Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers.

BACKGROUND: Knee osteoarthritis is the most prevalent chronic musculoskeletal debilitating disease. Current treatments are only symptomatic, and to improve this, we need a robust prediction model to stratify patients at an early stage according to the risk of joint structure disease progression. Some genetic factors, including single nucleotide polymorphism (SNP) genes and mitochondrial (mt)DNA haplogroups/clusters, have been linked to this disease. For the first time, we aim to determine, by using machine learning, whether some SNP genes and mtDNA haplogroups/clusters alone or combined could predict early knee osteoarthritis structural progressors. METHODS: Participants (901) were first classified for the probability of being structural progressors. Genotyping included SNP genes TP63, FTO, GNL3, DUS4L, GDF5, SUPT3H, MCF2L, and TGFA; mtDNA haplogroups H, J, T, Uk, and others; and clusters HV, TJ, KU, and C-others. They were considered for prediction with major risk factors of osteoarthritis, namely, age and body mass index (BMI). Seven supervised machine learning methodologies were evaluated. The support vector machine was used to generate gender-based models. The best input combination was assessed using sensitivity and synergy analyses. Validation was performed using tenfold cross-validation and an external cohort (TASOAC). RESULTS: From 277 models, two were defined. Both used age and BMI in addition for the first one of the SNP genes TP63, DUS4L, GDF5, and FTO with an accuracy of 85.0%; the second profits from the association of mtDNA haplogroups and SNP genes FTO and SUPT3H with 82.5% accuracy. The highest impact was associated with the haplogroup H, the presence of CT alleles for rs8044769 at FTO, and the absence of AA for rs10948172 at SUPT3H. Validation accuracy with the cross-validation (about 95%) and the external cohort (90.5%, 85.7%, respectively) was excellent for both models. CONCLUSIONS: This study introduces a novel source of decision support in precision medicine in which, for the first time, two models were developed consisting of (i) age, BMI, TP63, DUS4L, GDF5, and FTO and (ii) the optimum one as it has one less variable: age, BMI, mtDNA haplogroup, FTO, and SUPT3H. Such a framework is translational and would benefit patients at risk of structural progressive knee osteoarthritis.

A Machine Learning Model to Predict Knee Osteoarthritis Cartilage Volume Changes over Time Using Baseline Bone Curvature.

The hallmark of osteoarthritis (OA), the most prevalent musculoskeletal disease, is the loss of cartilage. By using machine learning (ML), we aimed to assess if baseline knee bone curvature (BC) could predict cartilage volume loss (CVL) at one year, and to develop a gender-based model. BC and cartilage volume were assessed on 1246 participants using magnetic resonance imaging. Variables included age, body mass index, and baseline values of eight BC regions. The outcome consisted of CVL at one year in 12 regions. Five ML methods were evaluated. Validation demonstrated very good accuracy for both genders (R ≥ 0.78), except the medial tibial plateau for the woman. In conclusion, we demonstrated, for the first time, that knee CVL at one year could be predicted using five baseline BC region values. This would benefit patients at risk of structural progressive knee OA.

Vastus medialis intramuscular fat is associated with reduced quadriceps strength, but not knee osteoarthritis severity.

BACKGROUND: Vastus medialis intramuscular fat has been proposed to be a modifiable determinant of knee cartilage loss in patients with knee osteoarthritis. The objective was to determine whether vastus medialis intramuscular fat relates to osteoarthritis severity and quadriceps muscle strength in patients with non-traumatic and post-traumatic knee osteoarthritis. METHODS: For this cross-sectional study, participants with knee osteoarthritis were classified into two groups: non-traumatic (n = 22; mean age = 60 years) and post-traumatic (n = 19; mean age = 56 years). Healthy adults were included (n = 22; mean age = 59 years). A 3-Tesla magnetic resonance imaging was used to measure vastus medialis cross-sectional area and intramuscular fat. Isometric knee extensor muscle torque was assessed using an isokinetic dynamometer and normalized to body mass (Nm/kg). Knee osteoarthritis severity was assessed using standing antero-posterior radiographs (Kellgren-Lawrence scores). Regression analyses examined relationships between 1) vastus medialis intramuscular fat with knee osteoarthritis severity and osteoarthritis group, after accounting for sex and body mass index, and 2) knee extensor muscle torque with vastus medialis intramuscular fat, after accounting for sex and vastus medialis cross-sectional area. FINDINGS: Vastus medialis intramuscular fat was positively associated with body mass index (B = 0.321, P < 0.001), but not with osteoarthritis severity or group (P > 0.05). Higher vastus medialis intramuscular fat was associated with reduced knee extensor muscle torque (B = -0.040, P = 0.018). INTERPRETATION: Greater vastus medialis intramuscular fat was associated with lower quadriceps muscle strength in patients with knee OA. It is unclear whether this is due to the accumulation of vastus medialis intramuscular fat or other potential factors, such as diet and physical inactivity.

