Urinary metabolites along with common and rare genetic variations are associated with incident chronic kidney disease

Amino acid metabolism in kidney health and disease

Amino acids form peptides and proteins and are therefore considered the main building blocks of life. The kidney has an important but under-appreciated role in the synthesis, degradation, filtration, reabsorption and excretion of amino acids, acting to retain useful metabolites while excreting potentially harmful and waste products from amino acid metabolism. A complex network of kidney transporters and enzymes guides these processes and moderates the competing concentrations of various metabolites and amino acid products. Kidney amino acid metabolism contributes to gluconeogenesis, nitrogen clearance, acid-base metabolism and provision of fuel for tricarboxylic acid cycle and urea cycle intermediates, and is thus a central hub for homeostasis. Conversely, kidney disease affects the levels and metabolism of a variety of amino acids. Here, we review the metabolic role of the kidney in amino acid metabolism and describe how different diseases of the kidney lead to aberrations in amino acid metabolism. Improved understanding of the metabolic and communication routes that are affected by disease could provide new mechanistic insights into the pathogenesis of kidney diseases and potentially enable targeted dietary or pharmacological interventions.

Differentiated metabolomic profiling reveals plasma amino acid signatures for primary glomerular disease

Primary glomerular disease (PGD) is an idiopathic cause of renal glomerular lesions that is characterized by proteinuria or hematuria and is the leading cause of chronic kidney disease (CKD). The identification of circulating biomarkers for the diagnosis of PGD requires a thorough understanding of the metabolic defects involved. In this study, ultra-high performance liquid chromatography-tandem mass spectrometry was performed to characterize the amino acid (AA) profiles of patients with pathologically diagnosed PGD, including minimal change disease (MCD), focal segmental glomerular sclerosis (FSGS), membranous nephropathy, and immunoglobulin A nephropathy. The plasma concentrations of asparagine and ornithine were low, and that of aspartic acid was high, in patients with all the pathologic types of PGD, compared to healthy controls. Two distinct diagnostic models were generated using the differential plasma AA profiles using logistic regression and receiver operating characteristic analyses, with areas under the curves of 1.000 and accuracies up to 100.0% in patients with MCD and FSGS. In conclusion, the progression of PGD is associated with alterations in AA profiles, The present findings provide a theoretical basis for the use of AAs as a non-invasive, real-time, rapid, and simple biomarker for the diagnosis of various pathologic types of PGD.

Lysine and salt-sensitive hypertension

PURPOSE OF REVIEW

Salt-sensitive (SS) hypertension and its associated kidney damage have been extensively studied, yet proper therapeutic strategies are lacking. The interest in altering the metabolome to affect renal and cardiovascular disease has been emerging. Here, we discuss the effect and potential mechanism behind the protective effect of lysine, an essential amino acid, on the progression of SS hypertension.

RECENT FINDINGS

We have recently demonstrated that administering lysine in an SS rodent model can control the progression of hypertension. Both the animal and pilot human studies showed that lysine can efficiently inhibit tubular reabsorption of albumin and protect the kidneys from further damage. In addition, we conducted multilevel omics studies that showed increased lysine conjugation and excretion, leading to the depletion of harmful metabolites and an increase in useful ones.

SUMMARY

Lysine's twofold action involves both mechanically flushing protein from proximal tubules to shield the kidneys and initiating metabolic adaptations in the kidneys. This results in a net positive impact on SS hypertension. While further research is necessary to apply the current findings in clinical settings, this study offers some evidence suggesting that lysine supplementation holds promise as a therapeutic approach for hypertensive kidney disease.

Intra-organ cell specific mitochondrial quantitative interactomics

Almost every organ consists of many cell types, each with its unique functions. Proteomes of these cell types are thus unique too. But it is reasonable to assume that interactome (inter and intra molecular interactions of proteins) are also distinct since protein interactions are what ultimately carry out the function. Podocytes and tubules are two cell types within kidney with vastly different functions: podocytes envelop the blood vessels in the glomerulus and act as filters while tubules are located downstream of the glomerulus and are responsible for reabsorption of important nutrients. It has been long known that for tubules mitochondria plays an important role as they require a lot of energy to carry out their functions. In podocytes, however, it has been assumed that mitochondria might not matter as much in both normal physiology and pathology1. Here we have applied quantitative cross-linking mass spectrometry to compare mitochondrial interactomes of tubules and podocytes using a transgenic mitochondrial tagging strategy to immunoprecipitate cell-specific mitochondria directly from whole kidney. We have uncovered that mitochondrial proteomes of these cell types are quite similar, although still showing unique features that correspond to known functions, such as high energy production through TCA cycle in tubules and requirements for detoxification in podocytes which are on the frontline of filtration where they encounter toxic compounds and therefore, as a non-renewing cell type they must have ways to protect themselves from cellular toxicity. But we gained much deeper insight with the interactomics data. We were able to find pathways differentially regulated in podocytes and tubules based on changing cross-link levels and not just protein levels. Among these pathways are betaine metabolism, lysine degradation, and many others. We have also demonstrated how quantitative interactomics could be used to detect different activity levels of an enzyme even when protein abundances of it are the same between cell types. We have validated this finding with an orthogonal activity assay. Overall, this work presents a new view of mitochondrial biology for two important, but functionally distinct, cell types within the mouse kidney showing both similarities and unique features. This data can continue to be explored to find new aspects of mitochondrial biology, especially in podocytes, where mitochondria has been understudied. In the future this methodology can also be applied to other organs to uncover differences in the function of cell types.

