{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kraus VB"],"funding":["NCCDPHP CDC HHS","NIA NIH HHS","ACL HHS","NIAMS NIH HHS"],"pagination":["329-337"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10925913"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["32(3)"],"pubmed_abstract":["<h4>Objective</h4>To better understand the pathogenesis of knee osteoarthritis (OA) through identification of serum diagnostics.<h4>Design</h4>We conducted multiple reaction monitoring mass spectrometry analysis of 107 peptides in baseline sera of two cohorts: the Foundation for National Institutes of Health (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 (ROC) and precision-recall (PR) curves.<h4>Results</h4>RF selected 23 peptides (19 proteins) and body mass index (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% confidence interval [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.<h4>Conclusions</h4>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."],"journal":["Osteoarthritis and cartilage"],"pubmed_title":["Serum proteomic biomarkers diagnostic of knee osteoarthritis."],"pmcid":["PMC10925913"],"funding_grant_id":["U01DP003206","P60 AR064166","R01 AR071450","P60 AR049465","U01 DP003206","P30 AR072580","P60 AR030701","P30 AG028716"],"pubmed_authors":["Golightly YM","Nelson AE","Li YJ","Soderblom EJ","Kraus VB","Reed A"],"additional_accession":[]},"is_claimable":false,"name":"Serum proteomic biomarkers diagnostic of knee osteoarthritis.","description":"<h4>Objective</h4>To better understand the pathogenesis of knee osteoarthritis (OA) through identification of serum diagnostics.<h4>Design</h4>We conducted multiple reaction monitoring mass spectrometry analysis of 107 peptides in baseline sera of two cohorts: the Foundation for National Institutes of Health (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 (ROC) and precision-recall (PR) curves.<h4>Results</h4>RF selected 23 peptides (19 proteins) and body mass index (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% confidence interval [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.<h4>Conclusions</h4>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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-04-07T21:14:28.475Z","creation":"2025-04-04T02:12:00.666Z"},"accession":"S-EPMC10925913","cross_references":{"pubmed":["37734705"],"doi":["10.1016/j.joca.2023.09.007"]}}