Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.Comment: 22 pages, 12 figures, 10 table

Comparison between 2D radiographic weight-bearing joint space width and 3D MRI non-weight-bearing cartilage thickness measures in the knee using non-weight-bearing 2D and 3D CT as an intermediary

Background: In knee osteoarthritis, radiographic joint space width (JSW) is frequently used as a surrogate marker for cartilage thickness; however, longitudinal changes in radiographic JSW have shown poor correlations with those of magnetic resonance imaging (MRI) cartilage thickness. There are fundamental differences between the techniques: radiographic JSW represents two-dimensional (2D), weight-bearing, bone-to-bone distance, while on MRI three-dimensional (3D) non-weight-bearing cartilage thickness is measured. In this exploratory study, computed tomography (CT) was included as a third technique, as it can measure bone-to-bone under non-weight-bearing conditions. The objective was to use CT to compare the impact of weight-bearing versus non-weight-bearing, as well as bone-to-bone JSW versus actual cartilage thickness, in the knee. Methods: Osteoarthritis patients (n = 20) who were treated with knee joint distraction were included. Weight-bearing radiographs, non-weight-bearing MRIs and CTs were acquired before and 2 years after treatment. The mean radiographic JSW and cartilage thickness of the most affected compartment were measured. From CT, the 3D median JSW was calculated and a 2D projectional image was rendered, positioned similarly and measured identically to the radiograph. Pearson correlations between the techniques were derived, both cross-sectionally and longitudinally. Results: Fourteen patients could be analyzed. Cross-sectionally, all comparisons showed moderate to strong significant correlations (R = 0.43–0.81; all p < 0.05). Longitudinal changes over time were small; only the correlations between 2D CT and 3D CT (R = 0.65; p = 0.01) and 3D CT and MRI (R = 0.62; p = 0.02) were statistically significant. Conclusion: The poor correlation between changes in radiographic JSW and MRI cartilage thickness appears primarily to result from the difference in weight-bearing, and less so from measuring bone-to-bone distance versus cartilage thickness

Changes in cartilage thickness and denuded bone area after knee joint distraction and high tibial osteotomy—post‐hoc analyses of two randomized controlled trials

High tibial osteotomy (HTO) and knee joint distraction (KJD) are joint‐preserving treat-ments that unload the more affected compartment (MAC) in knee osteoarthritis. This post‐hoc study compares two‐year cartilage‐thickness changes after treatment with KJD vs. HTO, and identifies factors predicting cartilage restoration. Patients indicated for HTO were randomized to KJD (KJDHTO) or HTO treatment. Patients indicated for total knee arthroplasty received KJD (KJDTKA). Outcomes were the MRI mean MAC cartilage thickness and percentage of denuded bone area (dABp) change two years after treatment, using radiographic joint space width (JSW) as the refer-ence. Cohen’s d was used for between‐group effect sizes. Post‐treatment, KJDHTO patients (n = 18) did not show significant changes. HTO patients (n = 33) displayed a decrease in MAC cartilage thickness and an increase in dABp, but an increase in JSW. KJDTKA (n = 18) showed an increase in MAC cartilage thickness and JSW, and a decrease in dABp. Osteoarthritis severity was the strongest predictor of cartilage restoration. Kellgren–Lawrence grade ≥3 showed significant restoration (p < 0.01) after KJD; grade ≤2 did not. Effect sizes between severe KJD and HTO patients were large for MAC MRI cartilage thickness (d = 1.09; p = 0.005) and dABp (d = 1.13; p = 0.003), but not radiographic JSW (d = 0.28; p = 0.521). This suggests that in knee osteoarthritis patients with high disease severity, KJD may be more efficient in restoring cartilage thickness

Towards Personalized Treatment in Haemophilia: The Role of Genetic Factors in Iron and Heme Control to Identify Patients at Risk for Haemophilic Arthropathy

The treatment landscape for haemophilia is changing rapidly, creating opportunities for personalized treatment. As major morbidity is still caused by haemophilic arthropathy, understanding the factors affecting joint damage and joint damage progression might lead to more individualized treatment regimens. We investigated the association of HFE mutations or HMOX1 polymorphisms affecting iron/heme handling with radiographic joint damage in 252 haemophilia patients (severe and moderate). Although iron levels and transferrin saturation were significantly increased in the 95 patients with an HFE mutation, neither carrying this mutation nor the HMOX1 polymorphism was associated with radiographic joint damage, and the same was true after adjustment for well-known factors associated with arthropathy. In conclusion, this study does not support the hypothesis that HFE mutations or HMOX1 polymorphisms can be used to predict the development of haemophilic arthropathy

Exploring the differences between radiographic joint space width and MRI cartilage thickness changes using data from the IMI-APPROACH cohort

