Larissa C. Motta, Camila M. de Almeida, Gabriely S. Folli, Marcos V. V. Lyrio, Bruno M. M. Siqueira, José Brango-Vanegas, Rosiane A. Costa, Octávio L. Franco, Ana Paula C. Figueiredo, Vandack A. Nobre Junior, Paulo R. Filgueiras, Valério G. Barauna, Paula F. Vassallo, Wanderson Romão
{"title":"非靶向代谢组学方法对肝移植患者急性肾损伤的早期诊断","authors":"Larissa C. Motta, Camila M. de Almeida, Gabriely S. Folli, Marcos V. V. Lyrio, Bruno M. M. Siqueira, José Brango-Vanegas, Rosiane A. Costa, Octávio L. Franco, Ana Paula C. Figueiredo, Vandack A. Nobre Junior, Paulo R. Filgueiras, Valério G. Barauna, Paula F. Vassallo, Wanderson Romão","doi":"10.1002/ansa.70025","DOIUrl":null,"url":null,"abstract":"<p>Acute kidney injury is a common complication in patients undergoing orthotopic liver transplantation, being associated with increased mortality and prolonged hospitalization. This study aimed to develop a novel analytical strategy for the early diagnosis of acute kidney injury in liver transplant recipients by combining an untargeted metabolomic approach with advanced chemometric techniques. Using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry coupled with partial least squares discriminant analysis, we successfully classified patient samples according to the severity of renal dysfunction. This integration of high-resolution mass spectrometry with multivariate analysis offers a minimally invasive, cost-effective and precise diagnostic method, requiring only a single serum sample per analysis. The study analysed 132 serum samples from 59 patients at three postoperative time points (0–6 h, 24 h and 48 h). The mass spectra revealed distinct metabolic profiles across different stages of acute kidney injury, highlighting biochemical shifts related to oxidative stress, inflammation and impaired renal function. The discriminant models demonstrated high sensitivity (80%–98%) and specificity (80%–97%), especially in distinguishing advanced stages of kidney injury, where metabolic alterations were most evident. These results support the use of this analytical workflow as a promising tool for early detection and monitoring of acute kidney injury, representing a significant advance in the application of untargeted metabolomics to clinical diagnostics. This work lays the foundation for future clinical translation of metabolomics-based diagnostics in liver transplantation, enabling more effective and timely therapeutic interventions.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"6 2","pages":""},"PeriodicalIF":4.1000,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70025","citationCount":"0","resultStr":"{\"title\":\"Early Diagnosis of Acute Kidney Injury in Liver Transplant Patients by an Untargeted Metabolomic Approach\",\"authors\":\"Larissa C. Motta, Camila M. de Almeida, Gabriely S. Folli, Marcos V. V. Lyrio, Bruno M. M. Siqueira, José Brango-Vanegas, Rosiane A. Costa, Octávio L. Franco, Ana Paula C. Figueiredo, Vandack A. Nobre Junior, Paulo R. Filgueiras, Valério G. Barauna, Paula F. Vassallo, Wanderson Romão\",\"doi\":\"10.1002/ansa.70025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Acute kidney injury is a common complication in patients undergoing orthotopic liver transplantation, being associated with increased mortality and prolonged hospitalization. This study aimed to develop a novel analytical strategy for the early diagnosis of acute kidney injury in liver transplant recipients by combining an untargeted metabolomic approach with advanced chemometric techniques. Using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry coupled with partial least squares discriminant analysis, we successfully classified patient samples according to the severity of renal dysfunction. This integration of high-resolution mass spectrometry with multivariate analysis offers a minimally invasive, cost-effective and precise diagnostic method, requiring only a single serum sample per analysis. The study analysed 132 serum samples from 59 patients at three postoperative time points (0–6 h, 24 h and 48 h). The mass spectra revealed distinct metabolic profiles across different stages of acute kidney injury, highlighting biochemical shifts related to oxidative stress, inflammation and impaired renal function. The discriminant models demonstrated high sensitivity (80%–98%) and specificity (80%–97%), especially in distinguishing advanced stages of kidney injury, where metabolic alterations were most evident. These results support the use of this analytical workflow as a promising tool for early detection and monitoring of acute kidney injury, representing a significant advance in the application of untargeted metabolomics to clinical diagnostics. This work lays the foundation for future clinical translation of metabolomics-based diagnostics in liver transplantation, enabling more effective and timely therapeutic interventions.</p>\",\"PeriodicalId\":93411,\"journal\":{\"name\":\"Analytical science advances\",\"volume\":\"6 2\",\"pages\":\"\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2025-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.70025\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analytical science advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ansa.70025\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical science advances","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ansa.70025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Early Diagnosis of Acute Kidney Injury in Liver Transplant Patients by an Untargeted Metabolomic Approach
Acute kidney injury is a common complication in patients undergoing orthotopic liver transplantation, being associated with increased mortality and prolonged hospitalization. This study aimed to develop a novel analytical strategy for the early diagnosis of acute kidney injury in liver transplant recipients by combining an untargeted metabolomic approach with advanced chemometric techniques. Using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry coupled with partial least squares discriminant analysis, we successfully classified patient samples according to the severity of renal dysfunction. This integration of high-resolution mass spectrometry with multivariate analysis offers a minimally invasive, cost-effective and precise diagnostic method, requiring only a single serum sample per analysis. The study analysed 132 serum samples from 59 patients at three postoperative time points (0–6 h, 24 h and 48 h). The mass spectra revealed distinct metabolic profiles across different stages of acute kidney injury, highlighting biochemical shifts related to oxidative stress, inflammation and impaired renal function. The discriminant models demonstrated high sensitivity (80%–98%) and specificity (80%–97%), especially in distinguishing advanced stages of kidney injury, where metabolic alterations were most evident. These results support the use of this analytical workflow as a promising tool for early detection and monitoring of acute kidney injury, representing a significant advance in the application of untargeted metabolomics to clinical diagnostics. This work lays the foundation for future clinical translation of metabolomics-based diagnostics in liver transplantation, enabling more effective and timely therapeutic interventions.