Background. Loss of kidney graft function due to interstitial fibrosis (IF) and tubular atrophy (TA) remains the main clinical challenge for long-term graft survival rate.

Methods. 158 deceased donor, primary kidney transplant (KT) patients were studied. 83 KT with 3 time point biopsies (pre-implantation, 3 and 9-mo post-KT (n=249) were evaluated using gene expression(GE) to identify markers involved in CAD progression using prospective and dynamic statistical analysis (training set). The group was classified as KTwith continuous normal function (eGFR >60 from KT) and normal histology (NFA), and with poor or declining function (CAD= continuous negative slope in eGFR and histological evidence of IF/TA). A group of patients with NAF at 4 years post-KT was used as a control (n=35). Patient follow up post-KT was 24-mo (36±4mo). An independent group of 35 KTRs was used for validation (n=105). In addition to the comparison analysis, advanced statistical modeling techniques were used to integrate donor-recipient molecular and clinicopathological data at multiple time points. This data was statistically combined using a binomial GEE model and/or a binomial GLMM as well as a standard logistic regression model. The marginal log-odds were estimated for the Y=1 risk group (CAD). A dynamic multivariate regression model was developed to forecast continuous risks of CAD integrating several predictive factors. Histological evaluation was performed by two independent pathologists.

Results. At 3-mo post-KT, 206 genes were differentially expressed groups (NFA vs CAD). For the same group of KT, 1,893 genes were differentially expressed at 9-mo with 103 being common with the differentially expressed genes ta 3-mo post-KT. The top canonical pathway for genes involved in progression to CAD was role of macrophages, fibroblasts and endothelia cells. Multiple scores were identified and independently validated. Preliminary study found in a composite score two genes LTF and MS4A4A (p=0.002, p=0.002, and p=0.047 at 3, 9, and 12 mo-post KT) as consistent biomarkers for CAD. These genes were also identified as part of the common genes involved in CAD group. The score performance was improved when clinical characteristics were combined to gene expression.

Conclusion. We developed a preliminary composite score model based on genes predictive of CAD at pre and post KT. The performance of the score improves when donor age and race and recipient gender were added to the score.

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