Understanding the molecular endotypes that influence clinical phenotypes is a critical step for the stratification of patients with osteoarthritis (OA) into therapeutic subtypes that can help the development of targeted disease-modifying OA drugs (DMOADs) to provide genuine, long-term clinical benefit.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Latourte, A., Kloppenburg, M. & Richette, P. Emerging pharmaceutical therapies for osteoarthritis. Nat. Rev. Rheumatol. 16, 673–688 (2020).
Oo, W. M. Prospects of disease-modifying osteoarthritis drugs. Clin. Geriatr. Med. 38, 397–432 (2022).
Oo, W. M., Little, C., Duong, V. & Hunter, D. J. The development of disease-modifying therapies for osteoarthritis (DMOADs): the evidence to date. Drug Des. Devel. Ther. 15, 2921–2945 (2021).
Mobasheri, A. et al. Molecular taxonomy of osteoarthritis for patient stratification, disease management and drug development: biochemical markers associated with emerging clinical phenotypes and molecular endotypes. Curr. Opin. Rheumatol. 31, 80–89 (2019).
Agache, I. & Akdis, C. A. Precision medicine and phenotypes, endotypes, genotypes, regiotypes, and theratypes of allergic diseases. J. Clin. Invest. 129, 1493–503 (2019).
Boer, C. G. et al. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell 184, 4784–4818.e17 (2021).
Angelini, F. et al. Osteoarthritis endotype discovery via clustering of biochemical marker data. Ann. Rheum. Dis. 81, 666–675 (2022).
Demanse, D. et al. Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database. Semin Arthritis Rheum. 58, 152140 (2023).
Saxer, F. et al. Prognostic value of B-score for predicting joint replacement in the context of osteoarthritis phenotypes: Data from the Osteoarthritis Initiative. Osteoarthr. Cartil. Open 6, 100458 (2024).
Arbeeva, L., Minnig, M. C., Yates, K. A. & Nelson, A. E. Machine learning approaches to the prediction of osteoarthritis phenotypes and outcomes. Curr. Rheumatol. Rep. 25, 213–225 (2023).
Acknowledgements
A.M. acknowledges support from the European Cooperation in Science and Technology (COST) Association, Action CA21110-Building an open European Network on OsteoArthritis research (NetwOArk). R.L. recognizes support from the US National Institutes of Health (grant P30 AR072580) and the Rheumatology Research Foundation.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Supplementary information
Rights and permissions
About this article
Cite this article
Mobasheri, A., Loeser, R. Clinical phenotypes, molecular endotypes and theratypes in OA therapeutic development. Nat Rev Rheumatol (2024). https://doi.org/10.1038/s41584-024-01126-4
Published:
DOI: https://doi.org/10.1038/s41584-024-01126-4