Clinical relevance of MRI knee abnormalities in Australian rules football players: a longitudinal study.

BACKGROUND/AIM: The clinical relevance of MRI knee abnormalities in athletes is unclear. This study aimed to determine the prevalence of MRI knee abnormalities in Australian Rules Football (ARF) players and describe their associations with pain, function, past and incident injury and surgery history. METHODS: 75 male players (mean age 21, range 16-30) from the Tasmanian State Football League were examined early in the playing season (baseline). History of knee injury/surgery and knee pain and function were assessed. Players underwent MRI scans of both knees at baseline. Clinical measurements and MRI scans were repeated at the end of the season, and incident knee injuries during the season were recorded. RESULTS: MRI knee abnormalities were common at baseline (67% bone marrow lesions, 16% meniscal tear/extrusion, 43% cartilage defects, 67% effusion synovitis). Meniscal tears/extrusion and synovial fluid volume were positively associated with knee symptoms, but these associations were small in magnitude and did not persist after further accounting for injury history. Players with a history of injury were at a greater risk of having meniscal tears/extrusion, effusion synovitis and greater synovial fluid volume. In contrast, players with a history of surgery were at a greater risk of having cartilage defects and meniscal tears/extrusion. Incident injuries were significantly associated with worsening symptoms, BML development and incident meniscal damage. CONCLUSIONS: MRI abnormalities are common in ARF players, are linked to a previous knee injury and surgery history, as well as incident injury but do not dictate clinical symptomatology.

A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening.

AIM: In osteoarthritis (OA) there is a need for automated screening systems for early detection of structural progressors. We built a comprehensive machine learning (ML) model that bridges major OA risk factors and serum levels of adipokines/related inflammatory factors at baseline for early prediction of at-risk knee OA patient structural progressors over time. METHODS: The patient- and gender-based model development used baseline serum levels of six adipokines, three related inflammatory factors and their ratios (36), as well as major OA risk factors [age and bone mass index (BMI)]. Subjects (677) were selected from the Osteoarthritis Initiative (OAI) progression subcohort. The probability values of being structural progressors (PVBSP) were generated using our previously published prediction model, including five baseline structural features of the knee, i.e. two X-rays and three magnetic resonance imaging variables. To identify the most important variables amongst the 47 studied in relation to PVBSP, we employed the ML feature classification methodology. Among five supervised ML algorithms, the support vector machine (SVM) demonstrated the best accuracy and use for gender-based classifiers development. Performance and sensitivity of the models were assessed. A reproducibility analysis was performed with clinical trial OA patients. RESULTS: Feature selections revealed that the combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP are the most important variables in predicting OA structural progressors in both genders. Classification accuracies for both genders in the testing stage (OAI) were >80%, with the highest sensitivity of CRP/MCP-1. Reproducibility analysis showed an accuracy ⩾92%; the ratio CRP/MCP-1 demonstrated the highest sensitivity in women and leptin/CRP in men. CONCLUSION: This is the first time that such a framework was built for predicting knee OA structural progressors. Using this automated ML patient- and gender-based model, early prediction of knee structural OA progression can be performed with high accuracy using only three baseline serum biomarkers and two risk factors. PLAIN LANGUAGE SUMMARY: Machine learning model for early knee osteoarthritis structural progression Knee osteoarthritis is a well-known debilitating disease leading to reduced mobility and quality of life - the main causes of chronic invalidity. Disease evolution can be slow and span many years; however, for some individuals, the progression/evolution can be fast. Current treatments are only symptomatic and conventional diagnosis of osteoarthritis is not very effective in early identification of patients who will progress rapidly. To improve therapeutic approaches, we need a robust prediction model to stratify osteoarthritis patients at an early stage according to risk of joint structure disease progression.We hypothesize that a prediction model using a machine learning system would enable such an early identification of individuals for whom osteoarthritis knee structure will degrade rapidly. Data were from the Osteoarthritis Initiative, a National Institute of Health (United States) databank, and the robustness and generalizability of the developed model was further evaluated using osteoarthritis patients from an external cohort. Using the supervised machine learning system (support vector machine), we developed an automated patient- and gender-based model enabling an early clinical prognosis for individuals at high risk of structural progressive osteoarthritis. In brief, this model employed at baseline (when the subject sees a physician) easily obtained features consisting of the two main osteoarthritis risk factors, age and bone mass index (BMI), in addition to the serum levels of three molecules. Two of these molecules belong to a family of factors names adipokines and one to a related inflammatory factor. In brief, the model comprising a combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP were found very robust for both genders, and the high accuracy persists when tested with an external cohort conferring the gender-based model generalizability. This study offers a new automated system for identifying early knee osteoarthritis structural progressors, which will significantly improve clinical prognosis with real time patient monitoring.

