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Impact of older donor age in kidney transplants in a biopsy-based observational study
Katelynn Madill-Thomsen, et al.
Katelynn Madill-Thomsen, et al.
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Clinical Research and Public Health Clinical Research Nephrology

Impact of older donor age in kidney transplants in a biopsy-based observational study

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Abstract

Because older donor age is a major concern when considering kidneys for potential transplantation, we explored the actual effect of donor age on the features of kidneys that have been transplanted. We studied the correlations of donor age with molecular injury and rejection scores in 4,502 kidney transplant biopsies assessed by microarrays as well as function and postbiopsy survival. We used multivariable analyses to correct for the correlations of donor age with other predictive variables: recipient age, time of biopsy after transplant, and deceased versus living donors. Older donor age correlated with lower glomerular filtration rate (GFR) and increased acute and chronic injury transcripts but had no effect on rejection, which was anticorrelated with recipient age. Acute injury transcripts peaked immediately after transplant and regressed. Older donor age had little effect on acute molecular injury immediately after transplant but strongly increased molecular injury scores at later times, peaking about 1-year after transplant, indicating that older age does not increase molecular injury but increases failed repair after injury. As expected, older donor age correlated with increased chronic injury and lower GFR, evident from the earliest time after transplant, pretransplant aging. However, despite substantial age-related effects, the quantitative contribution of donor aging to molecular injury, function, and survival was very small.

Authors

Katelynn Madill-Thomsen, Martina Mackova, Jessica Chang, Enver Akalin, Tarek Alhamad, Sanjiv Anand, Miha Arnol, Rajendra Baliga, Mirosław Banasik, Christopher Blosser, Georg Böhmig, Daniel Brennan, Jonathan Bromberg, Klemens Budde, Andrzej Chamienia, Kevin V. Chow, Michał Ciszek, Declan de Freitas, Dominika Dęborska-Materkowska, Alicja Dębska-Ślizień, Arjang Djamali, Leszek Domański, Magdalena Durlik, Gunilla Einecke, Farsad Eskandary, Richard Fatica, Iman Francis, Justyna Fryc, John Gill, Jagbir Gill, Maciej Glyda, Sita Gourishankar, Marta Gryczman, Gaurav Gupta, Petra Hruba, Peter Hughes, Arksarapuk Jittirat, Zeljka Jurekovic, Layla Kamal, Mahmoud Kamel, Sam Kant, Nika Kojc, Joanna Konopa, James Lan, Roslyn Mannon, Arthur Matas, Joanna Mazurkiewicz, Marius Miglinas, Thomas Mueller, Marek Myślak, Beata Naumnik, Anita Patel, Agnieszka Perkowska-Ptasińska, Michael Picton, Grzegorz Piecha, Emillio Poggio, Silvie Rajnochova Bloudickova, Thomas Schachtner, Sung Shin, Soroush Shojai, Majid Sikosana, Janka Slatinská, Katarzyna Smykal-Jankowiak, Ashish Solanki, Zeljka Veceric Haler, Ondrej Viklicky, Ksenija Vucur Simic, Matthew R. Weir, Andrzej Wiecek, Zbigniew Włodarczyk, Ziad Zaky, Philip F. Halloran

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Figure 5

Relationships between injury and rejection molecular features as well as donor age, recipient age, and time after transplant with injury PC1/2/3, and variable importance in predicting eGFR and 3-year postbiopsy graft survival.

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Relationships between injury and rejection molecular features as well as...
Factor maps in the N = 4,502 kidney transplant biopsy population showing the correlations between the input variables (red circles) and the principal components in the N = 4,502 kidney transplant biopsy population. Supplemental variables are shown in black. Donor age was also a supplemental variable and is shown in blue. The correlations between the PCA input variables and the PC scores are shown as factor maps in (A) PC2 versus PC1 and (B) PC2 versus PC3. C–F show relative variable importance plots from random survival forest analyses using injury PC1, PC2, and PC3; rejection PC1, PC2, and PC3; donor age; eGFR; recipient age; deceased donor status (DDvLD); and time of biopsy after transplant (TxBx) as predictors for eGFR and 3-year postbiopsy death-censored survival (1 random biopsy per transplant). (C) Selected variables predicting eGFR in all 4,502 biopsies; (D) predicting eGFR in 2,479 no rejection biopsies; (E) predicting 3-year survival after biopsy, including eGFR as an input variable and using donor age as a continuous variable; and (F) predicting 3-year survival after biopsy, including eGFR as an input variable and using donor age as a binary variable.

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