Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function
Renal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification o...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/12/1/57 |
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| author | Thomas Verissimo Anna Faivre Sebastian Sgardello Maarten Naesens Sophie de Seigneux Gilles Criton David Legouis |
| author_facet | Thomas Verissimo Anna Faivre Sebastian Sgardello Maarten Naesens Sophie de Seigneux Gilles Criton David Legouis |
| author_sort | Thomas Verissimo |
| collection | DOAJ |
| description | Renal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification of patients at risk of rapid graft failure is critical to optimize their management and slow the progression of the disease. In 42 kidney grafts undergoing protocol biopsies at reperfusion, we estimated the renal metabolome from RNAseq data. The estimated metabolites’ abundance was further used to predict the renal function within the first year of transplantation through a random forest machine learning algorithm. Using repeated K-fold cross-validation we first built and then tuned our model on a training dataset. The optimal model accurately predicted the one-year eGFR, with an out-of-bag root mean square root error (RMSE) that was 11.8 ± 7.2 mL/min/1.73 m<sup>2</sup>. The performance was similar in the test dataset, with a RMSE of 12.2 ± 3.2 mL/min/1.73 m<sup>2</sup>. This model outperformed classic statistical models. Reperfusion renal metabolome may be used to predict renal function one year after allograft kidney recipients. |
| first_indexed | 2024-03-10T00:58:02Z |
| format | Article |
| id | doaj.art-c44f757632214e988cf1d6861b48452e |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-10T00:58:02Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-c44f757632214e988cf1d6861b48452e2023-11-23T14:40:18ZengMDPI AGMetabolites2218-19892022-01-011215710.3390/metabo12010057Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft FunctionThomas Verissimo0Anna Faivre1Sebastian Sgardello2Maarten Naesens3Sophie de Seigneux4Gilles Criton5David Legouis6Laboratory of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, SwitzerlandLaboratory of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, SwitzerlandDepartment of Surgery, University Hospital of Geneva, 1205 Geneva, SwitzerlandService of Nephrology, University Hospitals of Leuven, 3000 Leuven, BelgiumLaboratory of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, SwitzerlandGeneva School of Economics and Management, University of Geneva, 1205 Geneva, SwitzerlandLaboratory of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, SwitzerlandRenal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification of patients at risk of rapid graft failure is critical to optimize their management and slow the progression of the disease. In 42 kidney grafts undergoing protocol biopsies at reperfusion, we estimated the renal metabolome from RNAseq data. The estimated metabolites’ abundance was further used to predict the renal function within the first year of transplantation through a random forest machine learning algorithm. Using repeated K-fold cross-validation we first built and then tuned our model on a training dataset. The optimal model accurately predicted the one-year eGFR, with an out-of-bag root mean square root error (RMSE) that was 11.8 ± 7.2 mL/min/1.73 m<sup>2</sup>. The performance was similar in the test dataset, with a RMSE of 12.2 ± 3.2 mL/min/1.73 m<sup>2</sup>. This model outperformed classic statistical models. Reperfusion renal metabolome may be used to predict renal function one year after allograft kidney recipients.https://www.mdpi.com/2218-1989/12/1/57AKI (acute kidney injury)renal transplantationmachine learningmetabolomics |
| spellingShingle | Thomas Verissimo Anna Faivre Sebastian Sgardello Maarten Naesens Sophie de Seigneux Gilles Criton David Legouis Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function Metabolites AKI (acute kidney injury) renal transplantation machine learning metabolomics |
| title | Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function |
| title_full | Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function |
| title_fullStr | Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function |
| title_full_unstemmed | Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function |
| title_short | Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function |
| title_sort | estimated renal metabolomics at reperfusion predicts one year kidney graft function |
| topic | AKI (acute kidney injury) renal transplantation machine learning metabolomics |
| url | https://www.mdpi.com/2218-1989/12/1/57 |
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