Dataset Information

Classification of patients with knee osteoarthritis in clinical phenotypes: Data from the osteoarthritis initiative.

ABSTRACT: The existence of phenotypes has been hypothesized to explain the large heterogeneity characterizing the knee osteoarthritis. In a previous systematic review of the literature, six main phenotypes were identified: Minimal Joint Disease (MJD), Malaligned Biomechanical (MB), Chronic Pain (CP), Inflammatory (I), Metabolic Syndrome (MS) and Bone and Cartilage Metabolism (BCM). The purpose of this study was to classify a sample of individuals with knee osteoarthritis (KOA) into pre-defined groups characterized by specific variables that can be linked to different disease mechanisms, and compare these phenotypes for demographic and health outcomes.599 patients were selected from the OAI database FNIH at 24 months' time to conduct the study. For each phenotype, cut offs of key variables were identified matching the results from previous studies in the field and the data available for the sample. The selection process consisted of 3 steps. At the end of each step, the subjects classified were excluded from the further classification stages. Patients meeting the criteria for more than one phenotype were classified separately into a 'complex KOA' group.Phenotype allocation (including complex KOA) was successful for 84% of cases with an overlap of 20%. Disease duration was shorter in the MJD while the CP phenotype included a larger number of Women (81%). A significant effect of phenotypes on WOMAC pain (F = 16.736 p <0.001) and WOMAC physical function (F = 14.676, p < 0.001) was identified after controlling for disease duration.This study signifies the feasibility of a classification of KOA subjects in distinct phenotypes based on subgroup-specific characteristics.

SUBMITTER: Dell'Isola A

PROVIDER: S-EPMC5766143 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Classification of patients with knee osteoarthritis in clinical phenotypes: Data from the osteoarthritis initiative.

Dell'Isola A A Steultjens M M

PloS one 20180112 1

<h4>Objectives</h4>The existence of phenotypes has been hypothesized to explain the large heterogeneity characterizing the knee osteoarthritis. In a previous systematic review of the literature, six main phenotypes were identified: Minimal Joint Disease (MJD), Malaligned Biomechanical (MB), Chronic Pain (CP), Inflammatory (I), Metabolic Syndrome (MS) and Bone and Cartilage Metabolism (BCM). The purpose of this study was to classify a sample of individuals with knee osteoarthritis (KOA) into pre- ...[more]

PMID: 29329325

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Project description: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.SignificanceWe 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.

Project description:OBJECTIVE:Osteoarthritis (OA) is a disease with a substantial public health burden. Quantitative assessments of periarticular bone may be a biomarker capable of monitoring early disease progression. The purpose of this study was to evaluate whether measures of periarticular bone associate with longitudinal structural progression. METHODS:We conducted a 12-18 months longitudinal study using the Osteoarthritis Initiative (OAI). Participants received knee dual-energy x-ray absorptiometry (DXA), trabecular magnetic resonance (MR) imaging, and x-rays. Knee DXAs generated proximal tibial medial:lateral periarticular bone mineral density (paBMD) measures. Proximal tibial trabecular MR images were assessed for trabecular morphometry: apparent bone volume fraction (BVF), trabecular number, thickness, and spacing. Weight-bearing x-rays were assessed for medial tibiofemoral joint space narrowing (JSN). Chi-squared analyses assessed whether periarticular bone measures were predictive of worsening medial tibiofemoral JSN, adjusted for age, sex, and BMI. RESULTS:In all, 444 participants, mean age 64.2 ± 9.2 years, BMI 29.5 ± 4.6kg/m2, and 52% male at baseline. Medial JSN (radiographic progression) occurred in 40 participants (9%). Higher baseline medial:lateral paBMD, apparent BVF, trabecular number and thickness, and lower baseline and decreased trabecular spacing were all associated with more progression of JSN in the medial compartment. From lowest to highest baseline medial:lateral paBMD quartile groups, 2%, 5%, 11%, and 18% had medial JSN progression, respectively, between the 36- and 48-month visits, p-values = 0.001 and 0.002 unadjusted and adjusted. The rate of change in medial:lateral paBMD, apparent BVF, and spacing were associated with more medial JSN. For rate of medial:lateral paBMD change from lowest to highest quartile, the proportion of each group that experienced medial JSN progression were 5%, 5%, 11%, and 18%, with an unadjusted and adjusted p-value of 0.005. CONCLUSION:Baseline and most rates of periarticular bone change associate with knee OA structural progression, highlighting the close relationship between subchondral bone and JSN. Future studies should focus on developing these measures as predictive and pathophysiological biomarkers, and evaluating their deployment in clinical trials testing bone-targeted therapeutics.

