Cohort profile: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) study: a 2-year, European, cohort study to describe, validate and predict phenotypes of osteoarthritis using clinical, imaging and biochemical markers

Mitonuclear epistasis involving TP63 and haplogroup Uk: Risk of rapid progression of knee OA in patients from the OAI

OBJECTIVE

To investigate genetic interactions between mitochondrial deoxyribonucleic acid (mtDNA) haplogroups and nuclear single nucleotide polymorphisms (nSNPs) to analyze their impact on the development of the rapid progression of knee osteoarthritis (OA).

DESIGN

A total of 1095 subjects from the Osteoarthritis Initiative, with a follow-up time of at least 48-months, were included. Appropriate statistical approaches were performed, including generalized estimating equations adjusting for age, gender, body mass index, contralateral knee OA, Western Ontario and McMaster Universities Osteoarthritis Index pain, previous injury in target knee and the presence of the mtDNA variant m.16519C. Additional genomic data consisted in the genotyping of Caucasian mtDNA haplogroups and eight nSNPs previously associated with the risk of knee OA in robust genome-wide association studies.

RESULTS

The simultaneous presence of the G allele of rs12107036 at TP63 and the haplogroup Uk significantly increases the risk of a rapid progression of knee OA (odds ratio = 1.670; 95% confidence interval [CI]: 1.031-2.706; adjusted p-value = 0.027). The assessment of the population attributable fraction showed that the highest proportion of rapid progressors was under the simultaneous presence of the G allele of rs12107036 and the haplogroup Uk (23.4%) (95%CI: 7.89-38.9; p-value < 0.05). The area under the curve of the cross-validation model (0.730) was very similar to the obtained for the predictive model (0.735). A nomogram was constructed to help clinicians to perform clinical trials or epidemiologic studies.

CONCLUSIONS

This study demonstrates the existence of a mitonuclear epistasis in OA, providing new mechanisms by which nuclear and mitochondrial variation influence the susceptibility to develop different OA phenotypes.

Prognostic model to predict the incidence of radiographic knee osteoarthritis

OBJECTIVE

Early diagnosis of knee osteoarthritis (KOA) in asymptomatic stages is essential for the timely management of patients using preventative strategies. We develop and validate a prognostic model useful for predicting the incidence of radiographic KOA (rKOA) in non-radiographic osteoarthritic subjects and stratify individuals at high risk of developing the disease.

METHODS

Subjects without radiographic signs of KOA according to the Kellgren and Lawrence (KL) classification scale (KL=0 in both knees) were enrolled in the OA initiative (OAI) cohort and the Prospective Cohort of A Coruña (PROCOAC). Prognostic models were developed to predict rKOA incidence during a 96-month follow-up period among OAI participants based on clinical variables and serum levels of the candidate protein biomarkers APOA1, APOA4, ZA2G and A2AP. The predictive capability of the biomarkers was assessed based on area under the curve (AUC), and internal validation was performed to correct for overfitting. A nomogram was plotted based on the regression parameters. Model performance was externally validated in the PROCOAC.

RESULTS

282 participants from the OAI were included in the development dataset. The model built with demographic, anthropometric and clinical data (age, sex, body mass index and WOMAC pain score) showed an AUC=0.702 for predicting rKOA incidence during the follow-up. The inclusion of ZA2G, A2AP and APOA1 data significantly improved the model's sensitivity and predictive performance (AUC=0.831). The simplest model, including only clinical covariates and ZA2G and A2AP serum levels, achieved an AUC=0.826. Both models were internally cross-validated. Predictive performance was externally validated in an independent dataset of 100 individuals from the PROCOAC (AUC=0.713).

CONCLUSION

A novel prognostic model based on common clinical variables and protein biomarkers was developed and externally validated to predict rKOA incidence over a 96-month period in individuals without any radiographic signs of disease. The resulting nomogram is a useful tool for stratifying high-risk populations and could potentially lead to personalised medicine strategies for treating OA.

What does artificial intelligence mean in rheumatology?

Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician's diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.

Constructing a clinical radiographic knee osteoarthritis database using artificial intelligence tools with limited human labor: A proof of principle

OBJECTIVE

To create a scalable and feasible retrospective consecutive knee osteoarthritis (OA) radiographic database with limited human labor using commercial and custom-built artificial intelligence (AI) tools.

METHODS

We applied four AI tools, two commercially available and two custom-built tools, to analyze 6 years of clinical consecutive knee radiographs from patients aged 35-79 at the University of Copenhagen Hospital, Bispebjerg-Frederiksberg Hospital, Denmark. The tools provided Kellgren-Lawrence (KL) grades, joint space widths, patella osteophyte detection, radiographic view detection, knee joint implant detection, and radiographic marker detection.

RESULTS

In total, 25,778 knee radiographs from 8575 patients were included in the database after excluding inapplicable radiographs, and 92.5% of the knees had a complete OA dataset. Using the four AI tools, we saved about 800 hours of radiologist reading time and only manually reviewed 16.0% of the images in the database.

CONCLUSIONS

This study shows that clinical knee OA databases can be built using AI with limited human reading time for uniform grading and measurements. The concept is scalable temporally and across geographic regions and could help diversify further OA research by efficiently including radiographic knee OA data from different populations globally. We can prevent data dredging and overfitting OA theories on existing trite cohorts by including various gene pools and continuous expansion of new clinical cohorts. Furthermore, the suggested tools and applied approaches provide an ability to retest previous hypotheses and test new hypotheses on real-life clinical data with current disease prevalence and trends.

