Lund University Publications

Inflammation-profiling reveals activated pathways and biomarkers with predictive potential in oligoarticular juvenile idiopathic arthritis

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Abstract

Background: We set out to profile the immune mechanisms active in treatment-naïve oligoarticular JIA (oJIA) to improve the knowledge of its immunopathogenesis, to identify potential biomarkers that can aid diagnosis, predictions and that correlate with clinical disease parameters. Methods: Using Olink proteomics (inflammation panel measuring 92 markers), we defined and compared the inflammation profiles of 38 plasma and 62 synovial fluid (SF) oJIA samples, 38 plasma samples from healthy age- and sex-matched controls (HC), 12 SF samples from non-arthritic controls and 26 SF samples from knee injury patients. Clinical data for the oJIA cohort were retrieved from the Swedish pediatric rheumatology quality register and medical charts.... (More)

Background: We set out to profile the immune mechanisms active in treatment-naïve oligoarticular JIA (oJIA) to improve the knowledge of its immunopathogenesis, to identify potential biomarkers that can aid diagnosis, predictions and that correlate with clinical disease parameters. Methods: Using Olink proteomics (inflammation panel measuring 92 markers), we defined and compared the inflammation profiles of 38 plasma and 62 synovial fluid (SF) oJIA samples, 38 plasma samples from healthy age- and sex-matched controls (HC), 12 SF samples from non-arthritic controls and 26 SF samples from knee injury patients. Clinical data for the oJIA cohort were retrieved from the Swedish pediatric rheumatology quality register and medical charts. Results: Plasma inflammation profiles of oJIA and HC were largely overlapping, with IL6 and MMP-1 significantly upregulated in oJIA. In SF, 48 differentially expressed proteins (DEPs) were identified in oJIA, highlighting immune pathways like leukocyte migration, cell chemotaxis and adaptive immunity. Comparative analysis revealed 13 proteins specific to oJIA. Correlations were found between DEPs in oJIA SF and clinical parameters (cJADAS-71, pain, health impact score). In plasma, IL6 and MMP-1 showed strong correlation with disease activity and pain, respectively. CXCL9, CXCL10 and CXCL11 were identified as potential predictive biomarkers for disease progression. Conclusions: The overlap in plasma inflammation profiles of oJIA and HCs suggests local rather than systemic inflammation in oJIA and underlines the need to study oJIA immunopathogenesis using SF samples. The oJIA SF inflammation profiles indicative of adaptive immune reactions separated oJIA from knee-injury patients and can be exploited for diagnostic purposes. Increased SF levels of CXCL9, CXCL10 and CXCL11 were associated with chronic disease progression and could serve as prognostic biomarkers and early treatment targets.

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