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The metabolomic quest for a biomarker in chronic kidney disease

The metabolomic quest for a biomarker in chronic kidney disease Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Clinical Kidney Journal, 2018, vol. 11, no. 5, 694–703 doi: 10.1093/ckj/sfy037 Advance Access Publication Date: 2 June 2018 CKJ Review CK J R EV IEW The metabolomic quest for a biomarker in chronic kidney disease Robert Davies School of Biomedical and Healthcare Sciences, University of Plymouth School of Biological Sciences, Plymouth, UK Correspondence and offprint requests to: Robert Davies; E-mail: robert.davies-8@students.plymouth.ac.uk ABSTRACT Chronic kidney disease (CKD) is a growing burden on people and on healthcare for which the diagnostics are niether disease-specific nor indicative of progression. Biomarkers are sought to enable clinicians to offer more appropriate patient- centred treatments, which could come to fruition by using a metabolomics approach. This mini-review highlights the current literature of metabolomics and CKD, and suggests additional factors that need to be considered in this quest for a biomarker, namely the diet and the gut microbiome, for more meaningful advances to be made. Keywords: biomarkers, chronic kidney disease, diet, metabolomics used as diagnostic tools but are invasive, and require skilled THE PROBLEM OF CHRONIC KIDNEY DISEASE professionals and resources to undertake [6]. Globally, chronic kidney disease (CKD) has increased by 36.9% be- Therefore, it would be beneficial to investigate other diag- tween 1990 and 2013 with increases in CKD due to diabetes by nostic measures to aid in further understanding CKD inception, 106.5%, hypertension by 29.4% and other causes by 58.8% [1]. progression and prognosis, to offer more suitable treatment Global CKD prevalence is increasing with different rates between options to patients and to advance and improve therapeutics countries, ethnicities and sexes, reflecting health inequalities, [7]. Indeed, in 2016, the International Society of Nephrology and even within these categories there are differences with re- identified key strategic points to enhance kidney-related re- spect to CKD aetiology [1–3]. search, of which diagnostic methods and CKD progression were Although estimated glomerular filtration rate (eGFR), albu- highlighted [6]. minuria and serum creatinine form part of the assessment As CKD is a condition of various aetiologies with complex net- along with clinical context and data [4, 5], there are limita- works of inter- and intra-molecular signalling, studies on CKD tions with the current diagnostic criteria. Estimation of GFR could utilize the ‘-omics’ approaches (Figure 1): genomics, tran- and creatinine is based on the Chronic Kidney Disease scriptomics, proteomics and metabolomics, which should enable Epidemiology Collaboration creatinine equation, which the clinician and researcher to have a better understanding of requires a correction factor for sex and those of African– the interconnecting genetic and molecular networks in CKD by Caribbean or African background [5] and is dependent on how the disease affects different body systems and responses to muscle mass; therefore, those who embody extremes of mus- stimuli such as diet, medication and the microbiome [8]. This cle mass such as bodybuilders, amputees and those with sar- mini-review will focus on contemporary human studies of CKD copenia or other muscle-wasting disorders may have utilizing a metabolomics approach published between 2016 and exaggerated and erroneous results. Kidney biopsies are also 2017. The research strategy for this involved reviewing relevant Received: 23.1.2018; Editorial decision: 16.4.2018 V C The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 694 Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 695 For metabolomics to be fruitful, metabolites need to be quantified. Biofluids and tissue samples can be used for metab- olomic analysis with technologies such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled with a preparatory chromatographic separation step of the sample such as capillary electrophoresis MS (CE-MS), liquid chromatography MS (LC-MS) or gas-chromatography (GC-MS) (Table 2). NMR is an analytical technique that uses magnetic fields to yield molecular information. MS is a method that measures the mass-to-charge ratio of an intact ion and tandem MS (MS/MS) can be used to measure selected isolated ions that are then fragmented, and the mass-to-charge ratio of each frag- ment is measured and used for analysis [28]. Both methods re- quire bioinformatic analysis for the data to be interpretable and meaningful. These methods provide information on identifica- tion and quantification of metabolites present in the sample. Additional factors such as sample preparation, sample matrix, and carryover effects should be considered when analysing and interpreting the data [24, 29]. For a more comprehensive review and analysis of metabolomic techniques and methodology con- sult references [24–29]. This progress in technology has enabled the identification of endogenous and exogenous metabolites as potential disease biomarkers, which could place personalized and precision patient-centred medicine within reach [30]. Metabolomics can be used to identify metabolites from a range of samples and for CKD the most pertinent are blood and urine [31], with the dialy- sate fluid also offering potential benefits. Blood As the current diagnostics for CKD are not indicative of disease progression, Rhee et al. [9] investigated progression of CKD FIGURE 1: Overview of ‘-omics’ approaches. within a CKD cohort stratifying by stable and rapid decline based on eGFR slope. This study incorporated a mix of ethnic literature using PubMed, Google Scholar and Plymouth backgrounds and CKD aetiologies reflecting the phenotype of University’s Library Primo databases for articles in English with CKD, and suggested lower levels of threonine, methionine and the following key words: ‘CKD’, ‘dialysis’, ‘metabolomics’, ‘bio- arginine as potential biomarkers of renal dysfunction by analy- marker’ used in various combinations. The bibliographies of sing plasma samples processed by LC-MS. In a study by Kimura identified articles with these key words were searched for addi- et al. [10] the authors aimed to identify prognostic biomarkers tional references. See Table 1 for a summary of included studies. for CKD progression and mortality in participants with CKD Stages 3–5 over a 4-year period. Plasma samples were processed using CE and LC-MS from which 16 metabolites were identified THE METABOLOMIC APPROACH with MasterHands software. These 16 metabolites were identi- For CKD, metabolomics may offer the best ‘-omics’ approach as fied as intrinsic to variable metabolic pathways including this involves examining the whole-body system by highlighting nucleotides, glycolysis and amino acids, with others unidenti- changes in metabolites from cellular processes evident in bodily fied. Medical history was noted including CKD aetiology and fluids [19, 20] demonstrating the phenotype of the disease. It is presence of comorbidities, and some medications were listed through metabolomics that a biomarker, or a panel of bio- but glycaemic agents were not. Furthermore, no changes in nu- markers, may be identified to ameliorate diagnosis and eluci- tritional status, dietary intake or weight were reported; there- date progression in those with CKD [7]. A new prognostic fore, it is unknown whether the identified metabolites could biomarker in CKD would not only be beneficial in enhancing arise from CKD or be derived from the diet, or gut microbiome patient-centred care and treatment management but also in as variation has been shown to occur both intra- and inter- elucidating the mechanisms by which the disease progresses individually in the blood metabolome largely due to dietary and how effective treatment is by monitoring the rate of change influences [32–34]. of the identified biomarker(s) [6]. Lee et al. [11] attempted to identify prognostic biomarkers The metabolomics approach has been applied to the study from blood serum comparing CKD patients with and without of various kidney diseases [21] but the discovery and implemen- diabetes, versus a healthy control group using NMR spectros- tation into clinical practice of specific disease biomarkers copy analysis. Participants were placed in groups based on remains elusive. The science of metabolomics has greatly ad- eGFR and diabetes diagnosis before enrolment, but the authors vanced due to the progress in technological developments in re- did not test participants in this study, which is a limitation as cent years, with better instrumentation and the ability to store, participants may have developed diabetes but have not yet analyse and share data with the concomitant development of been diagnosed. This study highlighted differences between bioinformatics and computational platforms [20, 22, 23]. healthy controls and the CKD groups with increases in Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 696 | R. Davies Table 1. Summary of metabolomic studies included in this mini-review Biological matrix Proposed metabolite Study population Metabolomic for metabolomic Bibliographic biomarkers group platform analysis Study outcome reference Uric acid, glucuronate, 200 rapidly declining LC-MS Plasma CKD progression Rhee et al. [9] 4-hydroxymandelate, eGFR, and 200 stable 3-methyladipate/pime- eGFR late, cytosine and homo- gentisate were higher in cases than in controls; threonine, methionine, phenylalanine and argi- nine were lower in cases than in controls Isethionate, saccharate, 112 participants with CE-MS Plasma Composite: predic- Kimura et al. [10] TMAO, 4-oxopentanoate, CKD Stages 3–5 not tive value for CKD cytidine, gluconate, glu- on dialysis at start progression to curonate, guanidinosuc- of study ESRF, requiring cinate, RRT, all-cause 2-hydroxyisobutyrate, death uridine, 5-oxoproline, pimelate, N-acetylneura- minate, 3-methylhisti- dine, citramalate, phthalate TMAO, creatinine, urea, glu- 291 pre-dialysis CKD NMR Serum Progression of CKD Lee et al. [11] cose, higher in CKD than patients with/with- healthy controls; argi- out type 2 diabetes nine, leucine, valine, glu- and 56 healthy tamine, tyrosine, controls pyruvate, citrate, acetate and formate decreased in CKD compared with healthy group C-Glycosyltryptophan, 158 patients with type GC-MS and LC-MS Serum eGFR decline and Niewczas et al. [12] pseudouridine, O-sulfo- 1 diabetes, protein- progression to tyrosine, N-acetylthreo- uria and CKD ESRF nine, N-acetylserine, Stage 3 N6-carbamoylthreonyla- denosine, N6-acetyllysine 4-Hydroxyphenylacetate, Discovery cohort of GC/LC-MS/MS Plasma Uraemic metabolites Tamura et al. [13] phenylacetylglutamine, 141 CKD patients on and impaired ex- hippurate and prolyl- dialysis and an in- ecutive function hydroxyproline dependent replica- tion cohort of 180 CKD patients on dialysis Kynurenine and its metabo- 27 CKD patients LC-MS/MS Serum Kidney function, Karu et al. [14] lites (quinolinic acid, tryptophan me- kynurenic acid, xanthur- tabolism, markers enic acid) and indoxyl for inflammation sulphate and oxidative stress, psychologi- cal/cognitive function Citrulline, dimethylamine, 15 patients with bi- NMR Urine Pathogenic path- Kalantari proline, acetoacetate, opsy-proven FSG ways and molecu- et al. [15] alphaketoisovaleric acid, lar changes in FSG valine, isobutyrate, disease D-Palmitylcarnitine, progression histidine and N-methylnicotinamide Urinary excretion rate of 27 First cohort: 22 non- GC-MS Plasma and Metabolic pathway Hallan et al. [16] metabolites and plasma diabetic CKD Stages urine analysis of CKD (continued) Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 697 Table 1. Continued Biological matrix Proposed metabolite Study population Metabolomic for metabolomic Bibliographic biomarkers group platform analysis Study outcome reference concentration of 33 3–4 and 10 healthy metabolites differed sig- adults. Second co- nificantly in CKD patients hort: 45 non-dia- versus controls. Citric betic CKD patients acid cycle was the most and 15 controls. significantly affected Additional 155 patients from the European Renal cDNA Bank cohort and 31 kidney biop- sies from healthy kidney transplant donors Significant differences in 80 ESRF haemodialysis LC-MS Plasma and Metabolic profile of Zhang et al. [17] concentration of 214 patients and 80 dialysate ESRF patients on metabolites between healthy controls dialysis healthy control and ESRF patients’ pre-dialysis plasma (126 increased and 88 reduced in ESRF group). Pre-dialysis ver- sus post-dialysis showed significant changes in 362 metabolites—including as yet unidentified metabolites TMAO and choline 80 controls