From biochemical markers to molecular endotypes of osteoarthritis: a review on validated biomarkers

INTRODUCTION

Osteoarthritis (OA) affects over 500 million people worldwide. OA patients are symptomatically treated, and current therapies exhibit marginal efficacy and frequently carry safety-risks associated with chronic use. No disease-modifying therapies have been approved to date leaving surgical joint replacement as a last resort. To enable effective patient care and successful drug development there is an urgent need to uncover the pathobiological drivers of OA and how these translate into disease endotypes. Endotypes provide a more precise and mechanistic definition of disease subgroups than observable phenotypes, and a panel of tissue- and pathology-specific biochemical markers may uncover treatable endotypes of OA.

AREAS COVERED

We have searched PubMed for full-text articles written in English to provide an in-depth narrative review of a panel of validated biochemical markers utilized for endotyping of OA and their association to key OA pathologies.

EXPERT OPINION

As utilized in IMI-APPROACH and validated in OAI-FNIH, a panel of biochemical markers may uncover disease subgroups and facilitate the enrichment of treatable molecular endotypes for recruitment in therapeutic clinical trials. Understanding the link between biochemical markers and patient-reported outcomes and treatable endotypes that may respond to given therapies will pave the way for new drug development in OA.

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study

Objectives

To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study.

Design

We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression.

Results

From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P ​+ ​S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81-0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52-0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P ​+ ​S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57).

Conclusions

The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.

Prognostic value of illness perception on changes in knee pain among elderly individuals: Two-year results from the Frederiksberg Cohort study

Objective

To investigate the prognostic value of illness perception (IP) on knee pain, quality of life (QoL) and functional level in elderly individuals reporting knee pain.

Design

A prospective cohort study of 1552 elderly with knee pain comparing two previously established clusters based on the Brief Illness Perception questionnaire. Cluster 1 ("Concerned optimists" [hypothesized unfavorable profile]; n ​= ​642) perceived their knee pain as a greater threat to them than Cluster 2 ("Unconcerned confident" [hypothesized favorable profile]; n ​= ​910). Primary outcome was the change from baseline to year 2 in the KOOS Pain subscale. Secondary outcomes were changes from baseline in quality of life (EuroQol-5 Domain and EQ VAS) and in the KOOS subscales Symptom, Activities of Daily Living, Knee-related QoL and Sports and recreation. Analyses were done on the original Intention-To-Survey (ITS) population, using repeated measures mixed linear models.

Results

Among the ITS population, 841 (54%) responded to the 2-year survey. There was a statistically significant but clinically irrelevant cluster difference in the 2-year change from baseline in KOOS pain (mean difference: 6.0 KOOS points [95% CI: 7.3 to -4.7]) explained by a minor improvement in Cluster 1: (6.2 points) and no changes in Cluster 2: (0.2 points). Comparable results were found across the secondary outcomes. Clinically irrelevant cluster changes in IP were seen.

Conclusion

In a cohort of people with knee pain, IP phenotype (i.e., Clusters) were of no prognostic value for the 2-year changes in pain, function, and QoL. Targeting IP may not be relevant in this patient population.

Trial registration number and date of registration

The Frederiksberg Cohort study was pre-registered at clinicaltrials.gov (NCT03472300) on March 21, 2018.

MRI-based synthetic CT in the detection of knee osteoarthritis: Comparison with CT

Magnetic resonance Imaging is the gold standard for assessment of soft tissues; however, X-ray-based techniques are required for evaluating bone-related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI-based bone visualization technique, compared with CT, for the scoring of knee osteoarthritis. sCT images were generated from the 3T T1-weighted gradient-echo MR images using a trained machine learning algorithm. Two readers scored the severity of osteoarthritis in tibiofemoral and patellofemoral joints according to OACT, which enables the evaluation of osteoarthritis, from its characteristics of joint space narrowing, osteophytes, cysts and sclerosis in CT (and sCT) images. Cohen's κ was used to assess the interreader agreement for each modality, and intermodality agreement of CT- and sCT-based scores for each reader. We also compared the confidence level of readers for grading CT and sCT images using confidence scores collected during grading. Inter-reader agreement for tibiofemoral and patellofemoral joints were almost-perfect for both modalities (κ = 0.83-0.88). The intermodality agreement of osteoarthritis scores between CT and sCT was substantial to almost-perfect for tibiofemoral (κ = 0.63 and 0.84 for the two readers) and patellofemoral joints (κ = 0.78 and 0.81 for the two readers). The analysis of diagnosis confidence scores showed comparable visual quality of the two modalities, where both are showing acceptable confidence levels for scoring OA. In conclusion, in this single-center study, sCT and CT were comparable for the scoring of knee OA.

Quantitative measurement of cartilage morphology in osteoarthritis: current knowledge and future directions

Quantitative measures of cartilage morphology ("cartilage morphometry") extracted from high resolution 3D magnetic resonance imaging (MRI) sequences have been shown to be sensitive to osteoarthritis (OA)-related change and also to treatment interventions. Cartilage morphometry is therefore nowadays widely used as outcome measure for observational studies and randomized interventional clinical trials. The objective of this narrative review is to summarize the current status of cartilage morphometry in OA research, to provide insights into aspects relevant for the design of future studies and clinical trials, and to give an outlook on future developments. It covers the aspects related to the acquisition of MRIs suitable for cartilage morphometry, the analysis techniques needed for deriving quantitative measures from the MRIs, the quality assurance required for providing reliable cartilage measures, and the appropriate participant recruitment criteria for the enrichment of study cohorts with knees likely to show structural progression. Finally, it provides an overview over recent clinical trials that relied on cartilage morphometry as a structural outcome measure for evaluating the efficacy of disease-modifying OA drugs (DMOAD).

Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes

PURPOSE OF REVIEW

Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and patients in decision making, based on machine-learned evidence, and (2) improve our understanding of pathophysiology and mechanisms underlying OA, providing new insights into disease management and prevention. The purpose of this review is to improve the ability of clinicians and OA researchers to understand the strengths and limitations of AI/ML methods in applications to OA research.

RECENT FINDINGS

AI/ML can assist clinicians by prediction of OA incidence and progression and by providing tailored personalized treatment. These methods allow using multidimensional multi-source data to understand the nature of OA, to identify different OA phenotypes, and for biomarker discovery. We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.

Magnetic resonance imaging-based bone imaging of the lower limb: Strategies for generating high-resolution synthetic computed tomography

This study aims at assessing approaches for generating high-resolution magnetic resonance imaging- (MRI-) based synthetic computed tomography (sCT) images suitable for orthopedic care using a deep learning model trained on low-resolution computed tomography (CT) data. To that end, paired MRI and CT data of three anatomical regions were used: high-resolution knee and ankle data, and low-resolution hip data. Four experiments were conducted to investigate the impact of low-resolution training CT data on sCT generation and to find ways to train models on low-resolution data while providing high-resolution sCT images. Experiments included resampling of the training data or augmentation of the low-resolution data with high-resolution data. Training sCT generation models using low-resolution CT data resulted in blurry sCT images. By resampling the MRI/CT pairs before the training, models generated sharper images, presumably through an increase in the MRI/CT mutual information. Alternatively, augmenting the low-resolution with high-resolution data improved sCT in terms of mean absolute error proportionally to the amount of high-resolution data. Overall, the morphological accuracy was satisfactory as assessed by an average intermodal distance between joint centers ranging from 0.7 to 1.2 mm and by an average intermodal root-mean-squared distances between bone surfaces under 0.7 mm. Average dice scores ranged from 79.8% to 87.3% for bony structures. To conclude, this paper proposed approaches to generate high-resolution sCT suitable for orthopedic care using low-resolution data. This can generalize the use of sCT for imaging the musculoskeletal system, paving the way for an MR-only imaging with simplified logistics and no ionizing radiation.

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

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.

Relevance of Biomarkers in Serum vs. Synovial Fluid in Patients with Knee Osteoarthritis

The association between structural changes and pain sensation in osteoarthritis (OA) remains unclear. Joint deterioration in OA leads to the release of protein fragments that can either systemically (serum) or locally (synovial fluid; SF) be targeted as biomarkers and describe structural changes and potentially pain. Biomarkers of collagen type I (C1M), type II (C2M), type III (C3M), type X (C10C), and aggrecan (ARGS) degradation were measured in the serum and SF of knee OA patients. Spearman's rank correlation was used to assess the correlation of the biomarkers' levels between serum and SF. Linear regression adjusted for confounders was used to evaluate the associations between the biomarkers' levels and clinical outcomes. The serum C1M levels were negatively associated with subchondral bone density. The serum C2M levels were negatively associated with KL grade and positively associated with minimum joint space width (minJSW). The C10C levels in SF were negatively associated with minJSW and positively associated with KL grade and osteophyte area. Lastly, the serum C2M and C3M levels were negatively associated with pain outcomes. Most of the biomarkers seemed to mainly be associated with structural outcomes. The overall biomarkers of extracellular matrix (ECM) remodeling in serum and SF may provide different information and reflect different pathogenic processes.

A meta-analysis and a functional study support the influence of mtDNA variant m.16519C on the risk of rapid progression of knee osteoarthritis

OBJECTIVES

To identify mitochondrial DNA (mtDNA) genetic variants associated with the risk of rapid progression of knee osteoarthritis (OA) and to characterise their functional significance using a cellular model of transmitochondrial cybrids.

METHODS

Three prospective cohorts contributed participants. The osteoarthritis initiative (OAI) included 1095 subjects, the Cohort Hip and Cohort Knee included 373 and 326 came from the PROspective Cohort of Osteoarthritis from A Coruña. mtDNA variants were screened in an initial subset of 450 subjects from the OAI by in-depth sequencing of mtDNA. A meta-analysis of the three cohorts was performed. A model of cybrids was constructed to study the functional consequences of harbouring the risk mtDNA variant by assessing: mtDNA copy number, mitochondrial biosynthesis, mitochondrial fission and fusion, mitochondrial reactive oxygen species (ROS), oxidative stress, autophagy and a whole transcriptome analysis by RNA-sequencing.

RESULTS

mtDNA variant m.16519C is over-represented in rapid progressors (combined OR 1.546; 95% CI 1.163 to 2.054; p=0.0027). Cybrids with this variant show increased mtDNA copy number and decreased mitochondrial biosynthesis; they produce higher amounts of mitochondrial ROS, are less resistant to oxidative stress, show a lower expression of the mitochondrial fission-related gene fission mitochondrial 1 and an impairment of autophagic flux. In addition, its presence modulates the transcriptome of cybrids, especially in terms of inflammation, where interleukin 6 emerges as one of the most differentially expressed genes.

