively collected at baseline, 1, 2, 4 and 6months post-transplant (n=116 patients, n=398 samples). Each sample was analyzed using both a commercial amino acid LC-MS/MS assay and a non-targeted metabolomics assay also based on MS/MS ion transitions. The results of both assays were independently statistically analyzed to identify amino acids associated with estimated glomerular filtration rates using correlation and partial least squares-discriminant analysis.

RESULTS

Although there was overlap between the results of the targeted and non-targeted metabolomics assays (tryptophan, 1-methyl histidine), there were also substantial inconsistencies, with the non-targeted assay resulting in more "hits" than the targeted assay. Without further verification of the hits detected by the non-targeted discovery assay, this would have led to different interpretation of the results. There were also false negative results when the non-targeted assay was used (hydroxy proline). Several of said discrepancies could be explained by loss of sensitivity during analytical runs for selected amino acids (serine and threonine), retention time shifts, signals above the range of linear detector response and integration of peaks not separated from background and interferences (aspartate) when the non-targeted metabolomics assay was used.

CONCLUSIONS

Whenever assessment of a specific pathway such as amino acids is the focus of interest, a targeted seems preferable to a non-targeted metabolomics assay.

Systematic biomarker discovery and coordinative validation for different primary nephrotic syndromes using gas chromatography-mass spectrometry

The goal of this study is to identify systematic biomarker panel for primary nephrotic syndromes from urine samples by applying a non-target metabolite profiling, and to validate their utility in independent sampling and analysis by multiplex statistical approaches. Nephrotic syndrome (NS) is a nonspecific kidney disorder, which is mostly represented by minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and membranous glomerulonephritis (MGN). Since urine metabolites may mirror disease-specific functional perturbations in kidney injury, we examined urine samples for distinctive metabolic changes to identify biomarkers for clinical applications. We developed unbiased multi-component covarianced models from a discovery set with 48 samples (12 healthy controls, 12 MCD, 12 FSGS, and 12 MGN). To extensively validate their diagnostic potential, new batch from 54 patients with primary NS were independently examined a year after. In the independent validation set, the model including citric acid, pyruvic acid, fructose, ethanolamine, and cysteine effectively discriminated each NS using receiver operating characteristic (ROC) analysis except MCD-MGN comparison; nonetheless an additional metabolite multi-composite greatly improved the discrimination power between MCD and MGN. Finally, we proposed the re-constructed metabolic network distinctively dysregulated by the different NSs that may deepen comprehensive understanding of the disease mechanistic, and help the enhanced identification of NS and therapeutic plans for future.

Specific Metabolic Profiles and Their Relationship to Insulin Resistance in Recent-Onset Type 1 and Type 2 Diabetes

CONTEXT

Insulin resistance reflects the inadequate insulin-mediated use of metabolites and predicts type 2 diabetes (T2D) but is also frequently seen in long-standing type 1 diabetes (T1D) and represents a major cardiovascular risk factor.

OBJECTIVE

We hypothesized that plasma metabolome profiles allow the identification of unique and common early biomarkers of insulin resistance in both diabetes types.

DESIGN, SETTING, AND PATIENTS

Two hundred ninety-five plasma metabolites were analyzed by mass spectrometry from patients of the prospective observational German Diabetes Study with T2D (n = 244) or T1D (n = 127) and known diabetes duration of less than 1 year and glucose-tolerant persons (CON; n = 129). Abundance of metabolites was tested for association with insulin sensitivity as assessed by hyperinsulinemic-euglycemic clamps and related metabolic phenotypes.

MAIN OUTCOMES MEASURES

Sixty-two metabolites with phenotype-specific patterns were identified using age, sex, and body mass index as covariates.

RESULTS

Compared with CON, the metabolome of T2D and T1D showed similar alterations in various phosphatidylcholine species and amino acids. Only T2D exhibited differences in free fatty acids compared with CON. Pairwise comparison of metabolites revealed alterations of 28 and 49 metabolites in T1D and T2D, respectively, when compared with CON. Eleven metabolites allowed differentiation between both diabetes types and alanine, α-amino-adipic acid, isoleucin, and stearic acid showed an inverse association with insulin sensitivity in both T2D and T1D combined.