and 179 LC-MS/MS Plasma TMAO, inflamma- Missailidis et al. [18] CKD Stages 3–5 tion and mortality patients in CKD patients FSG, focal segmental glomerulonephritis; RRT, renal replacement therapy. Table 2. Platforms for metabolomic analysis with possible advantages and disadvantages [24–27] Platform Advantages Disadvantages CE-MS Small sample volume Migration time variability High separation efficiency Poor concentration sensitivity High resolution Low sample loading capacity LC-MS Detects a large pool of metabolites Destructive of sample High sensitivity Time-consuming High resolution Sample preparation required GC-MS Wide dynamic range Requires thermal stability High resolution Destructive of sample High sensitivity Sample preparation required NMR Minimal sample preparation Low resolution Non-destructive of the sample Low sensitivity High reproducibility Expensive trimethylamine-N-oxide (TMAO), creatinine, urea and glucose be interpreted with caution. In another study on diabetes, that correlated with CKD progression. It was also shown that Niewczas et al. [12] monitored patients with CKD Stage 3 and levels of arginine, leucine, valine, glutamine, tyrosine, pyru- type 1 diabetes for a median of 11 years. Serum samples were vate, citrate, acetate and formate decreased in CKD patients analysed by Metabolon Inc. using GC-MS and LC-MS, and compared with the healthy group. However, these metabolites seven metabolites were identified that correlated with CKD are not specific to kidney disease and may be influenced by a progression. This study obtained repeat blood serum for myriad of other factors such as age, diet, nutritional status, metabolomic analysis, and urine samples for protein and renal medication, nutritional supplements and other diseases not function markers, which is a strength when investigating the accounted for in this study. Indeed, this study did not exclude progression of CKD. However, this study did not report on those with other CKD-associated conditions, namely hyper- medication use and may obfuscate the study results because tension and immune-related, subsequently, the results should an improvement in medication treatment regimens and Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 698 | R. Davies patient adherence to glycaemic, hypertensive and dyslipidae- plasma and dialysate effluent at regular timings during the dial- mic agents may slow the decline in kidney function, and hence ysis process but on a single occasion. The metabolome in the delay CKD progression [35]. plasma samples was compared with both groups using ultra Tamura et al.[13] sought to elucidate the impact of uraemic performance LC-MS and MetaboAnalyst 3.0 software, which metabolites on executive function in a cohort of dialysis showed that the haemodialysis process not only removed, as patients by using GC/LC-MS/MS in pre-dialysis plasma samples expected, uraemic products (TMAO, indoxyl sulphate, p-cresol and analysis performed by Metabolon Inc. Four metabolites sulphate, p-cresol glucuronide, uric acid and hippuric acid), were associated with impaired executive function in those fluid and excessive electrolytes, but a plethora of metabolites— with CKD: 4-hydroxyphenylacetate, phenylacetylglutamine, mainly amino acids (arginine, glutamine, alanine and phenylal- hippurate and prolyl-hydroxyproline. However, these metabo- anine) and lipids, which the authors concluded may be the lites can be derived from the diet and gut microbial metabolism, cause of increased mortality within the CKD population. These which this study did not investigate [36–39]. Furthermore, cog- changes in metabolites were also identified in the dialysate ef- nitive impairment could result from other confounding factors fluent when measured at the corresponding time intervals. in this study such as age, frailty, hypertension, incidence of However, the authors did not include information on the medi- neurological disorders and cardiovascular factors [40–46] rather cal comorbities or medications, CKD aetiology of the disease than the metabolites identified. In a similar study into cognitive group, or whether those in this group had any residual kidney decline, Karu et al. [14] identified kynurenine and its metabolites function. Although for the control group hypertension, cardio- (quinolinic acid, kynurenic acid, xanthurenic acid), and indoxyl vascular disease and diabetes were exclusion criteria, no indica- sulphate as being greatly elevated in CKD patients, especially in tion is given of diabetes prevalence within the ESRF group, those with cognitive impairment, compared with healthy con- which could have implications for interpreting the study’s trols. The proposed mechanism for this is that tryptophan is in- results. volved in the synthesis of the indoxyl sulphate (uraemic toxin) via colonic microbes [47, 48] and can affect brain activity DIET AND THE MICROBIOME—THE MISSING through the kynurenine pathway. However, the same con- LINKS? founding factors are attributable to this study as were for Tamura et al. [13], and hypertension was documented in 78% of Diet and nutritional status CKD participants in Karu et al. [14]; therefore, the cognitive de- Changes in amino acid metabolism are widely seen in those cline may be as a result of hypertension or exacerbated by the with CKD and on dialysis [9, 10, 17, 52, 53] but whether this is co-presence of accumulating uraemic toxins and hypertension. due to CKD progression, other diseases or concomitant with poor nutritional status and dietary protein intake remains elu- Urine sive, compounded by the fact that very few studies that include Metabolomic urinary analysis in CKD could be useful as it is an assessment of nutritional status or dietary intake. Diet is an non-invasive, easily obtained and provides a global state of important factor that should be assessed as those who display physiological function. However, caution should be employed malnutrition and protein-energy malnutrition have worse out- as there is evidence to suggest that urinary metabolites fluctu- comes and early mortality in CKD and on dialysis [54–56]. ate throughout the day suggesting vigilance should be taken Utilizing a subjective global assessment (SGA) tool will enable when interpreting results from such studies [49, 50]. Kalantari the clinician and researcher to understand and appreciate et al. [15] collected urine samples over 24 h from 15 patients with whether the metabolites identified result from nutritional sta- focal segmental glomerulosclerosis to identify 10 metabolites tus, dietary intake or from disease [57–59]. using NMR spectroscopy and ProMetab software, that were Consideration should also be made of dietary regimes as these deemed to be prognostic when compared with kidney biopsy can have influence over the metabolome and microbiome compo- results. This study implemented a diet on its participants for sition [60–62], such as differences between vegans, vegetarians, 24 h prior to collecting urine as a mitigating measure to control pescatarians and carnivores. In Wu et al. [63], healthy vegans con- for dietary influences on the urinary metabolome. However, sumed more carbohydrates, but less protein and fat, than healthy urine samples were collected in 2011 but no information is omnivores, resulting in a 25% difference between the identified given on how the samples were stored nor when the samples metabolites of omnivores and vegans of which lipid and amino were processed, which could limit the reliability of these results acid metabolites were significantly elevated in omnivores and the [50, 51]. Hallan et al. [16] used GC-MS and MetaboAnalyst 3.0 metabolites often associated with CKD hippurate, catechol sul- software on urinary samples in non-diabetic CKD patients phate and 3-hydroxyhippurate were increased in vegans com- showing decreased excretion of citric acid cycle metabolites cor- pared with omnivores. It is, therefore, necessary to account for roborated with analysis of kidney biopsies, showing a reduction differences in dietary intake when assessing the metabolites iden- in gene expression for citric acid cycle enzymes. These findings, tified in CKD patients as what could have been considered to be a however, could be accounted for by considering the nutritional potential biomarker may be derived from or greatly influenced by status and dietary intake of these participants, which this study factors other than kidney disease. Furthermore, it would also be did not do. informative to collect multiple samples across time-points for metabolomic analysis of dietary intake to understand how the Dialysate metabolome changes especially for amino acids [32, 64, 65]inCKD patients. Indeed, current suggested dietary protein requirements The only currently identified study that applied metabolomics to the effect of haemodialysis on the metabolome and dialysate for CKD patients are contentious and vary globally from 0.55 g/kg effluent was by Zhang et al. [17]. In this study, end-stage renal to 1g/kg[66–71] with greater requirements for those on dialysis, failure (ESRF) patients receiving dialysis were compared with 1.1–1.4 g/kg [58, 72], which may impact on the levels of amino matched heathy controls with samples collected from blood acids and uraemic toxins seen in these metabolomic CKD studies. Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 699 Microbiome was associated with intakes of coffee, fruit and wholegrains; and p-cresol sulphate and phenylacetylglutamine from the pu- Combining the study of diet and the microbiome in CKD studies trefaction of undigested dietary proteins by colonic bacteria. with metabolomics would enable elucidation of these complex Furthermore, Lees et al. [36] suggested that hippurate excretion and interwoven relationships, especially for TMAO and uraemic is associated with co-excretion of metabolic intermediates, es- toxins [73, 74]. Phenylacetylglutamine is associated with levels pecially citrate, succinate and 2-oxoglutarate, and has been as- of p-cresyl sulphate and indoxyl sulphate in CKD patients not sociated with a range of conditions besides kidney disease yet on dialysis and is considered to be a risk factor for cardio- including liver disease, hypertension, diabetes, atherosclerosis vascular disease and mortality [39], but whether it is as a result and psychiatric disorders, but is also dependent on intestinal of gut microbiome dysbiosis or due to impaired renal function is microbiota diversity. Diversity and abundance of human micro- yet to be elucidated. TMAO is derived from the gut microbiota biome varies widely even among healthy subjects and impor- and L-carnitine and choline precursors derived from dietary in- tant factors such as diet need to be considered due to its effect take of meat and eggs, and p-cresyl sulphate and sulphate are on microbiome composition and metabolism, and wider effects uraemic toxins derived from the metabolism of amino acids by on health status and disease [20, 83–85]. Furthermore, dietary commensal gut microbiota, consequently greatly influenced by advice given to those with CKD and on dialysis may negatively dietary intake [62, 75], and TMAO is implicated in greater mor- impact the microbiome of the kidney–gut axis due to reducing tality amongst those with CKD concomitant with progressing the ability to produce beneficial short-chain fatty acids [86, 87] impaired renal function [76] and increased cardiovascular as fruit and vegetable consumption is rationed to prevent elec- events [77]. Missailidis et al. [18] assessed plasma samples from those with CKD Stages 3–5 from various aetiologies, comorbid- trolyte derangement [57, 86, 88]. Short-chain fatty acids are ities and nutritional status using SGA, but lacked an assessment thought to be implicated in CKD through their deleterious de- of dietary intake. TMAO increased as CKD stage progressed and pletion and consequential effect on increasing oxidative stress, was associated with greater mortality in a 5-year follow-up. fibrosis and the immune response [86, 89]. Utilizing faecal sam- However, the nutritional status score also increased with pro- ples to assess the microbiome may offer further insights in the gressive CKD, which may have a more negative impact on mor- pathological progression of CKD [31] and provide potential dys- tality than the presence of TMAO. CKD patients had higher biotic targets for treatment of CKD [73]. TMAO levels than controls, which continued to increase as re- nal function declined. When a study participant received a kid- CONSIDERATIONS FOR FURTHER CKD ney transplant, TMAO levels decreased and nutritional status METABOLOMICS improved. It was demonstrated that CKD patients with the highest TMAO levels had a significantly lower survival rate, Single biomarker which the authors deemed to suggest that high levels of TMAO As studies incorporate metabolomics into their methodology to predicted reduced 5-year survival. Although this study did con- identify potential biomarkers, it should also be embedded how sider nutritional status by utilizing the SGA tool examining to evaluate the clinical usefulness of these biomarkers, to eluci- weight loss, anorexia and vomiting, muscle wasting, oedema date to what degree they can be used in clinical care, drug de- and loss of fat mass, it did not consider dietary intake or the velopment and therapy, and, ultimately, point-of-care testing microbiome, which can both have an impact on TMAO levels devices [30, 90]. A single biomarker may be elusive, but a panel [62, 73–75]. Furthermore, it could have collected faecal samples of biomarkers based on ratios of identified altered metabolites to assess the gut microbiome and its influence on TMAO levels may offer potential benefits [47] such as glutamate:glutamine, [31]. Stubbs et al. [78] also assessed the impact of TMAO on CKD and demonstrated the beneficial effect of transplant on de- which may indicate nervous system disorders and energy dys- creased levels of TMAO compared with pre-transplant, there- metabolism in uraemic patients, and tryptophan:kynurenine, which may indicate immune responses and increased athero- fore suggesting that increased levels of TMAO are a consequence of decreased renal function and urinary excretion. sclerosis risk in uraemic patients [53]. It would have been advantageous to assess levels of TMAO in conjunction with an assessment of dietary intake for a more Samples comprehensive investigation of the relationship between Results from metabolomic studies are not always reproducible TMAO, CKD, diet and the microbiome [79]. Stubbs et al.