Characteristic analysis of gut microbiota and screening model construction in children with autism spectrum disorder
Objective To investigate the differences of gut microbiota composition between children with autism spectrum disorder(ASD)and health children,and to construct a disease screening model using machine learning algorithm to provide a non-invasive method for autism screening based on biomarkers of gut microbiota.Methods From December 2019 to April 2023,this study recruited 149 ASD children aged 2.5 to 4.5 years from Jinan,Zunyi,Hong Kong and Shanghai,as the autism group.Additionally,149 healthy children matched 1∶1 by age and gender were recruited as the control group.Fecal samples were collected,and gut microbiota-related indices were gathered through 16S rRNA gene V3-V4 region sequencing for both groups.At the genus level,four machine learning algorithms,random forest,support vector machine,K-nearest neighbors,and naive bayes classifier,were used to construct an autism classification model in the model development dataset,identifying the most discriminative bacterial genus combinations,and the generalization ability of the models was evaluated in two independent external test datasets.Results ①The gut microbiota diversity of the autism group was significantly higher than that in the control group(Chao index=118.00,105.00;Shannon index=3.46,3.00;P=0.023,0.001).②There were significant differences in gut microbiota structure between autism children and control children(F=5.198,R2=0.052,P＜0.001).③ A total of 14 characteristic genera were identified.The genera with higher abundance in the autism group were Phocaeicola,Anaerobutyricum,Faecalibacterium,Blautia,Oscillibacter,Lachnospira,Parabacteroides,Flintibacter,and Anthropo gastromicrobium,and the genera with higher abundance in the control group were Ruthenibacterium,Flavonifractor,Bifidobacterium,Anaerostipes,and Eisenbergiella.④The random forest model based on the combination of 14 genera showed the best classification performance in the model development dataset,with the training set AUC of 100% (95% CI:100% -100% )and validation set AUC of 93.94% (95% CI:88.13% -99.74% ).In two independent external test datasets,the Naive Bayes model showed the best generalization performance,with AUC of 63.83% (95% CI:51.99% -75.67% )and 60.19% (95% CI:47.83% -72.55% ),respectively.Conclusion There are significant differences in the gut microbiota communities between autism children and control children,and specific gut microbiota biomarkers have the capability to classify autism disease states,suggesting that gut microbiota has potential significance as a non-invasive screening biomarker for early autism detection in children.
gut microbiotaautismchildrenbiomarkersmachine learning16S rRNA
国家科技期刊平台
NSTL
万方数据