Machine Learning-Based Individualized Survival Prediction Model for Total Knee Replacement in Osteoarthritis: Data From the Osteoarthritis Initiative.

OBJECTIVE: By using machine learning, our study aimed to build a model to predict risk and time to total knee replacement (TKR) of an osteoarthritic knee. METHODS: Features were from the Osteoarthritis Initiative (OAI) cohort at baseline. Using the lasso method for variable selection in the Cox regression model, we identified the 10 most important characteristics among 1,107 features. The prognostic power of the selected features was assessed by the Kaplan-Meier method and applied to 7 machine learning methods: Cox, DeepSurv, random forests algorithm, linear/kernel support vector machine (SVM), and linear/neural multi-task logistic regression models. As some of the 10 first-found features included similar radiographic measurements, we further looked at using the least number of features without compromising the accuracy of the model. Prediction performance was assessed by the concordance index, Brier score, and time-dependent area under the curve (AUC). RESULTS: Ten features were identified and included radiographs, bone marrow lesions of the medial condyle on magnetic resonance imaging, hyaluronic acid injection, performance measure, medical history, and knee-related symptoms. The methodologies Cox, DeepSurv, and linear SVM demonstrated the highest accuracy (concordance index scores of 0.85, Brier score of 0.02, and an AUC of 0.87). DeepSurv was chosen to build the prediction model to estimate the time to TKR for a given knee. Moreover, we were able to decrease the features to only 3 and maintain the high accuracy (concordance index of 0.85, Brier score of 0.02, and AUC of 0.86), which included bone marrow lesions, Kellgren/Lawrence grade, and knee-related symptoms, to predict risk and time of a TKR event. CONCLUSION: For the first time, we developed a model using the OAI cohort to predict with high accuracy if a given osteoarthritic knee would require TKR, when a TKR would be required, and who would likely progress fast toward this event.

Identification of the most important features of knee osteoarthritis structural progressors using machine learning methods.

OBJECTIVES: The aim was to identify the most important features of structural knee osteoarthritis (OA) progressors and classification using machine learning methods. METHODS: Participants, features and outcomes were from the Osteoarthritis Initiative. Features were from baseline (1107), including articular knee tissues (135) assessed by quantitative magnetic resonance imaging (MRI). OA progressors were ascertained by four outcomes: cartilage volume loss in medial plateau at 48 and 96 months (Prop_CV_48M, 96M), Kellgren-Lawrence (KL) grade ⩾ 2 and medial joint space narrowing (JSN) ⩾ 1 at 48 months. Six feature selection models were used to identify the common features in each outcome. Six classification methods were applied to measure the accuracy of the selected features in classifying the subjects into progressors and non-progressors. Classification of the best features was done using an automatic machine learning interface and the area under the curve (AUC). To prioritize the top five features, sparse partial least square (sPLS) method was used. RESULTS: For the classification of the best common features in each outcome, Multi-Layer Perceptron (MLP) achieved the highest AUC in Prop_CV_96M, KL and JSN (0.80, 0.88, 0.95), and Gradient Boosting Machine for Prop_CV_48M (0.70). sPLS showed the baseline top five features to predict knee OA progressors are the joint space width, mean cartilage thickness of the medial tibial plateau and sub-regions and JSN. CONCLUSION: In this comprehensive study using a large number of features (n = 1107) and MRI outcomes in addition to radiological outcomes, we identified the best features and classification methods for knee OA structural progressors. Data revealed baseline X-ray and MRI-based features could predict early OA knee progressors and that MLP is the best classification method.

Intra-articular corticosteroid knee injection induces a reduction in meniscal thickness with no treatment effect on cartilage volume: a case-control study.

