THE APPLICATION OF MASS SPECTROMETRY IN THE DIAGNOSIS OF ACUTE KIDNEY INJURY IN THE POSTOPERATIVE PERIOD OF PATIENTS UNDERGOING ORTHOTOPIC LIVER TRANSPLANTATION
Name: LARISSA CAMPOS MOTTA
Publication date: 09/06/2025
Examining board:
Name |
Role |
|---|---|
| MARCUS VINÍCIUS MELO DE ANDRADE | Examinador Externo |
| MARTINA BLANK | Examinador Externo |
| PAULA FRIZERA VASSALLO | Coorientador |
| RICARDO ALVES BERNARDO | Examinador Externo |
| RODRIGO REZENDE KITAGAWA | Examinador Interno |
Pages
Summary: Acute kidney injury (AKI) is a frequent and potentially fatal complication in patients undergoing orthotopic liver transplantation (OLT), with early diagnosis being essential for effective clinical management. In this context, the primary objective of this thesis was to investigate lipidomics- and mass spectrometry-based approaches for the early detection and monitoring of various stages of AKI in transplanted patients. The thesis is structured into four chapters: theoretical framework, objectives, and two scientific articles. The first study developed a methodology based on MALDI-TOF mass spectrometry combined with multivariate analysis (PLS-DA), aiming to distinguish AKI stages as defined by the KDIGO classification. The approach demonstrated high accuracy, especially in the more advanced stages of the disease, with sensitivity above 90% and low error rates. The second study focused on the in-depth lipid characterization of serum samples using UHPLC coupled to LTQ and Q-TOF systems, with data processing performed using MZmine, MetaboAnalyst 6.0, and lipid annotation via LipidMaps. Differentially expressed lipids were identified in the early stages of AKI, showing potential prognostic and diagnostic value. The findings reveal lipid metabolism alterations associated with AKI progression, reinforcing the role of lipidomics as a sensitive and specific tool for clinical applications. Collectively, the studies demonstrate that the application of omics sciences—particularly lipidomics— can provide important support for personalized medicine, contributing to risk stratification and therapeutic decision-making in critically ill patients. This thesis also highlights the importance of integrating advanced analytical and bioinformatics platforms for the discovery of clinical biomarkers in complex diseases.
