Search Results (85)

Sex-specific factors can influence the onset and progression of osteoarthritis (OA), yet the molecular mechanisms underlying their impact remain poorly defined. This study investigated whether plasma microRNAs (miRNAs) correlate to sex-dependent OA progression, based on evidence of enhanced spontaneous osteoclastogenesis in peripheral blood mononuclear cells (PBMCs) derived from OA patients. miRNAs were evaluated on OA-plasma (n = 20 men, 20 women with knee OA; KL grade I–II) and their role on OA signaling was investigated through bioinformatic analysis. Seven miRNAs were identified as significantly upregulated in men’ vs. women’ samples: hsa-miR-107, hsa-miR-23a-3p, hsa-miR-103a-3p, hsa-let-7g-5p, hsa-miR-22-3p, hsa-miR-106a-5p, hsa-miR-142-3p, and were associated with OA-related tissues and pathways. Notably, two common targets were identified: Adenosine Triphosphate Citrate Lyase (ACLY), a key enzyme linking citrate metabolism to epigenetic regulation, and phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), a component of the phosphatidylinositol-3-kinase PI3K/AKT/mTOR pathway. In men, increased miRNA expression may repress ACLY and PIK3R1, affecting catabolic gene expression, inflammation, and OA progression. Conversely, their lower expression in women may mitigate these effects by counterbalancing the OA-promoting influences driven by sex hormones. A functional validation is needed to confirm miRNA–ACLY/PIK3R1 interactions and their sex-specific roles in early OA pathophysiology. Full article

Background/Objectives: Knee osteoarthritis (KOA) is multifactorial, and longitudinal evidence isolating early predictors remains limited. We investigated predictors of incident KOA in community-dwelling older adult Japanese women. Methods: We analyzed 191 knees from 105 women aged ≥50 years (baseline Kellgren–Lawrence (KL) grade 0–1) and followed them for 8 years. Incident KOA was defined as KL ≥ 2 at the 8-year follow-up. Baseline measures included body mass index (BMI), physical function (one-leg stance, functional reach), Geriatric Locomotive Function Scale (GLFS-25), EuroQol 5-Dimension (EQ-5D) questionnaire, standing lateral whole-spine radiographs (sagittal spinopelvic parameters), and standing full-length anteroposterior (AP) lower-limb radiographs (coronal alignment parameters). Incident KOA was defined as KL ≥ 2 at follow-up. Group comparisons, multivariable logistic regression, and receiver operating characteristic analyses were conducted. Results: Incident KOA occurred in 58/191 knees (mean participant age 69.3 ± 6.1 years). Compared with non-incident knees, incident knees had higher BMI (23.8 vs. 21.1 kg/m²), higher GLFS-25, greater pelvic tilt and pelvic incidence minus lumbar lordosis (PI–LL) mismatch (11.5° vs. 5.3°), and lower EQ-5D, medial proximal tibial angle, and joint line obliquity. BMI was the strongest single predictor (area under the curve [AUC] 0.753). PI–LL mismatch showed limited standalone discrimination (AUC 0.596) but improved discrimination when combined with BMI (AUC 0.803). Conclusions: BMI was the primary predictor of incident KOA in this cohort. PI–LL mismatch, while not strongly discriminative alone, acted as a complementary marker consistent with sagittal-alignment-related mechanical stress. Results suggest that early screening and prevention should prioritize weight management, using spinopelvic parameters to refine risk stratification. Full article

Knee osteoarthritis (OA) is a complex condition with varying pain, functional limitations, and structural changes. Traditional classification using radiographic grades may not fully reflect individual patient experiences. This study aimed to establish WOMAC score cut-offs for KL grades and identify knee OA phenotypes through cluster analysis in a cohort of 99 adults, examining functional and radiological status, factors such as age, sex, body mass index (BMI), comorbidities, and psychological status. Receiver operating characteristic (ROC) analysis helped establish WOMAC cut-off scores related to KL grades, and cluster analysis identified phenotypic subgroups. The analysis showed that higher WOMAC scores correlated with advanced KL grades, leading to a five-tier classification of symptomatic severity: minimal or no symptoms (≤24), mild (25–41), moderate (42–69), severe (70–86), and extreme (≥87). Cluster analysis identified four distinct phenotypic groups: (1) younger patients exhibiting minimal symptoms and low KL grades; (2) individuals with moderate disease are characterized by functional deficits; (3) patients presenting with moderate-to-severe symptoms and significant joint narrowing; and (4) a subgroup experiencing severe pain, high levels of disability, advanced KL grades, elevated psychological distress, and an increased BMI. The study supports WOMAC cut-offs as key indicators of knee OA severity and shows that cluster analysis can reveal distinct phenotypes, underscoring the need for personalized management strategies in knee OA treatment. Full article

