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FRI0014 Multiplex Screening of Cytokines in The Search for Disease Activity Markers of SLE
  1. S. Eketjäll1,
  2. H. Idborg2,
  3. J.T. Gustafsson2,
  4. A. Zickert2,
  5. M. Kvarnström2,
  6. V. Oke2,
  7. P. Susanne2,
  8. P.-J. Jakobsson2,
  9. I. Gunnarsson2,
  10. E. Svenungsson2
  1. 1Science for Life Laboratory, Personal Healthcare and Biomarkers, AstraZeneca Translational Sciences Centre
  2. 2Rheumatology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden


Background There is presently no consensus on how to best measure disease activity in systemic lupus erythematosus (SLE). Available validated measures, such as SLE Disease Activity Index (SLEDAI) and SLE Activity Measure (SLAM), are composite scores that in addition to laboratory tests require investigation by a physician. These scores are insensitive to change and have failed to differentiate treatment responses in clinical trials. It would be an important step forward, for clinical practice and for clinical trials, if one or several biomarkers could be used as proxies for disease activity in SLE.

Objectives Our objective is to evaluate cytokines, in combination with basic laboratory tests, as potential disease activity biomarkers.

Methods In a cross-sectional setting we examined 433 patients with SLE (fulfilling four or more of the 1982 revised ACR criteria) and 322 age and gender matched population controls. Disease activity was assessed according to both SLEDAI and SLAM by a rheumatologist. Basic laboratory tests and analyses on MSD 30-plex cytokine assay (Mesoscale Discovery, K15054D) were performed on fasting blood samples (total >50 potential biomarkers). The discriminatory power for investigated biomarkers was tested between patients and controls. Spearman correlations with SLAM/SLEDAI scores were calculated among patients.

Results Many potential biomarkers discriminated between patients and controls. Best discriminatory power was observed for TNF-α (p=7x10–63), IL-6 (p=2.5x10–40), orosomucoid (p=7.1x10–38), plasma (P)-albumin (p=1.6x10–36) and sedimentation rate (SR) (p=5.6x10–34). The strongest correlations with SLEDAI/SLAM were observed for TNF-α (Spearman ρ=0.32, p=6.0x10–12 for SLEDAI and Spearman ρ=0.34, p=5.0x10–13 for SLAM), and for P-albumin (Spearman ρ=-0.33, p=9.0x10–13 for SLEDAI and Spearman ρ=-0.31, p=5.0x10–11 for SLAM). As SR is part of SLAM, expected positive correlations were observed (Spearman ρ=0.27, p=7.7x10-9 for SLEDAI and Spearman ρ=0.48, p=4.0x10–25 for SLAM).

Conclusions Of more than 50 investigated biomarkers TNF-α was the best discriminator between SLE patients and controls. Furthermore TNF-α was the biomarker, which correlated best with disease activity. These correlations were further improved by the ratio between TNF-α and P-albumin. We propose that the TNF-α/P-albumin ratio merits further investigations as a clinically useful biomarker for diagnostic and surveillance purposes in SLE.

Disclosure of Interest None declared

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