Multivariable Modeling of Biomarker Data From the Phase I Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium.

Autor: Hunter DJ; Royal North Shore Hospital and University of Sydney, Sydney, Australia., Deveza LA; Royal North Shore Hospital and University of Sydney, Sydney, Australia., Collins JE; Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts., Losina E; Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts., Katz JN; Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts., Nevitt MC; University of California, San Francisco., Lynch JA; University of California, San Francisco., Roemer FW; Boston University School of Medicine, Boston, Massachusetts, and University of Erlangen, Nuremberg, Erlangen, Germany., Guermazi A; Boston University School of Medicine, Boston, Massachusetts., Bowes MA; Imorphics Ltd., Manchester, UK., Dam EB; University of Copenhagen and BiomedIQ, Copenhagen, Denmark., Eckstein F; Paracelsus Medical University, Salzburg, Austria, and Chondrometrics GmbH, Ainring, Germany., Kwoh CK; University of Arizona College of Medicine, Tucson., Hoffmann S; Foundation for the National Institutes of Health, North Bethesda, Maryland., Kraus VB; Duke University School of Medicine, Durham, North Carolina.
Jazyk: angličtina
Zdroj: Arthritis care & research [Arthritis Care Res (Hoboken)] 2022 Jul; Vol. 74 (7), pp. 1142-1153. Date of Electronic Publication: 2022 May 03.
DOI: 10.1002/acr.24557
Abstrakt: Objective: To determine the optimal combination of imaging and biochemical biomarkers for use in the prediction of knee osteoarthritis (OA) progression.
Methods: The present study was a nested case-control trial from the Foundation of the National Institutes of Health OA Biomarkers Consortium that assessed study participants with a Kellgren/Lawrence grade of 1-3 who had complete biomarker data available (n = 539 to 550). Cases were participants' knees that had radiographic and pain progression between 24 and 48 months compared to baseline. Radiographic progression only was assessed in secondary analyses. Biomarkers (baseline and 24-month changes) that had a P value of <0.10 in univariate analysis were selected, including quantitative cartilage thickness and volume on magnetic resonance imaging (MRI), semiquantitative MRI markers, bone shape and area, quantitative meniscal volume, radiographic progression (trabecular bone texture [TBT]), and serum and/or urine biochemical markers. Multivariable logistic regression models were built using 3 different stepwise selection methods (complex models versus parsimonious models).
Results: Among baseline biomarkers, the number of locations affected by osteophytes (semiquantitative), quantitative central medial femoral and central lateral femoral cartilage thickness, patellar bone shape, and semiquantitative Hoffa-synovitis predicted OA progression in most models (C statistic 0.641-0.671). In most models, 24-month changes in semiquantitative MRI markers (effusion-synovitis, meniscal morphologic changes, and cartilage damage), quantitative central medial femoral cartilage thickness, quantitative medial tibial cartilage volume, quantitative lateral patellofemoral bone area, horizontal TBT (intercept term), and urine N-telopeptide of type I collagen predicted OA progression (C statistic 0.680-0.724). A different combination of imaging and biochemical biomarkers (baseline and 24-month change) predicted radiographic progression only, which had a higher C statistic of 0.716-0.832.
Conclusion: The present study highlights the combination of biomarkers with potential prognostic utility in OA disease-modifying trials. Properly qualified, these biomarkers could be used to enrich future trials with participants likely to experience progression of knee OA.
(© 2021 American College of Rheumatology.)
Databáze: MEDLINE
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