Machine learning identifies microbiome associations with MASLD biomarkers

Zeitschrift für Gastroenterologie, vol. 63

Abstract

Background: Gut microbiome plays a crucial role in liver health through gut-liver axis. It may contribute to metabolic-dysfunction-associated steatotic liver disease (MASLD), a global concern in individuals without significant alcohol consumption. MASLD is driven by metabolic factors like obesity and dyslipidemia. Investigating the link between the microbiome and MASLD-biomarkers (BMI, triglycerides) could help in detecting and preventing MASLD via microbiome modulation. Methods: Lifelines biobank's data, including over 16,700 participants, is utilized to extract 422 individuals with 16S rRNA gene amplicon sequencing and phenotypic data (age, gender, BMI, triglycerides, dietary intake). Due to the lack of data on the presence of MASLD ICD codes, BMI≥25 kg/m 2 and triglycerides≥1.7 mmol/L are used to identify patients at risk of MASLD. We then used machine learning models like Random Forest and XGBoost, with microbiome features (Shannon index, relative phyla abundance) and phenotypic data to define predictors of obesity and hypertriglyceridemia. Results: Firmicutes are found to be more abundant in obese patients, while Bacteroidetes are less abundant. For obesity, Random forest performed better with an AUC of 0.72 on test set (83 samples). For hypertriglyceridemia, XGBoost outperformed Random Forest with an AUC of 0.74 on test set (83 samples). Shannon index and microbiome phyla, including Bacteroidetes, Actinobacteria, and Desulfobacterota, are identified to be among top 7 features in classifying BMI and triglycerides and might also be important for MASLD. Conclusion: The study highlights the potential of identifying microbiome-based MASLD-biomarkers, and the role of machine-learning. Still, biopsy validation in MASLD is needed. Publication History Article published online: 20 January 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Authors 8

1. Thriveni Basavanapura Raju Aachen Department of Internal Medicine III

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Hospital, Department of Internal Medicine III, Gastroenterology, Hepatology and Metabolic Disease

2. Alina V. Beckmann Aachen

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   University Hospital RWTH Aachen, Aachen, Germany

3. Nicole Simone Treichel Aachen Functional Microbiome Research Group Institute of Medical Microbiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Functional Microbiome Research Group, Institute of Medical Microbiology, RWTH Aachen University Hospital, Aachen, Germany

4. Yazhou Chen Aachen Department of Internal Medicine III

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Hospital, Department of Internal Medicine III, Gastroenterology, Hepatology and Metabolic Disease

5. Benjamin P. M. Laevens Aachen Department of Internal Medicine III

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Hospital, Department of Internal Medicine III, Gastroenterology, Hepatology and Metabolic Disease

6. Kai Markus Schneider

   University Hospital Carl Gustav Carus · Technische Universität Dresden

   Affiliation as printed

   Department of Medicine 1, University Hospital Carl-Gustav-Carus Dresden, Technical University Dresden

7. Kirsten Schorning

   TU Dortmund University

   Affiliation as printed

   Technical University Dortmund, Dortmund, Germany

8. Carolin Victoria Schneider Aachen Department of Internal Medicine III

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Hospital, Department of Internal Medicine III, Gastroenterology, Hepatology and Metabolic Disease