Metabolic Rewiring and Communication: An Integrative View of Kidney Proximal Tubule Function

The kidney proximal tubule is a key organ for human metabolism. The kidney responds to stress with altered metabolite transformation and perturbed metabolic pathways, an ultimate cause for kidney disease. Here, we review the proximal tubule's metabolic function through an integrative view of transport, metabolism, and function, and embed it in the context of metabolome-wide data-driven research. Function (filtration, transport, secretion, and reabsorption), metabolite transformation, and metabolite signaling determine kidney metabolic rewiring in disease. Energy metabolism and substrates for key metabolic pathways are orchestrated by metabolite sensors. Given the importance of renal function for the inner milieu, we also review metabolic communication routes with other organs. Exciting research opportunities exist to understand metabolic perturbation of kidney and proximal tubule function, for example, in hypertension-associated kidney disease. We argue that, based on the integrative view outlined here, kidney diseases without genetic cause should be approached scientifically as metabolic diseases.

Integrative physiology of lysine metabolites

Lysine is an essential amino acid that serves as a building block in protein synthesis. Beside this, the metabolic activity of lysine has only recently been unraveled. Lysine metabolism is tissue specific and is linked to several renal, cardiovascular, and endocrinological diseases through human metabolomics datasets. As a free molecule, lysine takes part in the antioxidant response and engages in protein modifications, and its chemistry shapes both proteome and metabolome. In the proteome, it is an acceptor for a plethora of posttranslational modifications. In the metabolome, it can be modified, conjugated, and degraded. Here, we provide an update on integrative physiology of mammalian lysine metabolites such as α-aminoadipic acid, saccharopine, pipecolic acid, and lysine conjugates such as acetyl-lysine, and sugar-lysine conjugates such as advanced glycation end products. We also comment on their emerging associative and mechanistic links to renal disease, hypertension, diabetes, and cancer.

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.

Urinary Metabolomic Profile of Youth at Risk of Chronic Kidney Disease in Nicaragua

Key Points

Urinary concentrations of glycine, a molecule associated with thermoregulation, were elevated among youth from a high-risk region for chronic kidney disease of non-traditional etiology (CKDnt). Urinary concentrations of pyruvate, citric acid, and inosine were lower among youth at higher risk of CKDnt, suggesting renal stress. Metabolomic analyses may shed light on early disease processes or profiles or risk in the context of CKDnt.

Background

CKD of a nontraditional etiology (CKDnt) is responsible for high mortality in Central America, although its causes remain unclear. Evidence of kidney dysfunction has been observed among youth, suggesting that early kidney damage contributing to CKDnt may initiate in childhood.

Methods

Urine specimens of young Nicaraguan participants 12–23 years without CKDnt (n =136) were analyzed by proton nuclear magnetic resonance spectroscopy for 50 metabolites associated with kidney dysfunction. Urinary metabolite levels were compared by, regional CKDnt prevalence, sex, age, and family history of CKDnt using supervised statistical methods and pathway analysis in MetaboAnalyst. Magnitude of associations and changes over time were assessed through multivariable linear regression.

Results

In adjusted analyses, glycine concentrations were higher among youth from high-risk regions (β =0.82, [95% confidence interval, 0.16 to 1.85]; P = 0.01). Pyruvate concentrations were lower among youth with low eGFR (β = −0.36 [95% confidence interval, −0.57 to −0.04]; P = 0.03), and concentrations of other citric acid cycle metabolites differed by key risk factors. Over four years, participants with low eGFR experienced greater declines in 1-methylnicotinamide and 2-oxoglutarate and greater increases in citrate and guanidinoacetate concentrations.

Conclusion

Urinary concentration of glycine, a molecule associated with thermoregulation and kidney function preservation, was higher among youth in high-risk CKDnt regions, suggestive of greater heat exposure or renal stress. Lower pyruvate concentrations were associated with low eGFR, and citric acid cycle metabolites, such as pyruvate, likely relate to mitochondrial respiration rates in the kidneys. Participants with low eGFR experienced longitudinal declines in concentrations of 1-methylnicotinamide, an anti-inflammatory metabolite associated with anti-fibrosis in tubule cells. These findings merit further consideration in research on the origins of CKDnt.

Altered adolescents obesity metabolism is associated with hypertension: a UPLC-MS-based untargeted metabolomics study

Objective

This study aimed to explore the relationship between the plasma metabolites of adolescent obesity and hypertension and whether metabolite alterations had a mediating effort between adolescent obesity and hypertension.

Methods

We applied untargeted ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to detect the plasma metabolomic profiles of 105 adolescents. All participants were selected randomly based on a previous cross-sectional study. An orthogonal partial least squares- discriminant analysis (OPLS-DA), followed by univariate statistics and enrichment analysis, was used to identify differential metabolites. Using logistic regression for variable selection, an obesity-related metabolite score (OMS, OMS=∑k=1nβnmetabolite n) was constructed from the metabolites identified, and hypertension risk was estimated.

Results

In our study, based on P< 0.05, variable importance in projection (VIP) > 1.0, and impact value > 0.1, we identified a total of 12 differential metabolites. Significantly altered metabolic pathways were the sphingolipid metabolism, purine metabolism, pyrimidine metabolism, phospholipid metabolism, steroid hormone biosynthesis, tryptophan, tyrosine, and phenylalanine biosynthesis. The logistic regression selection resulted in a four-metabolite score (thymidine, sphingomyelin (SM) d40:1, 4-hydroxyestradiol, and L-lysinamide), which was positively associated with hypertension risk (odds ratio: 7.79; 95% confidence interval: 2.13, 28.47; for the quintile 4 compared with quartile 1 of OMS) after multivariable adjustment.

Conclusions

The OMS constructed from four differential metabolites was used to predict the risk of hypertension in adolescents. These findings could provide sensitive biomarkers for the early recognition of hypertension in adolescents with obesity.

Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease

Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.

Pathophysiology and genetics of salt-sensitive hypertension

Most hypertensive cases are primary and heavily associated with modifiable risk factors like salt intake. Evidence suggests that even small reductions in salt consumption reduce blood pressure in all age groups. In that regard, the ACC/AHA described a distinct set of individuals who exhibit salt-sensitivity, regardless of their hypertensive status. Data has shown that salt-sensitivity is an independent risk factor for cardiovascular events and mortality. However, despite extensive research, the pathogenesis of salt-sensitive hypertension is still unclear and tremendously challenged by its multifactorial etiology, complicated genetic influences, and the unavailability of a diagnostic tool. So far, the important roles of the renin-angiotensin-aldosterone system, sympathetic nervous system, and immune system in the pathogenesis of salt-sensitive hypertension have been studied. In the first part of this review, we focus on how the systems mentioned above are aberrantly regulated in salt-sensitive hypertension. We follow this with an emphasis on genetic variants in those systems that are associated with and/or increase predisposition to salt-sensitivity in humans.

Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury

Energy metabolism failure in proximal tubule cells (PTC) is a hallmark of chronic kidney injury. We combined transcriptomic, metabolomic and lipidomic approaches in experimental models and patient cohorts to investigate the molecular bases of the progression to chronic kidney allograft injury initiated by ischemia-reperfusion injury (IRI). The urinary metabolome of kidney transplant recipients with chronic allograft injury and who experienced severe IRI was significantly enriched with long chain fatty acids (FA). We identified a renal FA-related gene signature with low levels of Cpt2 and Acsm5 and high levels of Acsl4 and Acsm5 associated with IRI, transition to chronic injury, and established CKD in mouse models and kidney transplant recipients. The findings were consistent with the presence of Cpt2-, Acsl4+, Acsl5+, Acsm5- PTC failing to recover from IRI as identified by snRNAseq. In vitro experiments indicated that endoplasmic reticulum (ER) stress contributes to CPT2 repression, which, in turn, promotes lipids accumulation, drives profibrogenic epithelial phenotypic changes, and activates the unfolded protein response. ER stress through CPT2 inhibition and lipid accumulation, engages an auto-amplification loop leading to lipotoxicity and self-sustained cellular stress. Thus, IRI imprints a persistent FA metabolism disturbance in the proximal tubule sustaining the progression to chronic kidney allograft injury.

Accelerated lysine metabolism conveys kidney protection in salt-sensitive hypertension

Hypertension and kidney disease have been repeatedly associated with genomic variants and alterations of lysine metabolism. Here, we combined stable isotope labeling with untargeted metabolomics to investigate lysine's metabolic fate in vivo. Dietary 13C6 labeled lysine was tracked to lysine metabolites across various organs. Globally, lysine reacts rapidly with molecules of the central carbon metabolism, but incorporates slowly into proteins and acylcarnitines. Lysine metabolism is accelerated in a rat model of hypertension and kidney damage, chiefly through N-alpha-mediated degradation. Lysine administration diminished development of hypertension and kidney injury. Protective mechanisms include diuresis, further acceleration of lysine conjugate formation, and inhibition of tubular albumin uptake. Lysine also conjugates with malonyl-CoA to form a novel metabolite Nε-malonyl-lysine to deplete malonyl-CoA from fatty acid synthesis. Through conjugate formation and excretion as fructoselysine, saccharopine, and Nε-acetyllysine, lysine lead to depletion of central carbon metabolites from the organism and kidney. Consistently, lysine administration to patients at risk for hypertension and kidney disease inhibited tubular albumin uptake, increased lysine conjugate formation, and reduced tricarboxylic acid (TCA) cycle metabolites, compared to kidney-healthy volunteers. In conclusion, lysine isotope tracing mapped an accelerated metabolism in hypertension, and lysine administration could protect kidneys in hypertensive kidney disease.

1-Hydroxypyrene mediates renal fibrosis through aryl hydrocarbon receptor signalling pathway

BACKGROUND AND PURPOSE

In chronic kidney disease (CKD), patients inevitably reach end-stage renal disease and require renal transplant. Evidence suggests that CKD is associated with metabolite disorders. However, the molecular pathways targeted by metabolites remain enigmatic. Here, we describe roles of 1-hydroxypyrene in mediating renal fibrosis.

EXPERIMENTAL APPROACH

We analysed 5406 urine and serum samples from patients with Stage 1-5 CKD using metabolomics, and 1-hydroxypyrene was identified and validated using longitudinal and drug intervention cohorts as well as 5/6 nephrectomised and adenine-induced rats.

KEY RESULTS

We identified correlations between the urine and serum levels of 1-hydroxypyrene and the estimated GFR in patients with CKD onset and progression. Moreover, increased 1-hydroxypyrene levels in serum and kidney tissues correlated with decreased renal function in two rat models. Up-regulated mRNA expression of aryl hydrocarbon receptor and its target genes, including CYP1A1, CYP1A2 and CYP1B1, were observed in patients and rats with progressive CKD. Further we showed up-regulated mRNA expression of aryl hydrocarbon receptor and its three target genes, plus up-regulated nuclear aryl hydrocarbon receptor protein levels in mice and HK-2 cells treated with 1-hydroxypyrene, which caused accumulation of extracellular matrix components. Treatment with aryl hydrocarbon receptor short hairpin RNA or flavonoids inhibited mRNA expression of aryl hydrocarbon receptor and its target genes in 1-hydroxypyrene-induced HK-2 cells and mice.

