Serum Proteomic Biomarkers Diagnostic of Knee Osteoarthritis
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3 2024
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Source: Osteoarthritis Cartilage. 32(3):329-337
Details:
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Alternative Title:Osteoarthritis Cartilage
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Personal Author:
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Description:Objective:
To better understand the pathogenesis of knee osteoarthritis (OA) through identification of serum diagnostics.
Design:
We conducted multiple reaction monitoring mass spectrometry analysis of 107 peptides in baseline sera of two cohorts: the Foundation for NIH (n=596 Kellgren-Lawrence (KL) grade 1–3 knee OA participants); and the Johnston County Osteoarthritis Project (n=127 multi-joint controls free of radiographic OA of the hands, hips, knees (bilateral KL=0), and spine). Data were split into (70%) training and (30%) testing sets. Diagnostic peptide and clinical data predictors were selected by random forest (RF); selection was based on association (p<0.05) with OA status in multivariable logistic regression models. Model performance was based on area under the curve (AUC) of receiver operating characteristic and precision-recall (PR) curves.
Results:
RF selected 23 peptides (19 proteins) and BMI as diagnostic of OA. BMI weakly diagnosed OA (ROC-AUC 0.57, PR-AUC 0.812) and only symptomatic OA cases. ACTG was the strongest univariable predictor (ROC-AUC 0.705, PR-AUC 0.897). The final model (8 serum peptides) was highly diagnostic (ROC-AUC 0.833, 95% CI 0.751, 0.905; PR-AUC 0.929, 95% CI 0.876, 0.973) in the testing set and equally diagnostic of non-symptomatic and symptomatic cases (AUCs 0.830–0.835), and not significantly improved with addition of BMI. The STRING database predicted multiple high confidence interactions of the 19 diagnostic OA proteins.
Conclusions:
No more than 8 serum protein biomarkers were required to discriminate knee OA from non-OA. These biomarkers lend strong support to the involvement and cross-talk of complement and coagulation pathways in the development of OA.
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Pubmed ID:37734705
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Pubmed Central ID:PMC10925913
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Funding:
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Volume:32
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Issue:3
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Supporting Files:No Additional Files