Department of Pediatrics, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Department of Molecular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States.
Front Cell Infect Microbiol. 2021 Dec 7;11:711884. doi: 10.3389/fcimb.2021.711884. eCollection 2021.
Early diagnosis and treatment of pediatric Inflammatory bowel disease (PIBD) is challenging due to the complexity of the disease and lack of disease specific biomarkers. The novel machine learning (ML) technique may be a useful tool to provide a new route for the identification of early biomarkers for the diagnosis of PIBD.
In total, 66 treatment naive PIBD patients and 27 healthy controls were enrolled as an exploration cohort. Fecal microbiome profiling using 16S rRNA gene sequencing was performed. The correlation between microbiota and inflammatory and nutritional markers was evaluated using Spearman's correlation. A random forest model was used to set up an ML approach for the diagnosis of PIBD using 1902 markers. A validation cohort including 14 PIBD and 48 irritable bowel syndrome (IBS) was enrolled to further evaluate the sensitivity and accuracy of the model.
Compared with healthy subjects, PIBD patients showed a significantly lower diversity of the gut microbiome. The increased and were positively correlated with inflammatory markers and negatively correlated with nutrition markers, which indicated a more severe disease. A diagnostic ML model was successfully set up for differential diagnosis of PIBD integrating the top 11 OTUs. This diagnostic model showed outstanding performance at differentiating IBD from IBS in an independent validation cohort.
The diagnosis penal based on the ML of the gut microbiome may be a favorable tool for the precise diagnosis and treatment of PIBD. A study of the relationship between disease status and the microbiome was an effective way to clarify the pathogenesis of PIBD.
由于小儿炎症性肠病（PIBD）的复杂性和缺乏疾病特异性生物标志物，早期诊断和治疗具有挑战性。新型机器学习（ML）技术可能是一种有用的工具，可以为识别 PIBD 诊断的早期生物标志物提供新途径。
共纳入 66 例未经治疗的 PIBD 患者和 27 例健康对照作为探索队列。采用 16S rRNA 基因测序进行粪便微生物组分析。采用 Spearman 相关分析评估微生物群与炎症和营养标志物之间的相关性。采用随机森林模型建立基于 1902 个标志物的 ML 方法用于 PIBD 诊断。纳入 14 例 PIBD 和 48 例肠易激综合征（IBS）的验证队列进一步评估模型的敏感性和准确性。
与健康受试者相比，PIBD 患者的肠道微生物组多样性显著降低。增加的 和 与炎症标志物呈正相关，与营养标志物呈负相关，表明疾病更严重。成功建立了基于 ML 的肠道微生物组的诊断模型，用于 PIBD 的鉴别诊断，整合了前 11 个 OTUs。该诊断模型在独立验证队列中对区分 IBD 与 IBS 具有出色的性能。
基于 ML 的肠道微生物组的诊断可能是 PIBD 精确诊断和治疗的有利工具。研究疾病状态与微生物组之间的关系是阐明 PIBD 发病机制的有效途径。