CONCLUSIONS

The presence of the mtDNA variant m.16519C increases the risk of rapid progression of knee OA. Among the most modulated biological processes associated with this variant, inflammation and negative regulation of cellular process stand out. The design of therapies based on the maintenance of mitochondrial function is recommended.

Quantitative CT of the knee in the IMI-APPROACH osteoarthritis cohort: Association of bone mineral density with radiographic disease severity, meniscal coverage and meniscal extrusion

OBJECTIVE

Osteoarthritis (OA) is a highly prevalent chronic condition. The subchondral bone plays an important role in onset and progression of OA making it a potential treatment target for disease-modifying therapeutic approaches. However, little is known about changes of periarticular bone mineral density (BMD) in OA and its relation to meniscal coverage and meniscal extrusion at the knee. Thus, the aim of this study was to describe periarticular BMD in the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) cohort at the knee and to analyze the association with structural disease severity, meniscal coverage and meniscal extrusion.

DESIGN

Quantitative CT (QCT), MRI and radiographic examinations were acquired in 275 patients with knee osteoarthritis (OA). QCT was used to assess BMD at the femur and tibia, at the cortical bone plate (Cort) and at the epiphysis at three locations: subchondral (Sub), mid-epiphysis (Mid) and adjacent to the physis (Juxta). BMD was evaluated for the medial and lateral compartment separately and for subregions covered and not covered by the meniscus. Radiographs were used to determine the femorotibial angle and were evaluated according to the Kellgren and Lawrence (KL) system. Meniscal extrusion was assessed from 0 to 3.

RESULTS

Mean BMD differed significantly between each anatomic location at both the femur and tibia (p < 0.001) in patients with KL0. Tibial regions assumed to be covered with meniscus in patients with KL0 showed lower BMD at Sub (p < 0.001), equivalent BMD at Mid (p = 0.07) and higher BMD at Juxta (p < 0.001) subregions compared to regions not covered with meniscus. Knees with KL2-4 showed lower Sub (p = 0.03), Mid (p = 0.01) and Juxta (p < 0.05) BMD at the medial femur compared to KL0/1. Meniscal extrusion grade 2 and 3 was associated with greater BMD at the tibial Cort (p < 0.001, p = 0.007). Varus malalignment is associated with significant greater BMD at the medial femur and at the medial tibia at all anatomic locations.

CONCLUSION

BMD within the epiphyses of the tibia and femur decreases with increasing distance from the articular surface. Knees with structural OA (KL2-4) exhibit greater cortical BMD values at the tibia and lower BMD at the femur at the subchondral level and levels beneath compared to KL0/1. BMD at the tibial cortical bone plate is greater in patients with meniscal extrusion grade 2/3.

Test-retest precision and longitudinal cartilage thickness loss in the IMI-APPROACH cohort

OBJECTIVE

To investigate the test-retest precision and to report the longitudinal change in cartilage thickness, the percentage of knees with progression and the predictive value of the machine-learning-estimated structural progression score (s-score) for cartilage thickness loss in the IMI-APPROACH cohort - an exploratory, 5-center, 2-year prospective follow-up cohort.

DESIGN

Quantitative cartilage morphology at baseline and at least one follow-up visit was available for 270 of the 297 IMI-APPROACH participants (78% females, age: 66.4 ± 7.1 years, body mass index (BMI): 28.1 ± 5.3 kg/m², 55% with radiographic knee osteoarthritis (OA)) from 1.5T or 3T MRI. Test-retest precision (root mean square coefficient of variation) was assessed from 34 participants. To define progressor knees, smallest detectable change (SDC) thresholds were computed from 11 participants with longitudinal test-retest scans. Binary logistic regression was used to evaluate the odds of progression in femorotibial cartilage thickness (threshold: -211 μm) for the quartile with the highest vs the quartile with the lowest s-scores.

RESULTS

The test-retest precision was 69 μm for the entire femorotibial joint. Over 24 months, mean cartilage thickness loss in the entire femorotibial joint reached -174 μm (95% CI: [-207, -141] μm, 32.7% with progression). The s-score was not associated with 24-month progression rates by MRI (OR: 1.30, 95% CI: [0.52, 3.28]).

CONCLUSION

IMI-APPROACH successfully enrolled participants with substantial cartilage thickness loss, although the machine-learning-estimated s-score was not observed to be predictive of cartilage thickness loss. IMI-APPROACH data will be used in subsequent analyses to evaluate the impact of clinical, imaging, biomechanical and biochemical biomarkers on cartilage thickness loss and to refine the machine-learning-based s-score.

CLINICALTRIALS

GOV IDENTIFICATION

NCT03883568.

Comparison of 3D quantitative osteoarthritis imaging biomarkers from paired CT and MR images: data from the IMI-APPROACH study

INTRODUCTION

MRI bone surface area and femoral bone shape (B-score) measures have been employed as quantitative endpoints in DMOAD clinical trials. Computerized Tomography (CT) imaging is more commonly used for 3D visualization of bony anatomy due to its high bone-soft tissue contrast. We aimed to compare CT and MRI assessments of 3D imaging biomarkers.

METHODS

We used baseline and 24-month image data from the IMI-APPROACH 2-year prospective cohort study. Femur and tibia were automatically segmented using active appearance models, a machine-learning method, to measure 3D bone shape, area and 3D joint space width (3DJSW). Linear regression was used to test for correlation between measures. Limits of agreement and bias were tested using Bland-Altman analysis.

