Background
Juvenile systemic lupus erythematosus (jSLE) and juvenile idiopathic arthritis (jIA) are chronic autoimmune diseases with overlapping clinical manifestations, making early and accurate diagnosis challenging. This study aimed to utilize proton nuclear magnetic resonance (1H NMR)-based metabolomics to identify potential plasma biomarkers that can differentiate between jSLE, jIA, and healthy pediatric individuals.
Materials and methods
A total of 75 plasma samples were analyzed, including 28 from jSLE patients, 10 from jIA patients, and 37 from healthy controls. Metabolic profiling was performed using 1H NMR spectroscopy. Multivariate statistical analyses, including PCA, sPLS-DA, and OPLS-DA, were applied to identify disease-specific metabolic patterns. Diagnostic performance was evaluated using univariate and multivariate ROC curve analysis, with model validation through cross-validation and permutation testing.
Results
Distinct metabolic profiles were observed among the three groups. In the pairwise comparisons, a proposed diagnostic panel of key metabolites, including tyrosine, formate, histidine, isoleucine, and glucose, demonstrated excellent discriminative power. Univariate ROC analysis identified several metabolites with perfect discriminative capacity (AUC = 1.0) between jSLE or jIA and controls, such as tyrosine, formate, histidine, acetone, and lactate. In the comparison between jSLE and jIA, nine metabolites showed AUC > 0.91. Multivariate ROC curve analysis using PLS-DA and SVM classifiers with 5-fold cross-validation achieved outstanding classification performance, with AUC values reaching 1.0 for control vs. jSLE and control vs. jIA (using 5, 7, 10, and 16 metabolites), and 0.99 for jSLE vs. jIA. For all models, significant model fit and predictive capability (R2 and Q(Groot et al., 20172)) were found (p < 0.001), indicating robust model stability and strong predictive power. Collectively, the panel of five biomarkers yielded an AUC > 0.95 in all comparisons, highlighting its strong diagnostic potential.
Conclusion
This study identified metabolic biomarkers with strong diagnostic potential for differentiating jSLE, jIA, and healthy controls using NMR-based metabolomics. These findings offer promising tools for early differential diagnosis and lay the groundwork for future translational research in pediatric autoimmune diseases.
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