Project description:Background Longitudinal data on the association of quantitative thigh muscle MRI markers with knee osteoarthritis (KOA) outcomes are scarce. These associations are of clinical importance, with potential use for thigh muscle-directed disease-modifying interventions. Purpose To measure KOA-associated longitudinal changes in MRI-derived muscle cross-sectional area (CSA) and adipose tissue and their association with downstream symptom worsening and knee replacement (KR). Materials and Methods In a secondary analysis of the Osteoarthritis Initiative multicenter prospective cohort (February 2004 through October 2015), knees of participants with available good-quality thigh MRI scans at baseline and at least one follow-up visit were included and classified as with and without KOA according to baseline radiographic Kellgren-Lawrence grade of 2 or higher and matched for confounders with use of propensity score matching. An automated deep learning model for thigh MRI two-dimensional segmentation was developed and tested. Markers of muscle CSA and intramuscular adipose tissue (intra-MAT) were measured at baseline and 2nd- and 4th-year follow-up (period 1) and compared between knees with and without KOA by using linear mixed-effect regression models. Furthermore, in knees with KOA, the association of period 1 changes in muscle markers with risk of KR (Cox proportional hazards) and symptom worsening (mixed-effect models) during the 4th to 9th year (period 2) was evaluated. Results This study included 4634 matched thighs (2317 with and 2317 without KOA) of 2344 participants (mean age, 62 years ± 9 [SD]; 1292 women). Compared with those without, knees with KOA had a decrease in quadriceps CSA (mean difference, -8.21 mm2/year; P = .004) and an increase in quadriceps intra-MAT (1.98 mm2/year; P = .007). Decreased CSA and increased intra-MAT of quadriceps during period 1 was predictive of downstream (period 2) KOA symptom worsening (Western Ontario and McMaster Universities Osteoarthritis Index total score: odds ratio, 0.24 [negative association] [P < .001] and 1.38 [P = .012], respectively). Quadriceps CSA changes were negatively associated with higher future KR risk (hazard ratio, 0.70; P < .001). Conclusion Knee osteoarthritis was associated with longitudinal MRI-derived decreased quadriceps cross-sectional area and increased intramuscular adipose tissue. These potentially modifiable risk factors were predictive of downstream symptom worsening and knee replacement. Clinical trial registration no. NCT00080171 © RSNA, 2022 Online supplemental material is available for this article.

Project description:ObjectiveWe aimed to describe the natural history leading to end-stage knee osteoarthritis (esKOA), focusing on knee symptoms, radiographic severity, and the presence of limited mobility or instability.MethodsWe performed knee-based analyses of Osteoarthritis Initiative data from 7691 knees (4165 participants). We used a validated definition of esKOA that relied on meeting one of two criteria: (1) severe radiographic knee osteoarthritis (Kellgren-Lawrence [KL] grade=4) with moderate-to-intense pain (Likert WOMAC pain+function>11/88) or (2) KL grade<4 with intense or severe pain (WOMAC pain+function>22) and limited mobility (flexion contracture≥5°) or instability (based on a varus and valgus stress test). We also introduced an alternate definition of esKOA that relied on meeting one of two criteria that omitted physical exam findings:(1) severe radiographic knee osteoarthritis (KL grade=4) with at least moderate symptoms or (2) KL grade=2 or 3 with intense or severe symptoms and persistent knee pain (frequent knee pain during three or more months in the past year). We used descriptive statistics to explore the frequency of components of esKOA at the index visit when they had incident esKOA, at the annual visit before developing esKOA, and the interval change between those visits.ResultsOur analytic sample was mostly female (58%), without radiographic knee osteoarthritis (KL grade=0 or 1; 60%), without stability or mobility concerns (91%), and without persistent knee pain (77%). At the visit before incident esKOA, most knees already had moderate-to-severe radiographic osteoarthritis using the original (62%) or alternate (50%) definition (versus <15% for either definition of no esKOA). Over 80% of knees that reached the criteria for esKOA achieved this based on increased knee symptom severity - typically without worsening radiographic severity (80%).ConclusionRadiographic severity predisposed a knee to esKOA. However, worsening knee symptoms led to the development of incident esKOA. If investigators want to increase the chance of identifying incident esKOA as an outcome, they should enrich their study samples with people with moderate-to-severe radiographic osteoarthritis. Our findings also highlight the potential reversibility of esKOA (a knee that is classified with esKOA but later is not classified with esKOA). Reversibility is not a flaw of an outcome defining esKOA but rather a desirable clinical outcome to demonstrate a therapeutic intervention can help people with esKOA improve their knee symptoms and delay a knee replacement.

Dataset Information

Classification of patients with knee osteoarthritis in clinical phenotypes: Data from the osteoarthritis initiative.

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Classification of patients with knee osteoarthritis in clinical phenotypes: Data from the osteoarthritis initiative.

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