RESULTS

CT-MR pairs of the same knee were available from 434 participants (78% female). B-scores from CT and MR were strongly correlated (CCC = 0.967) with minimal bias of 0.1 (SDD = 0.227). Area measures were also correlated but showed a consistent bias (MR smaller). 3DJSW showed different biases (MR larger) in both lateral and medial compartments.

DISCUSSION

The strong correlation and small B-score bias suggests that B-score may be measured reliably using either modality. It is likely that the bone surface identified using MR and CT will be at slightly different positions within the bone/cartilage boundary. The negative bone area bias suggests the MR bone boundary is inside the CT boundary producing smaller areas for MR, consistent with the positive 3DJSW bias. The lateral-medial 3DJSW difference is possibly due to a difference in knee pose during acquisition (extended for CT, flexed for MR).

TRIAL REGISTRATION

NCT03883568.

Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort

OBJECTIVES

The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores.

METHODS

Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden's index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors.

RESULTS

Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively).

CONCLUSION

The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors.

TRIAL REGISTRATION

ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568.

Structural tissue damage and 24-month progression of semi-quantitative MRI biomarkers of knee osteoarthritis in the IMI-APPROACH cohort

Background

The IMI-APPROACH cohort is an exploratory, 5-centre, 2-year prospective follow-up study of knee osteoarthritis (OA). Aim was to describe baseline multi-tissue semiquantitative MRI evaluation of index knees and to describe change for different MRI features based on number of subregion-approaches and change in maximum grades over a 24-month period.

Methods

MRIs were acquired using 1.5 T or 3 T MRI systems and assessed using the semi-quantitative MRI OA Knee Scoring (MOAKS) system. MRIs were read at baseline and 24-months for cartilage damage, bone marrow lesions (BML), osteophytes, meniscal damage and extrusion, and Hoffa- and effusion-synovitis. In descriptive fashion, the frequencies of MRI features at baseline and change in these imaging biomarkers over time are presented for the entire sample in a subregional and maximum score approach for most features. Differences between knees without and with structural radiographic (R) OA are analyzed in addition.

Results

Two hundred eighty-nine participants had readable baseline MRI examinations. Mean age was 66.6 ± 7.1 years and participants had a mean BMI of 28.1 ± 5.3 kg/m2. The majority (55.3%) of included knees had radiographic OA. Any change in total cartilage MOAKS score was observed in 53.1% considering full-grade changes only, and in 73.9% including full-grade and within-grade changes. Any medial cartilage progression was seen in 23.9% and any lateral progression on 22.1%. While for the medial and lateral compartments numbers of subregions with improvement and worsening of BMLs were very similar, for the PFJ more improvement was observed compared to worsening (15.5% vs. 9.0%). Including within grade changes, the number of knees showing BML worsening increased from 42.2% to 55.6%. While for some features 24-months change was rare, frequency of change was much more common in knees with vs. without ROA (e.g. worsening of total MOAKS score cartilage in 68.4% of ROA knees vs. 36.7% of no-ROA knees, and 60.7% vs. 21.8% for an increase in maximum BML score per knee).

Conclusions

A wide range of MRI-detected structural pathologies was present in the IMI-APPROACH cohort. Baseline prevalence and change of features was substantially more common in the ROA subgroup compared to the knees without ROA.

Trial Registration

Clinicaltrials.gov identification: NCT03883568.

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers, given the "black box" nature of most algorithms, the unfamiliarity of the terms, and the lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this subject area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas, with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

Biological Targets of Multimolecular Therapies in Middle-Age Osteoarthritis

Knee osteoarthritis (OA) is a common condition, prevalent in middle-agedness, associated with chronic pain and impaired quality of life. Two interrelated biological processes fuel early OA progression: inflammation and structural tissues catabolism. Procatabolic and proinflammatory mediators are interconnected and form part of a self-perpetuating loop. They leverage OA research complexity because of the impossibility to discern certain spatiotemporal tissues' changes from others. Both are shared targets of versatile regenerative multimolecular therapies. In particular, platelet-rich plasma can interfere with inflammation and inflammatory pain. The therapeutic approach is to alter the vicious inflammatory loop by modifying the molecular composition of the synovial fluid, thereby paracrine cellular cross talk. Intra-articular injections of platelet-rich plasma can provide key factors balancing proinflammatory and anti-inflammatory factors, targeting macrophage dysfunction and modulating immune mechanisms within the knee.

The association of the lipid profile with knee and hand osteoarthritis severity: the IMI-APPROACH cohort

OBJECTIVE

To investigate the association of the lipidomic profile with osteoarthritis (OA) severity, considering the outcomes radiographic knee and hand OA, pain and function.

DESIGN

We used baseline data from the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) cohort, comprising persons with knee OA fulfilling the clinical American College of Rheumatology classification criteria. Radiographic knee and hand OA severity was quantified with Kellgren-Lawrence sum scores. Knee and hand pain and function were assessed with validated questionnaires. We quantified fasted plasma higher order lipids and oxylipins with liquid chromatography with tandem mass spectrometry (LC-MS/MS)-based platforms. Using penalised linear regression, we assessed the variance in OA severity explained by lipidomics, with adjustment for clinical covariates (age, sex, body mass index (BMI) and lipid lowering medication), measurement batch and clinical centre.

