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A “best-in-class” systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression
We aimed to identify markers in blood (serum) to predict clinically relevant knee osteoarthritis (OA) progression defined as the combination of both joint structure and pain worsening over 48 months. A set of 15 serum proteomic markers corresponding to 13 total proteins reached an area under the rec...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876540/ https://www.ncbi.nlm.nih.gov/pubmed/36696492 http://dx.doi.org/10.1126/sciadv.abq5095 |
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author | Zhou, Kaile Li, Yi-Ju Soderblom, Erik J. Reed, Alexander Jain, Vaibhav Sun, Shuming Moseley, M. Arthur Kraus, Virginia Byers |
author_facet | Zhou, Kaile Li, Yi-Ju Soderblom, Erik J. Reed, Alexander Jain, Vaibhav Sun, Shuming Moseley, M. Arthur Kraus, Virginia Byers |
author_sort | Zhou, Kaile |
collection | PubMed |
description | We aimed to identify markers in blood (serum) to predict clinically relevant knee osteoarthritis (OA) progression defined as the combination of both joint structure and pain worsening over 48 months. A set of 15 serum proteomic markers corresponding to 13 total proteins reached an area under the receiver operating characteristic curve (AUC) of 73% for distinguishing progressors from nonprogressors in a cohort of 596 individuals with knee OA. Prediction based on these blood markers was far better than traditional prediction based on baseline structural OA and pain severity (59%) or the current “best-in-class” biomarker for predicting OA progression, urinary carboxyl-terminal cross-linked telopeptide of type II collagen (58%). The generalizability of the marker set was confirmed in a second cohort of 86 individuals that yielded an AUC of 70% for distinguishing joint structural progressors. Blood is a readily accessible biospecimen whose analysis for these biomarkers could facilitate identification of individuals for clinical trial enrollment and those most in need of treatment. |
format | Online Article Text |
id | pubmed-9876540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98765402023-02-03 A “best-in-class” systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression Zhou, Kaile Li, Yi-Ju Soderblom, Erik J. Reed, Alexander Jain, Vaibhav Sun, Shuming Moseley, M. Arthur Kraus, Virginia Byers Sci Adv Biomedicine and Life Sciences We aimed to identify markers in blood (serum) to predict clinically relevant knee osteoarthritis (OA) progression defined as the combination of both joint structure and pain worsening over 48 months. A set of 15 serum proteomic markers corresponding to 13 total proteins reached an area under the receiver operating characteristic curve (AUC) of 73% for distinguishing progressors from nonprogressors in a cohort of 596 individuals with knee OA. Prediction based on these blood markers was far better than traditional prediction based on baseline structural OA and pain severity (59%) or the current “best-in-class” biomarker for predicting OA progression, urinary carboxyl-terminal cross-linked telopeptide of type II collagen (58%). The generalizability of the marker set was confirmed in a second cohort of 86 individuals that yielded an AUC of 70% for distinguishing joint structural progressors. Blood is a readily accessible biospecimen whose analysis for these biomarkers could facilitate identification of individuals for clinical trial enrollment and those most in need of treatment. American Association for the Advancement of Science 2023-01-25 /pmc/articles/PMC9876540/ /pubmed/36696492 http://dx.doi.org/10.1126/sciadv.abq5095 Text en Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Biomedicine and Life Sciences Zhou, Kaile Li, Yi-Ju Soderblom, Erik J. Reed, Alexander Jain, Vaibhav Sun, Shuming Moseley, M. Arthur Kraus, Virginia Byers A “best-in-class” systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression |
title | A “best-in-class” systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression |
title_full | A “best-in-class” systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression |
title_fullStr | A “best-in-class” systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression |
title_full_unstemmed | A “best-in-class” systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression |
title_short | A “best-in-class” systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression |
title_sort | “best-in-class” systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression |
topic | Biomedicine and Life Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876540/ https://www.ncbi.nlm.nih.gov/pubmed/36696492 http://dx.doi.org/10.1126/sciadv.abq5095 |
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