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Objectives SLE is a heterogeneous disease with diferent disease course and acitivity. Finding new biomarkers to characterize and predict disease behavior can be of great value in clinical practice and disease management.Methods Consecutive outpatient and inpatient patients with rheumatologist diagnosed SLE (>20 years) were enrolled, and two study visits were conducted six months apart with assessments of disease activity, current treatment, organ involvement, immunological findings and comorbidities. SLE disease activity was measured using SLEDAI 2K (Systemic Lupus Erythematosus Disease Activity Index 2K) score. Blood tests were taken to measure interferon levels using Simoa (Single Molecul Array) method and Olink proximity extension assay was used to measure inflammatory proteins. Principal component analysis, volcano plot and analysis of variance (ANOVA), Kruskal and Wilcoxon test were employed to evaluate significant differences in serum protein patterns between controls and patients, as well as among different SLE clusters (visualized by a heatmap). To evaluate the extent of the deviation in patients, age and gender matched control group was used.Results Among 62 patients for first and 49 patients for second study visit (mean age 49 (SD ±12.4) years, mean disease duration 12 (±10.1) years) 90% were females. At first and second visit mean SLEDAI 2K values were 4 (±4.0) and 4 (±4.7); interferon alpha levels were elevated in 78% of patients with median levels 133 and 117 fg/ml respectively. According to the biomarker analysis patients clustered into four groups. Serum proteins with the highest differences between the clusters at the first visit were CXCL11, CXCL10 and IL15RA and only CXCL11 at second visit. CXCL11 values remained quite stable during two study visits with no median percentage change in protein value. Proteins with largest absolute change in protein value over time were IL6, CCL3 and MCP3.Conclusions With measuring biomarkers concentration and their change over time in Estonian SLE patients, we have confirmed CXCL11 role and shown its stability as a biomarker in different disease clusters. Additional analysis with mixed-effect model will be needed to further evaluate serum biomarker changes in time.