Background/Objectives: Knee osteoarthritis (OA) is a major cause of pain and functional disability worldwide, leading to a growing interest in more durable and less invasive therapies. Micro-fragmented adipose tissue (MFAT) injections have emerged as a promising frontier in regenerative therapies using mesenchymal stem cells (MSCs). This study assessed the safety and effectiveness of MFAT injections for symptomatic knee OA while investigating the duration of treatment effects. Methods: This longitudinal study screened patients with symptomatic Kellgren-Lawrence (KL) grade II-IV knee OA who received single-dose MFAT injections. Outcomes were assessed using the Knee injury and Osteoarthritis Outcome Score (KOOS) subscales at baseline, 3, 6, and 12 months. A linear mixed effects model was performed to explore how age, BMI, sex, and OA severity influence outcomes. Results: Among 39 evaluable patients, mean baseline KOOS was 46.5 (SD 18.1). KOOS scores improved significantly across all subscales, peaking at six months and remaining higher than baseline at 12 months. Improvements exceeded clinically meaningful thresholds, including KL grades IV. Female patients reported significantly worse overall outcomes than male patients (p < 0.05). Minor self-limiting synovitis was reported in 18% of cases, and no severe adverse events were observed. Conclusions: MFAT infiltration may represent a safe, minimally invasive option to improve symptoms and delay surgery in patients with knee OA, including those with advanced disease. These findings highlight the potential role of MFAT as part of the treatment algorithm for knee OA, although strategies to sustain long-term benefits and confirmatory trials are needed. Full article

Background/Objectives: Spinal osteoarthritis (sOA) is a degenerative condition marked by pain, inflammation, and restricted mobility. While surgical interventions such as spinal decompression and stabilization are common, their impact on redox status and inflammatory markers remains underexplored. This study aimed to assess the effects of surgery on clinical, hematological, biochemical, and redox parameters in patients with sOA. Methods: A total of 25 patients diagnosed with sOA underwent spinal decompression and stabilization surgery. Preoperative and postoperative assessments included hematological and biochemical analyses, redox status evaluation (TAS, TOS, GSH, AOPP, SOD), and inflammatory markers such as IL-6. Disease severity was graded using the Kellgren–Lawrence (K-L) system. Results: Postoperatively, there was a significant decrease in neutrophil count (p = 0.014) and AOPP levels (p < 0.001), with a corresponding increase in lymphocyte count (p = 0.016), erythrocyte count (p = 0.036), and IL-6 levels (p = 0.008). TAS levels decreased (p = 0.006), while enzymatic antioxidants, such as SOD increased (p = 0.031). Erythrocyte GSH remained low, with a non-significant postoperative decrease. Patients with higher K-L grades exhibited greater redox imbalance, with reduced preoperative GSH and elevated postoperative superoxide anion, TOS, and SOD levels. More severe cases also showed decreased postoperative erythrocyte, hemoglobin, and PTH levels, and increased TAS and AOPP levels. Factorial analysis highlighted clusters associated with oxidative stress, inflammation, and clinical performance. Conclusions: The results underscore the complex relationship between inflammation, oxidative stress, and recovery in sOA. These findings suggest the importance of targeted postoperative strategies to support redox homeostasis and modulate inflammation in sOA patients. Full article