Although intra-articular corticosteroid injections (IACI) are commonly used for the treatment of knee osteoarthritis (OA), there is controversy regarding possible deleterious effects on joint structure. In this line, this study investigates the effects of IACI on the evolution of knee OA structural changes and pain. Participants for this nested case-control study were from the Osteoarthritis Initiative. Knees of participants who had received an IACI and had magnetic resonance images (MRI) were named cases (n = 93), and each matched with one control (n = 93). Features assessed at the yearly visits and their changes within the follow-up period were from MRI (cartilage volume, meniscal thickness, bone marrow lesions, bone curvature, and synovial effusion size), X-ray (joint space width), and clinical (Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC] pain score) data. Participants who received IACI experienced a transient and significantly greater rate of loss of the meniscal thickness (p = 0.006) and joint space width (p = 0.011) in the knee medial compartment in the year they received the injection, compared to controls. No significant effect of the IACI was found on the rate of cartilage loss nor on any other knee structural changes or WOMAC pain post-treatment. In conclusion, a single IACI in knee OA was shown to be safe with no negative impact on structural changes, but there was a transient meniscal thickness reduction, a phenomenon for which the clinical relevance is at present unknown.

Serum adipokines/related inflammatory factors and ratios as predictors of infrapatellar fat pad volume in osteoarthritis: Applying comprehensive machine learning approaches.

OBJECTIVE: The infrapatellar fat pad (IPFP) has been associated with knee osteoarthritis onset and progression. This study uses machine learning (ML) approaches to predict serum levels of some adipokines/related inflammatory factors and their ratios on knee IPFP volume of osteoarthritis patients. METHODS: Serum and MRI were from the OAI at baseline. Variables comprised the 3 main osteoarthritis risk factors (age, gender, BMI), 6 adipokines, 3 inflammatory factors, and their 36 ratios. IPFP volume was assessed on MRI with a ML methodology. The best variables and models were identified in Total-cohort (n = 678), High-BMI (n = 341) and Low-BMI (n = 337), using a selection approach based on ML methods. RESULTS: The best model for each group included three risk factors and adipsin/C-reactive protein combined for Total-cohort, adipsin/chemerin; High-BMI, chemerin/adiponectin HMW; and Low-BMI, interleukin-8. Gender separation improved the prediction (13-16%) compared to the BMI-based models. Reproducibility with osteoarthritis patients from a clinical trial was excellent (R: female 0.83, male 0.95). Pseudocodes based on gender were generated. CONCLUSION: This study demonstrates for the first time that the combination of the serum levels of adipokines/inflammatory factors and the three main risk factors of osteoarthritis could predict IPFP volume with high reproducibility, with the superior performance of the model accounting for gender separation.

Associations of Joint Line Tenderness and Patellofemoral Grind With Long-Term Knee Joint Outcomes: Data From the Osteoarthritis Initiative.

OBJECTIVE: To examine whether joint line tenderness and patellofemoral grind from physical examination were associated with cartilage volume loss, worsening of radiographic osteoarthritis, and the risk of total knee replacement. METHODS: This study examined 4,353 Osteoarthritis Initiative participants. For each measurement of joint line tenderness and patellofemoral grind, the patterns were defined as no (none at baseline and at 1 year), fluctuating (present at either time point), and persistent (present at both time points). Cartilage volume loss and worsening of radiographic osteoarthritis over 4 years were assessed using magnetic resonance imaging and radiographs, and total knee replacement over 6 years was assessed. RESULTS: A total of 35.0% of participants had joint line tenderness, and 15.8% had patellofemoral grind. Baseline patellofemoral grind, but not joint line tenderness, was associated with increased cartilage volume loss (1.08% per year versus 0.96% per year; P = 0.02) and an increased risk of total knee replacement (odds ratio [OR] 1.55 [95% confidence interval (95% CI) 1.11-2.17]; P = 0.01). While the patterns of joint line tenderness were not significantly associated with joint outcomes, participants with persistent patellofemoral grind had an increased rate of cartilage volume loss (1.30% per year versus 0.90% per year; P < 0.001) and an increased risk of total knee replacement (OR 2.10 [95% CI 1.30-3.38]; P = 0.002) compared with those participants without patellofemoral grind. CONCLUSION: Patellofemoral grind, but not joint line tenderness, may represent a clinical marker associated with accelerated cartilage volume loss over 4 years and an increased risk of total knee replacement over 6 years. This simple clinical examination may provide clinicians with an inexpensive way to identify those at higher risk of disease progression who should be targeted for surveillance and management.

The effect of alignment on knee osteoarthritis initiation and progression differs based on anterior cruciate ligament status: data from the Osteoarthritis Initiative.

