Department of Orthopaedics, Xinfeng County People's Hospital, Xinfeng, Jiangxi, China.
Department of ICU, GanZhou People's Hospital, GanZhou, Jiangxi, China.
Front Cell Infect Microbiol. 2024 Mar 8;14:1371371. doi: 10.3389/fcimb.2024.1371371. eCollection 2024.
Human gut microbiota has been shown to be significantly associated with various inflammatory diseases. Therefore, this study aimed to develop an excellent auxiliary tool for the diagnosis of juvenile idiopathic arthritis (JIA) based on fecal microbial biomarkers.
The fecal metagenomic sequencing data associated with JIA were extracted from NCBI, and the sequencing data were transformed into the relative abundance of microorganisms by professional data cleaning (KneadData, Trimmomatic and Bowtie2) and comparison software (Kraken2 and Bracken). After that, the fecal microbes with high abundance were extracted for subsequent analysis. The extracted fecal microbes were further screened by least absolute shrinkage and selection operator (LASSO) regression, and the selected fecal microbe biomarkers were used for model training. In this study, we constructed six different machine learning (ML) models, and then selected the best model for constructing a JIA diagnostic tool by comparing the performance of the models based on a combined consideration of area under receiver operating characteristic curve (AUC), accuracy, specificity, F1 score, calibration curves and clinical decision curves. In addition, to further explain the model, Permutation Importance analysis and Shapley Additive Explanations (SHAP) were performed to understand the contribution of each biomarker in the prediction process.
A total of 231 individuals were included in this study, including 203 JIA patients and Non-JIA individuals. In the analysis of diversity at the genus level, the alpha diversity represented by Shannon value was not significantly different between the two groups, while the belt diversity was slightly different. After selection by LASSO regression, 10 fecal microbe biomarkers were selected for model training. By comparing six different models, the XGB model showed the best performance, which average AUC, accuracy and F1 score were 0.976, 0.914 and 0.952, respectively, thus being used to construct the final JIA diagnosis model.
A JIA diagnosis model based on XGB algorithm was constructed with excellent performance, which may assist physicians in early detection of JIA patients and improve the prognosis of JIA patients.
人类肠道微生物群与各种炎症性疾病显著相关。因此，本研究旨在基于粪便微生物生物标志物开发一种用于诊断青少年特发性关节炎（JIA）的优秀辅助工具。
从 NCBI 中提取与 JIA 相关的粪便宏基因组测序数据，并通过专业数据清理（KneadData、Trimmomatic 和 Bowtie2）和比较软件（Kraken2 和 Bracken）将测序数据转换为微生物的相对丰度。之后，提取高丰度的粪便微生物进行后续分析。通过最小绝对值收缩和选择算子（LASSO）回归进一步筛选提取的粪便微生物，并使用所选粪便微生物生物标志物进行模型训练。在这项研究中，我们构建了六个不同的机器学习（ML）模型，然后通过综合考虑基于接收者操作特征曲线（AUC）、准确性、特异性、F1 评分、校准曲线和临床决策曲线的模型性能，选择最佳模型来构建 JIA 诊断工具。此外，为了进一步解释模型，进行了排列重要性分析和 Shapley 加性解释（SHAP），以了解每个生物标志物在预测过程中的贡献。
本研究共纳入 231 人，包括 203 例 JIA 患者和非 JIA 个体。在属水平的多样性分析中，两组间 Shannon 值代表的 alpha 多样性无显著差异，而带型多样性略有不同。经过 LASSO 回归选择后，选取 10 种粪便微生物生物标志物进行模型训练。通过比较六种不同的模型，XGB 模型表现出最佳性能，其平均 AUC、准确性和 F1 分数分别为 0.976、0.914 和 0.952，因此被用于构建最终的 JIA 诊断模型。
基于 XGB 算法构建了一种具有优异性能的 JIA 诊断模型，可能有助于医生早期发现 JIA 患者，并改善 JIA 患者的预后。
