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Metabolomic Profiling in Individuals with a Failing Kidney Allograft

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      Abstract

      Background

      Alteration of certain metabolites may play a role in the pathophysiology of renal allograft disease.

      Methods

      To explore metabolomic abnormalities in individuals with a failing kidney allograft, we analyzed by liquid chromatography-mass spectrometry (LC-MS/MS; for ex vivo profiling of serum and urine) and two dimensional correlated spectroscopy (2D COSY; for in vivo study of the kidney graft) 40 subjects with varying degrees of chronic allograft dysfunction stratified by tertiles of glomerular filtration rate (GFR; T1, T2, T3). Ten healthy non-allograft individuals were chosen as controls.

      Results

      LC-MS/MS analysis revealed a dose-response association between GFR and serum concentration of tryptophan, glutamine, dimethylarginine isomers (asymmetric [A]DMA and symmetric [S]DMA) and short-chain acylcarnitines (C4 and C12), (test for trend: T1-T3 = p<0.05; p = 0.01; p<0.001; p = 0.01; p = 0.01; p<0.05, respectively). The same association was found between GFR and urinary levels of histidine, DOPA, dopamine, carnosine, SDMA and ADMA (test for trend: T1-T3 = p<0.05; p<0.01; p = 0.001; p<0.05; p = 0.001; p<0.001; p<0.01, respectively). In vivo 2D COSY of the kidney allograft revealed significant reduction in the parenchymal content of choline, creatine, taurine and threonine (all: p<0.05) in individuals with lower GFR levels.

      Conclusions

      We report an association between renal function and altered metabolomic profile in renal transplant individuals with different degrees of kidney graft function.

      Related collections

      Most cited references 47

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      Estimating glomerular filtration rate from serum creatinine and cystatin C.

      Estimates of glomerular filtration rate (GFR) that are based on serum creatinine are routinely used; however, they are imprecise, potentially leading to the overdiagnosis of chronic kidney disease. Cystatin C is an alternative filtration marker for estimating GFR. Using cross-sectional analyses, we developed estimating equations based on cystatin C alone and in combination with creatinine in diverse populations totaling 5352 participants from 13 studies. These equations were then validated in 1119 participants from 5 different studies in which GFR had been measured. Cystatin and creatinine assays were traceable to primary reference materials. Mean measured GFRs were 68 and 70 ml per minute per 1.73 m(2) of body-surface area in the development and validation data sets, respectively. In the validation data set, the creatinine-cystatin C equation performed better than equations that used creatinine or cystatin C alone. Bias was similar among the three equations, with a median difference between measured and estimated GFR of 3.9 ml per minute per 1.73 m(2) with the combined equation, as compared with 3.7 and 3.4 ml per minute per 1.73 m(2) with the creatinine equation and the cystatin C equation (P=0.07 and P=0.05), respectively. Precision was improved with the combined equation (interquartile range of the difference, 13.4 vs. 15.4 and 16.4 ml per minute per 1.73 m(2), respectively [P=0.001 and P 30% of measured GFR, 8.5 vs. 12.8 and 14.1, respectively [P<0.001 for both comparisons]). In participants whose estimated GFR based on creatinine was 45 to 74 ml per minute per 1.73 m(2), the combined equation improved the classification of measured GFR as either less than 60 ml per minute per 1.73 m(2) or greater than or equal to 60 ml per minute per 1.73 m(2) (net reclassification index, 19.4% [P<0.001]) and correctly reclassified 16.9% of those with an estimated GFR of 45 to 59 ml per minute per 1.73 m(2) as having a GFR of 60 ml or higher per minute per 1.73 m(2). The combined creatinine-cystatin C equation performed better than equations based on either of these markers alone and may be useful as a confirmatory test for chronic kidney disease. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases.).
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        Review on uremic toxins: classification, concentration, and interindividual variability.

        The choice of the correct concentration of potential uremic toxins for in vitro, ex vivo, and in vivo experiments remains a major area of concern; errors at this level might result in incorrect decisions regarding therpeutic correction of uremia and related clinical complications. An encyclopedic list of uremic retention solutes was composed, containing their mean normal concentration (CN), their highest mean/median uremic concentration (CU), their highest concentration ever reported in uremia (CMAX), and their molecular weight. A literature search of 857 publications on uremic toxicity resulted in the selection of data reported in 55 publications on 90 compounds, published between 1968 and 2002. For all compounds, CU and/or CMAX exceeded CN. Molecular weight was lower than 500 D for 68 compounds; of the remaining 22 middle molecules, 12 exceeded 12,000 D. CU ranged from 32.0 ng/L (methionine-enkephalin) up to 2.3 g/L (urea). CU in the ng/L range was found especially for the middle molecules (10/22; 45.5%), compared with 2/68 (2.9%) for a molecular weight <500 D (P < 0.002). Twenty-five solutes (27.8%) were protein bound. Most of them had a molecular weight <500 D except for leptin and retinol-binding protein. The ratio CU/CN, an index of the concentration range over which toxicity is exerted, exceeded 15 in the case of 20 compounds. The highest values were registered for several guanidines, protein-bound compounds, and middle molecules, to a large extent compounds with known toxicity. A ratio of CMAX/CU <4, pointing to a Gaussian distribution, was found for the majority of the compounds (74/90; 82%). For some compounds, however, this ratio largely exceeded 4 [e.g., for leptin (6.81) or indole-3-acetic acid (10.37)], pointing to other influencing factors than renal function, such as gender, genetic predisposition, proteolytic breakdown, posttranslation modification, general condition, or nutritional status. Concentrations of retention solutes in uremia vary over a broad range, from nanograms per liter to grams per liter. Low concentrations are found especially for the middle molecules. A substantial number of molecules are protein bound and/or middle molecules, and many of these exert toxicity and are characterized by a high range of toxic over normal concentration (CU/CN ratio). Hence, uremic retention is a complex problem that concerns many more solutes than the current markers of urea and creatinine alone. This list provides a basis for systematic analytic approaches to map the relative importance of the enlisted families of toxins.
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          Circulating TNF receptors 1 and 2 predict ESRD in type 2 diabetes.