CONCLUSION AND IMPLICATIONS

Metabolite 1-hydroxypyrene was demonstrated to mediate renal fibrosis through activation of the aryl hydrocarbon receptor signalling pathway. Targeting aryl hydrocarbon receptor may be an alternative therapeutic strategy for CKD progression.

Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes

AIMS/HYPOTHESIS

This prospective, observational study examines associations between 51 urinary metabolites and risk of progression of diabetic nephropathy in individuals with type 1 diabetes by employing an automated NMR metabolomics technique suitable for large-scale urine sample collections.

METHODS

We collected 24-h urine samples for 2670 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy study and measured metabolite concentrations by NMR. Individuals were followed up for 9.0 ± 5.0 years until their first sign of progression of diabetic nephropathy, end-stage kidney disease or study end. Cox regressions were performed on the entire study population (overall progression), on 1999 individuals with normoalbuminuria and 347 individuals with macroalbuminuria at baseline.

RESULTS

Seven urinary metabolites were associated with overall progression after adjustment for baseline albuminuria and chronic kidney disease stage (p < 8 × 10^-4): leucine (HR 1.47 [95% CI 1.30, 1.66] per 1-SD creatinine-scaled metabolite concentration), valine (1.38 [1.22, 1.56]), isoleucine (1.33 [1.18, 1.50]), pseudouridine (1.25 [1.11, 1.42]), threonine (1.27 [1.11, 1.46]) and citrate (0.84 [0.75, 0.93]). 2-Hydroxyisobutyrate was associated with overall progression (1.30 [1.16, 1.45]) and also progression from normoalbuminuria (1.56 [1.25, 1.95]). Six amino acids and pyroglutamate were associated with progression from macroalbuminuria.

CONCLUSIONS/INTERPRETATION

Branched-chain amino acids and other urinary metabolites were associated with the progression of diabetic nephropathy on top of baseline albuminuria and chronic kidney disease. We found differences in associations for overall progression and progression from normo- and macroalbuminuria. These novel discoveries illustrate the utility of analysing urinary metabolites in entire population cohorts.

Comprehensive metabolomic profiling in early IgA nephropathy patients reveals urine glycine as a prognostic biomarker

Identification of a urinary metabolite biomarker with diagnostic or prognostic significance for early immunoglobulin A nephropathy (IgAN) is needed. We performed nuclear magnetic resonance‐based metabolomic profiling and identified 26 metabolites in urine samples. We collected urine samples from 201, 77, 47, 36 and 136 patients with IgAN, patients with membranous nephropathy, patients with minimal change disease, patients with lupus nephritis and healthy controls, respectively. We determined whether a metabolite level is associated with the prognosis of IgAN through Cox regression and continuous net reclassification improvement (cNRI). Finally, in vitro experiments with human kidney tubular epithelial cells (hTECs) were performed for experimental validation. As the results, the urinary glycine level was higher in the IgAN group than the control groups. A higher urinary glycine level was associated with lower risk of eGFR 30% decline in IgAN patients. The addition of glycine to a predictive model including clinicopathologic information significantly improved the predictive power for the prognosis of IgAN [cNRI 0.72 (0.28‐0.82)]. In hTECs, the addition of glycine ameliorated inflammatory signals induced by tumour necrosis factor‐α. Our study demonstrates that urinary glycine may have diagnostic and prognostic value for IgAN and indicates that urinary glycine is a protective biomarker for IgAN.

Urine Metabolite Levels, Adverse Kidney Outcomes, and Mortality in CKD Patients: A Metabolome-wide Association Study

RATIONALE & OBJECTIVE

Mechanisms underlying the variable course of disease progression in patients with chronic kidney disease (CKD) are incompletely understood. The aim of this study was to identify novel biomarkers of adverse kidney outcomes and overall mortality, which may offer insights into pathophysiologic mechanisms.

STUDY DESIGN

Metabolome-wide association study.

SETTING & PARTICIPANTS

5,087 patients with CKD enrolled in the observational German Chronic Kidney Disease Study.

EXPOSURES

Measurements of 1,487 metabolites in urine.

OUTCOMES

End points of interest were time to kidney failure (KF), a combined end point of KF and acute kidney injury (KF+AKI), and overall mortality.

ANALYTICAL APPROACH

Statistical analysis was based on a discovery-replication design (ratio 2:1) and multivariable-adjusted Cox regression models.

RESULTS

After a median follow-up of 4 years, 362 patients died, 241 experienced KF, and 382 experienced KF+AKI. Overall, we identified 55 urine metabolites whose levels were significantly associated with adverse kidney outcomes and/or mortality. Higher levels of C-glycosyltryptophan were consistently associated with all 3 main end points (hazard ratios of 1.43 [95% CI, 1.27-1.61] for KF, 1.40 [95% CI, 1.27-1.55] for KF+AKI, and 1.47 [95% CI, 1.33-1.63] for death). Metabolites belonging to the phosphatidylcholine pathway showed significant enrichment. Members of this pathway contributed to the improvement of the prediction performance for KF observed when multiple metabolites were added to the well-established Kidney Failure Risk Equation.

LIMITATIONS

Findings among patients of European ancestry with CKD may not be generalizable to the general population.

CONCLUSIONS

Our comprehensive screen of the association between urine metabolite levels and adverse kidney outcomes and mortality identifies metabolites that predict KF and represents a valuable resource for future studies of biomarkers of CKD progression.

Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.

