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
Statistics and Operations Research, University of North Carolina at Chapel Hill College of Arts and Sciences, Chapel Hill, North Carolina, USA.
Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Osteoarthr Cartil Open. 2023 Jan 24;5(1):100334. doi: 10.1016/j.ocarto.2023.100334. eCollection 2023 Mar.
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.
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 among multiple data blocks and to each block, and to determine statistical significance.
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.
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.
采用新方法，基于三个基线数据块的差异来识别膝关节骨关节炎的表型：1）股骨软骨厚度，2）胫骨软骨厚度，以及3）参与者特征和临床特征。
基线数据来自3321名骨关节炎倡议（OAI）参与者，他们有可用的软骨厚度图（6265个膝关节）和77项临床特征。软骨图是使用基于深度学习的分割方法和我们团队开发的基于图谱的分析，从三维双回波稳态（DESS）磁共振成像中获得的。基于角度的联合和个体变异解释（AJIVE）用于捕捉和量化多个数据块之间以及每个数据块内部的变异，并确定统计学意义。
三个数据块共享三种主要变异模式。模式1反映了男性、受教育程度较高者以及膝关节受力较大者的软骨总体较厚；模式2显示了凯尔格伦-劳伦斯分级加重、内侧软骨变薄与症状加重之间的关联；模式3对比了与体重指数（BMI）和对线不良相关的外侧和以内侧为主的软骨损伤。每个数据块还展示了个体独立的变异模式，这与膝关节骨关节炎症状和结构之间已知的不一致性相符，并反映了身体功能、症状和合并症等特征独立于结构损伤的重要性。
这项探索性分析将丰富的OAI数据集与用于确定和可视化软骨厚度的新方法相结合，强化了膝关节骨关节炎中已知的关联，同时为膝关节骨关节炎表型分析中的数据整合潜力提供了见解。
