Multivariable modeling of biomarker data from the phase 1 Foundation for the NIH Osteoarthritis Biomarkers Consortium
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Multivariable modeling of biomarker data from the phase 1 Foundation for the NIH Osteoarthritis Biomarkers Consortium. / Hunter, David J; Deveza, Leticia A; Collins, Jamie E; Losina, Elena; Nevitt, Michael C; Roemer, Frank W; Guermazi, Ali; Bowes, Michael A; Dam, Erik B; Eckstein, Felix; Lynch, John A; Katz, Jeffrey N; Kwoh, C Kent; Hoffmann, Steve; Kraus, Virginia B.
In: Arthritis Care & Research, Vol. 74, No. 7, 2022, p. 1142-1153.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Multivariable modeling of biomarker data from the phase 1 Foundation for the NIH Osteoarthritis Biomarkers Consortium
AU - Hunter, David J
AU - Deveza, Leticia A
AU - Collins, Jamie E
AU - Losina, Elena
AU - Nevitt, Michael C
AU - Roemer, Frank W
AU - Guermazi, Ali
AU - Bowes, Michael A
AU - Dam, Erik B
AU - Eckstein, Felix
AU - Lynch, John A
AU - Katz, Jeffrey N
AU - Kwoh, C Kent
AU - Hoffmann, Steve
AU - Kraus, Virginia B
N1 - This article is protected by copyright. All rights reserved.
PY - 2022
Y1 - 2022
N2 - OBJECTIVE: To determine the optimal combination of imaging and biochemical biomarkers to predict knee osteoarthritis (OA) progression.METHODS: Nested case-control study from the FNIH OA Biomarkers Consortium of participants with Kellgren-Lawrence grade 1-3 and complete biomarker data (n=539 to 550). Cases were knees with radiographic and pain progression between 24-48 months from baseline. Radiographic progression only was assessed in secondary analyses. Biomarkers (baseline and 24-month changes) with p<0.10 in univariate analysis were selected, including MRI (quantitative (Q) cartilage thickness and volume; semi-quantitative (SQ) MRI markers; bone shape and area; Q meniscal volume), radiographic (trabecular bone texture (TBT)), and serum and/or urine biochemical markers. Multivariable logistic regression models were built using three different step-wise selection methods (complex vs. parsimonious models).RESULTS: Among baseline biomarkers, the number of locations affected by osteophytes (SQ), Q central medial femoral and central lateral femoral cartilage thickness, patellar bone shape, and SQ Hoffa-synovitis predicted progression in most models (C-statistics 0.641-0.671). 24-month changes in SQ MRI markers (effusion-synovitis, meniscal morphology, and cartilage damage), Q central medial femoral cartilage thickness, Q medial tibial cartilage volume, Q lateral patellofemoral bone area, horizontal TBT (intercept term), and urine NTX-I predicted progression in most models (C-statistics 0.680-0.724). A different combination of imaging and biochemical biomarkers (baseline and 24-month change) predicted radiographic progression only, with higher C-statistics (0.716-0.832).CONCLUSION: This 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 progress.
AB - OBJECTIVE: To determine the optimal combination of imaging and biochemical biomarkers to predict knee osteoarthritis (OA) progression.METHODS: Nested case-control study from the FNIH OA Biomarkers Consortium of participants with Kellgren-Lawrence grade 1-3 and complete biomarker data (n=539 to 550). Cases were knees with radiographic and pain progression between 24-48 months from baseline. Radiographic progression only was assessed in secondary analyses. Biomarkers (baseline and 24-month changes) with p<0.10 in univariate analysis were selected, including MRI (quantitative (Q) cartilage thickness and volume; semi-quantitative (SQ) MRI markers; bone shape and area; Q meniscal volume), radiographic (trabecular bone texture (TBT)), and serum and/or urine biochemical markers. Multivariable logistic regression models were built using three different step-wise selection methods (complex vs. parsimonious models).RESULTS: Among baseline biomarkers, the number of locations affected by osteophytes (SQ), Q central medial femoral and central lateral femoral cartilage thickness, patellar bone shape, and SQ Hoffa-synovitis predicted progression in most models (C-statistics 0.641-0.671). 24-month changes in SQ MRI markers (effusion-synovitis, meniscal morphology, and cartilage damage), Q central medial femoral cartilage thickness, Q medial tibial cartilage volume, Q lateral patellofemoral bone area, horizontal TBT (intercept term), and urine NTX-I predicted progression in most models (C-statistics 0.680-0.724). A different combination of imaging and biochemical biomarkers (baseline and 24-month change) predicted radiographic progression only, with higher C-statistics (0.716-0.832).CONCLUSION: This 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 progress.
U2 - 10.1002/acr.24557
DO - 10.1002/acr.24557
M3 - Journal article
C2 - 33421361
VL - 74
SP - 1142
EP - 1153
JO - Arthritis Care & Research
JF - Arthritis Care & Research
SN - 2151-464X
IS - 7
ER -
ID: 255209488