RESULTS

In 216 participants (mean age 66 years, mean BMI 27.3 kg/m², 75% women) we quantified 603 higher order lipids (triacylglycerols, diacylglycerols, cholesteryl esters, ceramides, free fatty acids, sphingomyelins, phospholipids) and 28 oxylipins. Lipidomics explained 3% and 2% of the variance in radiographic knee and hand OA severity, respectively. Lipids were not associated with knee pain or function. Lipidomics accounted for 12% and 6% of variance in hand pain and function, respectively. The investigated OA severity outcomes were associated with the lipidomic fraction of bound and free arachidonic acid, bound palmitoleic acid, oleic acid, linoleic acid and docosapentaenoic acid.

CONCLUSIONS

Within the APPROACH cohort lipidomics explained a minor portion of the variation in OA severity, which was most evident for the outcome hand pain. Our results suggest that eicosanoids may be involved in OA severity.

GaitSmart motion analysis compared to commonly used function outcome measures in the IMI-APPROACH knee osteoarthritis cohort

Background

There are multiple measures for assessment of physical function in knee osteoarthritis (OA), but each has its strengths and limitations. The GaitSmart® system, which uses inertial measurement units (IMUs), might be a user-friendly and objective method to assess function. This study evaluates the validity and responsiveness of GaitSmart® motion analysis as a function measurement in knee OA and compares this to Knee Injury and Osteoarthritis Outcome Score (KOOS), Short Form 36 Health Survey (SF-36), 30s chair stand test, and 40m self-paced walk test.

Methods

The 2-year Innovative Medicines Initiative—Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee OA cohort was conducted between January 2018 and April 2021. For this study, available baseline and 6 months follow-up data (n = 262) was used. Principal component analysis was used to investigate whether above mentioned function instruments could represent one or more function domains. Subsequently, linear regression was used to explore the association between GaitSmart® parameters and those function domains. In addition, standardized response means, effect sizes and t-tests were calculated to evaluate the ability of GaitSmart® to differentiate between good and poor general health (based on SF-36). Lastly, the responsiveness of GaitSmart® to detect changes in function was determined.

Results

KOOS, SF-36, 30s chair test and 40m self-paced walk test were first combined into one function domain (total function). Thereafter, two function domains were substracted related to either performance based (objective function) or self-reported (subjective function) function. Linear regression resulted in the highest R² for the total function domain: 0.314 (R² for objective and subjective function were 0.252 and 0.142, respectively.). Furthermore, GaitSmart® was able to distinguish a difference in general health status, and is responsive to changes in the different aspects of objective function (Standardized response mean (SRMs) up to 0.74).

Conclusion

GaitSmart® analysis can reflect performance based and self-reported function and may be of value in the evaluation of function in knee OA. Future studies are warranted to validate whether GaitSmart® can be used as clinical outcome measure in OA research and clinical practice.

Use of machine learning in osteoarthritis research: a systematic literature review

Objective

The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA).

Methods

A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected.

Results

From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles.

Conclusion

This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.

Osteoarthritis endotype discovery via clustering of biochemical marker data

OBJECTIVES

Osteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning.

METHOD

Data quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression.

RESULTS

Three dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain.

CONCLUSIONS

This work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future.

TRIAL REGISTRATION NUMBER

NCT03883568.

Osteoarthritis in year 2021: biochemical markers

OBJECTIVE

To summarize recent scientific advances in protein-derived soluble biomarkers of osteoarthritis.

DESIGN

A systematic search on the PubMed electronic database of clinical studies on protein-derived soluble biochemical markers of osteoarthritis in humans that were published between January 1st 2020 and March 31th 2021. The studies were selected on the basis of objective criteria and summarized in a table. Then they were described in a narrative review.

RESULTS

Out of 1971 publications, 48 fulfilled all selection criteria and 16 were selected by the author for the narrative review. The papers were classified according their clinical significance as defined in the BIPEDS classification. Two papers investigated the "burden of disease", two were dedicated to "investigative biomarkers", four papers question the "prognosis", three the "efficacy of treatment" and five the "diagnosis and phenotyping" value of protein-derived biomarkers.

CONCLUSIONS

Currently, biomarkers research is focused on their use as tools to identify molecular endotypes and clinical phenotypes and to facilitate patient screening and monitoring in clinical trials. This approach should allow a more targeted management of patients suffering from osteoarthritis.

Neuropathic pain in the IMI-APPROACH knee osteoarthritis cohort: prevalence and phenotyping

Objectives

Osteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component.

Methods

Baseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component.

Results

OA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different.

Conclusions

OA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments.

Trial registration number

The study is registered under clinicaltrials.gov nr: NCT03883568.

Performance of knee image digital analysis of radiographs of patients with end-stage knee osteoarthritis

OBJECTIVE

Knee Image Digital Analysis (KIDA) is standardized radiographic analysis software for measuring osteoarthritis (OA) characteristics. It was validated in mild OA, but used for severe OA as well. The current goal was to evaluate the performance of KIDA in severe OA.

DESIGN

Of 103 patients, standardized radiographs were performed before and one and 2 years after treatment for severe OA. All radiographs were evaluated on subchondral bone density, joint space width (JSW), osteophytes, eminence height, and joint angle, twice within years by the same observer. Part of the radiographs were randomly selected for reevaluation twice within 1 month and evaluation by another observer. The intraclass correlation coefficient (ICC), smallest detectable difference (SDD) and coefficient of variation (CV) were calculated; the SDD and CV were compared to those in mild OA. The relation of severity with KIDA parameters and with observer differences was calculated with linear regression.