[78] did due to differences in patient demographics, samples used, not comment on the effect of diet and Missailidis et al. [18] con- methodology and computational analysis [20, 23, 91–94]. cluded that dietary changes could not explain the normalized Studies should report on when samples are taken and what pre- levels of TMAO after kidney transplant; however, it is not docu- sampling checks have been done to limit variability, as well as mented whether the participants receiving the transplant were on the time between sample acquisition and sample processing, urinating [80, 81] as this would allow TMAO levels to decrease because this may increase the possibility of metabolite degrada- due to it being excreted in the urine [82]. It remains unclear if tion and yielding false-positive results [50, 93, 95–99]. Each pa- TMAO can be used as a biomarker in CKD and cardiovascular tient is an individual, and each has their own individual dysfunction as it may just be a marker of poor renal clearance metabolic phenotype that is subject to dynamic daily changes or poor nutritional status; therefore, TMAO should be monitored due to diet and diet–microbiome interactions [32–34, 49]. It is in those with CKD along with an assessment of dietary intake problematic when studies rely on a single sample from which to and gut microbiome to further elucidate this mechanism and extrapolate prognostic markers with hindsight, and studies potential biomarker. seeking to investigate prognostic questions should have at least Very few studies have been identified that incorporate the study of the metabolome with dietary and microbiome consid- two measurements over the study time period in order to moni- erations. Pallister et al. [38] identified that metabolites are influ- tor dynamic changes and allow for more meaningful interpreta- enced by microbiome diversity, particularly hippurate, which tion of the data [90]. Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 700 | R. Davies The other ‘-omics’ 3. Carrero J-J, Hecking M, Ulasi I et al. Chronic kidney disease, gender, and access to care: a global perspective. Semin Current limitations with metabolomics in CKD studies stem Nephrol 2017; 37: 296–308 from the inability to identify all metabolites in the metabolome 4. Kellum JA, Lameire N, Aspelin P et al. Kidney Disease: and concomitant lack of overlap of metabolite coverage in com- Improving Global Outcomes (KDIGO) acute kidney injury parable studies and validation [100]. Corroborating identified work group. KDIGO clinical practice guideline for acute kid- metabolites with other physiological functions would make the ney injury. Kidney Int Suppl 2013; 2: 1–138 results more robust and may offer great benefits in ascertaining 5. NICE. Chronic Kidney Disease in Adults: Assessment and phenotypical data for an individual patient [16, 101], which will Management j Clinical Guideline [CG182] 2015. https://www. probably become even more useful with advances in technology nice.org.uk/guidance/cg182 (28 November 2017, date last and the ability to have wider coverage. Genetics coupled with accessed) metabolomics could provide valuable information on an indi- 6. Levin A, Tonelli M, Bonventre J et al. Global kidney health vidual’s metabolic profile, coined as metabotype, which has 2017 and beyond: a roadmap for closing gaps in care, re- been demonstrated in genome-wide association studies (GWAS) search, and policy. Lancet 2017; 390: 1888–1917 through the identification of genetic variation and its effect on 7. Hocher B, Adamski J. Metabolomics for clinical use and re- search in chronic kidney disease. Nat Rev Nephrol 2017; 13: metabolic functionality [19, 102–105]. GWAS data sets can be utilized by researchers to enrich the study of metabolic dysregu- 8. Trivedi DK, Hollywood KA, Goodacre R. Metabolomics for lation and be applied to metabolomic studies of CKD, but such the masses: the future of metabolomics in a personalized studies are currently lacking [106]. world. New Horiz Transl Med 2017; 3: 294–305 Advancement of metabolomics, and the wider ‘-omics’ fam- 9. Rhee EP, Clish CB, Wenger J et al. Metabolomics of chronic ily, requires a collaborative effort to share and store metabolite kidney disease progression: a case-control analysis in the data such as the CKDdb database [100]. Once the technology is chronic renal insufficiency cohort study. Am J Nephrol 2016; available, it could conceivably progress to readily available 43: 366–374 point-of-care devices such as lateral flow devices, dipsticks, 10. Kimura T, Yasuda K, Yamamoto R et al. Identification of breath testing and wearable technology utilizing biosensors and biomarkers for development of end-stage kidney disease in chemometric-based analyses [8] to monitor for disease incep- chronic kidney disease by metabolomic profiling. Sci Rep tion, progression and prognosis, with additional benefits arising 2016; 6: 26138 from measuring dietary and microbiome influences [107]. 11. Lee J, Choi J-Y, Kwon Y-K et al. Changes in serum metabo- lites with the stage of chronic kidney disease: comparison of diabetes and non-diabetes. Clin Chim Acta 2016; 459: CONCLUSIONS 123–131 This mini-review has highlighted the current need for better di- 12. Niewczas MA, Mathew AV, Croall S et al. Circulating modi- agnostic and prognostic markers for CKD. Further studies on fied metabolites and a risk of esrd in patients with type 1 di- CKD should utilize the metabolomic approach, but also examine abetes and chronic kidney disease. Diabetes Care 2017; 40: the diets and microbiome of the individual participants with 383–390 13. Tamura MK, Chertow GM, Depner TA et al. Metabolic profil- CKD. Multiple samples should be taken over a pre-determined ing of impaired cognitive function in patients receiving di- time period and assessed for changes in the metabolome and alysis. J Am Soc Nephrol 2016; 27: 3780–3787 cross-referenced with the CKD phenotype. Studies should also 14. Karu N, McKercher C, Nichols DS et al. Tryptophan metabo- stratify patients based on their ethnicity, sex and CKD aetiology, lism, its relation to inflammation and stress markers and and perform further analysis based on nutritional status, die- association with psychological and cognitive functioning: tary intake and on the microbiome particularly to elucidate the Tasmanian Chronic Kidney Disease pilot study. BMC interconnectedness of amino acid metabolism, uraemic toxins, Nephrol 2016; 17: 171 dietary factors and the gut microbiome. This approach would 15. Kalantari S, Nafar M, Samavat S et al. NMR-based metabolo- strengthen the research output on CKD fostering greater under- mics exploring urinary biomarkers correlated with protein- standing of how metabolites change, and through what influen- uria in focal segmental glomerulosclerosis: a pilot study: ces, so that biomarkers for CKD inception, prognosis and urine metabolomics based on NMR. Magn Reson Chem 2016; prognostics may be identified. 54: 821–826 16. Hallan S, Afkarian M, Zelnick LR et al. Metabolomics and gene expression analysis reveal down-regulation of the cit- CONFLICT OF INTEREST STATEMENT ric acid (TCA) cycle in non-diabetic CKD patients. EBioMedi- None declared. cine 2017; 26: 68–77 17. Zhang Z-H, Mao J-R, Chen H et al. Removal of uremic reten- tion products by hemodialysis is coupled with indiscrimi- REFERENCES nate loss of vital metabolites. Clin Biochem 2017; 50: 1. Abubakar II, Tillmann T, Banerjee A. Global, regional, and 1078–1086 national age-sex specific all-cause and cause-specific mor- 18. Missailidis C, Ha ¨ llqvist J, Qureshi AR et al. Serum tality for 240 causes of death, 1990-2013: a systematic trimethylamine-n-oxide is strongly related to renal func- analysis for the Global Burden of Disease Study 2013. Lancet tion and predicts outcome in chronic kidney disease. PLoS 2015; 385: 117–171 ONE 2016; 11: e0141738 2. Hill NR, Fatoba ST, Oke JL et al. Global prevalence of chronic 19. Atzler D, Schwedhelm E, Zeller T. Integrated genomics and kidney disease – a systematic review and meta-analysis. metabolomics in nephrology. Nephrol Dial Transplant 2014; PLoS ONE 2016; 11: e0158765 29: 1467–1474 Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 701 39. Poesen R, Claes K, Evenepoel P et al. Microbiota-derived 20. Beger RD, Dunn W, Schmidt MA et al. Metabolomics enables precision medicine: ‘A White Paper, Community phenylacetylglutamine associates with overall mortality Perspective’. Metabolomics 2016; 12: 149 and cardiovascular disease in patients with CKD. J Am Soc Nephrol 2016; 27: 3479–3487 21. Weiss RH, Kim K. Metabolomics in the study of kidney dis- eases. Nat Rev Nephrol 2011; 8: 22–33 40. Tamura M, Covinsky KE, Chertow GM et al. Functional sta- tus of elderly adults before and after initiation of dialysis. 22. Cisek K, Krochmal M, Klein J et al. The application of multi- N Engl J Med 2009; 361: 1539–1547 omics and systems biology to identify therapeutic targets 41. Tamura MK, Meyer JB, Saxena AB et al. Prevalence and sig- in chronic kidney disease. Nephrol Dial Transplant 2016; 31: nificance of stroke symptoms among patients receiving 2003–2011 maintenance dialysis. Neurology 2012; 79: 981–987 23. Meier R, Ruttkies C, Treutler H et al. Bioinformatics can 42. Bugnicourt J-M, Godefroy O, Chillon J-M et al. Cognitive dis- boost metabolomics research. J Biotechnol 2017; 261: 137–141 orders and dementia in CKD: the neglected kidney-brain 24. Aretz I, Meierhofer D. Advantages and pitfalls of mass spec- axis. J Am Soc Nephrol 2013; 24: 353–363 trometry based metabolome profiling in systems biology. 