Background. We studied urine metabolomics (UM) alone or combined with urine CXCL10 to non-invasively detect cellular infiltrates in renal allograft biopsies.

Methods. Urines (n=137) were obtained prior to biopsy in 113 patients with no (n=66), mild (borderline or subclinical; n=58) or severe (clinical; n=13) rejection. UM was performed with Direct Flow Injection-Tandem Mass Spectrometry (DFI-MS/MS) using multiple reaction monitoring. Urine CXCL10 was measured by ELISA. UM data was analyzed with PLS-DA, validated with leave-one-out cross-validation, and an optimal two-component model was developed. CXCL10 area under the curve (AUC) was determined and a net reclassification index (NRI) and integrated discrimination index (IDI) analyses were performed.

Results. A three-way PLS2 classifier demonstrated that UM discriminated the three groups (Cohen's kappa 0.601, 95%CI 0.46-0.74, p<0.001). UM and CXCL10 demonstrated an AUC 0.81 (95%CI 0.74-0.88) and 0.76 (95%CI 0.68-0.84) respectively; and a combined AUC 0.84 (95%CI 0.78-0.91) for detecting alloimmune inflammation that was improved by NRI and IDI. Urinary CXCL10 was the best discriminator, followed by urine acylcarnitines and hexose.

Conclusions. UM can accurately and non-invasively discriminate non-inflamed biopsies from subclinical and clinical inflammation, and the addition of urine CXCL10 had a modest but significant effect on overall diagnostic performance. These data suggest that UM and CXCL10 may be useful for non-invasive monitoring of alloimmune inflammation in renal transplant patients.

CITATION INFORMATION: Ho J, Sharma A, Mandal R, Wishart D, Wiebe C, Storsley L, Karpinski M, Gibson I, Nickerson P, Rush D. Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10. Am J Transplant. 2016;16 (suppl 3).

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