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

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.

Specific-cytokine associations with outcomes in knee osteoarthritis subgroups: breaking down disease heterogeneity with phenotyping

“…In this study, we identified a set of cytokines that are differentially associated with severity and radiographic progression across a recently proposed classification of inflammatory phenotypes in KOA (KOIP). In the last years, extensive research has been conducted on the role of several markers on OA severity and progression, including some of the ones evaluated in the present work [ 10 , 11 , 15 , 17 – 19 , 48 ]. Although these studies have provided valuable information about the pathophysiology of the disease, none of their results has been transferred to the clinical practice, either to improve the diagnosis or prognosis of their patients or to develop new therapeutic targets with a disease-modifying effect [ 14 ].…”

“…Previous studies have focused on identifying specific inflammatory markers, such as adipocytokines and cytokines found in the blood, synovial fluid, and synovial membrane of OA patients, which have been linked to pain, disability, and radiographic changes [ 15 , 16 ]. However, much of this research has provided inconclusive or inconsistent results regarding the strength and nature of the association between these cytokines and the severity and progression of OA [ 11 , 17 – 19 ].…”

“…The identification of these phenotypes could lead to better assessment of severity and prognosis biomarkers, resulting in significant clinical implications for patients’ stratification, therapy tailoring, and exploration of novel treatments [ 22 ]. In this regard, several groups have described distinct OA phenotypes characterized by the presentation of diverse features, such as clinical parameters [ 17 , 23 ], transcriptomics [ 24 , 25 ], metabolomic data [ 26 , 27 ], and other biochemical markers [ 18 , 28 ]. Despite these efforts, a comprehensive and clinically relevant classification system for OA has yet to be established.…”

Structural phenotypes of knee osteoarthritis: potential clinical and research relevance

“…Further, from a therapeutic perspective, identifying precise OA phenotypes would allow targeted treatments for specific subgroups and, ultimately, the identification of more efficacious treatments through successful clinical trials [ 6 ]. Previous studies have grouped knee OA patients into distinct phenotypes from different perspectives [ 16 – 20 ]. They have used different sets of characteristics to determine the phenotypes (e.g., imaging findings, biochemical profiles, clinical features) and used either outcome-based definitions (e.g., trajectories of clinical or structural progression) or definitions based on baseline characteristics with a subsequent association of the phenotypes with outcomes [ 21 ].…”

Methodological development of molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: the STEpUP OA Consortium

“…These in turn may relate to a broad range of aetiological factors that are associated with OA [22][23][24][25][26] . Efforts have been made to classify subgroups of people with OA based on epidemiological factors [27] , with several clinically defined phenotypes now suggested in the literature [9,[28][29][30] . A recent systematic review of 24 studies reported that up to 84% of people with OA could be assigned to at least one of six phenotypes [28,31] .…”