43. McAdams-DeMarco MA, Tan J, Salter ML. Frailty and cogni- Int J Mol Sci 2016; 17: 632 tive function in incident hemodialysis patients. Clin J Am 25. Lindon JC. Overview of NMR-based metabonomics. In: Soc Nephrol 2015; 10: 2181–2189 Lindon JC, Tranter GE, Koppenaal DW (eds). Encyclopedia of 44. Kallenberg MH, Kleinveld HA, Dekker FW. Functional and Spectroscopy and Spectrometry, 3rd edn. Oxford: Academic cognitive impairment, frailty, and adverse health outcomes Press, 2017, 517–526 in older patients reaching esrd–a systematic review. Clin J 26. Nassar AF, Wu T, Nassar SF et al. UPLC–MS for metabolo- Am Soc Nephrol 2016; 11: 1624–1639 mics: a giant step forward in support of pharmaceutical re- 45. Iyasere O, Okai D, Brown E. Cognitive function and ad- search. Drug Discov Today 2017; 22: 463–470 vanced kidney disease: longitudinal trends and impact on 27. Ramautar R, Somsen GW, de Jong GJ. CE–MS for metabolo- decision-making. Clin Kidney J 2017; 10: 89–94 mics: developments and applications in the period 2014– 46. Shirazian S, Grant CD, Aina O. Depression in chronic kidney 2016. Electrophoresis 2017; 38: 190–202 disease and end-stage renal disease: similarities and differ- 28. Patti GJ, Yanes O, Siuzdak G. Metabolomics: the apogee of ences in diagnosis, epidemiology, and management. Kidney the omics trilogy: Innovation. Nat Rev Mol Cell Biol 2012; 13: Int Rep 2017; 2: 94–107 263–269 47. Breit M, Weinberger KM. Metabolic biomarkers for chronic 29. Gowda GAN, Djukovic D. Overview of mass spectrometry- kidney disease. Appl Metabolomics 2016; 589: 62–80 based metabolomics: opportunities and challenges. In: 48. Leong S, Sirich T. Indoxyl sulfate—review of toxicity and Raftery D (ed). Mass Spectrometry in Metabolomics. New York, therapeutic strategies. Toxins 2016; 8: 358 NY: Springer, 2014, 3–12 49. Heinzmann SS, Merrifield CA, Rezzi S et al. Stability and ro- 30. Wishart DS. Emerging applications of metabolomics in bustness of human metabolic phenotypes in response to drug discovery and precision medicine. Nat Rev Drug Discov sequential food challenges. J Proteome Res 2012; 11: 643–655 2016; 15: 473 50. Budde K, Go ¨k O-N, Pietzner M et al. Quality assurance in the 31. Barrios C, Spector TD, Menni C. Blood, urine and faecal me- pre-analytical phase of human urine samples by 1H NMR tabolite profiles in the study of adult renal disease. Arch spectroscopy. Arch Biochem Biophys 2016; 589: 10–17 Biochem Biophys 2016; 589: 81–92 51. Capati A, Ijare OB, Bezabeh T. Diagnostic applications of nu- 32. Kim K, Mall C, Taylor SL et al. Mealtime, temporal, and daily clear magnetic resonance–based urinary metabolomics. variability of the human urinary and plasma metabolomes Magn Reson Insights 2017; 10: 1178623X17694346. https://www. in a tightly controlled environment. PLoS ONE 2014; 9: ncbi.nlm.nih.gov/pmc/articles/PMC5428226/ (13 December e86223 2017) 33. Dunn WB, Lin W, Broadhurst D et al. Molecular phenotyping 52. Duranton F, Lundin U, Gayrard N et al. Plasma and urinary of a UK population: defining the human serum metabo- amino acid metabolomic profiling in patients with different lome. Metabolomics 2015; 11: 9–26 levels of kidney function. Clin J Am Soc Nephrol 2014; 9: 37–45 34. Chaleckis R, Murakami I, Takada J et al. Individual variabil- 53. Liu S, Wang L, Hu C et al. Plasma metabolomics ity in human blood metabolites identifies age-related dif- profiling of maintenance hemodialysis based on capillary ferences. Proc Natl Acad Sci USA 2016; 113: 4252–4259 electrophoresis—time of flight mass spectrometry. Sci Rep 35. Aitken GR, Roderick PJ, Fraser S et al. Change in prevalence 2017; 7: 8150. doi: 10.1038/s41598-017-08327-w of chronic kidney disease in England over time: comparison 54. Magalhaes LP, dos Reis LM, Graciolli FG et al. Predictive fac- of nationally representative cross-sectional surveys from tors of one-year mortality in a cohort of patients undergo- 2003 to 2010. BMJ Open 2014; 4: e005480. doi: ing urgent-start hemodialysis. PLoS ONE 2017; 12: e0167895 10.1136/bmjopen-2014-005480 55. Sabatino A, Regolisti G, Karupaiah T et al. Protein-energy 36. Lees HJ, Swann JR, Wilson ID et al. Hippurate: the natural wasting and nutritional supplementation in patients with history of a mammalian–microbial cometabolite. J Proteome end-stage renal disease on hemodialysis. Clin Nutr 2017; 36: Res 2013; 12: 1527–1546 663–671 37. Va ´ zquez-Fresno R, Llorach R, Perera A et al. Clinical pheno- 56. Zha Y, Qian Q. Protein nutrition and malnutrition in CKD type clustering in cardiovascular risk patients for the iden- and ESRD. Nutrients 2017; 9: 208 tification of responsive metabotypes after red wine 57. Wright M, Jones C. Renal association clinical practice guide- polyphenol intake. J Nutr Biochem 2016; 28: 114–120 line on nutrition in CKD. Nephron Clin Pract 2011; 118: 38. Pallister T, Jackson MA, Martin TC et al. Hippurate as a c153–c164 metabolomic marker of gut microbiome diversity: modula- 58. Naylor HL, Jackson H, Walker GH et al. British Dietetic tion by diet and relationship to metabolic syndrome. Sci Rep Association evidence-based guidelines for the protein 2017; 7: 13670. doi: 10.1038/s41598-017-13722-4 requirements of adults undergoing maintenance Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 702 | R. Davies Metabolomics results from a randomized crossover inter- haemodialysis or peritoneal dialysis. J Hum Nutr Diet 2013; 26: 315–328 vention study. Mol Nutr Food Res 2016; 60: 1661–1672 59. Santin F, Rodrigues J, Brito FB et al. Performance of subjec- 76. Gruppen EG, Garcia E, Connelly MA et al. TMAO is associated with mortality: impact of modestly impaired tive global assessment and malnutrition inflammation score for monitoring the nutritional status of older adults renal function. Sci Rep 2017; 7: 13781. doi: 10.1038/ s41598-017-13739-9 on hemodialysis. Clin Nutr 2018; 37: 604–611 77. Kim RB, Morse BL, Djurdjev O et al. Advanced chronic kid- 60. Poesen R, Mutsaers HAM, Windey K et al. The influence of ney disease populations have elevated trimethylamine dietary protein intake on mammalian tryptophan and phe- N-oxide levels associated with increased cardiovascular nolic metabolites. PLoS ONE 2015; 10: e0140820 events. Kidney Int 2016; 89: 1144–1152 61. Schmidt JA, Rinaldi S, Scalbert A et al. Plasma concentra- 78. Stubbs JR, House JA, Ocque AJ et al. Serum trimethylamine- tions and intakes of amino acids in male meat-eaters, fish- n-oxide is elevated in ckd and correlates with coronary ath- eaters, vegetarians and vegans: a cross-sectional analysis erosclerosis burden. J Am Soc Nephrol 2016; 27: 305–313 in the EPIC-Oxford cohort. Eur J Clin Nutr 2016; 70: 306–312 79. Aron-Wisnewsky J, Cle ´ ment K. The gut microbiome, diet, 62. Cheung W, Keski-Rahkonen P, Assi N et al. A metabolomic and links to cardiometabolic and chronic disorders. Nat Rev study of biomarkers of meat and fish intake. Am J Clin Nutr Nephrol 2015; 12: 169 2017; 105: 600–608 80. Khosroshahi HT, Oskui R, Shoja MM et al. Time-dependent 63. Wu GD, Compher C, Chen EZ et al. Comparative metabolo- variations in urine output after renal transplantation. mics in vegans and omnivores reveal constraints on diet- Transplant Proc 2007; 39: 932–933 dependent gut microbiota metabolite production. Gut 2016; 81. Lai Q, Pretagostini R, Poli L et al. Early urine output predicts 65: 63–72 graft survival after kidney transplantation. Transplant Proc 64. Andersen M-BS, Rinnan A, Manach C et al. Untargeted 2010; 42: 1090–1092 metabolomics as a screening tool for estimating compli- 82. Moraes C, Fouque D, Amaral ACF et al. Trimethylamine ance to a dietary pattern. J Proteome Res 2014; 13: 1405–1418 n-oxide from gut microbiota in chronic kidney disease 65. Scalbert A, Brennan L, Manach C et al. The food metabo- patients: focus on diet. J Ren Nutr 2015; 25: 459–465 lome: a window over dietary exposure. Am J Clin Nutr 2014; 83. Huttenhower C, Gevers D, Knight R et al. Structure, function 99: 1286–1308 and diversity of the healthy human microbiome. Nature 66. Fouque D, Laville M. Low protein diets for chronic kidney 2012; 486: 207–214 disease in non diabetic adults. In: The Cochrane 84. Hall AB, Tolonen AC, Xavier RJ. Human genetic variation Collaboration (ed). Cochrane Database of Systematic Reviews. and the gut microbiome in disease. Nat Rev Genet 2017; 18: Chichester: John Wiley & Sons, Ltd, 2009; doi: 690–699 10.1002/14651858.CD001892.pub3 85. Mendes-Soares H, Chia N. Community metabolic modelling 67. Kovesdy CP, Kopple JD, Kalantar-Zadeh K. Management of approaches to understanding the gut microbiome: bridging protein-energy wasting in non-dialysis-dependent chronic biochemistry and ecology. Free Radic Biol Med 2017; 105: kidney disease: reconciling low protein intake with nutri- 102–109 tional therapy. Am J Clin Nutr 2013; 97: 1163–1177 86. Felizardo RJF, Castoldi A, Andrade-Oliveira V et al. The 68. Piccoli GB, Vigotti FN, Leone F et al. Low-protein diets in microbiota and chronic kidney diseases: a double-edged CKD: how can we achieve them? A narrative, pragmatic re- sword. Clin Transl Immunol 2016; 5: e86 view. Clin Kidney J 2015; 8: 61–70 87. Rı ´os-Covia ´ n D, Ruas-Madiedo P, Margolles A et al. Intestinal 69. Riccio E, Di Nuzzi A, Pisani A. Nutritional treatment in short chain fatty acids and their link with diet and human chronic kidney disease: the concept of nephroprotection. health. Front Microbiol 2016; 7: 185 Clin Exp Nephrol 2015; 19: 161–167 88. Mitch WE, Remuzzi G. Diets for patients with chronic kid- 70. Bellizzi V, Cupisti A, Locatelli F et al. Low-protein diets for ney disease, should we reconsider? BMC Nephrol 2016; 17: chronic kidney disease patients: the Italian experience. 80. doi: 10.1186/s12882-016-0283-x BMC Nephrol 2016; 17: 77 89. Li L, Ma L, Fu P. Gut microbiota–derived short-chain fatty 71. Di Iorio B, Di Micco L, Marzocco S et al. Very low-protein diet acids and kidney diseases. Drug Des Devel Ther 2017; 11: (VLPD) reduces metabolic acidosis in subjects with chronic 3531–3542 kidney disease: the “nutritional light signal” of the renal 90. Ravani P, Parfrey PS, Dicks E et al. Clinical research of acid load. Nutrients 2017; 9: 69 kidney diseases II: problems of study design. Nephrol Dial 72. Cano N, Fiaccadori E, Tesinsky P et al. ESPEN guidelines on Transplant 2007; 22: 2785–2794 enteral nutrition: adult renal failure. Clin Nutr 2006; 25: 91. Everett JR. A new paradigm for known metabolite identifi- 295–310 cation in metabonomics/metabolomics: metabolite identi- 73. Zhang LS, Davies SS. Microbial metabolism of dietary com- fication efficiency. Comput Struct Biotechnol J 2015; 13: ponents to bioactive metabolites: opportunities for new 131–144 therapeutic interventions. Genome Med 2016; 8: 46. http:// 92. Darshi M, Van Espen B, Sharma K. Metabolomics in diabetic genomemedicine.biomedcentral.com/articles/10.1186/ kidney disease: unraveling the biochemistry of a silent s13073-016-0296-x (28 November 2017, date last accessed) killer. Am J Nephrol 2016; 44: 92–103 74. Fernandez-Prado RE, Perez-Gomez M, Gracia-Iguacel C et al. 93. Emwas A-H, Roy R, McKay RT et al. Recommendations and Nutrients turned into toxins: microbiota modulation of nu- standardization of biomarker quantification using nmr- trient properties in chronic kidney disease. Nutrients 2017; based metabolomics with particular focus on urinary 9: 489 analysis. J Proteome Res 2016; 15: 360–373 75. Schmedes M, Aadland EK, Sundekilde UK et al. Lean-sea- 94. Hajduk J, Matysiak J, Kokot ZJ. Challenges in biomarker food intake decreases urinary markers of mitochondrial discovery with MALDI-TOF MS. Clin Chim Acta 2016; 458: lipid and energy metabolism in healthy subjects: 84–98 Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 703 metabolite dysfunction in patients with chronic kidney dis- 95. Townsend MK, Clish CB, Kraft P. Reproducibility of metabo- lomic profiles among men and women in 2 large cohort ease. Redox Biol 2017; 12: 505–521 studies. Clin Chem 2013; 59: 1657–1667 102. Illig T, Gieger C, Zhai G et al. A genome-wide perspective of 96. Breier M, Wahl S, Prehn C et al. Targeted metabolomics genetic variation in human metabolism. Nat Genet 2010; 42: identifies reliable and stable metabolites in human serum 137–141 and plasma samples. PLoS ONE 2014; 9: e89728 103. Suhre K, Shin S-Y, Petersen A-K Human metabolic individ- 97. Carayol M, Licaj I, Achaintre D et al. Reliability of serum uality in biomedical and pharmaceutical research. Nature metabolites over a two-year period: a targeted metabolo- 2011; 477: 54–60 mic approach in fasting and non-fasting samples from 104. Kastenmu ¨ ller G, Raffler J, Gieger C et al. Genetics of human EPIC. PLoS ONE 2015; 10: e0135437 metabolism: an update. Hum Mol Genet 2015; 24: R93–R101 98. Yin P, Lehmann R, Xu G. Effects of pre-analytical processes 105. Raffler J, Friedrich N, Arnold M et al. Genome-wide association on blood samples used in metabolomics studies. Anal study with targeted and non-targeted NMR metabolomics Bioanal Chem 2015; 407: 4879–4892 identifies 15 novel loci of urinary human metabolic individu- 99. Jobard E, Tre ´ dan O, Postoly D et al. A systematic evaluation ality. PLoS Genet 2015; 11: e1005487 106. Ko ¨ ttgen A, Raffler J, Sekula P et al. Genome-wide association of blood serum and plasma pre-analytics for metabolomics cohort studies. Int J Mol Sci 2016; 17: 2035 studies of metabolite concentrations (mGWAS): relevance 100. Fernandes M, Husi H. Establishment of a integrative multi- for nephrology. Semin Nephrol 2018; 38: 151–174 omics expression database CKDdb in the context of chronic 107. Holen T, Norheim F, Gundersen TE et al. Biomarkers for nu- kidney disease (CKD). Sci Rep 2017; 7: 40367 trient intake with focus on alternative sampling techni- 101. Chen Dan-Qian, Cao Gang, Chen Hua et al. Gene and protein ques. Genes Nutr 2016; 11. http://genesandnutrition. expressions and metabolomics exhibit activated redox sig- biomedcentral.com/articles/10.1186/s12263-016-0527-1 (28 nalling and wnt/b-catenin pathway are associated with November 2017, date last accessed) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clinical Kidney Journal Oxford University Press

The metabolomic quest for a biomarker in chronic kidney disease

Robert, Davies,
Clinical Kidney Journal , Volume 11 (5) – Oct 1, 2018
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© The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA.
