Autism spectrum disorder (ASD) is a neuro developmental disorder characterized by a series of abnormal social behaviors. The increasing prevalence of ASD has led to the discovery of a correlation with the intestinal microbiome in many studies. In our research, we evaluated 297 subjects, including 169 individuals with ASD and 128 neurotypical subjects, from the Sequence Read Archive database. We conducted a series of analyses, including alpha-diversity, phylogenetic profiles, and functional profiles, to explore the correlation between the gut microbiome and ASD. The principal component analysis (PCA) indicated that ASD and neurotypical subjects could be divided based on the unweighted UniFrac distance. The genera Prevotella, Roseburia, Ruminococcus, Megasphaera, and Catenibacterium might be biomarkers of ASD after linear discriminant analysis effect size (LEfSe) evaluation and Random Forest analysis, respectively. The functional analysis found six significant pathways between ASD and neurotypical subjects, including oxidative phosphorylation, nucleotide excision repair, peptidoglycan biosynthesis, photosynthesis, photosynthesis proteins, and two-component system. Based on these alterations of the intestinal microbiome in ASD subjects, we developed four machine learning models: random forest (RF), Multilayer Perceptron (MLP), kernelized support vector machines with the RBF kernel (SVMs), and Decision trees (DT). Notably, the RF model after RF selection was superior, with an F1 score of 0.74 and area under the curve of 0.827(0.004), suggesting the reliability and generalizability of predictive model. Besides, the validation performance of RF model after RF selection could be 0.75(0.01) on external cohort collected by our laboratory. Our study advances the understanding of human gut microbiome in ASD that designing and evaluating microbially based interventions of ASD.

自闭症谱系障碍（ASD）是一种神经发育障碍，其特征是一系列异常的社会行为。在许多研究中，ASD的患病率上升已导致发现与肠道微生物组的相关性。在我们的研究中，我们从Sequence Read Archive数据库中评估了297位受试者，包括169位ASD个体和128位神经型受试者。我们进行了一系列分析，包括α-多样性，系统发育谱和功能谱，以探索肠道微生物组与ASD之间的相关性。主成分分析（PCA）表明，可以根据未加权的UniFrac距离来划分ASD和神经型受试者。该属普氏菌，罗斯氏，瘤胃球菌属，巨球菌和连接杆菌线性判别分析效应大小（LEfSe）评估和随机森林分析后，可能分别是ASD的生物标记。功能分析发现ASD与典型神经元之间有六个重要途径，包括氧化磷酸化，核苷酸切除修复，肽聚糖生物合成，光合作用，光合作用蛋白和两组分系统。基于ASD受试者中肠道微生物组的这些变化，我们开发了四种机器学习模型：随机森林（RF），多层感知器（MLP），带有RBF内核的内核化支持向量机（SVM）和决策树（DT）。值得注意的是，选择RF后的RF模型具有较高的F1得分，F1得分为0.74，曲线下面积为0.827（0.004），表明预测模型的可靠性和可推广性。除了，在我们实验室收集的外部队列中，选择RF后，RF模型的验证性能可能为0.75（0.01）。我们的研究通过设计和评估基于微生物的ASD干预措施，提高了对ASD中人类肠道微生物组的了解。