CONCLUSION

Metabolome analyses from recent-onset T2D and T1D patients enables identification of defined diabetes type-specific differences and detection of biomarkers of insulin sensitivity. These analyses may help to identify novel clinical subphenotypes diabetes.

Profiling human blood serum metabolites by nuclear magnetic resonance spectroscopy: a comprehensive tool for the evaluation of hemodialysis efficiency

Hemodialysis remains the standard therapy to treat patients affected with end-stage renal disease by removing metabolites accumulated in blood plasma. The efficiency of hemodialysis is mainly monitored by urea clearance, which is routinely checked in clinical laboratory practice. However, there is mounting evidence that the clearance behavior of selected single metabolites is not sufficient to predict long-term outcome of treatment. To address this problem, we evaluated the potential of nuclear magnetic resonance spectroscopy for monitoring hemodialysis efficiency by comprehensive profiling of blood serum metabolites. We carried out a pilot study with a cohort of end-stage chronic kidney disease patients (n = 29), analyzing their serum prior and immediately after hemodialysis. To account for supposed variability in the accumulation of metabolites and efficiency of hemodialysis, patients' blood sera were repeatedly collected over a period of several months. Our results revealed that the metabolic profile in terms of concentrations varied considerably between patients but was comparably constant on the patient's level over the period of 4 months. Interestingly, also the individual clearance of the metabolites was characteristic for each patient. Thus, it is conceivable that the observed patient-dependent clearance patterns reflect to some extent the patients' long-term perspectives. We conclude that nuclear magnetic resonance spectroscopy is an optimal tool to complement traditional clinical methods based on a single variable, providing comprehensive and much more global information, which is crucial for patient evaluation and the development of improved treatments of kidney failure.

Metabolomics of renal venous plasma from individuals with unilateral renal artery stenosis and essential hypertension

Objective:

To compare the metabolite profiles of venous effluent from both kidneys of individuals with unilateral atherosclerotic renal artery stenosis (ARAS) in order to directly examine how impaired renal blood flow impacts small-molecule handling in humans.

Methods:

We applied liquid chromatography–mass spectrometry based metabolite profiling to venous plasma obtained from the stenotic (STK) and contralateral (CLK) kidneys of ARAS patients (n = 16), and both the kidneys of essential hypertensive controls (n = 11). Study samples were acquired during a 3-day protocol that included iothalamate clearance measurements, radiographic kidney phenotyping (Duplex ultrasound, multidetector computed tomography, and blood-oxygen-level-dependent MRI), and controlled sodium and caloric intake and antihypertensive treatment.

Results:

Partial least squares-discriminant analysis demonstrated clear separation of essential hypertensive kidney metabolite profiles versus STK and CLK metabolite profiles, but no separation between metabolite profiles of STK and CLK samples. All of the discriminating metabolites were similarly elevated in the STK and CLK samples, likely reflecting the lower glomerular filtration rate in the ARAS versus essential hypertensive individuals (mean 66.1 versus 89.2 ml/min per 1.73 m²). In a paired analysis within the ARAS group, no metabolite was significantly altered in STK compared with CLK samples; notably, creatinine was the same in STK and CLK samples (STK/CLK ratio = 1.0, P = 0.9). Results were unchanged in an examination of ARAS patients in the bottom half of renal tissue perfusion or oxygenation.

Conclusion:

Metabolite profiling does not differentiate venous effluent from STKs or CLKs in individuals with unilateral ARAS, despite the measurable loss of kidney volume and blood flow on the affected side. These findings are consistent with the kidney's ability to adapt to ARAS to maintain a range of metabolic functions.

Metabolomics and renal disease

PURPOSE OF REVIEW

This review summarizes recent metabolomics studies of renal disease, outlining some of the limitations of the literature to date.

