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

Journal: Annals of the rheumatic diseases
PMID: 30862608

Abstract

OBJECTIVES: 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.

Authors

  • Erika Van Nieuwenhove
    UZ Leuven, Leuven, Belgium.
  • Vasiliki Lagou
    Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK.
  • Lien Van Eyck
    UZ Leuven, Leuven, Belgium.
  • James Dooley
    VIB Center for Brain and Disease Research, Leuven, Belgium.
  • Ulrich Bodenhofer
    University of Applied Sciences Upper Austria - Campus Hagenberg, Hagenberg, Oberösterreich, Austria.
  • Carlos Roca
    Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Científicas (CSIC), Ramiro de Maeztu 9, 28040, Madrid, Spain.
  • Marijne Vandebergh
    Department of Neurosciences, KU Leuven - University of Leuven, Leuven, Belgium.
  • An Goris
    Department of Neurosciences, KU Leuven - University of Leuven, Leuven, Belgium.
  • Stéphanie Humblet-Baron
    VIB Center for Brain and Disease Research, Leuven, Belgium.
  • Carine Wouters
    UZ Leuven, Leuven, Belgium.
  • Adrian Liston
    VIB Center for Brain and Disease Research, Leuven, Belgium adrian.liston@babraham.ac.uk.

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