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Abstract

Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Clinical Kidney Journal, 2018, vol. 11, no. 5, 694–703 doi: 10.1093/ckj/sfy037 Advance Access Publication Date: 2 June 2018 CKJ Review CK J R EV IEW The metabolomic quest for a biomarker in chronic kidney disease Robert Davies School of Biomedical and Healthcare Sciences, University of Plymouth School of Biological Sciences, Plymouth, UK Correspondence and offprint requests to: Robert Davies; E-mail: robert.davies-8@students.plymouth.ac.uk ABSTRACT Chronic kidney disease (CKD) is a growing burden on people and on healthcare for which the diagnostics are niether disease-specific nor indicative of progression. Biomarkers are sought to enable clinicians to offer more appropriate patient- centred treatments, which could come to fruition by using a metabolomics approach. This mini-review highlights the current literature of metabolomics and CKD, and suggests additional factors that need to be considered in this quest for a biomarker, namely the diet and the gut microbiome, for more meaningful advances to be made. Keywords: biomarkers, chronic kidney disease, diet, metabolomics used as diagnostic tools but are invasive, and require skilled THE PROBLEM OF CHRONIC KIDNEY DISEASE professionals and resources to undertake [6]. Globally, chronic kidney disease (CKD) has increased by 36.9% be- Therefore, it would be beneficial to investigate other diag- tween 1990 and 2013 with increases in CKD due to diabetes by nostic measures to aid in further understanding CKD inception, 106.5%, hypertension by 29.4% and other causes by 58.8% [1]. progression and prognosis, to offer more suitable treatment Global CKD prevalence is increasing with different rates between options to patients and to advance and improve therapeutics countries, ethnicities and sexes, reflecting health inequalities, [7]. Indeed, in 2016, the International Society of Nephrology and even within these categories there are differences with re- identified key strategic points to enhance kidney-related re- spect to CKD aetiology [1–3]. search, of which diagnostic methods and CKD progression were Although estimated glomerular filtration rate (eGFR), albu- highlighted [6]. minuria and serum creatinine form part of the assessment As CKD is a condition of various aetiologies with complex net- along with clinical context and data [4, 5], there are limita- works of inter- and intra-molecular signalling, studies on CKD tions with the current diagnostic criteria. Estimation of GFR could utilize the ‘-omics’ approaches (Figure 1): genomics, tran- and creatinine is based on the Chronic Kidney Disease scriptomics, proteomics and metabolomics, which should enable Epidemiology Collaboration creatinine equation, which the clinician and researcher to have a better understanding of requires a correction factor for sex and those of African– the interconnecting genetic and molecular networks in CKD by Caribbean or African background [5] and is dependent on how the disease affects different body systems and responses to muscle mass; therefore, those who embody extremes of mus- stimuli such as diet, medication and the microbiome [8]. This cle mass such as bodybuilders, amputees and those with sar- mini-review will focus on contemporary human studies of CKD copenia or other muscle-wasting disorders may have utilizing a metabolomics approach published between 2016 and exaggerated and erroneous results. Kidney biopsies are also 2017. The research strategy for this involved reviewing relevant Received: 23.1.2018; Editorial decision: 16.4.2018 V C The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 694 Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 695 For metabolomics to be fruitful, metabolites need to be quantified. Biofluids and tissue samples can be used for metab- olomic analysis with technologies such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled with a preparatory chromatographic separation step of the sample such as capillary electrophoresis MS (CE-MS), liquid chromatography MS (LC-MS) or gas-chromatography (GC-MS) (Table 2). NMR is an analytical technique that uses magnetic fields to yield molecular information. MS is a method that measures the mass-to-charge ratio of an intact ion and tandem MS (MS/MS) can be used to measure selected isolated ions that are then fragmented, and the mass-to-charge ratio of each frag- ment is measured and used for analysis [28]. Both methods re- quire bioinformatic analysis for the data to be interpretable and meaningful. These methods provide information on identifica- tion and quantification of metabolites present in the sample. Additional factors such as sample preparation, sample matrix, and carryover effects should be considered when analysing and interpreting the data [24, 29]. For a more comprehensive review and analysis of metabolomic techniques and methodology con- sult references [24–29]. This progress in technology has enabled the identification of endogenous and exogenous metabolites as potential disease biomarkers, which could place personalized and precision patient-centred medicine within reach [30]. Metabolomics can be used to identify metabolites from a range of samples and for CKD the most pertinent are blood and urine [31], with the dialy- sate fluid also offering potential benefits. Blood As the current diagnostics for CKD are not indicative of disease progression, Rhee et al. [9] investigated progression of CKD FIGURE 1: Overview of ‘-omics’ approaches. within a CKD cohort stratifying by stable and rapid decline based on eGFR slope. This study incorporated a mix of ethnic literature using PubMed, Google Scholar and Plymouth backgrounds and CKD aetiologies reflecting the phenotype of University’s Library Primo databases for articles in English with CKD, and suggested lower levels of threonine, methionine and the following key words: ‘CKD’, ‘dialysis’, ‘metabolomics’, ‘bio- arginine as potential biomarkers of renal dysfunction by analy- marker’ used in various combinations. The bibliographies of sing plasma samples processed by LC-MS. In a study by Kimura identified articles with these key words were searched for addi- et al. [10] the authors aimed to identify prognostic biomarkers tional references. See Table 1 for a summary of included studies. for CKD progression and mortality in participants with CKD Stages 3–5 over a 4-year period. Plasma samples were processed using CE and LC-MS from which 16 metabolites were identified THE METABOLOMIC APPROACH with MasterHands software. These 16 metabolites were identi- For CKD, metabolomics may offer the best ‘-omics’ approach as fied as intrinsic to variable metabolic pathways including this involves examining the whole-body system by highlighting nucleotides, glycolysis and amino acids, with others unidenti- changes in metabolites from cellular processes evident in bodily fied. Medical history was noted including CKD aetiology and fluids [19, 20] demonstrating the phenotype of the disease. It is presence of comorbidities, and some medications were listed through metabolomics that a biomarker, or a panel of bio- but glycaemic agents were not. Furthermore, no changes in nu- markers, may be identified to ameliorate diagnosis and eluci- tritional status, dietary intake or weight were reported; there- date progression in those with CKD [7]. A new prognostic fore, it is unknown whether the identified metabolites could biomarker in CKD would not only be beneficial in enhancing arise from CKD or be derived from the diet, or gut microbiome patient-centred care and treatment management but also in as variation has been shown to occur both intra- and inter- elucidating the mechanisms by which the disease progresses individually in the blood metabolome largely due to dietary and how effective treatment is by monitoring the rate of change influences [32–34]. of the identified biomarker(s) [6]. Lee et al. [11] attempted to identify prognostic biomarkers The metabolomics approach has been applied to the study from blood serum comparing CKD patients with and without of various kidney diseases [21] but the discovery and implemen- diabetes, versus a healthy control group using NMR spectros- tation into clinical practice of specific disease biomarkers copy analysis. Participants were placed in groups based on remains elusive. The science of metabolomics has greatly ad- eGFR and diabetes diagnosis before enrolment, but the authors vanced due to the progress in technological developments in re- did not test participants in this study, which is a limitation as cent years, with better instrumentation and the ability to store, participants may have developed diabetes but have not yet analyse and share data with the concomitant development of been diagnosed. This study highlighted differences between bioinformatics and computational platforms [20, 22, 23]. healthy controls and the CKD groups with increases in Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 696 | R. Davies Table 1. Summary of metabolomic studies included in this mini-review Biological matrix Proposed metabolite Study population Metabolomic for metabolomic Bibliographic biomarkers group platform analysis Study outcome reference Uric acid, glucuronate, 200 rapidly declining LC-MS Plasma CKD progression Rhee et al. [9] 4-hydroxymandelate, eGFR, and 200 stable 3-methyladipate/pime- eGFR late, cytosine and homo- gentisate were higher in cases than in controls; threonine, methionine, phenylalanine and argi- nine were lower in cases than in controls Isethionate, saccharate, 112 participants with CE-MS Plasma Composite: predic- Kimura et al. [10] TMAO, 4-oxopentanoate, CKD Stages 3–5 not tive value for CKD cytidine, gluconate, glu- on dialysis at start progression to curonate, guanidinosuc- of study ESRF, requiring cinate, RRT, all-cause 2-hydroxyisobutyrate, death uridine, 5-oxoproline, pimelate, N-acetylneura- minate, 3-methylhisti- dine, citramalate, phthalate TMAO, creatinine, urea, glu- 291 pre-dialysis CKD NMR Serum Progression of CKD Lee et al. [11] cose, higher in CKD than patients with/with- healthy controls; argi- out type 2 diabetes nine, leucine, valine, glu- and 56 healthy tamine, tyrosine, controls pyruvate, citrate, acetate and formate decreased in CKD compared with healthy group C-Glycosyltryptophan, 158 patients with type GC-MS and LC-MS Serum eGFR decline and Niewczas et al. [12] pseudouridine, O-sulfo- 1 diabetes, protein- progression to tyrosine, N-acetylthreo- uria and CKD ESRF nine, N-acetylserine, Stage 3 N6-carbamoylthreonyla- denosine, N6-acetyllysine 4-Hydroxyphenylacetate, Discovery cohort of GC/LC-MS/MS Plasma Uraemic metabolites Tamura et al. [13] phenylacetylglutamine, 141 CKD patients on and impaired ex- hippurate and prolyl- dialysis and an in- ecutive function hydroxyproline dependent replica- tion cohort of 180 CKD patients on dialysis Kynurenine and its metabo- 27 CKD patients LC-MS/MS Serum Kidney function, Karu et al. [14] lites (quinolinic acid, tryptophan me- kynurenic acid, xanthur- tabolism, markers enic acid) and indoxyl for inflammation sulphate and oxidative stress, psychologi- cal/cognitive function Citrulline, dimethylamine, 15 patients with bi- NMR Urine Pathogenic path- Kalantari proline, acetoacetate, opsy-proven FSG ways and molecu- et al. [15] alphaketoisovaleric acid, lar changes in FSG valine, isobutyrate, disease D-Palmitylcarnitine, progression histidine and N-methylnicotinamide Urinary excretion rate of 27 First cohort: 22 non- GC-MS Plasma and Metabolic pathway Hallan et al. [16] metabolites and plasma diabetic CKD Stages urine analysis of CKD (continued) Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 697 Table 1. Continued Biological matrix Proposed metabolite Study population Metabolomic for metabolomic Bibliographic biomarkers group platform analysis Study outcome reference concentration of 33 3–4 and 10 healthy metabolites differed sig- adults. Second co- nificantly in CKD patients hort: 45 non-dia- versus controls. Citric betic CKD patients acid cycle was the most and 15 controls. significantly affected Additional 155 patients from the European Renal cDNA Bank cohort and 31 kidney biop- sies from healthy kidney transplant donors Significant differences in 80 ESRF haemodialysis LC-MS Plasma and Metabolic profile of Zhang et al. [17] concentration of 214 patients and 80 dialysate ESRF patients on metabolites between healthy controls dialysis healthy control and ESRF patients’ pre-dialysis plasma (126 increased and 88 reduced in ESRF group). Pre-dialysis ver- sus post-dialysis showed significant changes in 362 metabolites—including as yet unidentified metabolites TMAO and choline 80 controls and 179 LC-MS/MS Plasma TMAO, inflamma- Missailidis et