New Potential Biomarkers for Chronic Kidney Disease Management-A Review of the Literature

The prevalence of chronic kidney disease (CKD) is increasing worldwide, and the mortality rate continues to be unacceptably high. The biomarkers currently used in clinical practice are considered relevant when there is already significant renal impairment compromising the early use of potentially successful therapeutic interventions. More sensitive and specific biomarkers to detect CKD earlier on and improve patients' prognoses are an important unmet medical need. The aim of this review is to summarize the recent literature on new promising early CKD biomarkers of renal function, tubular lesions, endothelial dysfunction and inflammation, and on the auspicious findings from metabolomic studies in this field. Most of the studied biomarkers require further validation in large studies and in a broad range of populations in order to be implemented into routine CKD management. A panel of biomarkers, including earlier biomarkers of renal damage, seems to be a reasonable approach to be applied in clinical practice to allow earlier diagnosis and better disease characterization based on the underlying etiologic process.

Deep-DRM: a computational method for identifying disease-related metabolites based on graph deep learning approaches

MOTIVATION

The functional changes of the genes, RNAs and proteins will eventually be reflected in the metabolic level. Increasing number of researchers have researched mechanism, biomarkers and targeted drugs by metabolites. However, compared with our knowledge about genes, RNAs, and proteins, we still know few about diseases-related metabolites. All the few existed methods for identifying diseases-related metabolites ignore the chemical structure of metabolites, fail to recognize the association pattern between metabolites and diseases, and fail to apply to isolated diseases and metabolites.

RESULTS

In this study, we present a graph deep learning based method, named Deep-DRM, for identifying diseases-related metabolites. First, chemical structures of metabolites were used to calculate similarities of metabolites. The similarities of diseases were obtained based on their functional gene network and semantic associations. Therefore, both metabolites and diseases network could be built. Next, Graph Convolutional Network (GCN) was applied to encode the features of metabolites and diseases, respectively. Then, the dimension of these features was reduced by Principal components analysis (PCA) with retainment 99% information. Finally, Deep neural network was built for identifying true metabolite-disease pairs (MDPs) based on these features. The 10-cross validations on three testing setups showed outstanding AUC (0.952) and AUPR (0.939) of Deep-DRM compared with previous methods and similar approaches. Ten of top 15 predicted associations between diseases and metabolites got support by other studies, which suggests that Deep-DRM is an efficient method to identify MDPs.

CONTACT

liangcheng@hrbmu.edu.cn.

AVAILABILITY AND IMPLEMENTATION

https://github.com/zty2009/GPDNN-for-Identify-ing-Disease-related-Metabolites.

Potential Biomarkers of the Turnover, Mineralization, and Volume Classification: Results Using NMR Metabolomics in Hemodialysis Patients

Bone biopsy is still the gold standard to assess bone turnover (T), mineralization (M), and volume (V) in CKD patients, and serum biomarkers are not able to replace histomorphometry. Recently, metabolomics has emerged as a new technique that could allow for the identification of new biomarkers useful for disease diagnosis or for the understanding of pathophysiologic mechanisms, but it has never been assessed in the chronic kidney disease-mineral and bone disorder (CKD-MBD) scenario. In this study, we investigated the association between serum metabolites and the bone TMV classification in patients with end-stage renal disease by using serum NMR spectroscopy and bone biopsy of 49 hemodialysis patients from a single center in Brazil. High T was identified in 21 patients and was associated with higher levels of dimethylsulfone, glycine, citrate, and N-acetylornithine. The receiver-operating characteristic curve for the combination of PTH and these metabolites provided an area under the receiver-operating characteristic curve (AUC) of 0.86 (0.76 to 0.97). Abnormal M was identified in 30 patients and was associated with lower ethanol. The AUC for age, diabetes mellitus, and ethanol was 0.83 (0.71 to 0.96). Low V was identified in 17 patients and was associated with lower carnitine. The association of age, phosphate, and carnitine provided an AUC of 0.83 (0.70 to 0.96). Although differences among the curves by adding selected metabolites to traditional models were not statistically significant, the accuracy of the diagnosis according to the TMV classification seemed to be improved. This is the first study to evaluate the TMV classification system in relation to the serum metabolome assessed by NMR spectroscopy, showing that selected metabolites may help in the evaluation of bone phenotypes in CKD-MBD. © 2020 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans

The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite-locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the prioritization of causal genes, functional variants and target cell types. The combination of mQTLs with genetic and health information from 450,000 UK Biobank participants illuminated metabolic mediators, and hence, novel urinary biomarkers of disease risk. This comprehensive resource of genetic targets and their substrates is informative for ADME processes in humans and is relevant to basic science, clinical medicine and pharmaceutical research.

Omics studies for comprehensive understanding of immunoglobulin A nephropathy: state-of-the-art and future directions

Immunoglobulin A nephropathy (IgAN) is the most common worldwide primary glomerulonephritis with a strong autoimmune component. The disease shows variability in both clinical phenotypes and endpoints and can be potentially subdivided into more homogeneous subtypes through the identification of specific molecular biomarkers. This review focuses on the role of omics in driving the identification of potential molecular subtypes of the disease through the integration of multilevel data from genomics, transcriptomics, epigenomics, proteomics and metabolomics. First, the identification of molecular biomarkers, including mapping of the full spectrum of common and rare IgAN risk alleles, could permit a more precise stratification of IgAN patients. Second, the analysis of transcriptomic patterns and their modulation by epigenetic factors like microRNAs has the potential to increase our understanding in the pathogenic mechanisms of the disease. Third, the specificity of urinary proteomic and metabolomic signatures and the understanding of their functional relevance may contribute to the development of new non-invasive biomarkers for a better molecular characterization of the renal damage and its follow-up. All these approaches can give information for targeted therapeutic decisions and will support novel clinical decision making. In conclusion, we offer a framework of omic studies and outline barriers and potential solutions that should be used for improving the diagnosis and treatment of the disease. The ongoing decade is exploiting novel high-throughput molecular technologies and computational analyses for improving the diagnosis (precision nephrology) and treatment (personalized therapy) of the IgAN subtypes.

