Date: Saturday, June 2, 2018
Session Time: 5:30pm-7:30pm
Presentation Time: 5:30pm-7:30pm
Location: Hall 4EF
There is a lack of biomarkers for pre-kidney transplant immune risk stratification to avoid over- or under-immunosuppression. Since the circulating lipidome is integrally involved in inflammation, we hypothesized that lipidomic biomarkers could be helpful in the prediction of antibody-mediated rejection (AMR).
Serial serum samples collected over 1 year post-kidney transplant (KT; n = 150) from a prospective, observational cohort of 44 adult KT [AMR=16; stable controls (SC)=29) patients were assayed for 210 unique metabolites by quantitative mass spectrometry. An unsupervised hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) with regularized correction were used to identify group differences and select predictors of rejection, respectively. Combined models with addition of clinical data like calculated panel reactive antibody (cPRA) presence of significant donor-specific antibody (DSA) at the time of transplant were derived.
Analysis of day of (pre-)transplant (T0) samples, revealed a 7 lipid classifier which discriminated between AMR and SC with a misclassification rate of 8.9% [area under receiver operating characteristic curve (AUC) =0.95 (95%CI=0.84-0.98), R2=0.63]. A clinical model using cPRA and DSA was inferior and produced a misclassification rate of 15.6% [AUC=0.82 (95%CI=0.69-0.93), R2=0.41]. A combined model using 4 (of 7) lipid classifiers and DSA improved the AUC further to 0.98 (95% CI=0.89-1.0, R2=0.83) with a misclassification of only 2.2%. Specific classes of lipids (lyso-phosphatidylethanolamine and phosphatidylcholine species) were lower in AMR compared with SC. This might be biologically consistent with an inherent lack of pro-inflammatory modulation in the AMR patients. Serial analysis of SC patients demonstrated clear separation between T0 and 6 months post-transplant. T-tests revealed 8 lipids which were upregulated post-transplant. No changes were noted between 6 and 12 months post-transplant. LDA analysis of T0 vs that at time of AMR revealed up regulation of 6 unique lipids (after excluding lipid changes shared with SC group) at the time of rejection.
These preliminary findings suggest that a composite model using a 4 lipid classifier along with DSA could be used for prediction of antibody-mediated rejection. A validation cohort study is planned to verify these preliminary results.
CITATION INFORMATION: Gupta G., Bobba S., Contaifer D., Kimball P., King A., Kumar D., Levy M., Stern J., Wijesinghe D. A Pre-Transplant Blood-Based Lipid Signature for Prediction of Antibody-Mediated Rejection in Kidney Transplant Patients Am J Transplant. 2017;17 (suppl 3).
To cite this abstract in AMA style:Gupta G, Bobba S, Contaifer D, Kimball P, King A, Kumar D, Levy M, Stern J, Wijesinghe D. A Pre-Transplant Blood-Based Lipid Signature for Prediction of Antibody-Mediated Rejection in Kidney Transplant Patients [abstract]. https://atcmeetingabstracts.com/abstract/a-pre-transplant-blood-based-lipid-signature-for-prediction-of-antibody-mediated-rejection-in-kidney-transplant-patients/. Accessed October 22, 2019.
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