Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10

Julie Ho, Atul Sharma, Rupasri Mandal, David S Wishart, Chris Wiebe, Leroy Storsley, Martin Karpinski, Ian W Gibson, Peter W Nickerson, David N Rush, Julie Ho, Atul Sharma, Rupasri Mandal, David S Wishart, Chris Wiebe, Leroy Storsley, Martin Karpinski, Ian W Gibson, Peter W Nickerson, David N Rush

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.

Figures

FIGURE 1
FIGURE 1
Urinary metabolites can distinguish the severity of underlying alloimmune inflammation using a 3-way PLS2 classifier. Metabolomics significantly distinguishes no inflammation (blue), from mild (green) and severe (red) inflammation.
FIGURE 2
FIGURE 2
Urinary metabolites distinguish alloimmune inflammation, using a classifier trained on no inflammation versus any inflammation. A, 3D score plot demonstrates separation of no inflammation (blue), from mild (green) and severe (red) inflammation. The diagnostic performance of the (B) Full PLS-DA model (n = 34 metabolites); (C) Performance of the classification scores in the full cohort (n = 137 urines); (D) Representative metabolite scores in patients with sequential biopsies.
FIGURE 3
FIGURE 3
Urinary CXCL10 distinguishes alloimmune inflammation and improves the diagnostic performance of urinary metabolites. A, Urinary CXCL10 demonstrates increasing levels with increasing severity of alloimmune inflammation. B, The combination of urine CXCL10 and metabolites improves the overall diagnostic performance for alloimmune inflammation.
FIGURE 4
FIGURE 4
Urinary metabolites distinguish any alloimmune inflammation from ATN, using a classifier trained on any inflammation versus ATN. A, 3D score plot demonstrates separation of any inflammation (red) from ATN (blue). The diagnostic performance of the (B) Full PLS-DA model (n = 34 metabolites); (C) Performance of the classification scores in any inflammation (n = 71) versus ATN (n = 14).

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