Metabolomic and biochemical characterization of a new model of the transition of acute kidney injury to chronic kidney disease induced by folic acid

Background

Renal diseases represent a major public health problem. The demonstration that maladaptive repair of acute kidney injury (AKI) can lead to the development of chronic kidney disease (CKD) and end-stage renal disease has generated interest in studying the pathophysiological pathways involved. Animal models of AKI–CKD transition represent important tools to study this pathology. We hypothesized that the administration of multiple doses of folic acid (FA) would lead to a progressive loss of renal function that could be characterized through biochemical parameters, histological classification and nuclear magnetic resonance (NMR) profiling.

Methods

Wistar rats were divided into groups: the control group received a daily intraperitoneal (I.P.) injection of double-distilled water, the experimental group received a daily I.P. injection of FA (250 mg kg body weight⁻¹). Disease was classified according to blood urea nitrogen level: mild (40–80 mg dL⁻¹), moderate (100–200 mg dL⁻¹) and severe (>200 mg dL⁻¹). We analyzed through biochemical parameters, histological classification and NMR profiling.

Results

Biochemical markers, pro-inflammatory cytokines and kidney injury biomarkers differed significantly (P < 0.05) between control and experimental groups. Histology revealed that as damage progressed, the degree of tubular injury increased, and the inflammatory infiltrate was more evident. NMR metabolomics and chemometrics revealed differences in urinary metabolites associated with CKD progression. The main physiological pathways affected were those involved in energy production and amino-acid metabolism, together with organic osmolytes. These data suggest that multiple administrations of FA induce a reproducible model of the induction of CKD. This model could help to evaluate new strategies for nephroprotection that could be applied in the clinic.

Genetic Regulation of Liver Metabolites and Transcripts Linking to Biochemical-Clinical Parameters

Given the central metabolic role of the liver, hepatic metabolites and transcripts reflect the organismal physiological state. Biochemical-clinical plasma biomarkers, hepatic metabolites, transcripts, and single nucleotide polymorphism (SNP) genotypes of some 300 pigs were integrated by weighted correlation networks and genome-wide association analyses. Network-based approaches of transcriptomic and metabolomics data revealed linked of transcripts and metabolites of the pentose phosphate pathway (PPP). This finding was evidenced by using a NADP/NADPH assay and HDAC4 and G6PD transcript quantification with the latter coding for first limiting enzyme of this pathway and by RNAi knockdown experiments of HDAC4. Other transcripts including ARG2 and SLC22A7 showed link to amino acids and biomarkers. The amino acid metabolites were linked with transcripts of immune or acute phase response signaling, whereas the carbohydrate metabolites were highly enrich in cholesterol biosynthesis transcripts. Genome-wide association analyses revealed 180 metabolic quantitative trait loci (mQTL) (p < 10^-4). Trans-4-hydroxy-L-proline (p = 6 × 10^-9), being strongly correlated with plasma creatinine (CREA), showed strongest association with SNPs on chromosome 6 that had pleiotropic effects on PRODH2 expression as revealed by multivariate analysis. Consideration of shared marker association with biomarkers, metabolites, and transcripts revealed 144 SNPs associated with 44 metabolites and 69 transcripts that are correlated with each other, representing 176 mQTL and expression quantitative trait loci (eQTL). This is the first work to report genetic variants associated with liver metabolite and transcript levels as well as blood biochemical-clinical parameters in a healthy porcine model. The identified associations provide links between variation at the genome, transcriptome, and metabolome level molecules with clinically relevant phenotypes. This approach has the potential to detect novel biomarkers displaying individual variation and promoting predictive biology in medicine and animal breeding.

Big science and big data in nephrology

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.

Genome-Wide Association Studies of Metabolite Concentrations (mGWAS): Relevance for Nephrology

Metabolites are small molecules that are intermediates or products of metabolism, many of which are freely filtered by the kidneys. In addition, the kidneys have a central role in metabolite anabolism and catabolism, as well as in active metabolite reabsorption and/or secretion during tubular passage. This review article illustrates how the coupling of genomics and metabolomics in genome-wide association analyses of metabolites can be used to illuminate mechanisms underlying human metabolism, with a special focus on insights relevant to nephrology. First, genetic susceptibility loci for reduced kidney function and chronic kidney disease (CKD) were reviewed systematically for their associations with metabolite concentrations in metabolomics studies of blood and urine. Second, kidney function and CKD-associated metabolites reported from observational studies were interrogated for metabolite-associated genetic variants to generate and discuss complementary insights. Finally, insights originating from the simultaneous study of both blood and urine or by modeling intermetabolite relationships are summarized. We also discuss methodologic questions related to the study of metabolite concentrations in urine as well as among CKD patients. In summary, genome-wide association analyses of metabolites using metabolite concentrations quantified from blood and/or urine are a promising avenue of research to illuminate physiological and pathophysiological functions of the kidney.

Urinary Biomarkers of Renal Fibrosis

Renal fibrosis is the common pathological pathway of progressive CKD. The commonly used biomarkers in clinical practice are not optimal to detect injury or predict prognosis. Therefore, it is crucial to develop novel biomarkers to allow prompt intervention. Urine serves as a valuable resource of biomarker discovery for kidney diseases. Owing to the rapid development of omics platforms and bioinformatics, research on novel urinary biomarkers for renal fibrosis has proliferated in recent years. In this chapter, we discuss the current status and provide basic knowledge in this field. We present novel promising biomarkers including tubular injury markers, proteins related to activated inflammation/fibrosis pathways, CKD273, transcriptomic biomarkers, as well as metabolomic biomarkers. Furthermore, considering the complex nature of the pathogenesis of renal fibrosis, we also highlight the combination of biomarkers to further improve the diagnostic and prognostic performance.

