Clinical Kidney Journal, 2018, 1–10 doi: 10.1093/ckj/sfy037 CKJ Review CK J R E V I E W 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: email@example.com 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 . 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% . 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, . 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 . 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  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 . 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 firstname.lastname@example.org Downloaded from https://academic.oup.com/ckj/advance-article-abstract/doi/10.1093/ckj/sfy037/5032522 by Ed 'DeepDyve' Gillespie user on 12 July 2018 2| R. Davies 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 . 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 . Metabolomics can be used to identify metabolites from a range of samples and for CKD the most pertinent are blood and urine , 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.  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.  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 . 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) . Lee et al.  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  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/advance-article-abstract/doi/10.1093/ckj/sfy037/5032522 by Ed 'DeepDyve' Gillespie user on 12 July 2018 Metabolomics and CKD | 3 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.  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.  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.  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.  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.  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.  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.  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.  metabolites and plasma diabetic CKD Stages urine analysis of CKD (continued) Downloaded from https://academic.oup.com/ckj/advance-article-abstract/doi/10.1093/ckj/sfy037/5032522 by Ed 'DeepDyve' Gillespie user on 12 July 2018 4| R. Davies 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.  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.  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.  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/advance-article-abstract/doi/10.1093/ckj/sfy037/5032522 by Ed 'DeepDyve' Gillespie user on 12 July 2018 Metabolomics and CKD | 5 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 . plasma samples was compared with both groups using ultra Tamura et al. 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.  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. , and hypertension was documented in 78% of Diet and nutritional status CKD participants in Karu et al. ; 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.  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. , 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.  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]in CKD 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. . 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/advance-article-abstract/doi/10.1093/ckj/sfy037/5032522 by Ed 'DeepDyve' Gillespie user on 12 July 2018 6| R. Davies 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.  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 , 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  and increased cardiovascular as fruit and vegetable consumption is rationed to prevent elec- events . Missailidis et al.  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  and provide potential dys- tality than the presence of TMAO. CKD patients had higher biotic targets for treatment of CKD . 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  such as glutamate:glutamine, . Stubbs et al.  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 . 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 . Stubbs et al. did due to differences in patient demographics, samples used, not comment on the effect of diet and Missailidis et al.  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 . 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.  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 . Downloaded from https://academic.oup.com/ckj/advance-article-abstract/doi/10.1093/ckj/sfy037/5032522 by Ed 'DeepDyve' Gillespie user on 12 July 2018 Metabolomics and CKD | 7 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 . 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 . 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 . 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  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 . 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. 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Clinical Kidney Journal – Oxford University Press
Published: Jun 2, 2018
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