Metabolomics Research in Chronic Kidney Disease

Longitudinal Plasma Metabolome Patterns and Relation to Kidney Function and Proteinuria in Pediatric CKD

Key Points

Longitudinal untargeted metabolomics. Children with CKD have a circulating metabolome that changes over time.

Background

Understanding plasma metabolome patterns in relation to changing kidney function in pediatric CKD is important for continued research for identifying novel biomarkers, characterizing biochemical pathophysiology, and developing targeted interventions. There are a limited number of studies of longitudinal metabolomics and virtually none in pediatric CKD.

Methods

The CKD in Children study is a multi-institutional, prospective cohort that enrolled children aged 6 months to 16 years with eGFR 30–90 ml/min per 1.73 m2. Untargeted metabolomics profiling was performed on plasma samples from the baseline, 2-, and 4-year study visits. There were technologic updates in the metabolomic profiling platform used between the baseline and follow-up assays. Statistical approaches were adopted to avoid direct comparison of baseline and follow-up measurements. To identify metabolite associations with eGFR or urine protein-creatinine ratio (UPCR) among all three time points, we applied linear mixed-effects (LME) models. To identify metabolites associated with time, we applied LME models to the 2- and 4-year follow-up data. We applied linear regression analysis to examine associations between change in metabolite level over time (∆level) and change in eGFR (∆eGFR) and UPCR (∆UPCR). We reported significance on the basis of both the false discovery rate (FDR) <0.05 and P < 0.05.

Results

There were 1156 person-visits (N : baseline=626, 2-year=254, 4-year=276) included. There were 622 metabolites with standardized measurements at all three time points. In LME modeling, 406 and 343 metabolites associated with eGFR and UPCR at FDR <0.05, respectively. Among 530 follow-up person-visits, 158 metabolites showed differences over time at FDR <0.05. For participants with complete data at both follow-up visits (n =123), we report 35 metabolites with ∆level–∆eGFR associations significant at FDR <0.05. There were no metabolites with significant ∆level–∆UPCR associations at FDR <0.05. We report 16 metabolites with ∆level–∆UPCR associations at P < 0.05 and associations with UPCR in LME modeling at FDR <0.05.

Conclusions

We characterized longitudinal plasma metabolomic patterns associated with eGFR and UPCR in a large pediatric CKD population. Many of these metabolite signals have been associated with CKD progression, etiology, and proteinuria in previous CKD Biomarkers Consortium studies. There were also novel metabolite associations with eGFR and proteinuria detected.

Circulating Metabolomic Associations with Neurocognitive Outcomes in Pediatric CKD

BACKGROUND

Children with CKD are at risk for impaired neurocognitive functioning. We investigated metabolomic associations with neurocognition in children with CKD.

METHODS

We leveraged data from the Chronic Kidney Disease in Children (CKiD) study and the Neurocognitive Assessment and Magnetic Resonance Imaging Analysis of Children and Young Adults with Chronic Kidney Disease (NiCK) study. CKiD is a multi-institutional cohort that enrolled children aged 6 months to 16 years with eGFR 30-90 ml/min per 1.73 m 2 ( n =569). NiCK is a single-center cross-sectional study of participants aged 8-25 years with eGFR<90 ml/min per 1.73 m 2 ( n =60) and matched healthy controls ( n =67). Untargeted metabolomic quantification was performed on plasma (CKiD, 622 metabolites) and serum (NiCK, 825 metabolites) samples. Four neurocognitive domains were assessed: intelligence, attention regulation, working memory, and parent ratings of executive function. Repeat assessments were performed in CKiD at 2-year intervals. Linear regression and linear mixed-effects regression analyses adjusting for age, sex, delivery history, hypertension, proteinuria, CKD duration, and glomerular versus nonglomerular diagnosis were used to identify metabolites associated with neurocognitive z-scores. Analyses were performed with and without adjustment for eGFR.