Variability of Two Metabolomic Platforms in CKD

BACKGROUND AND OBJECTIVES

Nontargeted metabolomics can measure thousands of low-molecular-weight biochemicals, but important gaps limit its utility for biomarker discovery in CKD. These include the need to characterize technical and intraperson analyte variation, to pool data across platforms, and to outline analyte relationships with eGFR.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Plasma samples from 49 individuals with CKD (eGFR<60 ml/min per 1.73 m² and/or ≥1 g proteinuria) were examined from two study visits; 20 samples were repeated as blind replicates. To enable comparison across two nontargeted platforms, samples were profiled at Metabolon and the Broad Institute.

RESULTS

The Metabolon platform reported 837 known metabolites and 483 unnamed compounds (selected from 44,953 unknown ion features). The Broad Institute platform reported 594 known metabolites and 26,106 unknown ion features. Median coefficients of variation (CVs) across blind replicates were 14.6% (Metabolon) and 6.3% (Broad Institute) for known metabolites, and 18.9% for (Metabolon) unnamed compounds and 24.5% for (Broad Institute) unknown ion features. Median CVs for day-to-day variability were 29.0% (Metabolon) and 24.9% (Broad Institute) for known metabolites, and 41.8% for (Metabolon) unnamed compounds and 40.9% for (Broad Institute) unknown ion features. A total of 381 known metabolites were shared across platforms (median correlation 0.89). Many metabolites were negatively correlated with eGFR at P<0.05, including 35.7% (Metabolon) and 18.9% (Broad Institute) of known metabolites.

CONCLUSIONS

Nontargeted metabolomics quantifies >1000 analytes with low technical CVs, and agreement for overlapping metabolites across two leading platforms is excellent. Many metabolites demonstrate substantial intraperson variation and correlation with eGFR.

Amino acid transporters revisited: New views in health and disease

Amino acid transporters (AATs) are membrane-bound transport proteins that mediate transfer of amino acids into and out of cells or cellular organelles. AATs have diverse functional roles ranging from neurotransmission to acid-base balance, intracellular energy metabolism, and anabolic and catabolic reactions. In cancer cells and diabetes, dysregulation of AATs leads to metabolic reprogramming, which changes intracellular amino acid levels, contributing to the pathogenesis of cancer, obesity and diabetes. Indeed, the neutral amino acid transporters (NATs) SLC7A5/LAT1 and SLC1A5/ASCT2 are likely involved in several human malignancies. However, a clinical therapy that directly targets AATs has not yet been developed. The purpose of this review is to highlight the structural and functional diversity of AATs, their diverse physiological roles in different tissues and organs, their wide-ranging implications in human diseases and the emerging strategies and tools that will be necessary to target AATs therapeutically.

Metabolic Response in Rabbit Urine to Occurrence and Relief of Unilateral Ureteral Obstruction

Ureteral obstruction will lead clinically to hydronephrosis, which may further develop into partial or complete loss of kidney function and even cause permanent histological damage. However, there is little knowledge of metabolic responses during the obstructed process and its recoverability. In this study, a complete unilateral ureteral obstruction (CUUO) model was established in the rabbit, and ¹H NMR-based metabolomic analysis of urine was used to reveal the metabolic perturbations in rabbits caused by CUUO and the metabolic recovery after the CUUO was relieved. Univariate and multivariate statistical analyses were used to identify metabolic characteristics. The gradually decreased levels of 3-hydroxykynurenine, 3-methylhistidine, creatinine, guanidoacetate, meta- and para-hydroxyphenylacetate, and phenylacetylglycine and the gradually increased levels of acetate, alanine, citrate, glycine, lactate, and methionine in urine could be regarded as potential biomarkers for the occurrence and severity of ureteral obstruction. And the reduced levels of 3-methylhistidine, creatinine, guanidoacetate, hippurate, meta-hydroxyphenylacetate, and methylguanidine and the elevated levels of 2-aminoisobutyrate, acetylcholine, citrate, lactate, lysine, valine, and α-ketoglutarate in urine compared with the obstructed level could characterize the metabolic recovery of ureteral obstruction. Our results depicted the disturbed biochemical pathways involved in ureteral obstruction and demonstrated the practicability of recovering renal functions for the patients with severe hydronephrosis in clinical practice by removing causes for obstruction.

Predictive metabolomic signatures of end-stage renal disease: A multivariate analysis of population-based data

The variability of molecular signatures and predictive low molecular weight markers of chronic kidney disease (CKD) in different populations are poorly understood. Thus, in a large sample with 4763 people we compare the molecular signatures and metabolites with diagnostic relevance in plasma and urine of CKD patients of different geographical origins. From an integrated model based on dynamic networks and multivariate statistics, metabolites with predictive value obtained from targeted and untargeted molecular analysis, interactions between metabolic pathways affected by CKD, and the methodological quality of metabolomic studies were analyzed. The metabolites 3-methylhistidine, citrulline, kynurenine, p-cresol sulfate, urea, and citrate presented consistent expression in all population groups. Only increased kynurenine and p-cresol sulfate in plasma samples obtained acceptable scores as CKD biomarkers, independent of geographic origin. Metabolites such as leucine, alanine, isoleucine, serine, histidine, and citrate were nodal points, indicating that protein metabolism pathways are similarly impaired in Asian, European and North American patients. Based on our integrated model, we show that the metabolome of CKD patients exhibits a strong geographic influence, leading to unique metabolic signatures. Contrary to the likelihood of molecular similarities between geographically distinct populations, metabolic convergences in protein metabolism pathways and the molecules kynurenine and p-cresol sulfate were relevant as general predictors of CKD. In general, the quality assessment indicated that the current evidence is based on research models with variable methodological quality, whose limitations described in this study should be considered in the refinement of molecular approaches.