Knee osteoarthritis (KOA) is a most prevalent chronic muscoloskeletal disorder causing pain and functional impairment. Accurate predictions of KOA evolution are important for early interventions and preventive treatment planning. In this paper, we propose a novel dynamic hypergraph-based risk model (DyHRM) which integrates the encoder–decoder (ED) architecture with hypergraph convolutional neural networks (HGCNs). The risk model is used to generate longitudinal forecasts of KOA incidence and progression based on the knee evolution at a historical stage. DyHRM comprises two main parts, namely the dynamic hypergraph gated recurrent unit (DyHGRU) and the multi-view HGCN (MHGCN) networks. The ED-based DyHGRU follows the sequence-to-sequence learning approach. The encoder first transforms a knee sequence at the historical stage into a sequence of hidden states in a latent space. The Attention-based Context Transformer (ACT) is designed to identify important temporal trends in the encoder’s state sequence, while the decoder is used to generate sequences of KOA progression, at the prediction stage. MHGCN conducts multi-view spatial HGCN convolutions of the original knee data at each step of the historic stage. The aim is to acquire more comprehensive feature representations of nodes by exploiting different hyperedges (views), including the global shape descriptors of the cartilage volume, the injury history, and the demographic risk factors. In addition to DyHRM, we also propose the HyGraphSMOTE method to confront the inherent class imbalance problem in KOA datasets, between the knee progressors (minority) and non-progressors (majority). Embedded in MHGCN, the HyGraphSMOTE algorithm tackles data balancing in a systematic way, by generating new synthetic node sequences of the minority class via interpolation. Extensive experiments are conducted using the Osteoarthritis Initiative (OAI) cohort to validate the accuracy of longitudinal predictions acquired by DyHRM under different definition criteria of KOA incidence and progression. The basic finding of the experiments is that the larger the historic depth, the higher the accuracy of the obtained forecasts ahead. Comparative results demonstrate the efficacy of DyHRM against other state-of-the-art methods in this field. Full article

Background and objectives: Patient-specific components (PSC) represent an innovative option for total knee arthroplasty (TKA) in advanced osteoarthritis. Their effectiveness, however, closely relies on accurate positioning. Our study investigates the accuracy achieved by means of an inertial-based extramedullary cutting guide and the postoperative clinical and radiographic outcomes. Methods and materials: This was a prospective, single-arm, pilot study involving patients undergoing primary TKA with YourKnee^TM PSC. Femoral and tibial bone resections were performed using the Perseus inertial-based extramedullary cutting guide. Postoperative mechanical alignment and component positioning were assessed by computed tomography. Clinical outcomes were evaluated preoperatively and at 1, 3, 6, and 12 months postoperatively by main knee function and clinical outcome measures. Results: The study population included a small cohort (n= 12, four females/eight males, mean age 69 ± 5.65 years, mean BMI 25.7 ± 3.8 kg/m², KL grade > 3) with no control group. The mean absolute error between the planned and obtained Hip–Knee–Ankle angle was 1.36° ± 1.06 and within ±3° of all cases. Mean coronal alignment error was 1.87° ± 0.87 and 1.67° ± 0.75 for the femoral and tibial components, respectively. The mean sagittal alignment error was 1.89° ± 1.24 and 2.45° ± 0.87 for the femoral and the tibial components, respectively. Patients showed significant improvement in clinical and functional scores within the first 6 months: OKS increased from 20.64 ± 2.77 at the preoperative screening to 42.27 ± 4.34 (p < 0.0001), total KSS rose from 90.64 ± 17.25 to 169.36 ± 23.57 (p < 0.0001), and FJS reached 85.09 ± 17.14 at 6 months (p = 0.0031), indicating excellent functional recovery and forgotten joint effect. Knee ROM improved from 90.91° ± 11.14 to 110.36° ± 8.44 (p < 0.0001). After 6 months, outcome scores plateaued, suggesting an early stabilization of clinical benefits. No signs of radiolucency were detected on X-rays at 3- and 12-month follow-ups. Conclusions: The Perseus inertial-based extramedullary cutting guide used in combination with the YourKnee^TM PSCs resulted in accurate intraoperative prosthesis positioning and significant improvements in clinical and functional outcomes at 6 months after surgery. Despite the small sample size and absence of a control group, the results suggest that such combination represents a viable option to conventional surgical instrumentation and current off-the-shelf prosthetic designs. Full article

Introduction: Scattering of kernels due to angular dehiscence is a key bottleneck in mechanized harvesting of oilseed rape. Materials and Methods: In this study, a dual-track “genotype–phenotype” screening strategy was established by innovatively integrating high-throughput KASP molecular marker technology and a standardized random collision phenotyping system for the complex quantitative trait of angular resistance. Results: Through the systematic evaluation of 634 oilseed rape hybrid progenies, it was found that the KASP marker S12.68, targeting the cleavage resistance locus (BnSHP1) on chromosome C9, achieved a 73.34% introgression rate (465/634), which was significantly higher than the traditional breeding efficiency (<40%). Phenotypic characterization screened seven excellent resources with cracking resistance index (SRI) > 0.6, of which four reached the high resistance standard (SRI > 0.8), including the core materials NR21/KL01 (SRI = 1.0) and YuYou342/KL01 (SRI = 0.97). Six breeding intermediate materials (44.7–48.7% oil content, mycosphaerella resistance MR grade or above) were created, combining high resistance to chipping and excellent agronomic traits. For the first time, it was found that local germplasm YuYou342 (non-KL01-derived line) was purely susceptible at the S12.68 locus (SRI = 0.86), but its angiosperm vascular bundles density was significantly increased by 37% compared with that of the susceptible material 0911 (p < 0.01); and the material 187308 (SRI = 0.78), although purely susceptible at S12.68, had a 2.8-fold downregulation in expression of the angiosperm-related gene, BnIND1, and a 2.8-fold downregulation of expression of the angiosperm-related gene, BnIND1. expression was significantly downregulated 2.8-fold (q < 0.05), indicating the existence of a novel resistance mechanism independent of the primary effector locus. Conclusions: The results of this research provide an efficient technical platform and breakthrough germplasm resources for oilseed rape crack angle resistance breeding, which is of great practical significance for promoting the whole mechanized production. Full article

