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The bm12 Inducible Model of Systemic Lupus Erythematosus SLE in C57BL/6 Mice
Published on: November 1, 2015
The baseline interferon signature predicts disease severity over the subsequent 5 years in systemic lupus
Lloyd Mai1, Arundip Asaduzzaman1, Babak Noamani2
1Division of Rheumatology, Schroeder Arthritis Institute, University Health Network, Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Canada.
Objectives:
Type I interferons (IFNs) play an important role in the pathophysiology of systemic lupus erythematosus (SLE). While cross-sectional data suggest an association between IFN-induced gene expression and SLE disease activity, interest in this as a biomarker of flare has been tempered by a lack of fluctuation with disease activity in the majority of patients. This led us to question whether IFN-induced gene expression might instead be a biomarker of overall disease severity, with patients with high levels spending more time in an active disease state.
Methods:
Levels of five interferon-responsive genes were measured in the whole peripheral blood at baseline visit for 137 SLE patients subsequently followed for 5 years. Log transformed values were summed to yield a composite IFN5 score, and the correlation with various disease outcomes examined. Receiver operator characteristic analyses were performed for outcomes of interest. Kaplan-Meier curves were generated to compare the proportion of flare-free patients with high and low IFN5 scores over time.
Results:
The baseline IFN5 score was positively correlated with the adjusted mean SLE disease activity index-2000, number of flares, adjusted mean prednisone dose, and number of new immunosuppressive medications over the subsequent 5 years. Optimal cut-offs for the IFN5 score were determined using Youden's index and predicted more severe outcomes with 57-67% accuracy. A high baseline IFN5 level was associated with a significantly increased risk of subsequent flare.
Conclusions:
Measurement of the type I IFN signature is a useful tool for predicting the subsequent disease activity course.
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