Organ availability limits kidney transplantation, the best treatment for end-stage kidney disease. Deceased donor acceptance criteria have been relaxed to include older donors with higher risk of inferior posttransplant outcomes. More granular prediction models, based on deeper resolution organ assessment and understanding of damage processes, could substantially improve donor organ allocation and reduce graft dysfunction risk. Here, we profiled pre-implantation kidney biopsy proteomes from 185 deceased donors by high-resolution mass spectrometry and used machine learning to integrate and model these data, and donor and recipient clinical metadata to predict outcome. Our analysis and orthogonal validation on an independent cohort revealed 136 proteins predictive of outcome, 124 proteins of which showed donor-age modulated predictive effects. Observed associations with inflammatory, catabolic, lipid metabolism and apoptotic pathways may predispose donor kidneys to suboptimal posttransplant outcomes. Our work shows that integrating kidney proteome information with clinical metadata enhances the resolution of donor kidney quality stratification, and the highlighted biological mechanisms open new research directions in developing interventions during donor management or preservation to improve kidney transplantation outcome.
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