Quantification of the whole-body burden of radiographic osteoarthritis using factor analysis.
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2011
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INTRODUCTION: Although osteoarthritis (OA) commonly involves multiple joints, no widely accepted method for quantifying whole-body OA burden exists. Therefore, our aim was to apply factor analytic methods to radiographic OA (rOA) grades across multiple joint sites, representing both presence and severity, to quantify the burden of rOA. METHODS: We used cross-sectional data from the Johnston County Osteoarthritis Project. The sample (n = 2092) had a mean age of 65 ± 11 years, body mass index (BMI) 31 ± 7 kg/m2, with 33% men and 34% African Americans. A single expert reader (intra-rater κ = 0.89) provided radiographic grades based on standard atlases for the hands (30 joints, including bilateral distal and proximal interphalangeal [IP], thumb IP, metacarpophalangeal [MCP] and carpometacarpal [CMC] joints), knees (patellofemoral and tibiofemoral, 4 joints), hips (2 joints), and spine (5 levels [L1/2 to L5/S1]). All grades were entered into an exploratory common factor analysis as continuous variables. Stratified factor analyses were used to look for differences by gender, race, age, and cohort subgroups. RESULTS: Four factors were identified as follows: IP/CMC factor (20 joints), MCP factor (8 joints), Knee factor (4 joints), Spine factor (5 levels). These factors had high internal consistency reliability (Cronbach's α range 0.80 to 0.95), were not collapsible into a single factor, and had moderate between-factor correlations (Pearson correlation coefficient r = 0.24 to 0.44). There were no major differences in factor structure when stratified by subgroup. CONCLUSIONS: The 4 factors obtained in this analysis indicate that the variables contained within each factor share an underlying cause, but the 4 factors are distinct, suggesting that combining these joint sites into one overall measure is not appropriate. Using such factors to reflect multi-joint rOA in statistical models can reduce the number of variables needed and increase precision.
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Nelson, Amanda E, Robert F DeVellis, Jordan B Renner, Todd A Schwartz, Philip G Conaghan, Virginia B Kraus and Joanne M Jordan (2011). Quantification of the whole-body burden of radiographic osteoarthritis using factor analysis. Arthritis Res Ther, 13(5). p. R176. 10.1186/ar3501 Retrieved from https://hdl.handle.net/10161/10871.
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Virginia Byers Kraus
Virginia Byers Kraus, MD, PhD, is the Mary Bernheim Distinguished Professor of Medicine, Professor of Orthopaedic Surgery, Professor of Pathology and a faculty member of the Duke Molecular Physiology Institute in the Duke University School of Medicine. She is a practicing Rheumatologist with over 30 years’ experience in translational musculoskeletal research focusing on osteoarthritis, the most common of all arthritides. She trained at Brown University (ScB 1979), Duke University (MD 1982, PhD 1993) and the Duke University School of Medicine (Residency in Internal Medicine and Fellowship in Rheumatology). Her career has focused on elucidating osteoarthritis pathogenesis and translational research into the discovery and validation of biomarkers for early osteoarthritis detection, prediction of progression, monitoring of disease status, and facilitation of therapeutic developments. She is co-PI of the Foundation for NIH Biomarkers Consortium Osteoarthritis project. Trained as a molecular biologist and a Rheumatologist, she endeavors to study disease from bedside to bench.
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