Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative. Issue 1 (March 2023)

• Record Type: Journal Article
• Authors: Keefe, Thomas H.; Minnig, Mary Catherine; Arbeeva, Liubov; Niethammer, Marc; Xu, Zhenlin; Shen, Zhengyang; Chen, Boqi; Nissman, Daniel B.; Golightly, Yvonne M.; Marron, J.S.; Nelson, Amanda E.
• Is Part Of: Osteoarthritis and cartilage open. Volume 5:Issue 1(2023)
• Publication Date: 2023-03
• Subjects: Knee osteoarthritis -- cartilage thickness -- Modes of variation; Osteoarthritis -- Periodicals; Cartilage -- Periodicals; 616.7223005
• DOI: 10.1016/j.ocarto.2023.100334
• Languages: English
• ISSNs: 2665-9131

Abstract

Abstract: Objective: To employ novel methodologies to identify phenotypes in knee OA based on variation among three baseline data blocks: 1) femoral cartilage thickness, 2) tibial cartilage thickness, and 3) participant characteristics and clinical features. Methods: Baseline data were from 3321 Osteoarthritis Initiative (OAI) participants with available cartilage thickness maps (6265 knees) and 77 clinical features. Cartilage maps were obtained from 3D DESS MR images using a deep-learning based segmentation approach and an atlas-based analysis developed by our group. Angle-based Joint and Individual Variation Explained (AJIVE) was used to capture and quantify variation, both shared among multiple data blocks and individual to each block, and to determine statistical significance. Results: Three major modes of variation were shared across the three data blocks. Mode 1 reflected overall thicker cartilage among men, those with higher education, and greater knee forces; Mode 2 showed associations between worsening Kellgren-Lawrence Grade, medial cartilage thinning, and worsening symptoms; and Mode 3 contrasted lateral and medial-predominant cartilage loss associated with BMI and malalignment. Each data block also demonstrated individual, independent modes of variation consistent with the known discordance between symptoms and structure in knee OA and reflecting the importance of features such as physical function, symptoms, and comorbid conditions independent of structural damage. Conclusions: This exploratory analysis, combining the rich OAI dataset with novel methods for determining and visualizing cartilage thickness, reinforces known associations in knee OA while providing insights into the potential for data integration in knee OA phenotyping.