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Ann Rheum Dis. 2019 May;78(5):617-628. doi: 10.1136/annrheumdis-2018-214354. Epub 2019 Mar 12.

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes.

Author information

UZ Leuven, Leuven, Belgium.
VIB Center for Brain and Disease Research, Leuven, Belgium.
Department of Microbiology and Immunology, KU Leuven - University of Leuven, Leuven, Belgium.
Department of Neurosciences, KU Leuven - University of Leuven, Leuven, Belgium.
Institute of Bioinformatics, Linz, Austria.
LIT AI Lab, Linz Institute of Technology, Johannes Kepler University, Linz, Austria.
QUOMATIC.AI, Linz, Austria.
VIB Center for Brain and Disease Research, Leuven, Belgium
The Babraham Institute, Cambridge, United Kingdom.
Contributed equally



Juvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.


Here we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.


Immune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.


These results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.


B cells; T cells; autoimmune diseases; juvenile idiopathic arthritis

Conflict of interest statement

Competing interests: None declared.

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