RECENT FINDINGS

The application of metabolomics in nephrology research has expanded from the initial analyses of uremia to include both cross-sectional and longitudinal studies of earlier stages of kidney disease. Although these studies have nominated several potential markers of incident chronic kidney disease (CKD) and CKD progression, a lack of overlap in metabolite coverage has limited the ability to synthesize results across groups. Furthermore, direct examination of renal metabolite handling has underscored the substantial impact kidney function has on these potential markers (and many other circulating metabolites). In experimental studies, metabolomics has been used to identify a signature of decreased mitochondrial function in diabetic nephropathy and a preference for aerobic glucose metabolism in polycystic kidney disease. In each case, these studies have outlined novel therapeutic opportunities. Finally, as a complement to the longstanding interest in renal metabolite clearance, the microbiome has been increasingly recognized as the source of many plasma metabolites, including some with potential functional relevance to CKD and its complications.

SUMMARY

The high-throughput, high-resolution phenotyping enabled by metabolomics technologies has begun to provide insight on renal disease in clinical, physiologic, and experimental contexts.

Development and validation of a CE-MS method for the targeted assessment of amino acids in urine

A CE-ESI-MS method was developed and validated for the separation and quantitative analysis of amino acids (AA) in urine. Experimental parameters related to the CE-MS interface, BGE, and mass spectrometer (MS) settings were optimized providing a good separation of 27 AA, including the isomers L-leucine, L-isoleucine, and L-alloisoleucine, in less than 30 min. The sheath liquid was composed by 0.50% formic acid in 60% (v,v) methanol-water delivered at a flow rate of 5 μL/min. The BGE consisted of 0.80 mol/L formic acid at pH 1.96 and 15% methanol. A pH stacking procedure was implemented to enhance sensitivity (a 12.5% NH4 OH solution was injected at 0.5 psi/9 s prior to samples injected at 0.6 psi/20 s). The proposed method was validated according to FDA and ICH protocols exhibiting acceptable parameters. Analytical curves presented coefficients of determination from 0.996 to 0.9997 (with large F statistics and low p-values). LODs and quantification ranged from 0.63 to 29 μmol/L and from 1.9 to 86 μmol/L, respectively. Practical repeatability was obtained for all AA with coefficients of variation better than 0.55% CV (migration time) and 1.7% CV (peak area ratios; methionine sulfone as internal standard). Recoveries of AA in spiked urine ranged from 92.0 to 123% with few exceptions. Moreover, a successful quantification of AA in pooled control and test urine samples, which compose a vesicoureteral reflux cohort, was achieved showing the potential applicability of the proposed method for targeted metabolomics studies using CE-ESI-MS with an Ion Trap as mass analyzer.

Nitric oxide status in patients with chronic kidney disease

Patients with chronic kidney disease (CKD) are at an increased risk of cardiovascular (CVD) morbidity and mortality, mainly due to atherosclerosis. Decreased production or reduced bioavailability of nitric oxide (NO) can result in endothelial dysfunction (ED). Multiple mechanisms are known to cause a state of NO deficiency in patients with CKD. Patients in various stages of CKD grouped as group-1 (CKD stage 1 and 2), group-2 (CKD stage 3 and 4), group-3 (CKD stage 5) and healthy controls were included in the study. Each group of patients and controls comprised 25 subjects. Plasma nitrites, L-arginine, asymmetric dimethyl arginine (ADMA) and citrulline were measured in all the subjects. Patients in all stages of CKD had lower NO and higher ADMA levels compared to controls. Further, group-2 and group-3 patients had lower levels of NO and higher levels of ADMA than group-1 patients. L-arginine levels showed no difference between patients and controls. However, group-3 patients had lower L-arginine levels compared to group-1 patients. Citrulline levels were decreased in group-3 patients. NO production was decreased in patients in all stages of CKD. The decrease could be due to decreased availability of the substrate, L-arginine or due to an increased ADMA, a potent inhibitor of endothelial NO synthase. Therapeutic interventions directed towards improvement of NO production in addition to management of other CVD risk factors may prevent development of ED and facilitate proper management of CKD patients who are at increased risk for CVD.

