Dataset Information

Semiquantitative Imaging Biomarkers of Knee Osteoarthritis Progression: Data From the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium.

ABSTRACT:

Objective

To determine the association between changes in semiquantitative magnetic resonance imaging (MRI) biomarkers over 24 months and radiographic and pain progression over 48 months in knees with mild-to-moderate osteoarthritis (OA).

Methods

We undertook a nested case-control study as part of the Foundation for the National Institutes of Health Biomarkers Consortium Project. We used multivariable logistic regression models to examine the association between change over 24 months in semiquantitative MRI markers and radiographic and pain progression in knee OA. MRIs were read according to the MRI OA Knee Score system. We focused on changes in cartilage, osteophytes, meniscus, bone marrow lesions, Hoffa-synovitis, and effusion-synovitis.

Results

The most parsimonious model included changes in cartilage thickness and surface area, effusion-synovitis, Hoffa-synovitis, and meniscal morphology (C statistic 0.740). Compared with no worsening, worsening in cartilage thickness in ≥3 subregions was associated with 2.8-fold (95% confidence interval [95% CI] 1.3-5.9) greater odds of being a case, and worsening in cartilage surface area in ≥3 subregions was associated with 2.4-fold (95% CI 1.3-4.4) greater odds of being a case. Worsening of meniscal morphology in any region was associated with 2.2-fold (95% CI 1.3-3.8) greater odds of being a case. Worsening effusion-synovitis and Hoffa-synovitis were also associated with a greater odds of being a case (odds ratios 2.7 and 2.0, respectively).

Conclusion

Twenty-four-month changes in cartilage thickness, cartilage surface area, effusion-synovitis, Hoffa-synovitis, and meniscal morphology were independently associated with OA progression, suggesting that these factors may serve as efficacy biomarkers in clinical trials of disease-modifying interventions for knee OA.

SUBMITTER: Collins JE

PROVIDER: S-EPMC5599158 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Publications

Semiquantitative Imaging Biomarkers of Knee Osteoarthritis Progression: Data From the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium.

Collins Jamie E JE Losina Elena E Nevitt Michael C MC Roemer Frank W FW Guermazi Ali A Lynch John A JA Katz Jeffrey N JN Kent Kwoh C C Kraus Virginia B VB Hunter David J DJ

Arthritis & rheumatology (Hoboken, N.J.) 20161001 10

<h4>Objective</h4>To determine the association between changes in semiquantitative magnetic resonance imaging (MRI) biomarkers over 24 months and radiographic and pain progression over 48 months in knees with mild-to-moderate osteoarthritis (OA).<h4>Methods</h4>We undertook a nested case-control study as part of the Foundation for the National Institutes of Health Biomarkers Consortium Project. We used multivariable logistic regression models to examine the association between change over 24 mon ...[more]

PMID: 27111771

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Project description:OBJECTIVE:To investigate the association of cartilage thickness change over 24 months, as determined by magnetic resonance imaging (MRI), with knee osteoarthritis (OA) progression at 24-48 months. METHODS:This nested case-control study included 600 knees with a baseline Kellgren/Lawrence (K/L) grade of 1-3 from 600 Osteoarthritis Initiative (OAI) participants. Case knees (n?=?194) had both medial tibiofemoral radiographic joint space loss (?0.7 mm) and a persistent increase in the Western Ontario and McMaster Universities Osteoarthritis Index pain score (?9 on a 0-100 scale) 24-48 months from baseline. Control knees (n?=?406) included 200 with neither radiographic nor pain progression, 103 with radiographic progression only, and 103 with pain progression only. Medial and lateral femorotibial cartilage was segmented from sagittal 3T MRIs at baseline, 12 months, and 24 months. Logistic regression was used to assess the association of change in cartilage thickness, with a focus on the central medial femorotibial compartment, and OA progression. RESULTS:Central medial femorotibial compartment thickness loss was significantly associated with case status, with an odds ratio (OR) of 1.9 (95% confidence interval [95% CI] 1.6-2.3) (P < 0.0001). Association with case status reached P < 0.05 for both the central femur (OR 1.8 [95% CI 1.5-2.2]) and the central tibia (OR 1.6 [95% CI 1.3-1.9]). Lateral femorotibial compartment cartilage thickness loss, in contrast, was not significantly associated with case status. A reduction in central medial femorotibial compartment cartilage thickness was strongly associated with radiographic progression (OR 4.0 [95% CI 2.9-5.3]; P < 0.0001) and only weakly associated with pain progression (OR 1.3 [95% CI 1.1-1.6]; P < 0.01). CONCLUSION:Our findings indicate that loss of medial femorotibial cartilage thickness over 24 months is associated with the combination of radiographic and pain progression in the knee, with a stronger association for radiographic progression.

Project description:ObjectiveTo determine the optimal combination of imaging and biochemical biomarkers for use in the prediction of knee osteoarthritis (OA) progression.MethodsThe 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).ResultsAmong 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.ConclusionThe 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.

Project description:ObjectiveKnee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progression phenotypes that are potentially more responsive to interventions.DesignWe used publicly available data from the Foundation for the National Institutes of Health (FNIH) osteoarthritis (OA) Biomarkers Consortium, where radiographic (medial joint space narrowing of ≥0.7 mm), and pain progression (increase of ≥9 Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC] points) were defined at 48 months, as four mutually exclusive outcome groups (none, both, pain only, radiographic only), along with an extensive set of covariates. We applied distance weighted discrimination (DWD), direction-projection-permutation (DiProPerm) testing, and clustering methods to focus on the contrast (z-scores) between those progressing by both criteria ("progressors") and those progressing by neither ("non-progressors").ResultsUsing all observations (597 individuals, 59% women, mean age 62 years and BMI 31 kg/m2) and all 73 baseline variables available in the dataset, there was a clear separation among progressors and non-progressors (z = 10.1). Higher z-scores were seen for the magnetic resonance imaging (MRI)-based variables than for demographic/clinical variables or biochemical markers. Baseline variables with the greatest contribution to non-progression at 48 months included WOMAC pain, lateral meniscal extrusion, and serum N-terminal pro-peptide of collagen IIA (PIIANP), while those contributing to progression included bone marrow lesions, osteophytes, medial meniscal extrusion, and urine C-terminal crosslinked telopeptide type II collagen (CTX-II).ConclusionsUsing methods that provide a way to assess numerous variables of different types and scalings simultaneously in relation to an outcome of interest enabled a data-driven approach that identified key variables associated with a progression phenotype.

Dataset Information

Semiquantitative Imaging Biomarkers of Knee Osteoarthritis Progression: Data From the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium.

Objective

Methods

Results

Conclusion

Publications

Semiquantitative Imaging Biomarkers of Knee Osteoarthritis Progression: Data From the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium.

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