Abstract: SA-OR078
Effect of Biomarkers and Patient-Specific Factors on Kidney Rejection Risk
Session Information
- Transplantation: Clinical Controversies in Donation, Access, Monitoring, and Treatment
November 08, 2025 | Location: Room 370A, Convention Center
Abstract Time: 05:20 PM - 05:30 PM
Category: Transplantation
- 2102 Transplantation: Clinical
Authors
- Singla, Akhil, Northwestern University, Evanston, Illinois, United States
- Chen, Kenny, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
- Robelly, Blade H, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
- Rebello, Christabel, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
- Lantz, Connor, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
- Zhao, Lihui, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
- Lacombe, Ronnie, Eurofins Transplant Genomics, Lenexa, Kansas, United States
- Sinha, Rohita, Eurofins Transplant Genomics, Lenexa, Kansas, United States
- Hanna, Genevieve S, Eurofins Transplant Genomics, Lenexa, Kansas, United States
- Kleiboeker, Steven, Eurofins Transplant Genomics, Lenexa, Kansas, United States
- Park, Sookhyeon, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
- Mehrotra, Sanjay, Northwestern University, Evanston, Illinois, United States
- Friedewald, John J., Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
Background
Multiple biomarkers improve rejection prediction, but their relative contributions and optimal integration remain unclear. We evaluated five biomarkers–blood gene expression profiling (GEP), torque teno virus (TTV), donor-derived cell-free DNA (dd-cfDNA), and urine chemokines CXCL9/CXCL10–along with patient-specific factors to explain the risk of subclinical acute rejection (subAR) compared to stable graft function (TX).
Methods
We analyzed multi-center CTOT-08 trial data from 226 kidney transplant recipients over two years post-transplant, comprising 462 biopsy-paired samples (121 subAR, 341 TX). We performed stepwise multivariate logistic regression, incorporating these five biomarkers and recipient factors, including race, age, type of transplant, etc. We utilized an independent validation cohort with 54 subAR and 92 TX samples from 136 Northwestern University Mini-Kidney Biorepository subjects.
Results
Stepwise regression retained GEP, dd-cfDNA, log-transformed TTV viral load (logTTV), CXCL9, transplant type, and race as predictors of subAR versus TX within the first two years post-transplant. Each ten-fold increase in TTV viral load reduced the odds of subAR by 25.2% (95% CI: 12.3%-36.2%), whereas one-unit increases in GEP, dd-cfDNA, and CXCL9/creatinine raised subAR odds by 5% (95% CI: 3.5%-6.6%), 42.8% (95% CI: 18.6%-71.8%), and 5% (95% CI: 1%-9.5%), respectively. Recipients of living-donor kidneys had 49% (95% CI: 16.5%-68.9%) lower subAR risk than deceased-donor recipients, and race was not significant (p=0.244). In year 1 post-transplant, logTTV (p=0.004) and transplant type (p=0.014) were significant predictors but were not in year 2 (p>0.44), whereas CXCL9 was significant in year 2 (p=0.047) but not in year 1 (p=0.22). The model’s accuracy was 0.71 (95% CI: 0.67-0.75); AUC, 0.82; sensitivity, 0.85; specificity, 0.66; positive predictive value, 0.47; and negative predictive value, 0.93. In contrast, individual biomarkers showed an AUC<0.77, sensitivity<0.68, specificity<0.87, PPV<0.55, and NPV<0.84. The validation cohort showed similar results.
Conclusion
This analysis demonstrates how multiple biomarkers and patient-specific factors jointly assess rejection risk, enabling more precise, personalized monitoring in post-transplant care.
Funding
- NIDDK Support – Eurofins-Viracor