Nitric oxide synthesis capacity, ambulatory blood pressure and end organ damage in a black and white population: the SABPA study

Nitric oxide (NO) synthesis capacity is determined by the availability of substrate(s) such as L-arginine and the influence of nitric oxide synthase (NOS) inhibitors, asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA). These factors may be important in black South Africans with a very high prevalence of hypertension. We compared ambulatory blood pressure (BP), markers of end organ damage and NO synthesis capacity markers [L-arginine, L-homoarginine, L-citrulline, L-arginine:ADMA, ADMA, SDMA and dimethylarginine (DMA)], between black and white teachers (n = 390). Associations of nighttime BP and markers of end organ damage with NO synthesis capacity markers were also investigated. Although black men and women had higher BP and albumin-to-creatinine ratio (ACR) (all p < 0.001), they also had higher L-arginine, L-homoarginine, L-arginine:ADMA and lower SDMA and DMA levels (all p < 0.05). Only in white men ADMA concentrations associated positively with nighttime systolic blood pressure (R (2) = 0.20, β = 0.26, p = 0.009), nighttime diastolic blood pressure (R (2) = 0.23, β = 0.27, p = 0.007), carotid intima media thickness (cIMT) (R (2) = 0.36, β = 0.22, p = 0.008) and ACR (R (2) = 0.14, β = 0.32, p = 0.001). Our findings suggest that despite an adverse cardiovascular profile in blacks, their NO synthesis capacity profile seems favourable, and that other factors, such as NO inactivation, may prove to be more important.

Blood, urine and faecal metabolite profiles in the study of adult renal disease

Chronic kidney disease (CKD) is a major public health burden and to date traditional biomarkers of renal function (such as serum creatinine and cystatin C) are unable to identify at-risk individuals before the disease process is well under way. To help preventive strategies and maximize the potential for effective interventions, it is important to characterise the molecular changes that take place in the development of renal damage. Metabolomics is a promising tool to identify markers of renal disease since the kidneys are involved in the handling of major biochemical classes of metabolites. These metabolite levels capture a snap-shot of the metabolic profile of the individual, allowing for the potential identification of early biomarkers, and the monitoring of real-time kidney function. In this review, we describe the current status of the identification of blood/urine/faecal metabolic biomarkers in different entities of kidney diseases including: acute kidney injury, chronic kidney disease, renal transplant, diabetic nephropathy and other disorders.

Citrulline and Nonessential Amino Acids Prevent Fructose-Induced Nonalcoholic Fatty Liver Disease in Rats

BACKGROUND

Fructose induces nonalcoholic fatty liver disease (NAFLD). Citrulline (Cit) may exert a beneficial effect on steatosis.

OBJECTIVE

We compared the effects of Cit and an isonitrogenous mixture of nonessential amino acids (NEAAs) on fructose-induced NAFLD.

METHODS

Twenty-two male Sprague Dawley rats were randomly assigned into 4 groups (n = 4-6) to receive for 8 wk a 60% fructose diet, either alone or supplemented with Cit (1 g · kg(-1) · d(-1)), or an isonitrogenous amount of NEAAs, or the same NEAA-supplemented diet with starch and maltodextrin instead of fructose (controls). Nutritional and metabolic status, liver function, and expression of genes of hepatic lipid metabolism were determined.

RESULTS

Compared with controls, fructose led to NAFLD with significantly higher visceral fat mass (128%), lower lean body mass (-7%), insulin resistance (135%), increased plasma triglycerides (TGs; 67%), and altered plasma amino acid concentrations with decreased Arg bioavailability (-27%). This was corrected by both NEAA and Cit supplementation. Fructose caused a 2-fold increase in the gene expression of fatty acid synthase (Fas) and 70% and 90% decreases in that of carnitine palmitoyl-transferase 1a and microsomal TG transfer protein via a nearly 10-fold higher gene expression of sterol regulatory element-binding protein-1c (Srebp1c) and carbohydrate-responsive element-binding protein (Chrebp), and a 90% lower gene expression of peroxisome proliferator-activated receptor α (Ppara). NEAA or Cit supplementation led to a Ppara gene expression similar to controls and decreased those of Srebp1c and Chrebp in the liver by 50-60%. Only Cit led to Fas gene expression and Arg bioavailability similar to controls.

