University of Oulu

Angelini F, Widera P, Mobasheri A, et al Osteoarthritis endotype discovery via clustering of biochemical marker data. Annals of the Rheumatic Diseases 2022;81:666-675.

Osteoarthritis endotype discovery via clustering of biochemical marker data

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Author: Angelini, Federico1; Widera, Paweł1; Mobasheri, Ali2,3,4,5,6;
Organizations: 1School of Computing, Newcastle University, Newcastle upon Tyne, UK
2Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
3Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
4Rheumatology & Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands
5Department of Joint Surgery, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
6World Health Organization Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Aging, Liege, Belgium
7ImmunoScience, Nordic Bioscience, Herlev, Denmark
8Faculty of Medicine, Department of Clinical Sciences Lund, Orthopaedics, Lund University, Lund, Sweden
9Artialis SA, Liège, Belgium
10Center for Interdisciplinary Research on Medicines (CIRM), University of Liège, Liège, Belgium
11Rheumatology, Leiden Universitair Medisch Centrum, Leiden, The Netherlands
12Department of Clinical Epidemiology, Leiden Universitair Medisch Centrum, Leiden, The Netherlands
13Servicio de Reumatologia, INIBIC-Hospital Universitario A Coruña, A Coruña, Spain
14Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, Norway
15Institut national de la santé et de la recherche médicale, Sorbonne Université, Paris, France
16BioBone BV, Darmstadt, Germany
17GlaxoSmithKline USA, Philadelphia, Pennsylvania, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link:
Language: English
Published: BMJ, 2022
Publish Date: 2023-03-09


Objectives: Osteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning.

Methods: Data quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression.

Results: Three dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain.

Conclusions: This work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future.

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Series: Annals of the rheumatic diseases
ISSN: 0003-4967
ISSN-E: 1468-2060
ISSN-L: 0003-4967
Volume: 81
Issue: 5
Pages: 666 - 675
DOI: 10.1136/annrheumdis-2021-221763
Type of Publication: A1 Journal article – refereed
Field of Science: 1182 Biochemistry, cell and molecular biology
Funding: The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under Grant Agreement no 115770, resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in-kind contribution. See and http://wwwapproachprojecteu/.
Copyright information: © Author(s) (or their employer(s)) 2022. This article has been accepted for publication in Annals of the Rheumatic Diseases 2022 following peer review, and the Version of Record can be accessed online at