Characterization of gut microbiota and metabolites in renal transplant recipients during COVID-19 and prediction of one-year allograft function
Abstract Background The gut-lung-kidney axis is pivotal in immune-related kidney diseases, with gut dysbiosis potentially exacerbating the severity of Coronavirus disease 2019 (COVID-19) in recipients of kidney transplant. This study aimed to characterize the gut microbiome and metabolome in renal t...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-025-06090-5 |
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| Summary: | Abstract Background The gut-lung-kidney axis is pivotal in immune-related kidney diseases, with gut dysbiosis potentially exacerbating the severity of Coronavirus disease 2019 (COVID-19) in recipients of kidney transplant. This study aimed to characterize the gut microbiome and metabolome in renal transplant recipients with COVID-19 pneumonia over a one-year follow-up period. Methods A total of 30 renal transplant recipients were enrolled, comprising 17 with COVID-19 pneumonia, six with mild COVID-19, and seven without COVID-19. Fecal samples were collected at the onset of infection for gut microbiome and metabolome analysis. Generalized Estimating Equations (GEE) model and Latent Class Growth Mixed Model (LCGMM) were employed to dissect the relationships among clinical characteristics, laboratory tests, and gut microbiota and metabolites. Results Four microbial phyla (Deferribacteres, TM7, Fusobacteria, and Gemmatimonadetes) and 13 genera were significantly enriched across three recipients groups, correlating with baseline inflammatory response and allograft function. Additionally, 52 differentially expressed metabolites were identified, with seven significantly correlating with eight altered microbiota genera. LCGMM revealed two distinct classes of recipients, with those suffering from COVID-19 pneumonia exhibiting significantly elevated serum creatinine (Scr) trajectories over the one-year period. GEE further identified 12 genera and 181 metabolites closely associated with these trajectories; a multivariable model incorporating gut metabolites of 1-Caffeoylquinic Acid and PMK was found to effectively predict one-year allograft function. Conclusions Our study indicates a possible interaction between the composition of the gut microbiota and metabolites community and COVID-19 in renal transplant recipients, particularly in relation to disease severity and the prediction of one-year allograft function. |
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| ISSN: | 1479-5876 |