CONCLUSION

In our rat model, Cit and NEAAs effectively prevented fructose-induced NAFLD. On the basis of literature data and our findings, we propose that NEAAs may exert their effects specifically on the liver, whereas Cit presumably acts at both the hepatic and whole-body level, in part via improved peripheral Arg metabolism.

Urinary excretion of polyols and sugars in children with chronic kidney disease

BACKGROUND

The urinary concentrations of monosaccharides and polyols are used for diagnosing inborn errors of metabolism and renal tubular disorders. Reference values are age-related and depend on the method of detection. However, the influence of the renal function is often still neglected. In this study we examined the urinary excretion of monosaccharides and polyols in children with various degrees of chronic kidney disease (CKD), but with no known metabolic or renal tubular disorders.

CASE DIAGNOSIS/TREATMENT

In 25 patients with CKD stage 1-5, urinary concentrations of 18 monosaccharides and polyols were measured by gas chromatography-mass spectrometry (GC-MS) in random urinary samples and were compared with age-related reference values. Serum creatinine was measured at the time of the urine sample, and the height-independent estimated glomerular filtration rate (eGFR-Pottel) was calculated. Urinary excretions of monosaccharides and polyols were above the reference values in 8-88% of all patients. A significant difference between CKD stage 1-2 compared with CKD stage 3-5 was found for allose, arabitol and sorbitol (p < 0.05) and for arabinose, fucose, myoinositol, ribitol, xylitol, and xylose (p < 0.01).

CONCLUSIONS

We show that the excretion of polyols and sugars depends on eGFR, which warrants a cautious interpretation of the results in patients with CKD.

Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity

The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies.

Human metabolism is controlled through basic kinetic regulatory mechanisms, where the overall system aims to maintain a state of homeostasis. In response to external perturbations, such as environmental influences, nutrition or physical exercise, circulating metabolites show specific kinetic response patterns, which can be computationally modeled. In this work, we searched for dynamic metabolic biomarker candidates and analyzed specific kinetic mechanisms from longitudinal metabolic concentration data, obtained through a cycle ergometry stress test. In total, 110 metabolites measured from blood samples of 47 individuals were analyzed using tandem mass spectrometry (MS/MS). Dynamic biomarker candidates could be selected based on the amplitudes of changes in metabolite concentrations and the significance of statistical hypothesis testing. We were able to characterize specific kinetic patterns for groups of similarly behaving metabolites. Kinetic shape templates were identified, defining basic kinetic response patterns to physical exercise, such as sustained, early, late and other shape forms. The presented approach contributes to a better understanding of (patho)physiological biochemical mechanisms in human health, disease or during drug therapy, by offering tools for classifying dynamic biomarker candidates and for modeling and characterizing kinetic regulatory mechanisms from longitudinal experimental data.

Metabolic biomarkers for chronic kidney disease

Chronic kidney disease (CKD) is an increasingly recognized burden for patients and health care systems with high (and growing) global incidence and prevalence, significant mortality, and disproportionately high treatment costs. Yet, the available diagnostic tools are either impractical in clinical routine or have serious shortcomings impeding a well-informed disease management although optimized treatment strategies with proven benefits for the patients have become available. Advances in bioanalytical technologies have facilitated studies that identified genomic, proteomic, and metabolic biomarker candidates, and confirmed some of them in independent cohorts. This review summarizes the CKD-related markers discovered so far, and focuses on compounds and pathways, for which there is quantitative data, substantiating evidence from translational research, and a mechanistic understanding of the processes involved. Also, multiparametric marker panels have been suggested that showed promising diagnostic and prognostic performance in initial analyses although the data basis from prospective trials is very limited. Large-scale studies, however, are underway and will provide the information for validating a set of parameters and discarding others. Finally, the path from clinical research to a routine application is discussed, focusing on potential obstacles such as the use of mass spectrometry, and the feasibility of obtaining regulatory approval for targeted metabolomics assays.