INTRODUCTION/OBJECTIVE: Knee alignment and anterior cruciate ligament (ACL) injury are risk factors for knee osteoarthritis (OA). The objective was to examine interactions between knee alignment and ACL status on cartilage volume loss in participants with or at risk of knee OA. METHOD: Participants were from the Osteoarthritis Initiative, a longitudinal cohort study. Data were from baseline and 24- and 72-month follow-up visits. Participants with knee OA (progression subcohort) or at risk of knee OA (incidence subcohort) that had partial or full ACL tears (OA-ACL group; n=66) or an intact ACL (OA-only group, n=367) were selected. Femur-tibia angles from radiographs quantified knee alignment. Changes in tibial and femoral cartilage volumes were measured using magnetic resonance imaging. Hierarchical linear models examined if knee alignment, presence of ACL, and their interaction were related to cartilage volume loss after accounting for other variables. RESULTS: Interactions between alignment and ACL status were significantly related to cartilage volume loss in the lateral plateau (ß=-20.19, 95% confidence interval [CI]=-34.65 to -5.73) and lateral condyle (ß=-23.64, 95%CI=-43.06 to -4.23). Valgus alignment was related to lateral compartment cartilage loss in the OA-ACL group, but not in the OA-only group. Varus alignment was related to cartilage loss in the medial plateau (ß=7.49, 95%CI=0.17 to 14.80) and medial condyle (ß=19.70, 95%CI=5.96 to 33.44) in both groups. CONCLUSION: The impact of knee alignment on knee OA initiation and progression varies based on ACL status. Initial lateral compartment damage or changes in joint kinematics after ACL rupture might account for these findings.Key Points• The relationship between knee alignment and lateral compartment cartilage loss depended on the status of the anterior cruciate ligament in participants with knee osteoarthritis or at risk for knee osteoarthritis.• Valgus alignment was related to lateral compartment cartilage loss in participants with a deficient anterior cruciate ligament.• Varus alignment was related to medial compartment cartilage loss regardless of the status of the anterior cruciate ligament.

Association between metformin use and disease progression in obese people with knee osteoarthritis: data from the Osteoarthritis Initiative-a prospective cohort study.

OBJECTIVE: To examine whether metformin use was associated with knee cartilage volume loss over 4 years and risk of total knee replacement over 6 years in obese individuals with knee osteoarthritis. METHODS: This study analysed the Osteoarthritis Initiative participants with radiographic knee osteoarthritis (Kellgren-Lawrence grade ≥ 2) who were obese (body mass index [BMI] ≥ 30 kg/m2). Participants were classified as metformin users if they self-reported regular metformin use at baseline, 1-year and 2-year follow-up (n = 56). Non-users of metformin were defined as participants who did not report the use of metformin at any visit from baseline to 4-year follow-up (n = 762). Medial and lateral cartilage volume (femoral condyle and tibial plateau) were assessed using magnetic resonance imaging at baseline and 4 years. Total knee replacement over 6 years was assessed. General linear model and binary logistic regression were used for statistical analyses. RESULTS: The rate of medial cartilage volume loss was lower in metformin users compared with non-users (0.71% vs. 1.57% per annum), with a difference of - 0.86% per annum (95% CI - 1.58% to - 0.15%, p = 0.02), after adjustment for age, gender, BMI, pain score, Kellgren-Lawrence grade, self-reported diabetes, and weight change over 4 years. Metformin use was associated with a trend towards a significant reduction in risk of total knee replacement over 6 years (odds ratio 0.30, 95% CI 0.07-1.30, p = 0.11), after adjustment for age, gender, BMI, Kellgren-Lawrence grade, pain score, and self-reported diabetes. CONCLUSIONS: These data suggest that metformin use may have a beneficial effect on long-term knee joint outcomes in those with knee osteoarthritis and obesity. Randomised controlled trials are needed to confirm these findings and determine whether metformin would be a potential disease-modifying drug for knee osteoarthritis with the obese phenotype.

Knee effusion volume assessed by magnetic resonance imaging and progression of knee osteoarthritis: data from the Osteoarthritis Initiative.

