Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative Public Deposited

Downloadable Content

Download PDF
Creator
  • Nelson, Amanda E.
    • Affiliation: School of Medicine, Thurston Arthritis Research Center
  • Keefe, Thomas H.
    • Affiliation: College of Arts and Sciences, Department of Statistics and Operations Research
  • Schwartz, Todd A.
    • Affiliation: School of Medicine, Thurston Arthritis Research Center
  • Callahan, Leigh F.
    • Affiliation: School of Medicine, Thurston Arthritis Research Center
  • Loeser, Richard F.
    • Affiliation: School of Medicine, Thurston Arthritis Research Center
  • Golightly, Yvonne M.
    • Affiliation: School of Medicine, Thurston Arthritis Research Center
  • Arbeeva, Liubov
    • Affiliation: School of Medicine, Thurston Arthritis Research Center
  • Marron, J. S.
    • Affiliation: College of Arts and Sciences, Department of Statistics and Operations Research
Abstract
  • Objective To 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. Methods Data 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. Results The 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.
Date of publication
Keyword
DOI
Identifier
Resource type
  • Article
Rights statement
  • In Copyright
License
  • Attribution 4.0 International
Journal title
  • PLOS ONE
Journal volume
  • 17
Journal issue
  • 5
Language
  • English
Version
  • Publisher
ISSN
  • 1932-6203
Parents:

This work has no parents.

In Collection:

Items