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Genetic diagnostic profiling in axial spondyloarthritis: a real world study.
G.P. Thomas1, D. Willner2, P.C. Robinson3, A. Cortes4, R. Duan5, M. Rudwaleit6, N. Akkoc7, J. Braun8, C.-T. Chou9, W.P. Maksymowych10, S. Ozgocmen11, E. Roussou12, J. Sieper13, R. Valle-Oñate14, D. Van Der Heijde15, J. Wei16, P. Leo17, M.A. Brown18
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane; and Charles Sturt University, Wagga Wagga, New South Wales, Australia.
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane; and Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia.
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia.
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia.
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia.
- Klinikum Bielefeld and Charité University Medicine, Berlin, Germany; and Gent University, Gent, Belgium.
- Dokuz Eylul University Hospital, Izmir, Turkey.
- German Rheumatology Research Centre, Berlin, Germany.
- Division of Allergy, Immunology, Rheumatology, Department of Medicine, Taipei Veterans General Hospital, Taipei; and School of Medicine, National Yang-Ming University, Taipei, Taiwan.
- Department of Medicine, University of Alberta, Canada.
- Division of Rheumatology, Department of Physical Medicine and Rehabilitation, Erciyes University, Faculty of Medicine, Kayseri, Turkey.
- King George Hospital, London, UK.
- German Rheumatology Research Centre, Berlin; and Rheumatology, Med Klinik 1, Charite, Campus Benjamin Franklin, Berlin, Germany.
- Spondyloarthropathy Group-Division of Rheumatology, Hospital Militar Central/ Universidad de La Sabana, Colombia.
- Leiden University Medical Center, Leiden, the Netherlands.
- Chung Shan Medical University, Taichung, Taiwan.
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia.
- University of Queensland Diamantina Institute, Translational Research Institute; and Institute of Health and Biomedical Innovation, Queensland University of Technology, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Australia.
and the International Genetics of Ankylosing Spondylitis Consortium
CER9325
2017 Vol.35, N°2
PI 0229, PF 0233
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PMID: 27749235 [PubMed]
Received: 08/02/2016
Accepted : 25/07/2016
In Press: 07/10/2016
Published: 15/03/2017
Abstract
OBJECTIVES:
Spondyloarthritis (SpA) is often diagnosed late in the course of the disease and improved methods for early diagnosis are required. We have tested the ability of genetic profiling to diagnose axial SpA (axSpA) as a whole group, or ankylosing spondylitis (AS) alone, in a cohort of chronic back pain patients.
METHODS:
282 patients were recruited from centres in the United Kingdom, Germany, Taiwan, Canada, Columbia and Turkey as part of the ASAS classification criteria for axSpA study (ASAS cohort). Subjects were classified according to the ASAS axSpA criteria, and the modified New York Criteria for AS. Patients were genotyped for ~200,000 immune-mediated disease SNPs using the Illumina Immunochip.
RESULTS:
We first established the predictive accuracy of genetic data comparing 9,638 healthy controls and 4,428 AS cases from the homogenous International Genetics of AS (IGAS) Consortium Immunochip study which showed excellent predictive power (AUC=0.91). Genetic risk scores had lower predictive power (AUC=0.83) comparing ASAS cohort axSpA cases meeting the ASAS imaging criteria with IGAS controls. Comparing genetic risk scores showed moderate discriminatory capacity between IGAS AS and ASAS imaging positive cases (AUC 0.67±0.05), indicating that significant differences in genetic makeup exist between the cohorts.
CONCLUSIONS:
In a clinical setting of referred back pain patients suspected to have axial SpA we were unable to use genetic data to construct a predictive model better than that based on existing clinical data. Potential confounding factors include significant heterogeneity in the ASAS cohort, possibly reflecting the disease heterogeneity of axSpA, or differences between centres in ascertainment or classification performance.
