Tehran University of Medical Sciences Iranian Journal of Allergy, Asthma and Immunology 1735-1502 0 0 2026 07 14 1 10 Amirhesam Alirezaei Department of Nephrology, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran Ali Rostami-Asl Department of Nephrology, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran Aidin Niaki Shargh Department of Nephrology, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran Masoumeh Asgharpour Department of Nephrology, Rouhani Hospital, Babol University of Medical Sciences, Babol, Iran Fatemeh Rahmani Clinical Research Development Center, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran Amir-Reza Javanmard Iranian Dry Lab On Kidney Diseases (IDKD), School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Seyed Amirhossein Fazeli Iranian Dry Lab On Kidney Diseases (IDKD), School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran AND Division of Nephrology, Department of Internal Medicine, Taleghani General Hospital, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran AND Clinical Research and Development Center, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2026 02 20 2026 03 19 Antibody-mediated rejection (ABMR) remains a major cause of kidney graft dysfunction despite advances in immunosuppression. Recent evidence suggests that metabolic reprogramming, particularly de novo lipogenesis, may contribute to chronic allograft injury. This study aimed to identify gene signatures associated with lipid metabolism that are linked to chronic active ABMR in renal transplant recipients. Publicly available microarray datasets and clinical metadata were integrated for 100 biopsy specimens (55 ABMR and 45 controls). After normalization and batch correction, supervised machine-learning models—Random Forest, Support Vector Machine, and Convolutional Neural Network (CNN)—were trained to distinguish ABMR from control samples. Differentially expressed genes related to lipid metabolism pathways were identified and correlated with histopathologic and serologic parameters according to Banff diagnostic criteria. The CNN model achieved an accuracy of 88% and an AUC-ROC of 0.92, outperforming SVM and RF classifiers. Identified lipid metabolism-related gene signatures showed significant associations with markers of immune activation and graft injury, implicating de novo lipogenesis in the pathogenesis of chronic active ABMR. Functional enrichment analyses further supported dysregulation of fatty-acid biosynthesis pathways. Integrating transcriptomic profiling with artificial intelligence modeling uncovered lipid- related molecular patterns discriminating chronic active ABMR from stable grafts. These findings provide potential biomarkers and mechanistic insight into metabolic dysregulation underlying antibody-mediated graft injury, offering a foundation for future translational validation. https://ijaai.tums.ac.ir/index.php/ijaai/article/view/4755 https://ijaai.tums.ac.ir/index.php/ijaai/article/download/4755/2370