Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients
The assessment of kidney function within the first year following transplantation is crucial for predicting long-term graft survival. This study aimed to develop a robust and accurate model using metabolite profiles to predict early long-term outcomes in patient groups at the highest risk of early g...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2300736 |
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| author | Ihsan Yozgat Ulkem Cakır Muhittin Abdulkadir Serdar Sevgi Sahin Osman Ugur Sezerman Emirhan Nemutlu Ahmet Tarik Baykal Mustafa Serteser |
| author_facet | Ihsan Yozgat Ulkem Cakır Muhittin Abdulkadir Serdar Sevgi Sahin Osman Ugur Sezerman Emirhan Nemutlu Ahmet Tarik Baykal Mustafa Serteser |
| author_sort | Ihsan Yozgat |
| collection | DOAJ |
| description | The assessment of kidney function within the first year following transplantation is crucial for predicting long-term graft survival. This study aimed to develop a robust and accurate model using metabolite profiles to predict early long-term outcomes in patient groups at the highest risk of early graft loss. A group of 61 kidney transplant recipients underwent thorough monitoring during a one-year follow-up period, which included a one-week hospital stay and follow-up assessments at three and six months. Based on their 12-month follow-up serum creatinine levels: Group 2 had levels exceeding 1.5 mg/dl, while Group 1 had levels below 1.5 mg/dl. Metabolites were detected by mass spectrometer and first pre-processed. Univariate and multivariate statistical analyses were employed to identify significant differences between the two groups. Nineteen metabolites were found to differ significantly in the 1st week, and seventeen metabolites in the 3rd month (adjusted p-value < 0.05, quality control (QC) < 30, a fold change (FC) > 1.1 or a FC < 0.91, Variable Influence on Projection (VIP) > 1). However, no significant differences were observed in the 6th month. These distinctive metabolites mainly belonged to lipid, fatty acid, and amino acid categories. Ten models were constructed using a backward conditional approach, with the best performance seen in model 5 for Group 2 at the 1st-week mark (AUC 0.900) and model 3 at the 3rd-month mark (AUC 0.924). In conclusion, the models developed in the early stages may offer potential benefits in the management of kidney transplant patients. |
| format | Article |
| id | doaj-art-f1b6ceb2058b448f954c5cc63b90bf6f |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-f1b6ceb2058b448f954c5cc63b90bf6f2025-01-23T04:17:47ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146110.1080/0886022X.2023.2300736Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patientsIhsan Yozgat0Ulkem Cakır1Muhittin Abdulkadir Serdar2Sevgi Sahin3Osman Ugur Sezerman4Emirhan Nemutlu5Ahmet Tarik Baykal6Mustafa Serteser7Department of Medical Biotechnology, Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Istanbul, TurkeyDepartment of Nephrology, Acibadem University School of Medicine, Istanbul, TurkeyDepartment of Medical Biochemistry, Faculty of Medicine, Acibadem University, Istanbul, TurkeyDepartment of Nephrology, Acibadem University School of Medicine, Istanbul, TurkeyDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Acibadem University, Istanbul, TurkeyFaculty of Pharmacy, Department of Analytical Chemistry, Hacettepe University, Ankara, TürkiyeDepartment of Medical Biochemistry, Faculty of Medicine, Acibadem University, Istanbul, TurkeyDepartment of Medical Biochemistry, Faculty of Medicine, Acibadem University, Istanbul, TurkeyThe assessment of kidney function within the first year following transplantation is crucial for predicting long-term graft survival. This study aimed to develop a robust and accurate model using metabolite profiles to predict early long-term outcomes in patient groups at the highest risk of early graft loss. A group of 61 kidney transplant recipients underwent thorough monitoring during a one-year follow-up period, which included a one-week hospital stay and follow-up assessments at three and six months. Based on their 12-month follow-up serum creatinine levels: Group 2 had levels exceeding 1.5 mg/dl, while Group 1 had levels below 1.5 mg/dl. Metabolites were detected by mass spectrometer and first pre-processed. Univariate and multivariate statistical analyses were employed to identify significant differences between the two groups. Nineteen metabolites were found to differ significantly in the 1st week, and seventeen metabolites in the 3rd month (adjusted p-value < 0.05, quality control (QC) < 30, a fold change (FC) > 1.1 or a FC < 0.91, Variable Influence on Projection (VIP) > 1). However, no significant differences were observed in the 6th month. These distinctive metabolites mainly belonged to lipid, fatty acid, and amino acid categories. Ten models were constructed using a backward conditional approach, with the best performance seen in model 5 for Group 2 at the 1st-week mark (AUC 0.900) and model 3 at the 3rd-month mark (AUC 0.924). In conclusion, the models developed in the early stages may offer potential benefits in the management of kidney transplant patients.https://www.tandfonline.com/doi/10.1080/0886022X.2023.2300736Kidney transplantationlongitudinal metabolite profilingbiomarkerdiagnosisLong-Term renal graft survivaluntargeted metabolomics |
| spellingShingle | Ihsan Yozgat Ulkem Cakır Muhittin Abdulkadir Serdar Sevgi Sahin Osman Ugur Sezerman Emirhan Nemutlu Ahmet Tarik Baykal Mustafa Serteser Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients Renal Failure Kidney transplantation longitudinal metabolite profiling biomarker diagnosis Long-Term renal graft survival untargeted metabolomics |
| title | Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients |
| title_full | Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients |
| title_fullStr | Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients |
| title_full_unstemmed | Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients |
| title_short | Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients |
| title_sort | longitudinal non targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients |
| topic | Kidney transplantation longitudinal metabolite profiling biomarker diagnosis Long-Term renal graft survival untargeted metabolomics |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2300736 |
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