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

## TL;DR

This study uses machine learning and serum metabolites to predict liver transplant complications, offering a non-invasive diagnostic tool.

## Contribution

A novel machine learning model integrating serum metabolites and clinical data to predict liver graft pathology.

## Key findings

- The ML model achieved 79.66% accuracy in predicting graft pathology.
- Serine and serotonin were identified as top predictors for liver graft injury.
- The model showed high AUCs for binary predictions of MASH, biliary, and TCMR.

## 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.