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Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10
BACKGROUND: The goal of this study was to characterize urinary metabolomics for the noninvasive detection of cellular inflammation and to determine if adding urinary chemokine ligand 10 (CXCL10) improves the overall diagnostic discrimination. METHODS: Urines (n = 137) were obtained before biopsy in...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4946516/ https://www.ncbi.nlm.nih.gov/pubmed/27500268 http://dx.doi.org/10.1097/TXD.0000000000000589 |
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author | Ho, Julie Sharma, Atul Mandal, Rupasri Wishart, David S. Wiebe, Chris Storsley, Leroy Karpinski, Martin Gibson, Ian W. Nickerson, Peter W. Rush, David N. |
author_facet | Ho, Julie Sharma, Atul Mandal, Rupasri Wishart, David S. Wiebe, Chris Storsley, Leroy Karpinski, Martin Gibson, Ian W. Nickerson, Peter W. Rush, David N. |
author_sort | Ho, Julie |
collection | PubMed |
description | BACKGROUND: The goal of this study was to characterize urinary metabolomics for the noninvasive detection of cellular inflammation and to determine if adding urinary chemokine ligand 10 (CXCL10) improves the overall diagnostic discrimination. METHODS: Urines (n = 137) were obtained before biopsy in 113 patients with no (n = 66), mild (borderline or subclinical; n = 58), or severe (clinical; n = 13) rejection from a prospective cohort of adult renal transplant patients (n = 113). Targeted, quantitative metabolomics was performed with direct flow injection tandem mass spectrometry using multiple reaction monitoring (ABI 4000 Q-Trap). Urine CXCL10 was measured by enzyme-linked immunosorbent assay. A projection on latent structures discriminant analysis was performed and validated using leave-one-out cross-validation, and an optimal 2-component model developed. Chemokine ligand 10 area under the curve (AUC) was determined and net reclassification index and integrated discrimination index analyses were performed. RESULTS: PLS2 demonstrated that urinary metabolites moderately discriminated the 3 groups (Cohen κ, 0.601; 95% confidence interval [95% CI], 0.46-0.74; P < 0.001). Using binary classifiers, urinary metabolites and CXCL10 demonstrated an AUC of 0.81 (95% CI, 0.74-0.88) and 0.76 (95% CI, 0.68-0.84), respectively, and a combined AUC of 0.84 (95% CI, 0.78-0.91) for detecting alloimmune inflammation that was improved by net reclassification index and integrated discrimination index analyses. Urinary CXCL10 was the best univariate discriminator, followed by acylcarnitines and hexose. CONCLUSIONS: Urinary metabolomics can noninvasively discriminate noninflamed renal allografts from those with subclinical and clinical inflammation, and the addition of urine CXCL10 had a modest but significant effect on overall diagnostic performance. These data suggest that urinary metabolomics and CXCL10 may be useful for noninvasive monitoring of alloimmune inflammation in renal transplant patients. |
format | Online Article Text |
id | pubmed-4946516 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-49465162016-08-05 Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10 Ho, Julie Sharma, Atul Mandal, Rupasri Wishart, David S. Wiebe, Chris Storsley, Leroy Karpinski, Martin Gibson, Ian W. Nickerson, Peter W. Rush, David N. Transplant Direct Kidney Transplantation BACKGROUND: The goal of this study was to characterize urinary metabolomics for the noninvasive detection of cellular inflammation and to determine if adding urinary chemokine ligand 10 (CXCL10) improves the overall diagnostic discrimination. METHODS: Urines (n = 137) were obtained before biopsy in 113 patients with no (n = 66), mild (borderline or subclinical; n = 58), or severe (clinical; n = 13) rejection from a prospective cohort of adult renal transplant patients (n = 113). Targeted, quantitative metabolomics was performed with direct flow injection tandem mass spectrometry using multiple reaction monitoring (ABI 4000 Q-Trap). Urine CXCL10 was measured by enzyme-linked immunosorbent assay. A projection on latent structures discriminant analysis was performed and validated using leave-one-out cross-validation, and an optimal 2-component model developed. Chemokine ligand 10 area under the curve (AUC) was determined and net reclassification index and integrated discrimination index analyses were performed. RESULTS: PLS2 demonstrated that urinary metabolites moderately discriminated the 3 groups (Cohen κ, 0.601; 95% confidence interval [95% CI], 0.46-0.74; P < 0.001). Using binary classifiers, urinary metabolites and CXCL10 demonstrated an AUC of 0.81 (95% CI, 0.74-0.88) and 0.76 (95% CI, 0.68-0.84), respectively, and a combined AUC of 0.84 (95% CI, 0.78-0.91) for detecting alloimmune inflammation that was improved by net reclassification index and integrated discrimination index analyses. Urinary CXCL10 was the best univariate discriminator, followed by acylcarnitines and hexose. CONCLUSIONS: Urinary metabolomics can noninvasively discriminate noninflamed renal allografts from those with subclinical and clinical inflammation, and the addition of urine CXCL10 had a modest but significant effect on overall diagnostic performance. These data suggest that urinary metabolomics and CXCL10 may be useful for noninvasive monitoring of alloimmune inflammation in renal transplant patients. Lippincott Williams & Wilkins 2016-05-19 /pmc/articles/PMC4946516/ /pubmed/27500268 http://dx.doi.org/10.1097/TXD.0000000000000589 Text en Copyright © 2016 The Authors. Transplantation Direct. Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (http://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially. |
spellingShingle | Kidney Transplantation Ho, Julie Sharma, Atul Mandal, Rupasri Wishart, David S. Wiebe, Chris Storsley, Leroy Karpinski, Martin Gibson, Ian W. Nickerson, Peter W. Rush, David N. Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10 |
title | Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10 |
title_full | Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10 |
title_fullStr | Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10 |
title_full_unstemmed | Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10 |
title_short | Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10 |
title_sort | detecting renal allograft inflammation using quantitative urine metabolomics and cxcl10 |
topic | Kidney Transplantation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4946516/ https://www.ncbi.nlm.nih.gov/pubmed/27500268 http://dx.doi.org/10.1097/TXD.0000000000000589 |
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