RESULTS

Intra-observer ICCs were higher in the 98 severe radiographs reanalyzed within 1 month (all >0.8) than the 293 reanalyzed within years (all >0.5; most >0.8) and than inter-observer ICCs (all >0.7). SDDs and CVs were smaller when reanalyzed within a month and comparable to those in mild OA. Some parameters showed bias between readings. Severity showed significant relation with osteophytes and JSW parameters, and with the observer variation in these parameters (all P < 0.04).

CONCLUSIONS

KIDA is a well-performing tool also for severe OA. In order to decrease variability and SDDs, images should be analyzed in a limited time frame and randomized order.

A deep learning method for predicting knee osteoarthritis radiographic progression from MRI

Background

The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.

Methods

Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months.

Results

Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the medial joint space for JSN progression and the intra-articular space for pain prediction.

Conclusions

This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02634-4.

Pathology-pain relationships in different osteoarthritis animal model phenotypes: it matters what you measure, when you measure, and how you got there

OBJECTIVE

To determine whether osteoarthritis (OA) pain characteristics and mechanistic pathways in pre-clinical models are phenotype-specific.

DESIGN

Male 11-week-old C57BL6 mice had unilateral medial-meniscal-destabilization (DMM) or antigen-induced-arthritis (AIA), vs sham-surgery/immunised-controls (Sham/Im-CT). Pain behaviour (allodynia, mechanical- and thermal-hyperalgesia, hindlimb static weight-bearing, stride-length) and lumbar dorsal root ganglia (DRG) gene-expression were measured at baseline, day-3, week-1/-2/-4/-8/-16, and pain-behaviour:gene-expression:joint-pathology associations investigated.

RESULTS

DMM and AIA induced structural OA defined by progressively increasing cartilage erosion, subchondral bone sclerosis and osteophyte size and maturation. All pain-behaviours were modified, with model-specific differences in severity and temporal pattern. Tactile allodynia developed acutely in both models and persisted to week-16. During early-OA (wk4-8) there was; reduced right hindlimb weight-bearing in AIA; thermal-hyperalgesia and reduced stride-length in DMM. During chronic-OA (wk12-16); mechanical-hyperalgesia and reduced right hindlimb weight-bearing were observed in DMM only. There were no associations in either model between different pain-behaviour outcomes. A coordinated DRG-expression profile was observed in sham and Im-CT for all 11 genes tested, but not in AIA and DMM. At wk-16 despite equivalent joint pathology, changes in DRG-expression (Calca, Trpa1, Trpv1, Trpv4) were observed only in DMM. In AIA mechanical-hyperalgesia was associated with Trpv1 (r = -0.79) and Il1b (r = 0.53). In DMM stride-length was associated with Calca, Tac1, Trpv1, Trpv2, Trpv4 and Adamts5 (r = 0.4-0.57). DRG gene-expression change was correlated with subchondral-bone sclerosis in DMM, and cartilage damage in AIA. Positive pain-behaviour:joint-pathology associations were only present in AIA - for synovitis, subchondral-bone resorption, chondrocyte-hypertrophy and cartilage damage.

CONCLUSION

Pain and peripheral sensory neuronal responses are OA-phenotype-specific with distinct pathology:pain-outcome:molecular-mechanism relationships.

Baseline clinical characteristics of predicted structural and pain progressors in the IMI-APPROACH knee OA cohort

OBJECTIVES

To describe the relations between baseline clinical characteristics of the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) participants and their predicted probabilities for knee osteoarthritis (OA) structural (S) progression and/or pain (P) progression.

METHODS

Baseline clinical characteristics of the IMI-APPROACH participants were used for this study. Radiographs were evaluated according to Kellgren and Lawrence (K&L grade) and Knee Image Digital Analysis. Knee Injury and Osteoarthritis Outcome Score (KOOS) and Numeric Rating Scale (NRS) were used to evaluate pain. Predicted progression scores for each individual were determined using machine learning models. Pearson correlation coefficients were used to evaluate correlations between scores for predicted progression and baseline characteristics. T-tests and χ² tests were used to evaluate differences between participants with high versus low progression scores.

RESULTS

Participants with high S progressions score were found to have statistically significantly less structural damage compared with participants with low S progression scores (minimum Joint Space Width, minJSW 3.56 mm vs 1.63 mm; p<0.001, K&L grade; p=0.028). Participants with high P progression scores had statistically significantly more pain compared with participants with low P progression scores (KOOS pain 51.71 vs 82.11; p<0.001, NRS pain 6.7 vs 2.4; p<0.001).

CONCLUSIONS

The baseline minJSW of the IMI-APPROACH participants contradicts the idea that the (predicted) course of knee OA follows a pattern of inertia, where patients who have progressed previously are more likely to display further progression. In contrast, for pain progressors the pattern of inertia seems valid, since participants with high P score already have more pain at baseline compared with participants with a low P score.

Relationship between motion, using the GaitSmartTM system, and radiographic knee osteoarthritis: an explorative analysis in the IMI-APPROACH cohort

Objectives

To assess underlying domains measured by GaitSmart^TMparameters and whether these are additional to established OA markers including patient reported outcome measures (PROMs) and radiographic parameters, and to evaluate if GaitSmart analysis is related to the presence and severity of radiographic knee OA.

Methods

GaitSmart analysis was performed during baseline visits of participants of the APPROACH cohort (n = 297). Principal component analyses (PCA) were performed to explore structure in relationships between GaitSmart parameters alone and in addition to radiographic parameters and PROMs. Logistic and linear regression analyses were performed to analyse the relationship of GaitSmart with the presence (Kellgren and Lawrence grade ≥2 in at least one knee) and severity of radiographic OA (ROA).

