Certain metabolic biomarkers with a high diagnostic efficiency for systemic lupus erythematosus (SLE) have been identified, according to study results published in the Clinical Immunology.

Researchers aimed to integrate serum metabolomics and lipidomics to identify biomarkers of organ involvement in the disorder. They utilized high-resolution mass spectrometry-based metabolomics and lipidomics to identify various metabolites and lipids.

A total of 163 participants were recruited, of whom 133 were patients with SLE and 30 were healthy controls. All of the patients with SLE met at least 4 of the American College of Rheumatology classification criteria for SLE and had no history of other autoimmune diseases. Disease was evaluated with the use of the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI). Inactive SLE was defined as an SLEDAI score of 0 to 4 (reported in 63 patients), whereas active SLE was defined as an SLEDAI score of over 4 (reported in 70 patients).


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The types of organs involved in SLE were established according to clinical manifestations and laboratory testing. From the 133 patients with SLE, 103 patients with significantly different phenotypes were screened and divided into the following 4 groups: (1) 30 patients with kidney involvement (KI) only; (2) 29 patients with skin involvement (SI) only; (3) 14 patients with blood system involvement (BI) only; and
(4) 30 patients with multisystem involvement (MI).

Overall, 95 differential metabolites and 54 lipids were identified between patients with SLE and healthy controls. The differential metabolites screened in metabolomics included primarily lipid and lipid-like molecules, organic acids and their derivatives, organoheterocyclic compounds, nucleosides, nucleotides, and analogues. The differential lipids screened in lipidomics included mainly triacylglycerol, phosphatidylcholine, fatty acids, acylcarnitine, and sphingomyelin.

The researchers found that a combination of 4 metabolites could differentiate patients with SLE from healthy controls with an area under the curve (AUC) accuracy of 0.998. They combined 3 lipids to distinguish inactive SLE from active SLE (AUC, 0.767). They then identified the biomarkers for different organ phenotypes of SLE. The AUCs for identification of patients with SLE with KI, SI, BI, and MI were 0.766, 0.718, 0.951, and 0.999, respectively.

Limitations of the study included the fact that although SLE has many clinical phenotypes, the biomarkers of only 4 disease phenotypes were evaluated. Disease phenotypes such as nervous system involvement and joint involvement were not examined. The metabolic biomarkers for SLE warrant validation in a larger cohort of patients with the disorder.

The study authors concluded that the current study succeeded in identifying biomarkers associated with various clinical phenotypes in patients with SLE, which could facilitate a more precise diagnosis and evaluation of disease progression in individuals with the disorder.

Reference  

Zhang W, Zhao H, Du P, et al. Integration of metabolomics and lipidomics reveals serum biomarkers for systemic lupus erythematosus with different organs involvement. Clin Immunol. 2022;241:109057. doi:10.1016/j.clim.2022.109057