Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients

Abstract

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 1^st week, and seventeen metabolites in the 3^rd 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 6^th 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 3^rd-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.

Keywords:

• Kidney transplantation
• longitudinal metabolite profiling
• biomarker
• diagnosis
• Long-Term renal graft survival
• untargeted metabolomics

Acknowledgments

The authors express our sincere gratitude to the patients who participated in this study, as their willingness to contribute and provide valuable insights is highly appreciated. Furthermore, the authors extend their heartfelt thanks to Dr. Gülay Yılmaz, we also acknowledge the invaluable contributions of Derya Candan, the organ transplant coordinator, Nilgun Demiroz, the organ transplant responsible nurse, and Sukran Cakan, the laboratory responsible technician, all of whom played crucial roles in the success of this study. Their commitment, professionalism, and support throughout the research process were truly integral.

Author contribution

Ihsan Yozgat: Investigation, Conceptualization, Writing - Original draft preparation; Investigation, Methodology, Data curation, Formal analysis, Software; Ulkem Cakir: Conceptualization, Data curation, Investigation, Methodology, Writing—review & editing; Muhittin Abdulkadir Serdar: Conceptualization, Data curation, Formal analysis, Methodology, Writing—review & editing; Sevgi Sahin: Conceptualization, Investigation, Methodology, Writing—review & editing; Osman Ugur Sezerman: Data curation, Formal analysis, Methodology, Software, Writing—review & editing; Emirhan Nemutlu: Conceptualization, Formal analysis, Methodology, Writing—review & editing; Ahmet Tarik Baykal: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Software, Supervision, Writing—review & editing; Mustafa Serteser: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing—review & editing.

Disclosure statement

The authors declare that there are no conflicts of interest.

Additional information

Funding

The financial support for this research was provided by Acıbadem Labmed laboratories in Turkey. Additionally, the study received support from the 2244 Industrial Ph.D. program, which is administered by The Scientific and Technological Research Council of Turkey (TUBITAK) under Grant# 118C082.