Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Cristina Baciu
ORCID logo ,
Soumita Ghosh
,
Sara Naimimohasses
,
Arya Rahmani
,
Elisa Pasini
,
Maryam Naghibzadeh
ORCID logo ,
Azhie Amirhossein
,
Mamatha Bhat
*
Version 1 : Received: 28 March 2024 / Approved: 29 March 2024 / Online: 29 March 2024 (11:48:58 CET)

How to cite: Baciu, C.; Ghosh, S.; Naimimohasses, S.; Rahmani, A.; Pasini, E.; Naghibzadeh, M.; Amirhossein, A.; Bhat, M. Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning. Preprints 2024, 2024031827. https://doi.org/10.20944/preprints202403.1827.v1 Baciu, C.; Ghosh, S.; Naimimohasses, S.; Rahmani, A.; Pasini, E.; Naghibzadeh, M.; Amirhossein, A.; Bhat, M. Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning. Preprints 2024, 2024031827. https://doi.org/10.20944/preprints202403.1827.v1

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 efficacy of the model was assessed through the evaluation of the Out-of-Bag (OOB) error estimate. 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 3 models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest), the AUCs were 0.882, 0.972 and 0.96 respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Keywords

metabolomics; liver transplant; T-cell mediated rejection; metabolic dysfunction-associated steatohepatitis (MASH); biliary complications.

Subject

Public Health and Healthcare, Primary Health Care

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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