al. [18] CKD Stages 3–5 tion and mortality patients in CKD patients FSG, focal segmental glomerulonephritis; RRT, renal replacement therapy. Table 2. Platforms for metabolomic analysis with possible advantages and disadvantages [24–27] Platform Advantages Disadvantages CE-MS Small sample volume Migration time variability High separation efficiency Poor concentration sensitivity High resolution Low sample loading capacity LC-MS Detects a large pool of metabolites Destructive of sample High sensitivity Time-consuming High resolution Sample preparation required GC-MS Wide dynamic range Requires thermal stability High resolution Destructive of sample High sensitivity Sample preparation required NMR Minimal sample preparation Low resolution Non-destructive of the sample Low sensitivity High reproducibility Expensive trimethylamine-N-oxide (TMAO), creatinine, urea and glucose be interpreted with caution. In another study on diabetes, that correlated with CKD progression. It was also shown that Niewczas et al. [12] monitored patients with CKD Stage 3 and levels of arginine, leucine, valine, glutamine, tyrosine, pyru- type 1 diabetes for a median of 11 years. Serum samples were vate, citrate, acetate and formate decreased in CKD patients analysed by Metabolon Inc. using GC-MS and LC-MS, and compared with the healthy group. However, these metabolites seven metabolites were identified that correlated with CKD are not specific to kidney disease and may be influenced by a progression. This study obtained repeat blood serum for myriad of other factors such as age, diet, nutritional status, metabolomic analysis, and urine samples for protein and renal medication, nutritional supplements and other diseases not function markers, which is a strength when investigating the accounted for in this study. Indeed, this study did not exclude progression of CKD. However, this study did not report on those with other CKD-associated conditions, namely hyper- medication use and may obfuscate the study results because tension and immune-related, subsequently, the results should an improvement in medication treatment regimens and Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 698 | R. Davies patient adherence to glycaemic, hypertensive and dyslipidae- plasma and dialysate effluent at regular timings during the dial- mic agents may slow the decline in kidney function, and hence ysis process but on a single occasion. The metabolome in the delay CKD progression [35]. plasma samples was compared with both groups using ultra Tamura et al.[13] sought to elucidate the impact of uraemic performance LC-MS and MetaboAnalyst 3.0 software, which metabolites on executive function in a cohort of dialysis showed that the haemodialysis process not only removed, as patients by using GC/LC-MS/MS in pre-dialysis plasma samples expected, uraemic products (TMAO, indoxyl sulphate, p-cresol and analysis performed by Metabolon Inc. Four metabolites sulphate, p-cresol glucuronide, uric acid and hippuric acid), were associated with impaired executive function in those fluid and excessive electrolytes, but a plethora of metabolites— with CKD: 4-hydroxyphenylacetate, phenylacetylglutamine, mainly amino acids (arginine, glutamine, alanine and phenylal- hippurate and prolyl-hydroxyproline. However, these metabo- anine) and lipids, which the authors concluded may be the lites can be derived from the diet and gut microbial metabolism, cause of increased mortality within the CKD population. These which this study did not investigate [36–39]. Furthermore, cog- changes in metabolites were also identified in the dialysate ef- nitive impairment could result from other confounding factors fluent when measured at the corresponding time intervals. in this study such as age, frailty, hypertension, incidence of However, the authors did not include information on the medi- neurological disorders and cardiovascular factors [40–46] rather cal comorbities or medications, CKD aetiology of the disease than the metabolites identified. In a similar study into cognitive group, or whether those in this group had any residual kidney decline, Karu et al. [14] identified kynurenine and its metabolites function. Although for the control group hypertension, cardio- (quinolinic acid, kynurenic acid, xanthurenic acid), and indoxyl vascular disease and diabetes were exclusion criteria, no indica- sulphate as being greatly elevated in CKD patients, especially in tion is given of diabetes prevalence within the ESRF group, those with cognitive impairment, compared with healthy con- which could have implications for interpreting the study’s trols. The proposed mechanism for this is that tryptophan is in- results. volved in the synthesis of the indoxyl sulphate (uraemic toxin) via colonic microbes [47, 48] and can affect brain activity DIET AND THE MICROBIOME—THE MISSING through the kynurenine pathway. However, the same con- LINKS? founding factors are attributable to this study as were for Tamura et al. [13], and hypertension was documented in 78% of Diet and nutritional status CKD participants in Karu et al. [14]; therefore, the cognitive de- Changes in amino acid metabolism are widely seen in those cline may be as a result of hypertension or exacerbated by the with CKD and on dialysis [9, 10, 17, 52, 53] but whether this is co-presence of accumulating uraemic toxins and hypertension. due to CKD progression, other diseases or concomitant with poor nutritional status and dietary protein intake remains elu- Urine sive, compounded by the fact that very few studies that include Metabolomic urinary analysis in CKD could be useful as it is an assessment of nutritional status or dietary intake. Diet is an non-invasive, easily obtained and provides a global state of important factor that should be assessed as those who display physiological function. However, caution should be employed malnutrition and protein-energy malnutrition have worse out- as there is evidence to suggest that urinary metabolites fluctu- comes and early mortality in CKD and on dialysis [54–56]. ate throughout the day suggesting vigilance should be taken Utilizing a subjective global assessment (SGA) tool will enable when interpreting results from such studies [49, 50]. Kalantari the clinician and researcher to understand and appreciate et al. [15] collected urine samples over 24 h from 15 patients with whether the metabolites identified result from nutritional sta- focal segmental glomerulosclerosis to identify 10 metabolites tus, dietary intake or from disease [57–59]. using NMR spectroscopy and ProMetab software, that were Consideration should also be made of dietary regimes as these deemed to be prognostic when compared with kidney biopsy can have influence over the metabolome and microbiome compo- results. This study implemented a diet on its participants for sition [60–62], such as differences between vegans, vegetarians, 24 h prior to collecting urine as a mitigating measure to control pescatarians and carnivores. In Wu et al. [63], healthy vegans con- for dietary influences on the urinary metabolome. However, sumed more carbohydrates, but less protein and fat, than healthy urine samples were collected in 2011 but no information is omnivores, resulting in a 25% difference between the identified given on how the samples were stored nor when the samples metabolites of omnivores and vegans of which lipid and amino were processed, which could limit the reliability of these results acid metabolites were significantly elevated in omnivores and the [50, 51]. Hallan et al. [16] used GC-MS and MetaboAnalyst 3.0 metabolites often associated with CKD hippurate, catechol sul- software on urinary samples in non-diabetic CKD patients phate and 3-hydroxyhippurate were increased in vegans com- showing decreased excretion of citric acid cycle metabolites cor- pared with omnivores. It is, therefore, necessary to account for roborated with analysis of kidney biopsies, showing a reduction differences in dietary intake when assessing the metabolites iden- in gene expression for citric acid cycle enzymes. These findings, tified in CKD patients as what could have been considered to be a however, could be accounted for by considering the nutritional potential biomarker may be derived from or greatly influenced by status and dietary intake of these participants, which this study factors other than kidney disease. Furthermore, it would also be did not do. informative to collect multiple samples across time-points for metabolomic analysis of dietary intake to understand how the Dialysate metabolome changes especially for amino acids [32, 64, 65]inCKD patients. Indeed, current suggested dietary protein requirements The only currently identified study that applied metabolomics to the effect of haemodialysis on the metabolome and dialysate for CKD patients are contentious and vary globally from 0.55 g/kg effluent was by Zhang et al. [17]. In this study, end-stage renal to 1g/kg[66–71] with greater requirements for those on dialysis, failure (ESRF) patients receiving dialysis were compared with 1.1–1.4 g/kg [58, 72], which may impact on the levels of amino matched heathy controls with samples collected from blood acids and uraemic toxins seen in these metabolomic CKD studies. Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 699 Microbiome was associated with intakes of coffee, fruit and wholegrains; and p-cresol sulphate and phenylacetylglutamine from the pu- Combining the study of diet and the microbiome in CKD studies trefaction of undigested dietary proteins by colonic bacteria. with metabolomics would enable elucidation of these complex Furthermore, Lees et al. [36] suggested that hippurate excretion and interwoven relationships, especially for TMAO and uraemic is associated with co-excretion of metabolic intermediates, es- toxins [73, 74]. Phenylacetylglutamine is associated with levels pecially citrate, succinate and 2-oxoglutarate, and has been as- of p-cresyl sulphate and indoxyl sulphate in CKD patients not sociated with a range of conditions besides kidney disease yet on dialysis and is considered to be a risk factor for cardio- including liver disease, hypertension, diabetes, atherosclerosis vascular disease and mortality [39], but whether it is as a result and psychiatric disorders, but is also dependent on intestinal of gut microbiome dysbiosis or due to impaired renal function is microbiota diversity. Diversity and abundance of human micro- yet to be elucidated. TMAO is derived from the gut microbiota biome varies widely even among healthy subjects and impor- and L-carnitine and choline precursors derived from dietary in- tant factors such as diet need to be considered due to its effect take of meat and eggs, and p-cresyl sulphate and sulphate are on microbiome composition and metabolism, and wider effects uraemic toxins derived from the metabolism of amino acids by on health status and disease [20, 83–85]. Furthermore, dietary commensal gut microbiota, consequently greatly influenced by advice given to those with CKD and on dialysis may negatively dietary intake [62, 75], and TMAO is implicated in greater mor- impact the microbiome of the kidney–gut axis due to reducing tality amongst those with CKD concomitant with progressing the ability to produce beneficial short-chain fatty acids [86, 87] impaired renal function [76] and increased cardiovascular as fruit and vegetable consumption is rationed to prevent elec- events [77]. Missailidis et al. [18] assessed plasma samples from those with CKD Stages 3–5 from various aetiologies, comorbid- trolyte derangement [57, 86, 88]. Short-chain fatty acids are ities and nutritional status using SGA, but lacked an assessment thought to be implicated in CKD through their deleterious de- of dietary intake. TMAO increased as CKD stage progressed and pletion and consequential effect on increasing oxidative stress, was associated with greater mortality in a 5-year follow-up. fibrosis and the immune response [86, 89]. Utilizing faecal sam- However, the nutritional status score also increased with pro- ples to assess the microbiome may offer further insights in the gressive CKD, which may have a more negative impact on mor- pathological progression of CKD [31] and provide potential dys- tality than the presence of TMAO. CKD patients had higher biotic targets for treatment of CKD [73]. TMAO levels than controls, which continued to increase as re- nal function declined. When a study participant received a kid- CONSIDERATIONS FOR FURTHER CKD ney transplant, TMAO levels decreased and nutritional status METABOLOMICS improved. It was demonstrated that CKD patients with the highest TMAO levels had a significantly lower survival rate, Single biomarker which the authors deemed to suggest that high levels of TMAO As studies incorporate metabolomics into their methodology to predicted reduced 5-year survival. Although this study did con- identify potential biomarkers, it should also be embedded how sider nutritional status by utilizing the SGA tool examining to evaluate the clinical usefulness of these biomarkers, to eluci- weight loss, anorexia and vomiting, muscle wasting, oedema date to what degree they can be used in clinical care, drug de- and loss of fat mass, it did not consider dietary intake or the velopment and therapy, and, ultimately, point-of-care testing microbiome, which can both have an impact on TMAO levels devices [30, 90]. A single biomarker may be elusive, but a panel [62, 73–75]. Furthermore, it could have collected faecal samples of biomarkers based on ratios of identified altered metabolites to assess the gut microbiome and its influence on TMAO levels may offer potential benefits [47] such as glutamate:glutamine, [31]. Stubbs et al. [78] also assessed the impact of TMAO on CKD and demonstrated the beneficial effect of transplant on de- which may indicate nervous system disorders and energy dys- creased levels of TMAO compared with pre-transplant, there- metabolism in uraemic patients, and tryptophan:kynurenine, which may indicate immune responses and increased athero- fore suggesting that increased levels of TMAO are a consequence of decreased renal function and urinary excretion. sclerosis risk in uraemic patients [53]. It would have been advantageous to assess levels of TMAO in conjunction with an assessment of dietary intake for a more Samples comprehensive investigation of the relationship between Results from metabolomic studies are not always reproducible TMAO, CKD, diet and the microbiome [79]. Stubbs et al.