Background/Objectives: This Phase 3, randomised, double-blind, multicentre trial evaluated the efficacy and safety of a novel hyaluronic acid hydrogel cross-linked with hexamethylenediamine (HMDA-HA) compared to a conventional 1,4-butanediol diglycidyl ether cross-linked HA (BDDE-HA) in patients with mild-to-moderate knee osteoarthritis (OA). Methods: A total of 223 adults (mean age 63.5 years; 167 women) with Kellgren–Lawrence (KL) grade I–III knee OA were randomised 1:1 to receive two intra-articular injections of HMDA-HA or BDDE-HA at baseline and at 24 weeks. The primary endpoint was changes from baseline in weight-bearing pain (WBP) on a 100 mm visual analogue scale (VAS) at Week 12, assessed in the per-protocol population. A non-inferiority margin of 10 mm was predefined. Secondary outcomes included global assessments, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index scores, responder rates, and rescue medication use [ClinicalTrials.gov: NCT06307847]. Results: At Week 12, least squares mean change (standard error [SE]) in WBP was −23.72 (1.88) mm in the HMDA-HA group (n = 83) and −25.99 (1.76) mm in the BDDE-HA group (n = 95), yielding a difference of 2.26 mm (95% confidence interval [CI]: −2.83 to 7.34; p = 0.3825), thus demonstrating the non-inferiority of HMDA-HA to BDDE-HA. Secondary outcomes were comparable between groups. A total of 136 adverse events were reported: 44 (41.1%) in the HMDA-HA group and 32 (28.1%) in the BDDE-HA group, with no treatment-related adverse drug reactions. Conclusions: A single-injection intra-articular regimen of HMDA-HA was effective and safe for the treatment of adult patients with mild-to-moderate knee OA. Full article

Osteoarthritis (OA) is the most prevalent degenerative joint disorder worldwide, causing significant declines in quality of life. The osteochondral junction (OCJ), a critical structural interface between deep cartilage and subchondral bone, plays an essential role in OA progression but is challenging to assess using conventional magnetic resonance imaging (MRI) due to its short T2 relaxation times. This study aimed to evaluate the utility of ultrashort echo time (UTE) MRI biomarkers, including macromolecular fraction (MMF), magnetization transfer ratio (MTR), and T2*, for in vivo quantification of OCJ changes in knee OA for the first time. Forty-five patients (mean age: 53.8 ± 17.0 years, 50% female) were imaged using 3D UTE-MRI sequences on a 3T clinical MRI scanner. Patients were stratified into two OA groups based on radiographic Kellgren–Lawrence (KL) scores: normal/subtle (KL = 0–1) (n = 21) and mild to moderate (KL = 2–3) (n = 24). Quantitative analysis revealed significantly lower MMF (15.8  ±  1.4% vs. 13.6 ± 1.2%, p < 0.001) and MTR (42.5 ± 2.5% vs. 38.2  ±  2.3%, p < 0.001) in the higher KL 2–3 group, alongside a higher trend in T2* values (19.7  ±  2.6 ms vs. 21.6  ±  3.8 ms, p = 0.06). Moreover, MMF and MTR were significantly negatively correlated with KL grades (r = −0.66 and −0.59; p < 0.001, respectively), while T2* showed a weaker positive correlation (r = 0.26, p = 0.08). Receiver operating characteristic (ROC) analysis demonstrated superior diagnostic accuracy for MMF (AUC = 0.88) and MTR (AUC = 0.86) compared to T2* (AUC = 0.64). These findings highlight UTE-MT techniques (i.e., MMF and MTR) as promising imaging tools for detecting OCJ degeneration in knee OA, with potential implications for earlier and more accurate diagnosis and disease monitoring. Full article