Objective: To examine whether baseline knee joint effusion volume and the change in effusion volume over 1 year are associated with cartilage volume loss, progression of radiographic OA (ROA) over 4 years and risk of total knee replacement over 6 years. Methods: This study included 4115 Osteoarthritis Initiative participants with knee joint effusion volume quantified by MRI at baseline. The change in effusion volume over 1 year was assessed. Cartilage volume loss and progression of ROA over 4 years were assessed using MRI and X-ray and total knee replacement over 6 years was assessed. Multiple linear regression and binary logistic regression were used for data analyses. Results: Baseline knee effusion volume (per 5 ml) was positively associated with a loss of medial and lateral cartilage volume [regression coefficient 0.13%/year (95% CI 0.10, 0.17) and 0.13%/year (95% CI 0.10, 0.16), respectively, both P < 0.001], progression of ROA [odds ratio (OR) 1.28 (95% CI 1.20, 1.37), P < 0.001], and risk of knee replacement [OR 1.12 (95% CI 1.05, 1.20), P = 0.001]. A 5 ml increase in knee effusion volume over 1 year was positively associated with medial cartilage volume loss [regression coefficient 0.09%/year (95% CI 0.04, 0.15), P = 0.001], progression of ROA [OR 1.21 (95% CI 1.11, 1.33), P < 0.001] and risk of knee replacement [OR 1.24 (95% CI 1.12, 1.37), P < 0.001]. Conclusions: Knee joint effusion volume assessed from MRI provides a continuous and sensitive measure that was associated with cartilage volume loss, progression of ROA and risk of total knee replacement. It may provide a method to identify individuals with an inflammatory OA phenotype who are at higher risk of disease progression.

Knee pain as a predictor of structural progression over 4 years: data from the Osteoarthritis Initiative, a prospective cohort study.

BACKGROUND: There is evidence that knee pain not only is a consequence of structural deterioration in osteoarthritis (OA) but also contributes to structural progression. Clarifying this is important because targeting the factors related to knee pain may offer a clinical approach for slowing the progression of knee OA. The aim of this study was to examine whether knee pain over 1 year predicted cartilage volume loss, incidence and progression of radiographic osteoarthritis (ROA) over 4 years. METHODS: Osteoarthritis Initiative participants with no ROA (Kellgren-Lawrence grade ≤ 1) (n = 2120) and with ROA (Kellgren-Lawrence grade > 2) (n = 2249) were examined. Knee pain was assessed at baseline and 1 year using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Knee pain patterns were categorised as no pain (WOMAC pain < 5 at baseline and 1 year), fluctuating pain (WOMAC pain > 5 at either time point) and persistent pain (WOMAC pain > 5 at both time points). Cartilage volume, incidence and progression of ROA were assessed using magnetic resonance imaging and x-rays at baseline and 4-years. RESULTS: In both non-ROA and ROA, greater baseline WOMAC knee pain score was associated with increased medial and lateral cartilage volume loss (p ≤ 0.001), incidence (OR 1.07, 95% CI 1.01-1.13) and progression (OR 1.07, 95% CI 1.03-1.10) of ROA. Non-ROA and ROA participants with fluctuating and persistent knee pain had increased cartilage volume loss compared with those with no pain (p for trend ≤ 0.01). Non-ROA participants with fluctuating knee pain had increased risk of incident ROA (OR 1.62, 95% CI 1.04-2.54), corresponding to a number needed to harm of 19.5. In ROA the risk of progressive ROA increased in participants with persistent knee pain (OR 1.82, 95% CI 1.28-2.60), corresponding to a number needed to harm of 9.6. CONCLUSIONS: Knee pain over 1 year predicted accelerated cartilage volume loss and increased risk of incident and progressive ROA. Early management of knee pain and controlling knee pain over time by targeting the underlying mechanisms may be important for preserving knee structure and reducing the burden of knee OA.

Exploring determinants predicting response to intra-articular hyaluronic acid treatment in symptomatic knee osteoarthritis: 9-year follow-up data from the Osteoarthritis Initiative.

BACKGROUND: The weight of recommendation for intra-articular therapies such as hyaluronic acid injections varies from one set of guidelines to another, and they have not yet reached unanimity with respect to the usefulness of intra-articular hyaluronic acid (IAHA) injections for the symptomatic treatment of knee osteoarthritis (OA). Among the reasons for the controversy is that the current literature provides inconsistent results and conclusions about such treatment. This study aimed at identifying determinants associated with a better response to IAHA treatment in knee OA. METHODS: Subjects were selected from the Osteoarthritis Initiative database. Participants were subjects who had radiographic OA, received one IAHA treatment, and had data on demographics and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores at visits before (T0) and after (T1; within 6 months) treatment. Pain was analyzed for demographic, clinical, and imaging characteristics at T0 and change over time (T0 to T1). Subjects with WOMAC pain > 0 at T0 were subdivided into Low, Moderate, and High pain groups based on tertile analysis. Further analyses were done with the High pain group (score ≥ 8), which was divided into responders (improvement in pain ≥ 20%) and nonresponders (unchanged or worsening of pain). RESULTS: Participants (n = 310) received a total of 404 treatments (one per knee). In the Low and Moderate pain groups vs the High pain group, participants had significantly lower score at T0 (p < 0.001), and the Low vs High pain group had significantly lower BMI (p = 0.002), greater joint space width (JSW) (p = 0.010) and knee cartilage volume (p ≤ 0.009), and smaller synovial effusion (p = 0.033). In the High pain group, responders vs nonresponders were usually younger (p = 0.014), with greater cartilage volume in the medial compartment (p = 0.046), a trend toward greater JSW, and a significant improvement in all WOMAC scores (p < 0.001), while nonresponders showed worsening of symptoms. CONCLUSIONS: This study identified reliable predictive determinants that can distinguish patients who could best benefit from IAHA treatment: high levels of knee pain, younger, and less severe structural damage. These could be implemented in clinical practice as a useful guide for physicians.