[78] did due to differences in patient demographics, samples used, not comment on the effect of diet and Missailidis et al. [18] con- methodology and computational analysis [20, 23, 91–94]. cluded that dietary changes could not explain the normalized Studies should report on when samples are taken and what pre- levels of TMAO after kidney transplant; however, it is not docu- sampling checks have been done to limit variability, as well as mented whether the participants receiving the transplant were on the time between sample acquisition and sample processing, urinating [80, 81] as this would allow TMAO levels to decrease because this may increase the possibility of metabolite degrada- due to it being excreted in the urine [82]. It remains unclear if tion and yielding false-positive results [50, 93, 95–99]. Each pa- TMAO can be used as a biomarker in CKD and cardiovascular tient is an individual, and each has their own individual dysfunction as it may just be a marker of poor renal clearance metabolic phenotype that is subject to dynamic daily changes or poor nutritional status; therefore, TMAO should be monitored due to diet and diet–microbiome interactions [32–34, 49]. It is in those with CKD along with an assessment of dietary intake problematic when studies rely on a single sample from which to and gut microbiome to further elucidate this mechanism and extrapolate prognostic markers with hindsight, and studies potential biomarker. seeking to investigate prognostic questions should have at least Very few studies have been identified that incorporate the study of the metabolome with dietary and microbiome consid- two measurements over the study time period in order to moni- erations. Pallister et al. [38] identified that metabolites are influ- tor dynamic changes and allow for more meaningful interpreta- enced by microbiome diversity, particularly hippurate, which tion of the data [90]. Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 700 | R. Davies The other ‘-omics’ 3. Carrero J-J, Hecking M, Ulasi I et al. Chronic kidney disease, gender, and access to care: a global perspective. Semin Current limitations with metabolomics in CKD studies stem Nephrol 2017; 37: 296–308 from the inability to identify all metabolites in the metabolome 4. Kellum JA, Lameire N, Aspelin P et al. Kidney Disease: and concomitant lack of overlap of metabolite coverage in com- Improving Global Outcomes (KDIGO) acute kidney injury parable studies and validation [100]. Corroborating identified work group. KDIGO clinical practice guideline for acute kid- metabolites with other physiological functions would make the ney injury. Kidney Int Suppl 2013; 2: 1–138 results more robust and may offer great benefits in ascertaining 5. NICE. Chronic Kidney Disease in Adults: Assessment and phenotypical data for an individual patient [16, 101], which will Management j Clinical Guideline [CG182] 2015. https://www. probably become even more useful with advances in technology nice.org.uk/guidance/cg182 (28 November 2017, date last and the ability to have wider coverage. Genetics coupled with accessed) metabolomics could provide valuable information on an indi- 6. Levin A, Tonelli M, Bonventre J et al. Global kidney health vidual’s metabolic profile, coined as metabotype, which has 2017 and beyond: a roadmap for closing gaps in care, re- been demonstrated in genome-wide association studies (GWAS) search, and policy. Lancet 2017; 390: 1888–1917 through the identification of genetic variation and its effect on 7. Hocher B, Adamski J. Metabolomics for clinical use and re- search in chronic kidney disease. Nat Rev Nephrol 2017; 13: metabolic functionality [19, 102–105]. GWAS data sets can be utilized by researchers to enrich the study of metabolic dysregu- 8. Trivedi DK, Hollywood KA, Goodacre R. Metabolomics for lation and be applied to metabolomic studies of CKD, but such the masses: the future of metabolomics in a personalized studies are currently lacking [106]. world. New Horiz Transl Med 2017; 3: 294–305 Advancement of metabolomics, and the wider ‘-omics’ fam- 9. Rhee EP, Clish CB, Wenger J et al. Metabolomics of chronic ily, requires a collaborative effort to share and store metabolite kidney disease progression: a case-control analysis in the data such as the CKDdb database [100]. Once the technology is chronic renal insufficiency cohort study. Am J Nephrol 2016; available, it could conceivably progress to readily available 43: 366–374 point-of-care devices such as lateral flow devices, dipsticks, 10. Kimura T, Yasuda K, Yamamoto R et al. Identification of breath testing and wearable technology utilizing biosensors and biomarkers for development of end-stage kidney disease in chemometric-based analyses [8] to monitor for disease incep- chronic kidney disease by metabolomic profiling. Sci Rep tion, progression and prognosis, with additional benefits arising 2016; 6: 26138 from measuring dietary and microbiome influences [107]. 11. Lee J, Choi J-Y, Kwon Y-K et al. Changes in serum metabo- lites with the stage of chronic kidney disease: comparison of diabetes and non-diabetes. Clin Chim Acta 2016; 459: CONCLUSIONS 123–131 This mini-review has highlighted the current need for better di- 12. Niewczas MA, Mathew AV, Croall S et al. Circulating modi- agnostic and prognostic markers for CKD. Further studies on fied metabolites and a risk of esrd in patients with type 1 di- CKD should utilize the metabolomic approach, but also examine abetes and chronic kidney disease. Diabetes Care 2017; 40: the diets and microbiome of the individual participants with 383–390 13. Tamura MK, Chertow GM, Depner TA et al. Metabolic profil- CKD. Multiple samples should be taken over a pre-determined ing of impaired cognitive function in patients receiving di- time period and assessed for changes in the metabolome and alysis. J Am Soc Nephrol 2016; 27: 3780–3787 cross-referenced with the CKD phenotype. Studies should also 14. Karu N, McKercher C, Nichols DS et al. Tryptophan metabo- stratify patients based on their ethnicity, sex and CKD aetiology, lism, its relation to inflammation and stress markers and and perform further analysis based on nutritional status, die- association with psychological and cognitive functioning: tary intake and on the microbiome particularly to elucidate the Tasmanian Chronic Kidney Disease pilot study. BMC interconnectedness of amino acid metabolism, uraemic toxins, Nephrol 2016; 17: 171 dietary factors and the gut microbiome. This approach would 15. Kalantari S, Nafar M, Samavat S et al. NMR-based metabolo- strengthen the research output on CKD fostering greater under- mics exploring urinary biomarkers correlated with protein- standing of how metabolites change, and through what influen- uria in focal segmental glomerulosclerosis: a pilot study: ces, so that biomarkers for CKD inception, prognosis and urine metabolomics based on NMR. Magn Reson Chem 2016; prognostics may be identified. 54: 821–826 16. Hallan S, Afkarian M, Zelnick LR et al. Metabolomics and gene expression analysis reveal down-regulation of the cit- CONFLICT OF INTEREST STATEMENT ric acid (TCA) cycle in non-diabetic CKD patients. EBioMedi- None declared. cine 2017; 26: 68–77 17. Zhang Z-H, Mao J-R, Chen H et al. Removal of uremic reten- tion products by hemodialysis is coupled with indiscrimi- REFERENCES nate loss of vital metabolites. Clin Biochem 2017; 50: 1. Abubakar II, Tillmann T, Banerjee A. Global, regional, and 1078–1086 national age-sex specific all-cause and cause-specific mor- 18. Missailidis C, Ha ¨ llqvist J, Qureshi AR et al. Serum tality for 240 causes of death, 1990-2013: a systematic trimethylamine-n-oxide is strongly related to renal func- analysis for the Global Burden of Disease Study 2013. Lancet tion and predicts outcome in chronic kidney disease. PLoS 2015; 385: 117–171 ONE 2016; 11: e0141738 2. Hill NR, Fatoba ST, Oke JL et al. Global prevalence of chronic 19. Atzler D, Schwedhelm E, Zeller T. Integrated genomics and kidney disease – a systematic review and meta-analysis. metabolomics in nephrology. Nephrol Dial Transplant 2014; PLoS ONE 2016; 11: e0158765 29: 1467–1474 Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 701 39. Poesen R, Claes K, Evenepoel P et al. Microbiota-derived 20. Beger RD, Dunn W, Schmidt MA et al. Metabolomics enables precision medicine: ‘A White Paper, Community phenylacetylglutamine associates with overall mortality Perspective’. Metabolomics 2016; 12: 149 and cardiovascular disease in patients with CKD. J Am Soc Nephrol 2016; 27: 3479–3487 21. Weiss RH, Kim K. Metabolomics in the study of kidney dis- eases. Nat Rev Nephrol 2011; 8: 22–33 40. Tamura M, Covinsky KE, Chertow GM et al. Functional sta- tus of elderly adults before and after initiation of dialysis. 22. Cisek K, Krochmal M, Klein J et al. The application of multi- N Engl J Med 2009; 361: 1539–1547 omics and systems biology to identify therapeutic targets 41. Tamura MK, Meyer JB, Saxena AB et al. Prevalence and sig- in chronic kidney disease. Nephrol Dial Transplant 2016; 31: nificance of stroke symptoms among patients receiving 2003–2011 maintenance dialysis. Neurology 2012; 79: 981–987 23. Meier R, Ruttkies C, Treutler H et al. Bioinformatics can 42. Bugnicourt J-M, Godefroy O, Chillon J-M et al. Cognitive dis- boost metabolomics research. J Biotechnol 2017; 261: 137–141 orders and dementia in CKD: the neglected kidney-brain 24. Aretz I, Meierhofer D. Advantages and pitfalls of mass spec- axis. J Am Soc Nephrol 2013; 24: 353–363 trometry based metabolome profiling in systems biology. 