          Levels of proinflammatory cytokines associate with risk for developing type 2 diabetes but whether chronic inflammation contributes to the development of diabetic complications, such as ESRD, is unknown. In the 1990s, we recruited 410 patients with type 2 diabetes for studies of diabetic nephropathy and recorded their characteristics at enrollment. During 12 years of follow-up, 59 patients developed ESRD (17 per 1000 patient-years) and 84 patients died without ESRD (24 per 1000 patient-years). Plasma markers of systemic inflammation, endothelial dysfunction, and the TNF pathway were measured in the study entry samples. Of the examined markers, only TNF receptors 1 and 2 (TNFR1 and TNFR2) associated with risk for ESRD. These two markers were highly correlated, but ESRD associated more strongly with TNFR1. The cumulative incidence of ESRD for patients in the highest TNFR1 quartile was 54% after 12 years but only 3% for the other quartiles (P<0.001). In Cox proportional hazard analyses, TNFR1 predicted risk for ESRD even after adjustment for clinical covariates such as urinary albumin excretion. Plasma concentration of TNFR1 outperformed all tested clinical variables with regard to predicting ESRD. Concentrations of TNFRs moderately associated with death unrelated to ESRD. In conclusion, elevated concentrations of circulating TNFRs in patients with type 2 diabetes at baseline are very strong predictors of the subsequent progression to ESRD in subjects with and without proteinuria.
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            Author and article information

            Affiliations
            [1 ]Nephrology Division, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States of America
            [2 ]Transplant Medicine, IRCCS Ospedale San Raffaele, Milan, Italy
            [3 ]Section on Genetics and Epidemiology, Joslin Diabetes Center, Harvard Medical School, Boston, MA, United States of America
            [4 ]San Giovanni Battista Hospital and University of Turin, Division of Nephrology, Dialysis, and Transplantation, Turin, Italy
            [5 ]Biomedical Engineering, University of Texas, Austin, TX, United States of America
            [6 ]Universita’ Vita-Salute San Raffaele, Milan, Italy
            [7 ]Medicine, Al-Azhar University, Cairo, Egypt
            [8 ]Radiology, San Raffaele Scientific Institute, Milan, Italy
            [9 ]Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America
            [10 ]Transplantation Research Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
            Universidade Federal de Sao Paulo, BRAZIL
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            • Conceptualization: RB PF MAN AC AL SM.

            • Data curation: RB PF AL SM ST MBN FD.

            • Formal analysis: RB MAN SM AL AC.

            • Funding acquisition: PF AC AL.

            • Investigation: RB MAN LB SM ST FD MBN AVV VU VDZ BEE AS FDC AL.

            • Methodology: RB MAN PF.

            • Project administration: RB PF MAN SM AL AC.

            • Resources: PF RB LB SB.

            • Software: RB MAN.

            • Supervision: AC AL AC BL.

            • Validation: RB MAN LB SM ST FD MBN AVV VU VDZ BEE AS FDC AL.

            • Writing – original draft: RB MAN PF.

            • Writing – review & editing: RB MAN LB SM ST FD MBN AVV VU VDZ BEE MV AS FDC AL AC PF.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            4 January 2017
            2017
            : 12
            : 1
            28052095 5214547 10.1371/journal.pone.0169077 PONE-D-16-15738
            © 2017 Bassi et al

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Counts
            Figures: 3, Tables: 1, Pages: 14
            Product
            Funding
            This work was supported by a AST Genentech/Novartis Clinical Science Fellowship grant to RB and an Italian Society of Diabetes (AMD-SID) Pasquale di Coste Award to RB. RB was supported by a JDRF Post-Doctoral Research Fellowship grant. MAN received a JDRF Career Development Award (5-CDA-2015-89-A-B). PF was supported by an American Heart Association (AHA) Grant-In-Aid and the Italian Ministry of Health (grant RF-2010-2303119). PF also received support from the EFSD/Sanofi European Research Programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Biology and Life Sciences
            Anatomy
            Renal System
            Kidneys
            Medicine and Health Sciences
            Anatomy
            Renal System
            Kidneys
            Medicine and Health Sciences
            Surgical and Invasive Medical Procedures
            Transplantation
            Organ Transplantation
            Renal Transplantation
            Medicine and Health Sciences
            Surgical and Invasive Medical Procedures
            Urinary System Procedures
            Renal Transplantation
            Biology and Life Sciences
            Physiology
            Renal Physiology
            Glomerular Filtration Rate
            Medicine and Health Sciences
            Physiology
            Renal Physiology
            Glomerular Filtration Rate
            Biology and Life Sciences
            Biochemistry
            Metabolism
            Metabolites
            Biology and Life Sciences
            Biochemistry
            Metabolism
            Metabolomics
            Research and analysis methods
            Spectrum analysis techniques
            NMR spectroscopy
            Correlation Spectroscopy
            Two-Dimensional Correlation Spectroscopy
            Research and analysis methods
            Spectrum analysis techniques
            NMR spectroscopy
            Two-dimensional NMR spectroscopy
            Two-Dimensional Correlation Spectroscopy
            Biology and Life Sciences
            Anatomy
            Body Fluids
            Urine
            Medicine and Health Sciences
            Anatomy
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            Biology and Life Sciences
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            Medicine and Health Sciences
            Physiology
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            Biology and Life Sciences
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