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

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

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

RNA sequencing to predict response to TNF-\u3b1 inhibitors reveals possible mechanism for nonresponse in smokers

Several studies have employed microarray-based profiling to predict response to tumor necrosis factor-alpha inhibitors (TNFi) in rheumatoid arthritis (RA); yet efforts to validate these targets have failed to show predictive abilities acceptable for clinical practice

Development and validation of rheumatoid arthritis disease activity indices including HandScan (optical spectral transmission) scores

Objective: To develop and validate a composite rheumatoid arthritis (RA) disease activity index using optical spectral transmission (OST) scores obtained with the HandScan, replacing tender and swollen joint counts. Methods: RA patients from a single center routinely undergoing HandScan measurements with at least 1 concurrent OST score and Disease Activity Score in 28 joints (DAS28) were included. Data were extracted from medical records. Linear regression analyses with the DAS28 as the outcome were performed to create a disease activity index (DAS-OST). OST score, erythrocyte sedimentation rate (ESR), and patient global assessment (PtGA) visual analog scale (VAS), sex, age, disease duration, and rheumatoid factor status were evaluated as independent variables. Final models were derived based on the statistical significance of coefficients and model fit. Of the data, two-thirds were used for development and one-third for validation; external validation was performed in a cohort from another center. Agreement between DAS-OST and DAS28 was assessed using the Bland-Altman plot method and intraclass correlation coefficient (ICC). Diagnostic value of the DAS-OST was determined for established definitions of remission, low disease activity (LDA), and high disease activity (HDA). Results: Data of 3,358 observations from 1,505 unique RA patients were extracted. DAS-OST was defined as: –0.44 + OST × 0.03 + male × –0.11 + LN(ESR) × 0.77 + PtGA VAS × 0.03. The ICCs between DAS-OST and DAS28 were 0.88 (95% confidence interval [95% CI] 0.87–0.90) and 0.82 (95% CI 0.75–0.86) and measurement errors were 0.58 and 0.87 in internal and external validation, respectively. Sensitivity for remission, LDA, and HDA was 79%, 91%, and 43%, respectively, and specificity was 92%, 80%, and 96% in external validation. Conclusion: Using the HandScan, RA disease activity can be accurately estimated if combined with ESR, PtGA VAS, and sex into a disease activity index (DAS-OST)

Structural changes after ankle joint distraction in haemophilic arthropathy: an explorative study investigating biochemical markers and 3D joint space width

INTRODUCTION: Ankle joint distraction (AJD) is a promising treatment for patients with severe haemophilic ankle arthropathy (HAA). However, some patients showed no clinical improvement after AJD and these differences may be related to structural differences. AIM: Primarily to quantify the structural changes after AJD in patients with HAA by the use of 3D joint space width (JSW) measurements and biochemical markers and secondarily to correlate these findings with clinical pain/function. METHODS: Patients with haemophilia A/B who underwent AJD were included for this study. Bone contours on MRI (performed before and 12 and 36 months after AJD) were drawn manually and percentage change in JSW was calculated. Blood/urine (before and 6, 12, 24 and 36 months after AJD) was collected for biomarker measurement (COMP, CS846, C10C, CALC2, PRO-C2, CTX-II) and combined indexes of markers were calculated. Mixed effects models were used for analyses on group level. Structural changes were compared with clinical parameters. RESULTS: Eight patients were evaluated. On group level, percentage changes in JSW showed a slight decrease after 12 months followed by a non-statistically significant increase in JSW after 36 months compared to baseline. Biochemical marker collagen/cartilage formation also showed an initial decrease, followed by a trend towards net formation 12, 24 and 36 months after AJD. On individual patient level, no clear correlations between structural changes and clinical parameters were observed. CONCLUSION: Cartilage restoration activity on group level in patients with HAA after AJD was in concordance with clinical improvements. Correlating structural modifications with clinical parameters in the individual patient remains difficult