43. McAdams-DeMarco MA, Tan J, Salter ML. Frailty and cogni- Int J Mol Sci 2016; 17: 632 tive function in incident hemodialysis patients. Clin J Am 25. Lindon JC. Overview of NMR-based metabonomics. In: Soc Nephrol 2015; 10: 2181–2189 Lindon JC, Tranter GE, Koppenaal DW (eds). Encyclopedia of 44. Kallenberg MH, Kleinveld HA, Dekker FW. Functional and Spectroscopy and Spectrometry, 3rd edn. Oxford: Academic cognitive impairment, frailty, and adverse health outcomes Press, 2017, 517–526 in older patients reaching esrd–a systematic review. Clin J 26. Nassar AF, Wu T, Nassar SF et al. UPLC–MS for metabolo- Am Soc Nephrol 2016; 11: 1624–1639 mics: a giant step forward in support of pharmaceutical re- 45. Iyasere O, Okai D, Brown E. Cognitive function and ad- search. Drug Discov Today 2017; 22: 463–470 vanced kidney disease: longitudinal trends and impact on 27. Ramautar R, Somsen GW, de Jong GJ. CE–MS for metabolo- decision-making. Clin Kidney J 2017; 10: 89–94 mics: developments and applications in the period 2014– 46. Shirazian S, Grant CD, Aina O. Depression in chronic kidney 2016. Electrophoresis 2017; 38: 190–202 disease and end-stage renal disease: similarities and differ- 28. Patti GJ, Yanes O, Siuzdak G. Metabolomics: the apogee of ences in diagnosis, epidemiology, and management. Kidney the omics trilogy: Innovation. Nat Rev Mol Cell Biol 2012; 13: Int Rep 2017; 2: 94–107 263–269 47. Breit M, Weinberger KM. Metabolic biomarkers for chronic 29. Gowda GAN, Djukovic D. Overview of mass spectrometry- kidney disease. Appl Metabolomics 2016; 589: 62–80 based metabolomics: opportunities and challenges. In: 48. Leong S, Sirich T. Indoxyl sulfate—review of toxicity and Raftery D (ed). Mass Spectrometry in Metabolomics. New York, therapeutic strategies. Toxins 2016; 8: 358 NY: Springer, 2014, 3–12 49. Heinzmann SS, Merrifield CA, Rezzi S et al. Stability and ro- 30. Wishart DS. Emerging applications of metabolomics in bustness of human metabolic phenotypes in response to drug discovery and precision medicine. Nat Rev Drug Discov sequential food challenges. J Proteome Res 2012; 11: 643–655 2016; 15: 473 50. Budde K, Go ¨k O-N, Pietzner M et al. Quality assurance in the 31. Barrios C, Spector TD, Menni C. Blood, urine and faecal me- pre-analytical phase of human urine samples by 1H NMR tabolite profiles in the study of adult renal disease. Arch spectroscopy. Arch Biochem Biophys 2016; 589: 10–17 Biochem Biophys 2016; 589: 81–92 51. Capati A, Ijare OB, Bezabeh T. Diagnostic applications of nu- 32. Kim K, Mall C, Taylor SL et al. Mealtime, temporal, and daily clear magnetic resonance–based urinary metabolomics. variability of the human urinary and plasma metabolomes Magn Reson Insights 2017; 10: 1178623X17694346. https://www. in a tightly controlled environment. PLoS ONE 2014; 9: ncbi.nlm.nih.gov/pmc/articles/PMC5428226/ (13 December e86223 2017) 33. Dunn WB, Lin W, Broadhurst D et al. Molecular phenotyping 52. Duranton F, Lundin U, Gayrard N et al. Plasma and urinary of a UK population: defining the human serum metabo- amino acid metabolomic profiling in patients with different lome. Metabolomics 2015; 11: 9–26 levels of kidney function. Clin J Am Soc Nephrol 2014; 9: 37–45 34. Chaleckis R, Murakami I, Takada J et al. Individual variabil- 53. Liu S, Wang L, Hu C et al. Plasma metabolomics ity in human blood metabolites identifies age-related dif- profiling of maintenance hemodialysis based on capillary ferences. Proc Natl Acad Sci USA 2016; 113: 4252–4259 electrophoresis—time of flight mass spectrometry. Sci Rep 35. Aitken GR, Roderick PJ, Fraser S et al. Change in prevalence 2017; 7: 8150. doi: 10.1038/s41598-017-08327-w of chronic kidney disease in England over time: comparison 54. Magalhaes LP, dos Reis LM, Graciolli FG et al. Predictive fac- of nationally representative cross-sectional surveys from tors of one-year mortality in a cohort of patients undergo- 2003 to 2010. BMJ Open 2014; 4: e005480. doi: ing urgent-start hemodialysis. PLoS ONE 2017; 12: e0167895 10.1136/bmjopen-2014-005480 55. Sabatino A, Regolisti G, Karupaiah T et al. Protein-energy 36. Lees HJ, Swann JR, Wilson ID et al. Hippurate: the natural wasting and nutritional supplementation in patients with history of a mammalian–microbial cometabolite. J Proteome end-stage renal disease on hemodialysis. Clin Nutr 2017; 36: Res 2013; 12: 1527–1546 663–671 37. Va ´ zquez-Fresno R, Llorach R, Perera A et al. Clinical pheno- 56. Zha Y, Qian Q. Protein nutrition and malnutrition in CKD type clustering in cardiovascular risk patients for the iden- and ESRD. Nutrients 2017; 9: 208 tification of responsive metabotypes after red wine 57. Wright M, Jones C. Renal association clinical practice guide- polyphenol intake. J Nutr Biochem 2016; 28: 114–120 line on nutrition in CKD. Nephron Clin Pract 2011; 118: 38. Pallister T, Jackson MA, Martin TC et al. Hippurate as a c153–c164 metabolomic marker of gut microbiome diversity: modula- 58. Naylor HL, Jackson H, Walker GH et al. British Dietetic tion by diet and relationship to metabolic syndrome. Sci Rep Association evidence-based guidelines for the protein 2017; 7: 13670. doi: 10.1038/s41598-017-13722-4 requirements of adults undergoing maintenance Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 702 | R. Davies Metabolomics results from a randomized crossover inter- haemodialysis or peritoneal dialysis. J Hum Nutr Diet 2013; 26: 315–328 vention study. Mol Nutr Food Res 2016; 60: 1661–1672 59. Santin F, Rodrigues J, Brito FB et al. Performance of subjec- 76. Gruppen EG, Garcia E, Connelly MA et al. TMAO is associated with mortality: impact of modestly impaired tive global assessment and malnutrition inflammation score for monitoring the nutritional status of older adults renal function. Sci Rep 2017; 7: 13781. doi: 10.1038/ s41598-017-13739-9 on hemodialysis. Clin Nutr 2018; 37: 604–611 77. Kim RB, Morse BL, Djurdjev O et al. Advanced chronic kid- 60. Poesen R, Mutsaers HAM, Windey K et al. The influence of ney disease populations have elevated trimethylamine dietary protein intake on mammalian tryptophan and phe- N-oxide levels associated with increased cardiovascular nolic metabolites. PLoS ONE 2015; 10: e0140820 events. Kidney Int 2016; 89: 1144–1152 61. Schmidt JA, Rinaldi S, Scalbert A et al. Plasma concentra- 78. Stubbs JR, House JA, Ocque AJ et al. Serum trimethylamine- tions and intakes of amino acids in male meat-eaters, fish- n-oxide is elevated in ckd and correlates with coronary ath- eaters, vegetarians and vegans: a cross-sectional analysis erosclerosis burden. J Am Soc Nephrol 2016; 27: 305–313 in the EPIC-Oxford cohort. Eur J Clin Nutr 2016; 70: 306–312 79. Aron-Wisnewsky J, Cle ´ ment K. The gut microbiome, diet, 62. Cheung W, Keski-Rahkonen P, Assi N et al. A metabolomic and links to cardiometabolic and chronic disorders. Nat Rev study of biomarkers of meat and fish intake. Am J Clin Nutr Nephrol 2015; 12: 169 2017; 105: 600–608 80. Khosroshahi HT, Oskui R, Shoja MM et al. Time-dependent 63. Wu GD, Compher C, Chen EZ et al. Comparative metabolo- variations in urine output after renal transplantation. mics in vegans and omnivores reveal constraints on diet- Transplant Proc 2007; 39: 932–933 dependent gut microbiota metabolite production. Gut 2016; 81. Lai Q, Pretagostini R, Poli L et al. Early urine output predicts 65: 63–72 graft survival after kidney transplantation. Transplant Proc 64. Andersen M-BS, Rinnan A, Manach C et al. Untargeted 2010; 42: 1090–1092 metabolomics as a screening tool for estimating compli- 82. Moraes C, Fouque D, Amaral ACF et al. Trimethylamine ance to a dietary pattern. J Proteome Res 2014; 13: 1405–1418 n-oxide from gut microbiota in chronic kidney disease 65. Scalbert A, Brennan L, Manach C et al. The food metabo- patients: focus on diet. J Ren Nutr 2015; 25: 459–465 lome: a window over dietary exposure. Am J Clin Nutr 2014; 83. Huttenhower C, Gevers D, Knight R et al. Structure, function 99: 1286–1308 and diversity of the healthy human microbiome. Nature 66. Fouque D, Laville M. Low protein diets for chronic kidney 2012; 486: 207–214 disease in non diabetic adults. In: The Cochrane 84. Hall AB, Tolonen AC, Xavier RJ. Human genetic variation Collaboration (ed). Cochrane Database of Systematic Reviews. and the gut microbiome in disease. Nat Rev Genet 2017; 18: Chichester: John Wiley & Sons, Ltd, 2009; doi: 690–699 10.1002/14651858.CD001892.pub3 85. Mendes-Soares H, Chia N. Community metabolic modelling 67. Kovesdy CP, Kopple JD, Kalantar-Zadeh K. Management of approaches to understanding the gut microbiome: bridging protein-energy wasting in non-dialysis-dependent chronic biochemistry and ecology. Free Radic Biol Med 2017; 105: kidney disease: reconciling low protein intake with nutri- 102–109 tional therapy. Am J Clin Nutr 2013; 97: 1163–1177 86. Felizardo RJF, Castoldi A, Andrade-Oliveira V et al. The 68. Piccoli GB, Vigotti FN, Leone F et al. Low-protein diets in microbiota and chronic kidney diseases: a double-edged CKD: how can we achieve them? A narrative, pragmatic re- sword. Clin Transl Immunol 2016; 5: e86 view. Clin Kidney J 2015; 8: 61–70 87. Rı ´os-Covia ´ n D, Ruas-Madiedo P, Margolles A et al. Intestinal 69. Riccio E, Di Nuzzi A, Pisani A. Nutritional treatment in short chain fatty acids and their link with diet and human chronic kidney disease: the concept of nephroprotection. health. Front Microbiol 2016; 7: 185 Clin Exp Nephrol 2015; 19: 161–167 88. Mitch WE, Remuzzi G. Diets for patients with chronic kid- 70. Bellizzi V, Cupisti A, Locatelli F et al. Low-protein diets for ney disease, should we reconsider? BMC Nephrol 2016; 17: chronic kidney disease patients: the Italian experience. 80. doi: 10.1186/s12882-016-0283-x BMC Nephrol 2016; 17: 77 89. Li L, Ma L, Fu P. Gut microbiota–derived short-chain fatty 71. Di Iorio B, Di Micco L, Marzocco S et al. Very low-protein diet acids and kidney diseases. Drug Des Devel Ther 2017; 11: (VLPD) reduces metabolic acidosis in subjects with chronic 3531–3542 kidney disease: the “nutritional light signal” of the renal 90. Ravani P, Parfrey PS, Dicks E et al. Clinical research of acid load. Nutrients 2017; 9: 69 kidney diseases II: problems of study design. Nephrol Dial 72. Cano N, Fiaccadori E, Tesinsky P et al. ESPEN guidelines on Transplant 2007; 22: 2785–2794 enteral nutrition: adult renal failure. Clin Nutr 2006; 25: 91. Everett JR. A new paradigm for known metabolite identifi- 295–310 cation in metabonomics/metabolomics: metabolite identi- 73. Zhang LS, Davies SS. Microbial metabolism of dietary com- fication efficiency. Comput Struct Biotechnol J 2015; 13: ponents to bioactive metabolites: opportunities for new 131–144 therapeutic interventions. Genome Med 2016; 8: 46. http:// 92. Darshi M, Van Espen B, Sharma K. Metabolomics in diabetic genomemedicine.biomedcentral.com/articles/10.1186/ kidney disease: unraveling the biochemistry of a silent s13073-016-0296-x (28 November 2017, date last accessed) killer. Am J Nephrol 2016; 44: 92–103 74. Fernandez-Prado RE, Perez-Gomez M, Gracia-Iguacel C et al. 93. Emwas A-H, Roy R, McKay RT et al. Recommendations and Nutrients turned into toxins: microbiota modulation of nu- standardization of biomarker quantification using nmr- trient properties in chronic kidney disease. Nutrients 2017; based metabolomics with particular focus on urinary 9: 489 analysis. J Proteome Res 2016; 15: 360–373 75. Schmedes M, Aadland EK, Sundekilde UK et al. Lean-sea- 94. Hajduk J, Matysiak J, Kokot ZJ. Challenges in biomarker food intake decreases urinary markers of mitochondrial discovery with MALDI-TOF MS. Clin Chim Acta 2016; 458: lipid and energy metabolism in healthy subjects: 84–98 Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/694/5032522 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Metabolomics and CKD | 703 metabolite dysfunction in patients with chronic kidney dis- 95. Townsend MK, Clish CB, Kraft P. Reproducibility of metabo- lomic profiles among men and women in 2 large cohort ease. Redox Biol 2017; 12: 505–521 studies. Clin Chem 2013; 59: 1657–1667 102. Illig T, Gieger C, Zhai G et al. A genome-wide perspective of 96. Breier M, Wahl S, Prehn C et al. Targeted metabolomics genetic variation in human metabolism. Nat Genet 2010; 42: identifies reliable and stable metabolites in human serum 137–141 and plasma samples. PLoS ONE 2014; 9: e89728 103. Suhre K, Shin S-Y, Petersen A-K Human metabolic individ- 97. Carayol M, Licaj I, Achaintre D et al. Reliability of serum uality in biomedical and pharmaceutical research. Nature metabolites over a two-year period: a targeted metabolo- 2011; 477: 54–60 mic approach in fasting and non-fasting samples from 104. Kastenmu ¨ ller G, Raffler J, Gieger C et al. Genetics of human EPIC. PLoS ONE 2015; 10: e0135437 metabolism: an update. Hum Mol Genet 2015; 24: R93–R101 98. Yin P, Lehmann R, Xu G. Effects of pre-analytical processes 105. Raffler J, Friedrich N, Arnold M et al. Genome-wide association on blood samples used in metabolomics studies. Anal study with targeted and non-targeted NMR metabolomics Bioanal Chem 2015; 407: 4879–4892 identifies 15 novel loci of urinary human metabolic individu- 99. Jobard E, Tre ´ dan O, Postoly D et al. A systematic evaluation ality. PLoS Genet 2015; 11: e1005487 106. Ko ¨ ttgen A, Raffler J, Sekula P et al. Genome-wide association of blood serum and plasma pre-analytics for metabolomics cohort studies. Int J Mol Sci 2016; 17: 2035 studies of metabolite concentrations (mGWAS): relevance 100. Fernandes M, Husi H. Establishment of a integrative multi- for nephrology. Semin Nephrol 2018; 38: 151–174 omics expression database CKDdb in the context of chronic 107. Holen T, Norheim F, Gundersen TE et al. Biomarkers for nu- kidney disease (CKD). Sci Rep 2017; 7: 40367 trient intake with focus on alternative sampling techni- 101. Chen Dan-Qian, Cao Gang, Chen Hua et al. Gene and protein ques. Genes Nutr 2016; 11. http://genesandnutrition. expressions and metabolomics exhibit activated redox sig- biomedcentral.com/articles/10.1186/s12263-016-0527-1 (28 nalling and wnt/b-catenin pathway are associated with November 2017, date last accessed)

Journal

Clinical Kidney Journal – Oxford University Press

Published: Oct 1, 2018

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