Wang Wenjuan, Fu Pengcheng
School of Life and Pharmaceutical Sciences, Hainan University, 58 Renmin Avenue, Haikou 570228, China.
State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, 58 Renmin Avenue, Haikou 570228, China.
Microorganisms. 2023 Jan 22;11(2):291. doi: 10.3390/microorganisms11020291.
The study of human gut microbiota has attracted increasing interest in the fields of life science and healthcare. However, the complicated and interconnected associations between gut microbiota and human diseases are still difficult to determine in a predictive fashion. Artificial intelligence such as machine learning (ML) and deep learning can assist in processing and interpreting biological datasets. In this study, we aggregated data from different studies based on the species composition and relative abundance of gut microbiota in children with autism spectrum disorder (ASD) and typically developed (TD) individuals and analyzed the commonalities and differences of ASD-associated microbiota across cohorts. We established a predictive model using an ML algorithm to explore the diagnostic value of the gut microbiome for the children with ASD and identify potential biomarkers for ASD diagnosis. The results indicated that the Shenzhen cohort achieved a higher area under the receiver operating characteristic curve (AUROC) value of 0.984 with 97% accuracy, while the Moscow cohort achieved an AUROC value of 0.81 with 67% accuracy. For the combination of the two cohorts, the average prediction results had an AUROC of 0.86 and 80% accuracy. The results of our cross-cohort analysis suggested that a variety of influencing factors, such as population characteristics, geographical region, and dietary habits, should be taken into consideration in microbial transplantation or dietary therapy. Collectively, our prediction strategy based on gut microbiota can serve as an enhanced strategy for the clinical diagnosis of ASD and assist in providing a more complete method to assess the risk of the disorder.
人类肠道微生物群的研究在生命科学和医疗保健领域引起了越来越多的关注。然而，肠道微生物群与人类疾病之间复杂且相互关联的关系仍难以以预测的方式确定。机器学习（ML）和深度学习等人工智能可以协助处理和解释生物数据集。在本研究中，我们基于自闭症谱系障碍（ASD）儿童和发育正常（TD）个体的肠道微生物群的物种组成和相对丰度，汇总了来自不同研究的数据，并分析了不同队列中与ASD相关的微生物群的共性和差异。我们使用ML算法建立了一个预测模型，以探索肠道微生物组对ASD儿童的诊断价值，并识别ASD诊断的潜在生物标志物。结果表明，深圳队列在受试者工作特征曲线（AUROC）下的面积值为0.984，准确率为97%，而莫斯科队列的AUROC值为0.81，准确率为67%。对于两个队列的组合，平均预测结果的AUROC为0.86，准确率为80%。我们的跨队列分析结果表明，在微生物移植或饮食治疗中应考虑多种影响因素，如人群特征、地理区域和饮食习惯。总体而言，我们基于肠道微生物群的预测策略可以作为ASD临床诊断的增强策略，并有助于提供一种更完整的方法来评估该疾病的风险。
