Gut microbiota in children with juvenile idiopathic arthritis: characteristics, biomarker identification, and usefulness in clinical prediction

Abstract

Background: Recent studies have suggested that the gut microbiota is altered in children with juvenile idiopathic arthritis (JIA). However, age, sex, and body mass index (BMI) were not matched in the previous studies, and the results are inconsistent. We conducted an age-, sex-, and BMI-matched cross-sectional study to characterize the gut microbiota in children with JIA, and evaluate its potential in clinical prediction. Methods: A total of 40 patients with JIA and 42 healthy controls, ranging from 1 to 16 years, were enrolled in this study. Fecal samples were collected for 16S rDNA sequencing. The data were analyzed using QIIME software and R packages. Specifically, the random forest model was used to identify biomarkers, and the receiver operating characteristic curve and the decision curve analysis were used to evaluate model performance. Results: A total of 39 fecal samples from patients with JIA, and 42 fecal samples from healthy controls were sequenced successfully. The Chao 1 and Shannon-Wiener index in the JIA group were significantly lower than those in the control group, and the Bray-Curtis dissimilarity also differed significantly between the two groups. The relative abundance of 4 genera, Anaerostipes, Dialister, Lachnospira, and Roseburia, decreased significantly in the JIA group compared to those in the control group. The 4 genera included microbes that produce short-chain fatty acids (SCFAs) and were negatively correlated with some rheumatic indices. Moreover, 12 genera were identified as potential biomarkers by using the nested cross-validation function of the random forest. A random forest model constructed using these genera was able to differentiate the patients with JIA from the healthy controls, and the area under the receiver operating characteristic curve was 0.7975. The decision curve analysis indicated that the model had usefulness in clinical practice.

A practical guide to amplicon and metagenomic analysis of microbiome data

“…In microbiome research, machine learning is used for taxonomic classification, beta-diversity analysis, binning, and compositional analysis of particular features. Commonly used machine learning methods include random forest (Vangay et al, 2019 ; Qian et al, 2020 ), Adaboost (Wilck et al, 2017 ), and deep learning (Galkin et al, 2018 ) to classify groups by selecting biomarkers or regression analysis to show experimental condition-dependent changes in biomarker abundance (Table 2 ).…”

Exploring the Oral Microbiome in Rheumatic Diseases, State of Art and Future Prospective in Personalized Medicine with an AI Approach

“…In this regard, although promising, the results seem less consistent than in other areas as of few studies with a limited sample size have been carried out only on fecal samples. In particular, in a cohort of 39 patients with juvenile idiopathic arthritis, a random forest algorithm was used to discriminate patients from healthy controls (AUC 0.80) by integrating information of 12 genera including Anaerostipes , Dialister , Lachnospira , and Roseburia from fecal samples [ 102 ]. Size and quality of datasets are of utmost importance in ML.…”

A case of mistaken identity: an enthesitis-related arthritis case report

“…12 This has been further substantiated in a paper by Qian et al in which they found that four genera of short-chain fatty acid (SCFA) producing microbes were useful as a clinical predictor of JIA. 2 As SCFAs have important immunomodulatory functions including differentiation of anti-inflammatory regulatory T cells, IL-10 production, and proinflammatory TH17 suppression, the growing evidence continues to find the deregulatory actions of the immune system as the guilty culprit. This is supported by studies that show that alterations in Tcell populations may promote inflammation and modulation of these populations may be targets for treatment.…”

“…1 JIA has an estimated prevalence of 1 in 1000 making it one of the most common rheumatological conditions affecting children leading to complications such as joint damage and vision loss. 1,2 This childhood condition is inflammatory in nature leading to extraarticular manifestations, permanent joint damage, and significant morbidity with persistence into adulthood. 1 The International league of associations of rheumatology (ILAR) developed a classification system in the mid 1990's, the most recent update in 2001, subdividing JIA ABSTRACT Juvenile idiopathic arthritis is the most common inflammatory rheumatological condition affecting children.…”