Journal article
Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning
Metabolites, Vol.14(5), p.254
05/01/2024
DOI: 10.3390/metabo14050254
PMCID: PMC11122840
PMID: 38786731
Abstract
Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model's efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.
Details
- Title: Subtitle
- Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning
- Creators
- Cristina Baciu - University Health NetworkSoumita Ghosh - University Health NetworkSara Naimimohasses - University Health NetworkArya Rahmani - University Health NetworkElisa Pasini - University Health NetworkMaryam Naghibzadeh - University Health NetworkAmirhossein Azhie - University Health NetworkMamatha Bhat - University Health Network
- Resource Type
- Journal article
- Publication Details
- Metabolites, Vol.14(5), p.254
- DOI
- 10.3390/metabo14050254
- PMID
- 38786731
- PMCID
- PMC11122840
- NLM abbreviation
- Metabolites
- ISSN
- 2218-1989
- eISSN
- 2218-1989
- Publisher
- Mdpi
- Number of pages
- 18
- Grant note
- American Society of Transplantation
- Language
- English
- Date published
- 05/01/2024
- Academic Unit
- Gastroenterology and Hepatology; Internal Medicine
- Record Identifier
- 9984772260802771
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