RESULTS

There were multiple metabolite associations with neurocognition observed in at least two of the analytic samples (CKiD baseline, CKiD follow-up, and NiCK CKD). Most of these metabolites were significantly elevated in children with CKD compared with healthy controls in NiCK. Notable signals included associations with parental ratings of executive function: phenylacetylglutamine, indoleacetylglutamine, and trimethylamine N-oxide-and with intelligence: γ -glutamyl amino acids and aconitate.

CONCLUSIONS

Several metabolites were associated with neurocognitive dysfunction in pediatric CKD, implicating gut microbiome-derived substances, mitochondrial dysfunction, and altered energy metabolism, circulating toxins, and redox homeostasis.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_11_17_CJN0000000000000318.mp3.

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

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

Untargeted plasma 1H NMR-based metabolomic profiling in different stages of chronic kidney disease

Chronic kidney disease (CKD) is a serious public health issue affecting thousands of people worldwide. CKD diagnosis is usually made by Estimated Glomerular Filtration Rate (eGFR) and albuminuria, which limit the knowledge of the mechanisms behind CKD progression. The aim of the present study was to identify changes in the metabolomic profile that occur as CKD advances. In this sense, 77 plasma samples from patients with CDK were evaluated by 1D and 2D Nuclear Magnetic Resonance Spectroscopy (NMR). The NMR data showed significant changes in the metabolomic profile of CKD patients and the control group. Principal component analysis (PCA) clustered CKD and control patients into three distinct groups, control, stage 1 (G1)-stage 4 (G4) and stage 5 (G5). Lactate, glucose, acetate and creatinine were responsible for discriminating the control group from all the others CKD stages. Valine, alanine, glucose, creatinine, glutamate and lactate were responsible for the clustering of G1-G4 stages. G5 was discriminated by calcium ethylenediamine tetraacetic acid, magnesium ethylenediamine tetraacetic acid, creatinine, betaine/choline/trimethylamine N-oxide (TMAO), lactate and acetate. CKD G5 plasma pool which was submitted in MetaboAnalyst 4.0 platform (MetPA) analysis and showed 13 metabolic pathways involved in CKD physiopathology. Metabolic changes associated with glycolysis and gluconeogenesis allowed discriminating between CKD and control patients. The determination of involved molecules in TMAO generation in G5 suggests an important role in this uremic toxin linked to CKD and cardiovascular diseases. The aforementioned results propose the feasibility of metabolic assessment of CKD by NMR during treatment and disease progression.

Metabolomic profiles in serum and urine uncover novel biomarkers in children with nephrotic syndrome

BACKGROUND

Nephrotic syndrome is common in children and adults worldwide, and steroid-sensitive nephrotic syndrome (SSNS) accounts for 80%. Aberrant metabolism involvement in early SSNS is sparsely studied, and its pathogenesis remains unclear. Therefore, the goal of this study was to investigate the changes in initiated SSNS patients-related metabolites through serum and urine metabolomics and discover the novel potential metabolites and metabolic pathways.

METHODS

Serum samples (27 SSNS and 56 controls) and urine samples (17 SSNS and 24 controls) were collected. Meanwhile, the non-targeted analyses were performed by ultra-high-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-QTOF-MS) to determine the changes in SSNS. We applied the causal inference model, the DoWhy model, to assess the causal effects of several selected metabolites. An ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to validate hits (D-mannitol, dulcitol, D-sorbitol, XMP, NADPH, NAD, bilirubin, and α-KG-like) in 41 SSNS and 43 controls. In addition, the metabolic pathways were explored.

RESULTS

Compared to urine, the metabolism analysis of serum samples was more clearly discriminated at SSNS. 194 differential serum metabolites and five metabolic pathways were obtained in the SSNS group. Eight differential metabolites were identified by establishing the diagnostic model for SSNS, and four variables had a positive causal effect. After validation by targeted MS, except XMP, others have similar trends like the untargeted metabolic analysis.

CONCLUSION

With untargeted metabolomics analysis and further targeted quantitative analysis, we found seven metabolites may be new biomarkers for risk prediction and early diagnosis for SSNS.

