Crossroads of metabolism and CKD

Sensitive and useful biomarkers for predicting chronic kidney disease (CKD) and its prognosis are urgently needed. Using a nontargeted metabolomic approach, Hu et al. explored an established CKD cohort, Modification of Diet in Renal Disease, as well as a validation cohort, African American Study of Kidney Disease and Hypertension. They identified 3 serum metabolites (ribonate, fumarate, and allantoin) significantly associated with mortality in CKD even after statistical adjustment by multiple clinical covariates including glomerular filtration rate and urinary protein. Their finding develops a new aspect for diagnosing and predicting the prognosis of CKD. Sensitive and useful biomarkers for predicting chronic kidney disease (CKD) and its prognosis are urgently needed. Using a nontargeted metabolomic approach, Hu et al. explored an established CKD cohort, Modification of Diet in Renal Disease, as well as a validation cohort, African American Study of Kidney Disease and Hypertension. They identified 3 serum metabolites (ribonate, fumarate, and allantoin) significantly associated with mortality in CKD even after statistical adjustment by multiple clinical covariates including glomerular filtration rate and urinary protein. Their finding develops a new aspect for diagnosing and predicting the prognosis of CKD. Because of its high prevalence and mortality, chronic kidney diseases (CKD) are a global burden of health. Comprehensive metabolomics has been exploited to decipher the pathophysiologic mechanisms of CKD progression.1Hocher B. Adamski J. Metabolomics for clinical use and research in chronic kidney disease.Nat Rev Nephrol. 2017; 13: 269-284Crossref PubMed Scopus (188) Google Scholar Metabolites lie downstream of the transcription and translation process, and metabolomics reflects the functional outcome of genes, transcriptome, and protein synthesis. The kidney function itself directly influences the circulating metabolites through glomerular filtration, tubular secretion/reabsorption, and metabolism.2Kalim S. Rhee E.P. An overview of renal metabolomics.Kidney Int. 2017; 91: 61-69Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar In CKD, it is plausible to use metabolomics to search for biomarkers that can predict disease progression and long-term outcome. However, because the backgrounds of patients with CKD (age, sex, race, nutrition, kidney function, etiology of kidney diseases) are complicated, as are the circulating metabolites, the selection of a target sample cohort and study design for the analysis of overwhelming data sets needs to be carefully determined.1Hocher B. Adamski J. Metabolomics for clinical use and research in chronic kidney disease.Nat Rev Nephrol. 2017; 13: 269-284Crossref PubMed Scopus (188) Google Scholar, 2Kalim S. Rhee E.P. An overview of renal metabolomics.Kidney Int. 2017; 91: 61-69Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar The conditions of sample collection also have a great influence on the results. Here, Hu et al.3Hu J.-R. Coresh J. Inker L.A. et al.Serum metabolites are associated with all-cause mortality in chronic kidney disease.Kidney Int. 2018; 94: 381-389Abstract Full Text Full Text PDF PubMed Scopus (31) Google Scholar (2018) reported an elegant strategy to identify the serum metabolites associated with mortality in CKD, through a discovery and replication study using 2 established and distinctive CKD cohorts, Modification of Diet in Renal Disease (MDRD) Study and African American Study of Kidney Disease and Hypertension (AASK). The MDRD cohort included only 8% African Americans with 10% diabetic patients, excluding patients with diabetic kidney diseases and patients with diabetes treated with insulin. On the other hand, the AASK cohort was all African American because of its principal study concept and excluded all patients with diabetes. The mean glomerular filtration rate (GFR) was 30 and 47 ml/min per 1.73 m2 for MDRD and AASK, respectively. In a sense, it was unique of this study to avoid the strong influence of diabetes and diabetic kidney diseases on the metabolic profile of patients with CKD. Blood was collected while patients were fasting and was immediately separated into serum and plasma (for a metabolite analysis, it is easier to avoid metabolic deviation when the patient is fasting). When comparing the serum and plasma samples, some metabolites were detected at higher concentrations, which means the serum might be suitable for a discovery study in metabolomics.4Yu Z. Kastenmuller G. He Y. et al.Differences between human plasma and serum metabolite profiles.PloS One. 2011; 6: e21230Crossref PubMed Scopus (305) Google Scholar For the MDRD study, blood was allowed to clot at room temperature between 30 minutes and 2 hours, and the separated serum was stocked at –70 °C for up to a month before shipment on dry ice to the central laboratory. For the AASK study, the blood was allowed to clot at room temperature for 30 minutes and the serum was stocked at –20 °C for a few days until shipment on dry ice to the AASK central laboratory after aliquot storage at –70 °C. Thus, the serum preparation and stock conditions in the 2 study cohorts were suitable for reliable metabolomics analysis.5Anton G. Wilson R. Yu Z.H. et al.Pre-analytical sample quality: metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples.PloS One. 2015; 10: e0121495Crossref PubMed Scopus (73) Google Scholar In a discovery study of the MDRD cohort, 6 metabolites from 622 investigated nondrug metabolites were found to be significantly related to all-cause mortality during a median follow-up of 16.5 years. The 6 metabolites were glutamine, alpha-ketoglutarate, ribonate, fumarate, allantoin, and gamma-glutamylglutamine. In a replication study of the AASK cohort, the authors