Machine Learning-Based Identification of Novel Exosome-Derived Metabolic Biomarkers for the Diagnosis of Systemic Lupus Erythematosus and Differentiation of Renal Involvement.

OBJECTIVE

This study aims to investigate the exosome-derived metabolomics profiles in systemic lupus erythematosus (SLE), identify differential metabolites, and analyze their potential as diagnostic markers for SLE and lupus nephritis (LN).

METHODS

Totally, 91 participants were enrolled between February 2023 and January 2024 including 58 SLE patients [30 with nonrenal-SLE and 28 with Lupus nephritis (LN)] and 33 healthy controls (HC). Ultracentrifugation was used to isolate serum exosomes, which were analyzed for their metabolic profiles using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Endogenous metabolites were identified via public metabolite databases. Random Forest, Lasso regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were employed to screen key metabolites, and a prediction model was constructed for SLE diagnosis and LN discrimination. ROC curves were constructed to determine the potential of these differential exosome-derived metabolites for the diagnosis of SLE. Furthermore, Spearman's correlation was employed to evaluate the potential links between exosome-derived metabolites and the clinical parameters which reflect disease progression.

RESULTS

A total of 586 endogenous serum exosome-derived metabolites showed differential expression, with 225 exosome-derived metabolites significantly upregulated, 88 downregulated and 273 exhibiting no notable changes in the HC and SLE groups. Machine learning algorithms revealed three differential metabolites: Pro-Asn-Gln-Met-Ser, C24:1 sphingolipid, and protoporphyrin IX, which exhibited AUC values of 0.998, 0.992 and 0.969 respectively, for distinguishing between the SLE and HC groups, with a combined AUC of 1.0. In distinguishing between the LN and SLE groups, the AUC values for these metabolites were 0.920, 0.893 and 0.865, respectively, with a combined AUC of 0.931, demonstrating excellent diagnostic performance. Spearman correlation analysis revealed that Pro-Asn-Gln-Met-Ser and protoporphyrin IX were positively correlated with the SLE Disease Activity Index (SLEDAI) scores, urinary protein/creatinine ratio (ACR) and urinary protein levels, while C24:1 sphingolipid exhibited a negative correlation.

CONCLUSIONS

This study provides the first comprehensive characterization of the exosome-derived metabolites in SLE and established a promising prediction model for SLE and LN discrimination. The correlation between exosome-derived metabolites and key clinical parameters strongly indicated their potential role in SLE pathological progression.