Levels of serum biomarkers from a two-year multicentre trial are associated with treatment response on knee osteoarthritis cartilage loss as assessed by magnetic resonance imaging: an exploratory study.

BACKGROUND: There is an obvious need to identify biomarkers that could predict patient response to an osteoarthritis (OA) treatment. This post hoc study explored in a 2-year randomized controlled trial in patients with knee OA, the likelihood of some serum biomarkers to be associated with a better response to chondroitin sulfate in reducing cartilage volume loss. METHODS: Eight biomarkers were studied: hyaluronic acid (HA), C reactive protein (CRP), adipsin, leptin, N-terminal propeptide of collagen IIα (PIIANP), C-terminal crosslinked telopeptide of type I collagen (CTX-1), matrix metalloproteinase-1 (MMP-1), and MMP-3. Patients were treated with chondroitin sulfate (1200 mg/day; n = 57) or celecoxib (200 mg/day; n = 62). Serum biomarkers were measured at baseline. The cartilage volume at baseline and its loss at 2 years were assessed by quantitative magnetic resonance imaging (MRI). Statistical analysis included analysis of covariance. RESULTS: As data from the original MOSAIC trial showed no differences in cartilage volume and loss in the lateral compartment of the knee joint between the two treatment groups in any comparison, only the medial compartment and its subregions were studied. Stratification according to the median biomarker levels was used to discriminate treatment effect. In patients with levels of biomarkers of inflammation (HA, leptin and adipsin) lower than the median, those treated with chondroitin sulfate demonstrated less cartilage volume loss in the medial compartment, condyle, and plateau (p ≤ 0.047). In contrast, patients treated with chondroitin sulfate with higher levels of MMP-1 and MMP-3, biomarkers of cartilage catabolism, had less cartilage volume loss in the medial compartment, condyle, and plateau (p ≤ 0.050). Patients with higher levels of PIIANP and CTX-1, biomarkers related to collagen anabolism and bone catabolism, respectively, had reduced cartilage volume loss in the medial condyle (p ≤ 0.026) in the chondroitin sulfate group. CONCLUSION: This study is suggestive of a potentially greater response to chondroitin sulfate treatment on cartilage volume loss in patients with knee OA with low level of inflammation and/or greater level of cartilage catabolism. TRIAL REGISTRATION: This is a post hoc study. Original trial registration: ClinicalTrials.gov, NCT01354145 . Registered on 13 May 2011.

Bone curvature changes can predict the impact of treatment on cartilage volume loss in knee osteoarthritis: data from a 2-year clinical trial.

Objectives: Knee bone curvature assessed by MRI was associated with OA cartilage loss. A recent knee OA trial demonstrated the superiority of chondroitin sulfate over celecoxib (comparator) at reducing cartilage volume loss (CVL) in the medial compartment (condyle). The main objectives were to identify which baseline bone curvature regions of interest (BCROI) best associated with CVL and investigate whether baseline BCROI and 2-year change are correlated with the protective effect of chondroitin sulphate on CVL. Methods: This post hoc analysis of a clinical trial used the according-to-protocol population (chondroitin sulphate, n = 57; celecoxib, n = 63) baseline and 2-year MRI to assess bone curvature and CVL. Global optimum search identified the BCROI in the medial condyle using celecoxib as reference. Statistical analyses were performed with Pearson's correlation, Mann-Whitney U -test, Student's t -test and analysis of covariance. Results: The BCROI including the medial posterior condyle and lateral central condyle was found to correlate best with medial condyle CVL at 2 years ( r = 0.33, P = 0.008). In patients with a baseline BCROI value less than the median (more flattened bone), chondroitin sulphate demonstrated a protective effect on CVL compared with celecoxib in the medial compartment (P = 0.037). In patients with 2-year BCROI changes greater than the median (greater severity of bone flattening), chondroitin sulphate protected against CVL in the medial compartment, condyle and central plateau (P ⩽ 0.030). Conclusion: This study is the first to demonstrate the feasibility and usefulness of bone curvature measurements to predict effectiveness of OA treatment on CVL. The results identify bone curvature as a potential novel biomarker for knee OA clinical trials.