Serum Metabolites and Kidney Outcomes: The Atherosclerosis Risk in Communities Study

Rationale & Objective

Novel metabolite biomarkers of kidney failure with replacement therapy (KFRT) may help identify people at high risk for adverse kidney outcomes and implicated pathways may aid in developing targeted therapeutics.

Study Design

Prospective cohort.

Setting & Participants

The cohort included 3,799 Atherosclerosis Risk in Communities study participants with serum samples available for measurement at visit 1 (1987-1989).

Exposure

Baseline serum levels of 318 metabolites.

Outcomes

Incident KFRT, kidney failure (KFRT, estimated glomerular filtration rate <15 mL/min/1.73 m², or death from kidney disease).

Analytical Approach

Because metabolites are often intercorrelated and represent shared pathways, we used a high dimension reduction technique called Netboost to cluster metabolites. Longitudinal associations between clusters of metabolites and KFRT and kidney failure were estimated using a Cox proportional hazards model.

Results

Mean age of study participants was 53 years, 61% were African American, and 13% had diabetes. There were 160 KFRT cases and 357 kidney failure cases over a mean of 23 years. The 314 metabolites were grouped in 43 clusters. Four clusters were significantly associated with risk of KFRT and 6 were associated with kidney failure (including 3 shared clusters). The 3 shared clusters suggested potential pathways perturbed early in kidney disease: cluster 5 (15 metabolites involved in alanine, aspartate, and glutamate metabolism as well as 5-oxoproline and several gamma-glutamyl amino acids), cluster 26 (6 metabolites involved in sugar and inositol phosphate metabolism), and cluster 34 (21 metabolites involved in glycerophospholipid metabolism). Several individual metabolites were also significantly associated with both KFRT and kidney failure, including glucose and mannose, which were associated with higher risk of both outcomes, and 5-oxoproline, gamma-glutamyl amino acids, linoleoylglycerophosphocholine, 1,5-anhydroglucitol, which were associated with lower risk of both outcomes.

Limitations

Inability to determine if the metabolites cause or are a consequence of changes in kidney function.

Conclusions

We identified several clusters of metabolites reproducibly associated with development of KFRT. Future experimental studies are needed to validate our findings as well as continue unraveling metabolic pathways involved in kidney function decline.

Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research

Chronic kidney disease, including renal failure (RF), is a global public health problem. The clinical diagnosis mainly depends on the change of estimated glomerular filtration rate, which usually lags behind disease progression and likely has limited clinical utility for the early detection of this health problem. Now, we employed Q-Exactive HFX Orbitrap LC-MS/MS based metabolomics to reveal the metabolic profile and potential biomarkers for RF screening. 27 RF patients and 27 healthy controls were included as the testing groups, and comparative analysis of results using different techniques, such as multivariate pattern recognition and univariate statistical analysis, was applied to screen and elucidate the differential metabolites. The dot plots and receiver operating characteristics curves of identified different metabolites were established to discover the potential biomarkers of RF. The results exhibited a clear separation between the two groups, and a total of 216 different metabolites corresponding to 13 metabolic pathways were discovered to be associated with RF; and 44 metabolites showed high levels of sensitivity and specificity under curve values of close to 1, thus might be used as serum biomarkers for RF. In summary, for the first time, our untargeted metabolomics study revealed the distinct metabolic profile of RF, and 44 metabolites with high sensitivity and specificity were discovered, 3 of which have been reported and were consistent with our observations. The other metabolites were first reported by us. Our findings might provide a feasible diagnostic tool for identifying populations at risk for RF through detection of serum metabolites.

Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function

Renal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification of patients at risk of rapid graft failure is critical to optimize their management and slow the progression of the disease. In 42 kidney grafts undergoing protocol biopsies at reperfusion, we estimated the renal metabolome from RNAseq data. The estimated metabolites' abundance was further used to predict the renal function within the first year of transplantation through a random forest machine learning algorithm. Using repeated K-fold cross-validation we first built and then tuned our model on a training dataset. The optimal model accurately predicted the one-year eGFR, with an out-of-bag root mean square root error (RMSE) that was 11.8 ± 7.2 mL/min/1.73 m2. The performance was similar in the test dataset, with a RMSE of 12.2 ± 3.2 mL/min/1.73 m2. This model outperformed classic statistical models. Reperfusion renal metabolome may be used to predict renal function one year after allograft kidney recipients.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

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

Perspectives in systems nephrology

Chronic kidney diseases (CKD) are a major health problem affecting approximately 10% of the world’s population and posing increasing challenges to the healthcare system. While CKD encompasses a broad spectrum of pathological processes and diverse etiologies, the classification of kidney disease is currently based on clinical findings or histopathological categorizations. This descriptive classification is agnostic towards the underlying disease mechanisms and has limited progress towards the ability to predict disease prognosis and treatment responses. To gain better insight into the complex and heterogeneous disease pathophysiology of CKD, a systems biology approach can be transformative. Rather than examining one factor or pathway at a time, as in the reductionist approach, with this strategy a broad spectrum of information is integrated, including comprehensive multi-omics data, clinical phenotypic information, and clinicopathological parameters. In recent years, rapid advances in mathematical, statistical, computational, and artificial intelligence methods enable the mapping of diverse big data sets. This holistic approach aims to identify the molecular basis of CKD subtypes as well as individual determinants of disease manifestation in a given patient. The emerging mechanism-based patient stratification and disease classification will lead to improved prognostic and predictive diagnostics and the discovery of novel molecular disease-specific therapies.

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.

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies

Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the 'thorough' removal of unwanted variations, the collective consideration of multiple criteria ('intragroup variation', 'marker stability' and 'classification capability') was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were 'first' discovered to be non-CWP. 'Then', 21 new strategies that combined the 'sample'-based method with the 'metabolite'-based one were found to be CWP. 'Finally', a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.

Metabolomics of the diabetic nephropathy: behind the fingerprint of development and progression indicators

Current diagnostic methods are not very sensitive to detect the initial stages diabetic nephropathy of type 2. In this work, a review of metabolomic approximation studies for the identification of biomarkers of this disease with potential to differentiate between early stages, evaluate and direct treatment and help slow kidney damage. Using public (Pubmed and Google Scholar) and private (Scopus and Web of Knowledge) databases, a systematic search of the information published related to metabolomics of diabetic nephropathy in different biospecimens (urine, serum, plasma and blood) was made. Later, the MetaboAnalyst 4.0 software was used to identify the metabolic pathways associated with these metabolites. Groups of potential metabolites were identified for monitoring diabetic nephropathy with the available literature data. In the urine, oxide-3-hydroxyisovalerate, TMAO, aconite and citrate and hydroxypropionate derivatives are highlighted; meanwhile, in the serum: citrate, creatinine, arginine and its derivatives; and in the plasma: amino acids such as histidine, methionine and arginine has a potential contribution. Using MetaboAnalyst 4.0 the metabolic pathways related to these metabolites were related. The search for biomarkers to measure the progression of diabetic nephropathy, together with analytical strategies for their detection and quantification, are the starting point for designing new methods of clinical chemistry analysis. The association between the metabolic pathway dysfunction could be useful for the overall assessment of the treatment and clinical follow-up of this disease.

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

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

Metabolomics Analysis Reveals the Protection Mechanism of Huangqi-Danshen Decoction on Adenine-Induced Chronic Kidney Disease in Rats

