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Published on: October 31, 2012
The novel proteomic signature for cardiac allograft vasculopathy
Dongmei Wei1, Sander Trenson2, Jan M Van Keer3
1Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, Leuven, BE-3000, Belgium.
Insights
This study developed a urinary proteomic signature to detect cardiac allograft vasculopathy (CAV) after heart transplantation. This non-invasive biomarker shows promise for early CAV screening and improved patient outcomes.
Area of Science:
- Urology
- Cardiology
- Proteomics
- Biomarkers
Background:
- Cardiac allograft vasculopathy (CAV) is a primary cause of long-term mortality and re-transplantation following heart transplantation.
- Current non-invasive screening methods for CAV are limited, necessitating the development of novel diagnostic tools.
Purpose of the Study:
- To identify and validate a urinary proteomic signature for the early, non-invasive detection of cardiac allograft vasculopathy (CAV).
Main Methods:
- Utilized capillary electrophoresis coupled with mass spectrometry to analyze the urinary proteome of 217 heart transplant recipients.
- Employed extreme gradient boost machine learning to develop a 27-peptide proteomic signature for CAV discrimination.
- Validated the signature's performance in independent derivation and validation cohorts.
Main Results:
- The proteomic signature achieved an AUC of 0.83 in the derivation cohort and 0.71 in the validation cohort.
- In the validation cohort, the signature demonstrated 68.4% sensitivity, 73.2% specificity, and 71.6% accuracy for CAV detection.
- The signature significantly improved diagnostic accuracy when added to existing clinical risk models, indicated by improved discrimination and reclassification metrics.
Conclusions:
- A pilot study successfully identified and validated a urinary proteomic signature for CAV surveillance.
- The identified peptides, predominantly collagen fragments, offer insights into CAV pathogenesis.
- This proteomic signature represents a potential non-invasive approach for CAV monitoring and may guide personalized treatment strategies.
Aims:
Cardiac allograft vasculopathy (CAV) is the major long-term complication after heart transplantation, leading to mortality and re-transplantation. As available non-invasive biomarkers are scarce for CAV screening, we aimed to identify a proteomic signature for CAV.
Methods And Results:
We measured urinary proteome by capillary electrophoresis coupled with mass spectrometry in 217 heart transplantation recipients (mean age: 55.0 ± 14.4 years; women: 23.5%), including 76 (35.0%) patients with CAV diagnosed by coronary angiography. We randomly and evenly grouped participants into the derivation cohort (n = 108, mean age: 56.4 ± 13.8 years; women: 22.2%; CAV: n = 38) and the validation cohort (n = 109, mean age: 56.4 ± 13.8 years; women: 24.8%, CAV: n = 38), stratified by CAV. Using the decision tree-based machine learning methods (extreme gradient boost), we constructed a proteomic signature for CAV discrimination in the derivation cohort and verified its performance in the validation cohort. The proteomic signature that consisted of 27 peptides yielded areas under the curve of 0.83 [95% confidence interval (CI): 0.75-0.91, P < 0.001] and 0.71 (95% CI: 0.60-0.81, P = 0.001) for CAV discrimination in the derivation and validation cohort, respectively. With the optimized threshold of 0.484, the sensitivity, specificity, and accuracy for CAV differentiation in the validation cohort were 68.4%, 73.2%, and 71.6%, respectively. With adjustment of potential clinical confounders, the signature was significantly associated with CAV [adjusted odds ratio: 1.31 (95% CI: 1.07-1.64) for per 0.1% increment in the predicted probability, P = 0.012]. Diagnostic accuracy significantly improved by adding the signature to the logistic model that already included multiple clinical risk factors, suggested by the integrated discrimination improvement of 9.1% (95% CI: 2.5-15.3, P = 0.005) and net reclassification improvement of 83.3% (95% CI: 46.7-119.5, P < 0.001). Of the 27 peptides, the majority were the fragments of collagen I (44.4%), collagen III (18.5%), collagen II (3.7%), collagen XI (3.7%), mucin-1 (3.7%), xylosyltransferase 1 (3.7%), and protocadherin-12 (3.7%). Pathway analysis performed in Reactome Pathway Database revealed that the multiple pathways involved by the signature were related to the pathogenesis of CAV, such as collagen turnover, platelet aggregation and coagulation, cell adhesion, and motility.
Conclusions:
This pilot study identified and validated a urinary proteomic signature that provided a potential approach for the surveillance of CAV. These proteins might provide insights into CAV pathological processes and call for further investigation into personalized treatment targets.
