ArticlePloS one2022
Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
- Artificial intelligence and big data for precision regenerative medicine in knee osteoarthritis: endotyping, responder prediction, and clinical translation.Frontiers in bioengineering and biotechnology · 2026Review
- Patterns of Shared Variation in Knee Ultrasound for Osteoarthritis: A Machine Learning Approach.Osteoarthritis imaging · 2025Article
- Powerful significance testing for unbalanced clusters.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Terminologies and definitions used to classify patients with osteoarthritis: a scoping review.BMC rheumatology · 2025Article
- Article
- Clustering Methods in Rheumatic and Musculoskeletal Disease Research: An Educational Guide to Best Research Practices.The Journal of rheumatology · 2024Review
- Central Sensitization: The Missing Link Between Psychological Distress and Poor Outcome Following Primary Total Knee Arthroplasty.The Journal of arthroplasty · 2024Article
- Specific-cytokine associations with outcomes in knee osteoarthritis subgroups: breaking down disease heterogeneity with phenotyping.Arthritis research & therapy · 2024Article
- Article
- Metabolic and inflammatory profiles define phenotypes with clinical relevance in female knee osteoarthritis patients with joint effusion.Rheumatology (Oxford, England) · 2023Article
- Structural phenotypes of knee osteoarthritis: potential clinical and research relevance.Skeletal radiology · 2023Review
- Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.Current rheumatology reports · 2023Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo apply biclustering, a methodology originally developed for analysis of gene expression data, to simultaneously cluster observations and clinical features to explore candidate phenotypes of knee osteoarthritis (KOA) for the first time.
methodsData from the baseline Osteoarthritis Initiative (OAI) visit were cleaned, transformed, and standardized as indicated (leaving 6461 knees with 86 features). Biclustering produced submatrices of the overall data matrix, representing similar observations across a subset of variables. Statistical validation was determined using the novel SigClust procedure. After identifying biclusters, relationships with key outcome measures were assessed, including progression of radiographic KOA, total knee arthroplasty, loss of joint space width, and worsening Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, over 96 months of follow-up.
resultsThe final analytic set included 6461 knees from 3330 individuals (mean age 61 years, mean body mass index 28 kg/m2, 57% women and 86% White). We identified 6 mutually exclusive biclusters characterized by different feature profiles at baseline, particularly related to symptoms and function. Biclusters represented overall better (#1), similar (#2, 3, 6), and poorer (#4, 5) prognosis compared to the overall cohort of knees, respectively. In general, knees in biclusters #4 and 5 had more structural progression (based on Kellgren-Lawrence grade, total knee arthroplasty, and loss of joint space width) but tended to have an improvement in WOMAC pain scores over time. In contrast, knees in bicluster #1 had less incident and progressive KOA, fewer total knee arthroplasties, less loss of joint space width, and stable pain scores compared with the overall cohort. SIGNIFICANCE: We identified six biclusters within the baseline OAI dataset which have varying relationships with key outcomes in KOA. Such biclusters represent potential phenotypes within the larger cohort and may suggest subgroups at greater or lesser risk of progression over time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.