Huangqi-Danshen decoction (HDD) is a commonly used drug pair for clinical treatment of chronic kidney disease (CKD) in traditional Chinese medicine with good efficacy. However, the potential mechanisms of this action have not been well elucidated. The aim of this study was to explore the metabolic profiling variations in response to HDD treatment in a CKD rat model. CKD rat model was induced by adding 0.75% adenine to the diet for 4 weeks. The rats in the treatment group received HDD extract orally at the dose of 4.7 g/kg/day during the experiment. At the end of the experiment, serum and kidney samples were collected for biochemical and pathological examination. Ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS) was used to analyze metabolic profiling variations in the kidney. The results showed that treatment with HDD markedly attenuated kidney injury and improved renal function. A total of 28 metabolites contributing to CKD phenotype were found and identified in the kidney samples. The primary metabolic pathways disordered in the kidney of CKD rats were glycerophospholipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, and citrate cycle. Partial least squares discriminant analysis (PLS-DA) score plot showed that the three groups of renal samples were obviously divided into three categories, and the metabolic trajectory of the HDD treatment group moved to the control group. (E)-Piperolein A, phosphatidylcholines (PC) (18:1/22:6), phosphatidylinositols (PI) (13:0/18:1), PI (15:0/20:3), phosphatidylserines (PS) (O-20:0/12:0), and triglyceride (TG) (22:4/24:0/O-18:0) represented potential biomarkers of the renoprotective effects of HDD against CKD. In conclusion, HDD has renoprotective effect against adenine-induced CKD, which may be mediated via partially restoration of perturbed metabolism in the kidney.

Serum Metabolomic Alterations Associated with Proteinuria in CKD

BACKGROUND AND OBJECTIVES

Data are scarce on blood metabolite associations with proteinuria, a strong risk factor for adverse kidney outcomes. We sought to investigate associations of proteinuria with serum metabolites identified using untargeted profiling in populations with CKD.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Using stored serum samples from the African American Study of Kidney Disease and Hypertension (AASK; n=962) and the Modification of Diet in Renal Disease (MDRD) study (n=620), two rigorously conducted clinical trials with per-protocol measures of 24-hour proteinuria and GFR, we evaluated cross-sectional associations between urine protein-to-creatinine ratio and 637 known, nondrug metabolites, adjusting for key clinical covariables. Metabolites significantly associated with proteinuria were tested for associations with CKD progression.

RESULTS

In the AASK and the MDRD study, respectively, the median urine protein-to-creatinine ratio was 80 (interquartile range [IQR], 28-359) and 188 (IQR, 54-894) mg/g, mean age was 56 and 52 years, 39% and 38% were women, 100% and 7% were black, and median measured GFR was 48 (IQR, 35-57) and 28 (IQR, 18-39) ml/min per 1.73 m2. Linear regression identified 66 serum metabolites associated with proteinuria in one or both studies after Bonferroni correction (P<7.8×10-5), 58 of which were statistically significant in a meta-analysis (P<7.8×10-4). The metabolites with the lowest P values (P<10-27) were 4-hydroxychlorthalonil and 1,5-anhydroglucitol; all six quantified metabolites in the phosphatidylethanolamine pathway were also significant. Of the 58 metabolites associated with proteinuria, four were associated with ESKD in both the AASK and the MDRD study.

CONCLUSIONS

We identified 58 serum metabolites with cross-sectional associations with proteinuria, some of which were also associated with CKD progression.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2019_02_07_CJASNPodcast_19_03_.mp3.

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.

The incessant search for renal biomarkers: is it really justified?

PURPOSE OF REVIEW

This review summarizes the most recent and relevant findings in the search for novel biomarkers for the most common renal diseases.

RECENT FINDINGS

Unprecedented, fast-paced technical advances in biomedical research have offered an opportunity to identify novel and more specific renal biomarkers in several clinical settings. However, despite the huge efforts made, the molecules identified so far have generally failed to provide relevant information beyond what has already been generated by established biomarkers, such as serum creatinine and proteinuria, whereas the complexity and costs of these technology platforms hamper their widespread implementation.

SUMMARY

No novel renal biomarkers have added clear-cut additional value in clinical decision-making. The only exception is anti-phospholipase A2 receptor antibodies, which have been implemented successfully as a diagnostic and prognostic biomarker of membranous nephropathy. This achievement, along with the large number of ongoing collaborative projects worldwide, should lead the renal community to be quite confident regarding the successful qualification of novel and effective diagnostic, prognostic and therapeutic response biomarkers for kidney diseases, hopefully in the next few years.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

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