[Abstract] Objective: Longitudinal weight-bearing radiographic joint space width (JSW) and non-weight-bearing MRI-based cartilage thickness changes often show weak correlations. The current objective was to investigate these correlations, and to explore the influence of different factors that could contribute to longitudinal differences between the two methods. Methods: The current study included 178 participants with medial osteoarthritis (OA) out of the 297 knee OA participants enrolled in the IMI-APPROACH cohort. Changes over 2 years in medial JSW (ΔJSWmed), minimum JSW (ΔJSWmin), and medial femorotibial cartilage thickness (ΔMFTC) were assessed using linear regression, using measurements from radiographs and MRI acquired at baseline, 6 months, and 1 and 2 years. Pearson R correlations were calculated. The influence of cartilage quality (T2 mapping), meniscal extrusion (MOAKS scoring), potential pain-induced unloading (difference in knee-specific pain scores), and increased loading (BMI) on the correlations was analyzed by dividing participants in groups based on each factor separately, and comparing correlations (slope and strength) between groups using linear regression models. Result: Correlations between ΔMFTC and ΔJSWmed and ΔJSWmin were statistically significant (p < 0.004) but weak (R < 0.35). Correlations were significantly different between groups based on cartilage quality and on meniscal extrusion: only patients with the lowest T2 values and with meniscal extrusion showed significant moderate correlations. Pain-induced unloading or BMI-induced loading did not influence correlations. Conclusions: While the amount of loading does not seem to make a difference, weight-bearing radiographic JSW changes are a better reflection of non-weight-bearing MRI cartilage thickness changes in knees with higher quality cartilage and with meniscal extrusion

Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis

Objective: To predict response to anti–tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment. Methods: Gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study. Results: Transcription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up-regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models. Conclusion: Our findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti-TNF treatment

Myostatin and CXCL11 promote nervous tissue macrophages to maintain osteoarthritis pain

Pain is the most debilitating symptom of knee osteoarthritis (OA) that can even persist after total knee replacement. The severity and duration of pain do not correlate well with joint tissue alterations, suggesting other mechanisms may drive pain persistence in OA. Previous work identified that macrophages accumulate in the dorsal root ganglia (DRG) containing the somas of sensory neurons innervating the injured knee joint in a mouse OA model and acquire a M1-like phenotype to maintain pain. Here we aimed to unravel the mechanisms that govern DRG macrophage accumulation and programming. The accumulation of F4/80 +iNOS + (M1-like) DRG macrophages was detectable at day 3 after mono-iodoacetate (MIA)-induced OA in the mouse. Depletion of macrophages prior to induction of OA resolved pain-like behaviors by day 7 without affecting the initial development of pain-like behaviors. Analysis of DRG transcript identified CXCL11 and myostatin. CXCL11 and myostatin were increased at 3 weeks post OA induction, with CXCL11 expression partially localized in satellite glial cells and myostatin in sensory neurons. Blocking CXCL11 or myostatin prevented the persistence of OA pain, without affecting the initiation of pain. CXCL11 neutralization reduced the number of total and F4/80 +iNOS + DRG macrophages, whilst myostatin inhibition diminished the programming of F4/80 +iNOS + DRG macrophages. Intrathecal injection of recombinant CXCL11 did not induce pain-associated behaviors. In contrast, intrathecal myostatin increased the number of F4/80 +iNOS + DRG macrophages concurrent with the development of mechanical hypersensitivity that was prevented by macrophages depletion or CXCL11 blockade. Finally, myostatin inhibition during established OA, resolved pain and F4/80 +iNOS + macrophage accumulation in the DRG. In conclusion, DRG macrophages maintain OA pain, but are not required for the induction of OA pain. Myostatin is a key ligand in neuro-immune communication that drives the persistence of pain in OA through nervous tissue macrophages and represent a novel therapeutic target for the treatment of OA pain

Identifying multivariate disease trajectories and potential phenotypes of early knee osteoarthritis in the CHECK cohort

OBJECTIVE: To gain better understanding of osteoarthritis (OA) heterogeneity and its predictors for distinguishing OA phenotypes. This could provide the opportunity to tailor prevention and treatment strategies and thus improve care. DESIGN: Ten year follow-up data from CHECK (1002 early-OA subjects with first general practitioner visit for complaints ≤6 months before inclusion) was used. Data were collected on WOMAC (pain, function, stiffness), quantitative radiographic tibiofemoral (TF) OA characteristics, and semi-quantitative radiographic patellofemoral (PF) OA characteristics. Using functional data analysis, distinctive sets of trajectories were identified for WOMAC, TF and PF characteristics, based on model fit and clinical interpretation. The probabilities of knee membership to each trajectory were used in hierarchical cluster analyses to derive knee OA phenotypes. The number and composition of potential phenotypes was selected again based on model fit (silhouette score) and clinical interpretation. RESULTS: Five trajectories representing different constant levels or changing WOMAC scores were identified. For TF and PF OA, eight and six trajectories respectively were identified based on (changes in) joint space narrowing, osteophytes and sclerosis. Combining the probabilities of knees belonging to these different trajectories resulted in six clusters ('phenotypes') of knees with different degrees of functional (WOMAC) and radiographic (PF) parameters; TF parameters were found not to significantly contribute to clustering. Including baseline characteristics as well resulted in eight clusters of knees, dominated by sex, menopausal status and WOMAC scores, with only limited contribution of PF features. CONCLUSIONS: Several stable and progressive trajectories of OA symptoms and radiographic features were identified, resulting in phenotypes with relatively independent symptomatic and radiographic features. Sex and menopausal status may be especially important when phenotyping knee OA patients, while radiographic features contributed less. Possible phenotypes were identified that, after validation, could aid personalized treatments and patients selection