Results

Two hundred and eighty-four successful GaitSmart analyses were performed. The PCA identified five underlying GaitSmart domains. Radiographic parameters and PROMs formed additional domains indicating that GaitSmart largely measures separate concepts. Several GaitSmart domains were related to the presence of ROA as well as the severity of joint damage in addition to demographics and PROMs with an area under the receiver operating characteristic curve of 0.724 and explained variances (adjusted R²) of 0.107, 0.132 and 0.147 for minimum joint space width, osteophyte area and mean subchondral bone density, respectively.

Conclusions

GaitSmart analysis provides additional information over established OA outcomes. GaitSmart parameters are also associated with the presence of ROA and extent of radiographic severity over demographics and PROMS. These results indicate that Gaitsmart^TM may be an additional outcome measure for the evaluation of OA.

Making the patient voice heard in a research consortium: experiences from an EU project (IMI-APPROACH)

APPROACH is an EU-wide research consortium with the goal to identify different subgroups of knee osteoarthritis to enable future differential diagnosis and treatment. During a 2-year clinical study images, biomarkers and clinical data are collected from people living with knee osteoarthritis and data are analyzed to confirm patterns that can indicate such different subgroups. A Patient Council (PC) has been set up at project initiation and consists of five people from Norway, The Netherlands and UK. Initially, this group of individuals had to learn how to effectively work with each other and with the researchers. Today, the PC is a strong team that is fully integrated in the consortium and acknowledged by researchers as an important sounding board. The article describes this journey looking at formal processes of involvement - organizational structure, budget, meetings - and more informal processes such as building relationships and changing researcher perceptions. It describes how the PC helped improve the experience and engagement of study participants by providing input to the clinical protocol and ensuring effective communication (e.g. through direct interactions with participants and newsletters). Furthermore, the PC is helping with dissemination of results and project advocacy, and overall provides the patient perspective to researchers. Additionally, the authors experienced and describe the intangible benefits such as a shift in researcher attitudes and a sense of community and purpose for PC members. Importantly, learnings reported in this article also include the challenges, such as effective integration of the PC with researchers' work in the early phase of the project. TRIAL REGISTRATION: US National Library of Medicine, NCT03883568 , retrospectively registered 21 March 2019.

The DIGICOD cohort: A hospital-based observational prospective cohort of patients with hand osteoarthritis - methodology and baseline characteristics of the population

OBJECTIVE

Despite its prevalence, there are few worldwide hand osteoarthritis (HOA) cohorts. The main objective of DIGItal COhort Design (DIGICOD) cohort is to investigate prognostic clinical, biological, genetic and imaging factors of clinical worsening after 6years follow-up.

METHODS

DIGICOD is a hospital-based prospective cohort including patients>35years-old with symptomatic HOA fulfilling: (i) ACR criteria for HOA with≥2 symptomatic joints among proximal/distal interphalangeal joints or 1st interphalangeal joint with Kellgren-Lawrence (KL)≥2; or (ii) symptomatic thumb base OA with KL≥2. Main exclusion criteria were inflammatory arthritis and crystal arthropathies. Annual clinical evaluations were scheduled with imaging (X-rays of the hands and of other OA symptomatic joints) and biological sampling every 3years. Hand radiographs are scored using KL and anatomical Verbruggen-Veys scores. Follow-up visits are ongoing. Cohort methodology and baseline characteristics are presented.

RESULTS

Between April 2013 and June 2017, from the 436 HOA included patients, 426 have been analysed of whom 357 (84%) are women. Mean age±standard deviation was 66.7±7.3years and mean disease duration was 12.6±9.6years. Metabolic syndrome affected 151 (36.5%) patients. Mean Visual Analog Scale (VAS) hand pain (0-100mm) was 44.4±26.7mm at activity. Mean FIHOA (0-100) was 19.9±18.6. Elevated serum CRP level (≥5mg/L) involved 10% patients. Mean KL score (0-128) was 46.7±18 and the mean number of joint with KL≥2 was 15.1±6.3. Erosive HOA (defined as≥1 Erosive or Remodeling phase joint according to Verbruggen-Veys score) involved 195/426 (45.8%) patients and the median number (interquartile range) of erosive joints in erosive patients was 3.0 (1.0-5.0).

CONCLUSION

DIGICOD is a unique prospective HOA cohort with a long-term 6years standardized assessment and has included severe radiologically HOA patients with a high prevalence of erosive disease.

Bone phenotypes in rheumatology - there is more to bone than just bone

Osteoarthritis, rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis, all have one clear common denominator; an altered turnover of bone. However, this may be more complex than a simple change in bone matrix and mineral turnover. While these diseases share a common tissue axis, their manifestations in the area of pathology are highly diverse, ranging from sclerosis to erosion of bone in different regions. The management of these diseases will benefit from a deeper understanding of the local versus systemic effects, the relation to the equilibrium of the bone balance (i.e., bone formation versus bone resorption), and the physiological and pathophysiological phenotypes of the cells involved (e.g., osteoblasts, osteoclasts, osteocytes and chondrocytes). For example, the process of endochondral bone formation in chondrocytes occurs exists during skeletal development and healthy conditions, but also in pathological conditions. This review focuses on the complex molecular and cellular taxonomy of bone in the context of rheumatological diseases that alter bone matrix composition and maintenance, giving rise to different bone turnover phenotypes, and how biomarkers (biochemical markers) can be applied to potentially describe specific bone phenotypic tissue profiles.