applied conservative Bonferroni correction, and 3 out of 6 metabolites in MDRD were significantly replicated in AASK over a median follow-up of 9.7 years after statistical adjustment by clinical covariates, including 125 I-iothalamate clearance-based kidney function and 24-hour urine protein measurements. The 3 replicated metabolites were ribonate, fumarate, and allantoin. Ribonate is an intermediate of the pentose phosphate pathway. Fumarate is an intermediate in the citric acid cycle and also an inhibitor of the hydroxylation of hypoxia-inducible factor. Allonate is a byproduct of urate and is associated with the purine pathway. Furthermore, the study arms of MDRD and AASK were also adjusted. The MDRD study consisted of 2 studies based on the GFR at enrollment (study A with a GFR of 25–55 ml/min per 1.73 m2, and study B with a GFR of 13–24 ml/min per 1.73 m2) with 2 levels of blood pressure control and 3 levels of dietary protein and phosphorus control.6Klahr S. Levey A.S. Beck G.J. et al.The effects of dietary protein restriction and blood-pressure control on the progression of chronic renal disease. Modification of Diet in Renal Disease Study Group.N Engl J Med. 1994; 330: 877-884Crossref PubMed Scopus (2051) Google Scholar The AASK trial was designed to access the effects of 3 class medications in the initial antihypertensive therapy (ramipril, metoprolol, and amlodipine) and 2 levels of blood pressure control on the GFR decline in hypertension.7Wright Jr., J.T. Bakris G. Greene T. et al.Effect of blood pressure lowering and antihypertensive drug class on progression of hypertensive kidney disease: results from the AASK trial.JAMA. 2002; 288: 2421-2431Crossref PubMed Scopus (1629) Google Scholar All of these 3 replicated metabolites (ribonate, fumarate, and allantoin) were positively associated with mortality across the 2 study cohorts after correction for multiple comparisons. It is important that 3 metabolites replicated in 2 established CKD cohorts were significantly associated with long-term mortality beyond the conventional CKD markers, measured GFR, and 24-hour urinary protein. Accordingly, under a carefully designed study, ribonate, fumarate, and allantoin were the markers associated with long-term mortality of CKD patients independently of the renal function. Because human cells lack the enzyme uricase, which converts uric acid, and allantoin is a nonenzymatic oxidative product of uric acid in humans,8Kand'ar R. Zakova P. Allantoin as a marker of oxidative stress in human erythrocytes.Clin Chem Lab Med. 2008; 46: 1270-1274Crossref PubMed Scopus (51) Google Scholar the increased level of allantoin in the circulation indicated an increased oxidative stress condition under CKD leading to the clinical outcome (Figure 1). On the other hand, rioborate is a new marker and its precise role is still unclear. Further evaluation will be necessary. However, several unanswered issues in this study will need further investigation. A study to evaluate the metabolomics and mortality association in cohorts with better conserved renal function will be necessary because the mean GFR was 30 and 47 ml/min per 1.73 m2 for MDRD and AASK, respectively. Even though adjustments for diabetes as clinical covariates in the metabolomics data in the MDRD samples were done in this study, it would be interesting to investigate the same untargeted metabolomics in an established CKD cohort mainly because of diabetic kidney diseases. In addition, the previously reported representative "uremic toxins" indoxyl sulfate, p-cresyl sulfate, and trimethylamine-N-oxide have been reported to be associated with cardiovascular diseases, mortality, and the progression of atherosclerosis.9Duranton F. Cohen G. De Smet R. et al.Normal and pathologic concentrations of uremic toxins.J Am Soc Nephrol. 2012; 23: 1258-1270Crossref PubMed Scopus (628) Google Scholar The authors mentioned that those studies were done by targeted metabolomics analysis and not all the previous studies included GFR as a covariate. Further quantification of metabolites is needed in multiple, clinical time courses from patients with CKD or in different CKD stage populations to validate the pathophysiologic consequences. In conclusion, this study provided a new framework for exploring the risk factors for mortality in CKD using an untargeted metabolomics and a discovery-replication study approach. This approach could be used to search for new biomarkers predicting long-term outcomes in various disease cohorts. Further confirmation of the metabolites using more quantitative methods and validation in another CKD cohort will ensure the new investigational targets beyond conventional CKD risk factors for the development of novel biomarkers for CKD and therapeutic strategies against CKD-related mortality. All the authors declared no competing interests. Serum metabolites are associated with all-cause mortality in chronic kidney diseaseKidney InternationalVol. 94Issue 2PreviewChronic kidney disease (CKD) involves significant metabolic abnormalities and has a high mortality rate. Because the levels of serum metabolites in patients with CKD might provide insight into subclinical disease states and risk for future mortality, we determined which serum metabolites reproducibly associate with mortality in CKD using a discovery and replication design. Metabolite levels were quantified via untargeted liquid chromatography and mass spectroscopy from serum samples of 299 patients with CKD in the Modification of Diet in Renal Disease (MDRD) study as a discovery cohort. Full-Text PDF Open ArchiveSuzuki T, Abe T. Crossroads of metabolism and CKD. Kidney Int. 2018;94:242–243Kidney InternationalVol. 94Issue 4PreviewIn the above-mentioned article, in the title, "Crossloads" was corrected to "Crossroads". Full-Text PDF Open Archive