Background Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with symptoms ranging from skin related problems to more severe cardiovascular effects. The heterogenous representation of the disease might be the reason for the lack of efficient treatment. We hypothesize that subgroups of SLE can be characterized by different biochemical pathways and that specific biomarkers along these pathways can be identified.

Objectives Our objective is to investigate new diagnostic entities of SLE and suggest new therapeutic and prognostic biomarkers, that can be validated and implemented in clinical practice to improve human health care.

Methods In this study we have utilized the Karolinska lupus cohort that consists of 320 SLE patients and 320 age-matched controls. Two main subgroups were defined: One group was defined as having SSA and SSB antibodies and a negative lupus anticoagulant test (LAC), i.e., a “Sjögren-like” group. The other group was defined as being negative for SSA and SSB antibodies but positive in the LAC test. According to previous studies these patients are at increased risk for cardiovascular events as compared to the “Sjörgren-like” group. A pilot study was designed and EDTA-plasma from selected patients in these two groups and controls were analysed using a proteomic and metabolomic approach. Pathway analysis was then performed on the obtained data.

Results The pilot study showed that it was possible to differentiate the two subgroups of SLE based on the proteomic profile. From the proteins found to be significantly different between the groups, several proteins known to be involved in SLE were detected, e.g. Apolipoprotein A1 and complement factor 3. In addition, proteins that to our knowledge have not been reported earlier to correlate with SLE, e.g., Apolipoprotein M, were detected and are subject for further investigations. Apolipoprotein E was one of the proteins that was found to be significantly different between the two subgroups of SLE and will be investigated in the entire cohort. Preliminary data from metabolomics demonstrate that it is possible to separate patients from controls and we found for example that Tryptophan levels were lower in SLE patients. Pathway analyses of proteomics and metabolomics data strongly predict that the changes in SLE patients compared to controls are associated with inflammation and immunity related pathways.

Conclusions This project will provide new knowledge about SLE taking several complex systems into account simultaneously. Using selected biomarkers it will be possible to identify more homogenous patient populations for clinical trials and thereby increase the efficacy. The systems biology approach is likely to identify pathways that may lead to better understanding of the disease, identification of novel drug targets and biomarkers supporting improved diagnosis of SLE.

Disclosure of Interest None Declared