Chondroitin sulfate efficacy versus celecoxib on knee osteoarthritis structural changes using magnetic resonance imaging: a 2-year multicentre exploratory study.

BACKGROUND: In osteoarthritis (OA) treatment, although chondroitin sulfate (CS) was found in a number of studies using radiography to have a structure-modifying effect, to date CS use is still under debate. A clinical study using quantitative magnetic resonance imaging (qMRI) is therefore of the utmost importance. Here we report data from a 24-month, randomised, double-blind, double-dummy, controlled, comparative exploratory study of knee OA. The primary endpoint was to determine the effect of CS 1200 mg/day versus celecoxib 200 mg/day on cartilage volume loss (CVL) in the lateral compartment over time as measured by qMRI. Secondary endpoints included assessment of the OA structural changes and signs and symptoms of OA. METHODS: qMRI was performed at baseline and at 12 and 24 months. CVL, bone marrow lesion size, and synovial thickness were evaluated using qMRI. The primary statistical analysis was carried out on the modified intention-to-treat (mITT) population (n = 138) using chi-squared, Fisher's exact, Wilcoxon Mann-Whitney, and Student's t tests and analysis of covariance. Analyses were also conducted on the according-to-protocol (ATP; n = 120) population. RESULTS: In the adjusted mITT analysis, compared with celecoxib treatment, patients treated with CS had a significant reduced CVL at 24 months in the medial compartment (celecoxib -8.1 % ± 4.2, CS -6.3 % ± 3.2; p = 0.018) and medial condyle (-7.7 % ± 4.7, -5.5 % ± 3.9; p = 0.008); no significant effect was seen in the lateral compartment. In the ATP population, CS reduced CVL in the medial compartment at 12 months (celecoxib -5.6 % ± 3.0, CS -4.5 % ± 2.6; p = 0.049) and 24 months (celecoxib -8.4 % ± 4.2, CS -6.6 % ± 3.3; p = 0.021), and in the medial condyle at 24 months (celocoxib -8.1 % ± 4.7, CS -5.7 % ± 4.0; p = 0.010). A trend towards a statistically reduced synovial thickness (celecoxib +17.96 ± 33.73 mm, CS -0.66 ± 22.72 mm; p = 0.076) in the medial suprapatellar bursa was observed in CS patients. Both groups experienced a marked reduction in the incidence of patients with joint swelling/effusion and in symptoms over time. Data showed similar good safety profiles including cardiovascular adverse events for both drugs. CONCLUSION: This study demonstrated, for the first time in a 2-year randomised controlled trial using qMRI, the superiority of CS over celecoxib at reducing CVL in knee OA patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01354145 . Registered 13 May 2011.

Long-Term Effects of Glucosamine and Chondroitin Sulfate on the Progression of Structural Changes in Knee Osteoarthritis: Six-Year Followup Data From the Osteoarthritis Initiative.

OBJECTIVE: To examine the long-term (6-year) effect of combined glucosamine (Glu) and chondroitin sulfate (CS) treatment on cartilage volume in knee osteoarthritis (OA). METHODS: Participants were from the Osteoarthritis Initiative progression and incidence subcohorts, had magnetic resonance imaging (MRI) of the target knee at baseline and 6 years, joint space width >1 mm, and data available on Glu/CS consumption (n = 1,593). They were stratified into 2 main groups based on whether or not they had medial meniscal extrusion at baseline. The group with meniscal extrusion (n = 429) was further stratified into subgroups based on exposure or no exposure to Glu/CS as follows: not exposed, 1 year, 2-3 years, and 4-6 years. Cartilage volume was assessed using fully automated quantitative MRI technology. RESULTS: The Jonckheere-Terpstra trend test indicated that treatment with Glu/CS significantly reduced the cartilage volume loss in the global knee, associated with the lateral compartment. Multivariate analysis further demonstrated that the extent of the treatment's positive effect was related to exposure time to treatment, the protective effect at 6 years being significant in participants exposed to ≥2 years of treatment. CONCLUSION: These findings provide future support for the long-term protective structure-modifying effects of Glu/CS treatment in knee OA subjects.