Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10. Issue 6 (June 2016)

• Record Type: Journal Article
• Authors: Ho, Julie; Sharma, Atul; Mandal, Rupasri; Wishart, David S.; Wiebe, Chris; Storsley, Leroy; Karpinski, Martin; Gibson, Ian W.; Nickerson, Peter W.; Rush, David N.
• Is Part Of: Transplantation direct. Volume 2:Issue 6(2016)
• Publication Date: 2016-06
• Subjects: Transplantation of organs, tissues, etc -- Periodicals; Transplantation -- Periodicals; 362.19795
• DOI: 10.1097/TXD.0000000000000589
• Languages: English
• ISSNs: 2373-8731

Abstract

Abstract : 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.