Classification model
To investigate the potential utility of gut microbial and SCFAs profiles in C-ASD prediction, we built random forest models based on faecal taxonomic or SCFAs features to discriminate them. First, 16 candidate bacteria were selected according to the LEfSe analysis (LDA > 3). The discriminant model based on the 16 representative genera effectively distinguished the C-ASD group from the TD group (AUC = 0.924, 95% CI: 0.896–0.953) through fivefold cross-validation of the random forest model. However, the PPA-derived model did not improve the classification accuracy (AUC = 0.62, 95% CI: 0.565–0.675) (Fig. Receiver operating characteristic curve of training data based on selected features.