Halloran, P.F. and Reeve, J. and Aliabadi, A. and Cadeiras, M. and Crespo-Leiro, M.G. and Depasquale, E.C. and Deng, M. and Goekler, J. and Kim, D.H. and Kobashigawa, J. and Parkes, M. and Macdonald, P. and Potena, L. and Stehlik, J. and Zuckermann, A. (2020) Mapping the Injury Phenotypes of Heart Transplant. The Journal of Heart and Lung Transplantation, 39 (4). pp. S58-S59. ISSN 10532498
Halloran, P.F. and Reeve, J. and Aliabadi, A. and Cadeiras, M. and Crespo-Leiro, M.G. and Depasquale, E.C. and Deng, M. and Goekler, J. and Kim, D.H. and Kobashigawa, J. and Parkes, M. and Macdonald, P. and Potena, L. and Stehlik, J. and Zuckermann, A. (2020) Mapping the Injury Phenotypes of Heart Transplant. The Journal of Heart and Lung Transplantation, 39 (4). pp. S58-S59. ISSN 10532498
Halloran, P.F. and Reeve, J. and Aliabadi, A. and Cadeiras, M. and Crespo-Leiro, M.G. and Depasquale, E.C. and Deng, M. and Goekler, J. and Kim, D.H. and Kobashigawa, J. and Parkes, M. and Macdonald, P. and Potena, L. and Stehlik, J. and Zuckermann, A. (2020) Mapping the Injury Phenotypes of Heart Transplant. The Journal of Heart and Lung Transplantation, 39 (4). pp. S58-S59. ISSN 10532498
Abstract
Purpose: In previous studies we used microarray analysis to characterize the rejection phenotypes of heart transplant endomyocardial biopsies. Although these phenotypes were associated with graft survival, gene-based analyses indicated that survival was more strongly associated with injury- than with rejection-related genes. We therefore built a second model using injury genes, analogous to our earlier rejection model, in order to have an independent classification system more concordant with outcomes. Methods: We used microarrays to analyzed gene expression of previously annotated injury-associated transcripts in 1320 biopsies (645 patients) from 13 centers in the INTERHEART study. Categories were defined using unsupervised archetypal analysis. These categories and those from the rejection analysis were used to predict low LVEF (<=55), and 3-year graft survival, using random forest analysis. Results: The injury analysis identified four phenotypes: I1severe; I2late; I3early; and I4no-injury. These were related to the rejection phenotypes: R1normal, R2TCMR, R3ABMR, R4injury, and R5minor. Comparison with the rejection classification showed that severe and late injury were often but not always associated with rejection. TCMR was almost always injured, but ABMR was less consistent. When injury and rejection phenotypes were combined in random forests, the injury scores were the best predictors of low rejection fraction (LVEF) (Figure 1A), and graft loss, although R4injury was also important (Figure 1B). Conclusion: Parenchymal injury and late changes (atrophy-fibrosis) can be mapped in heart transplant biopsies, and their presentation correlates with low LVEF and lower 3-year survival. Injury is often, but not always, associated with rejection. Severe acute injury and the late fibrosis phenotypes are often associated with TCMR. Thus parenchymal injury is the intermediate phenotype by which rejection mediates disturbed function and survival. ClinicalTrials.gov #NCT02670408.
Metadata
Subjects: | R Medicine > R Medicine (General) |
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Depositing User: | Repository Administrator |
Date Deposited: | 02 Jun 2020 03:42 |
Last Modified: | 02 Jun 2020 03:42 |