Protein carbamylation is associated with heart failure and mortality in diabetic patients with end-stage renal disease

Serum carbamylated albumin (C-Alb) levels are associated with excess mortality in patients with diabetic end stage renal disease. To gain insight into the pathophysiology of carbamylation, we determined associations between C-Alb and causes of death in patients on chronic hemodialysis. The Die Deutsche Diabetes Dialyse Studie (4D study) was a randomized controlled trial testing the effects of atorvastatin on survival in diabetic patients on dialysis during a median follow-up of 4 years. We stratified 1,161 patients by C-Alb to see if differences in carbamylation altered the effects of atorvastatin on survival. Baseline C-Alb significantly correlated with serum cardiac stress markers troponin T and N-terminal pro-B-type-natriuretic peptide, and was associated with history of heart failure and arrhythmia. C-Alb was strongly associated with 1-year adjusted risk of CV mortality, sudden cardiac death and the 4-year risk of death from congestive heart failure (Hazard Ratios of 3.06, 3.78 and 4.64, respectively), but not with myocardial infarction or stroke. Patients with low C-Alb, treated with atorvastatin, experienced a significant improvement in their 4-year survival (Hazard Ratio 0.692). High C-Alb levels are associated with ongoing cardiac damage, risk of congestive heart failure and sudden cardiac death. Thus, carbamylation and uremic cardiomyopathy are associated in patients with diabetes mellitus and kidney disease. Additionally, statins were specifically beneficial to hemodialysis patients with low C-Alb.

Serum metabolomic profiling and incident CKD among African Americans

BACKGROUND AND OBJECTIVES

Novel biomarkers that more accurately reflect kidney function and predict future CKD are needed. The human metabolome is the product of multiple physiologic or pathophysiologic processes and may provide novel insight into disease etiology and progression. This study investigated whether estimated kidney function would be associated with multiple metabolites and whether selected metabolomic factors would be independent risk factors for incident CKD.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

In total, 1921 African Americans free of CKD with a median of 19.6 years follow-up among the Atherosclerosis Risk in Communities Study were included. A total of 204 serum metabolites quantified by untargeted gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry was analyzed by both linear regression for the cross-sectional associations with eGFR (specified by the Chronic Kidney Disease Epidemiology Collaboration equation) and Cox proportional hazards model for the longitudinal associations with incident CKD.

RESULTS

Forty named and 34 unnamed metabolites were found to be associated with eGFR specified by the Chronic Kidney Disease Epidemiology Collaboration equation with creatine and 3-indoxyl sulfate showing the strongest positive (2.8 ml/min per 1.73 m(2) per +1 SD; 95% confidence interval, 2.1 to 3.5) and negative association (-14.2 ml/min per 1.73 m(2) per +1 SD; 95% confidence interval, -17.0 to -11.3), respectively. Two hundred four incident CKD events with a median follow-up time of 19.6 years were included in the survival analyses. Higher levels of 5-oxoproline (hazard ratio, 0.70; 95% confidence interval, 0.60 to 0.82) and 1,5-anhydroglucitol (hazard ratio, 0.68; 95% confidence interval, 0.58 to 0.80) were significantly related to lower risk of incident CKD, and the associations did not appreciably change when mutually adjusted.

CONCLUSIONS

These data identify a large number of metabolites associated with kidney function as well as two metabolites that are candidate risk factors for CKD and may provide new insights into CKD biomarker identification.

Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = -0.8031; p<0.0001 and ρ = -0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = -0.6557; p = 0.0001 and ρ = -0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.