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  • Angelini, FedericoUniversity of Newcastle upon Tyne (author)

Osteoarthritis endotype discovery via clustering of biochemical marker data

  • Article/chapterEnglish2022

Publisher, publication year, extent ...

  • 2022-03-04
  • BMJ,2022

Numbers

  • LIBRIS-ID:oai:lup.lub.lu.se:d012f8c9-9ff7-4338-b278-2773b7b339b9
  • https://lup.lub.lu.se/record/d012f8c9-9ff7-4338-b278-2773b7b339b9URI
  • https://doi.org/10.1136/annrheumdis-2021-221763DOI

Supplementary language notes

  • Language:English
  • Summary in:English

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Classification

  • Subject category:art swepub-publicationtype
  • Subject category:ref swepub-contenttype

Notes

  • 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. Method 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. Trial registration number NCT03883568.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Widera, PawełUniversity of Newcastle upon Tyne (author)
  • Mobasheri, AliUniversity Medical Center Utrecht,World Health Organization Centre for Health Development,Sun Yat-sen University (author)
  • Blair, JosephNordic Bioscience AS (author)
  • Struglics, AndréLund University,Lunds universitet,Ortopedi, Lund,Sektion III,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Lund OsteoArthritis Division - Nedbrytning av ledbrosk: en biologisk process som leder till artros,Forskargrupper vid Lunds universitet,Orthopaedics (Lund),Section III,Department of Clinical Sciences, Lund,Faculty of Medicine,Lund OsteoArthritis Division - Molecular marker research group,Lund University Research Groups(Swepub:lu)ort-as1 (author)
  • Uebelhoer, MelanieArtialis SA (author)
  • Henrotin, YvesArtialis SA,University of Liège (author)
  • Marijnissen, Anne C.A.University Medical Center Utrecht (author)
  • Kloppenburg, MargreetLeiden University Medical Centre (author)
  • Blanco, Francisco J.University of A Coruña (author)
  • Haugen, Ida K.Diakonhjemmet Hospital (author)
  • Berenbaum, FrancisParis-Sorbonne University (author)
  • Ladel, ChristophBioBone Bv (author)
  • Larkin, JonathanGlaxoSmithKline (author)
  • Bay-Jensen, Anne C.Nordic Bioscience AS (author)
  • Bacardit, JaumeUniversity of Newcastle upon Tyne (author)
  • University of Newcastle upon TyneUniversity Medical Center Utrecht (creator_code:org_t)

Related titles

  • In:Annals of the Rheumatic Diseases: BMJ81:5, s. 666-6750003-49671468-2060

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