Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease

Abstract

AbstractAlcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725