A metabolite-GWAS (mGWAS) approach to unveil chronic kidney disease progression

In this issue, McMahon et al. report that, by combining phenotypic, metabolomic, and genetic data, they could better detect chronic kidney disease at the early stages and provide insight into its pathobiology. The most significant findings of the study are that several urinary metabolites (e.g., glycine and histidine) were identified as early risk factors for chronic kidney disease, and metabolites with genomewide association study analysis identified associations of urinary metabolites (i.e., lysine and N^G-monomethyl-l-arginine) with single-nucleotide polymorphisms of SLC7A9.

Genome-Wide Association Studies of Metabolites in Patients with CKD Identify Multiple Loci and Illuminate Tubular Transport Mechanisms

Background The kidneys have a central role in the generation, turnover, transport, and excretion of metabolites, and these functions can be altered in CKD. Genetic studies of metabolite concentrations can identify proteins performing these functions.Methods We conducted genome-wide association studies and aggregate rare variant tests of the concentrations of 139 serum metabolites and 41 urine metabolites, as well as their pairwise ratios and fractional excretions in up to 1168 patients with CKD.Results After correction for multiple testing, genome-wide significant associations were detected for 25 serum metabolites, two urine metabolites, and 259 serum and 14 urinary metabolite ratios. These included associations already known from population-based studies. Additional findings included an association for the uremic toxin putrescine and variants upstream of an enzyme catalyzing the oxidative deamination of polyamines (AOC1, P-min=2.4×10^-12), a relatively high carrier frequency (2%) for rare deleterious missense variants in ACADM that are collectively associated with serum ratios of medium-chain acylcarnitines (P-burden=6.6×10^-16), and associations of a common variant in SLC7A9 with several ratios of lysine to neutral amino acids in urine, including the lysine/glutamine ratio (P=2.2×10^-23). The associations of this SLC7A9 variant with ratios of lysine to specific neutral amino acids were much stronger than the association with lysine concentration alone. This finding is consistent with SLC7A9 functioning as an exchanger of urinary cationic amino acids against specific intracellular neutral amino acids at the apical membrane of proximal tubular cells.Conclusions Metabolomic indices of specific kidney functions in genetic studies may provide insight into human renal physiology.

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.

How Omics Data Can Be Used in Nephrology

Advances in technology and computing now permit the high-throughput analysis of multiple domains of biological information, including the genome, transcriptome, proteome, and metabolome. These omics approaches, particularly comprehensive analysis of the genome, have catalyzed major discoveries in science and medicine, including in nephrology. However, they also generate large complex data sets that can be difficult to synthesize from a clinical perspective. This article seeks to provide an overview that makes omics technologies relevant to the practicing nephrologist, framing these tools as an extension of how we approach patient care in the clinic. More specifically, omics technologies reinforce the impact that genetic mutations can have on a range of kidney disorders, expand the catalogue of uremic molecules that accumulate in blood with kidney failure, enhance our ability to scrutinize urine beyond urinalysis for insight on renal pathology, and enable more extensive characterization of kidney tissue when a biopsy is performed. Although assay methodologies vary widely, all omics technologies share a common conceptual framework that embraces unbiased discovery at the molecular level. Ultimately, the application of these technologies seeks to elucidate a more mechanistic and individualized approach to the diagnosis and treatment of human disease.

Metabolomic Alterations Associated with Cause of CKD

BACKGROUND AND OBJECTIVES

Causes of CKD differ in prognosis and treatment. Metabolomic indicators of CKD cause may provide clues regarding the different physiologic processes underlying CKD development and progression.

DESIGN, SETTING, PARTICIPANTS & MEASUREMENTS

Metabolites were quantified from serum samples of participants in the Modification of Diet in Renal Disease (MDRD) Study, a randomized controlled trial of dietary protein restriction and BP control, using untargeted reverse phase ultraperformance liquid chromatography tandem mass spectrometry quantification. Known, nondrug metabolites (n=687) were log-transformed and analyzed to discover associations with CKD cause (polycystic kidney disease, glomerular disease, and other cause). Discovery was performed in Study B, a substudy of MDRD with low GFR (n=166), and replication was performed in Study A, a substudy of MDRD with higher GFR (n=423).

RESULTS

Overall in MDRD, average participant age was 51 years and 61% were men. In the discovery study (Study B), 29% of participants had polycystic kidney disease, 28% had glomerular disease, and 43% had CKD of another cause; in the replication study (Study A), the percentages were 28%, 24%, and 48%, respectively. In the discovery analysis, adjusted for demographics, randomization group, body mass index, hypertensive medications, measured GFR, log-transformed proteinuria, and estimated protein intake, seven metabolites (16-hydroxypalmitate, kynurenate, homovanillate sulfate, N2,N2-dimethylguanosine, hippurate, homocitrulline, and 1,5-anhydroglucitol) were associated with CKD cause after correction for multiple comparisons (P<0.0008). Five of these metabolite associations (16-hydroxypalmitate, kynurenate, homovanillate sulfate, N2,N2-dimethylguanosine, and hippurate) were replicated in Study A (P<0.007), with all replicated metabolites exhibiting higher levels in polycystic kidney disease and lower levels in glomerular disease compared with CKD of other causes.

CONCLUSIONS

Metabolomic profiling identified several metabolites strongly associated with cause of CKD.