ArticleAmerican journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons2026
Recipient plasma metabolomics as a predictor of heart transplant severe primary graft dysfunction.
Article in American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Primary graft dysfunction (PGD) is a leading cause of morbidity and mortality following orthotopic heart transplantation (OHT), yet its biology remains unclear, and metabolomics research is limited. A single-center study was conducted on 62 OHT recipients. Preoperative plasma samples underwent metabolomics analysis via liquid chromatography-mass spectrometry. Metabolic signatures of severe PGD were identified using partial least squares discriminant analysis and pathway enrichment analysis of untargeted data, with additional analysis of annotated and identified metabolites. Partial least squares discriminant analysis of combined mass spectral signals from preoperative plasma showed clustering of patients who developed severe PGD. Pathway enrichment highlighted alterations in arachidonic acid, tryptophan, tyrosine, and branched-chain amino acid metabolism as key preoperative predictors of severe PGD. Preoperative blood levels of 75 metabolites, spanning inflammatory lipids, amino acids, and redox-active species, differed (P < .05) between patients who developed severe PGD and those who did not, although only 3 remained significantly different after correction for multiple comparisons (q < .05). Taken together, these exploratory findings demonstrate that distinct preoperative metabolic profiles exist in OHT recipients who develop severe PGD and support hypothesis generation on candidate metabolic signatures to identify patients at high risk. They suggest that the plasma metabolome may be a powerful tool for studying PGD biology.
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