Background: Knee Osteoarthritis (KOA) is a prevalent and debilitating joint disorder that significantly impacts quality of life, particularly in aging populations. Accurate and consistent classification of KOA severity, typically using the Kellgren-Lawrence (KL) grading system, is crucial for effective diagnosis, treatment planning, and monitoring disease progression. However, traditional KL grading is known for its inherent subjectivity and inter-rater variability, which underscores the pressing need for objective, automated, and reliable classification methods. Methods: This study investigates the performance of an EfficientNetB5 deep learning model, enhanced with transfer learning from the ImageNet dataset, for the task of classifying KOA severity into five distinct KL grades (0–4). We utilized a publicly available Kaggle dataset comprising 9786 knee X-ray images. A key aspect of our methodology was a comprehensive data-centric preprocessing pipeline, which involved an initial phase of outlier removal to reduce noise, followed by systematic label correction using the Cleanlab framework to identify and rectify potential inconsistencies within the original dataset labels. Results: The final EfficientNetB5 model, trained on the preprocessed and Cleanlab-remediated data, achieved an overall accuracy of 82.07% on the test set. This performance represents a significant improvement over previously reported benchmarks for five-class KOA classification on this dataset, such as ResNet-101 which achieved 69% accuracy. The substantial enhancement in model performance is primarily attributed to Cleanlab’s robust ability to detect and correct mislabeled instances, thereby improving the overall quality and reliability of the training data and enabling the model to better learn and capture complex radiographic patterns associated with KOA. Class-wise performance analysis indicated strong differentiation between healthy (KL Grade 0) and severe (KL Grade 4) cases. However, the “Doubtful” (KL Grade 1) class presented ongoing challenges, exhibiting lower recall and precision compared to other grades. When evaluated against other architectures like MobileNetV3 and Xception for multi-class tasks, our EfficientNetB5 demonstrated highly competitive results. Conclusions: The integration of an EfficientNetB5 model with a rigorous data-centric preprocessing approach, particularly Cleanlab-based label correction and outlier removal, provides a robust and significantly more accurate method for five-class KOA severity classification. While limitations in handling inherently ambiguous cases (such as KL Grade 1) and the small sample size for severe KOA warrant further investigation, this study demonstrates a promising pathway to enhance diagnostic precision. The developed pipeline shows considerable potential for future clinical applications, aiding in more objective and reliable KOA assessment. Full article

Background: Knee osteoarthritis (KOA) affects 37% of individuals aged ≥ 60 years in the national health survey, causing pain, discomfort, and reduced functional independence. Methods: This study aims to automate the assessment of KOA severity by training deep learning models using the Kellgren–Lawrence grading system (class 0~4). A total of 15,000 images were used, with 3000 images collected for each grade. The learning models utilized were DenseNet201, ResNet101, and EfficientNetV2, and their performance in lesion classification was evaluated and compared. Statistical metrics, including accuracy, precision, recall, and F1-score, were employed to assess the feasibility of applying deep learning models for KOA classification. Results: Among these four metrics, DenseNet201 achieved the highest performance, while the ResNet101 model recorded the lowest. DenseNet201 demonstrated the best performance with an overall accuracy of 73%. The model’s accuracy by K-L grade was 80.7% for K-L Grade 0, 53.7% for K-L Grade 1, 72.7% for K-L Grade 2, 75.3% for K-L Grade 3, and 82.7% for K-L Grade 4. The model achieved a precision of 73.2%, a recall of 73%, and an F1-score of 72.7%. Conclusions: These results highlight the potential of deep learning models for assisting specialists in diagnosing the severity of KOA by automatically assigning K-L grades to patient data. Full article

Knee osteoarthritis (KOA) is a highly prevalent muscoloskeletal joint disorder affecting a significant portion of the population worldwide. Accurate predictions of KOA progression can assist clinicians in drawing preventive strategies for patients. In this paper, we present an integrated approach based on hypergraph convolutional networks (HGCNs) for longitudinal predictions of KOA grades and progressions from MRI images. We propose two novel models, namely, the C_Shape.Net and the predictor network. The C_Shape.Net operates on a hypergraph of volumetric nodes, especially designed to represent the surface and volumetric features of the cartilage. It encompasses deep HGCN convolutions, graph pooling, and readout operations in a hierarchy of layers, providing, at the output, expressive 3D shape descriptors of the cartilage volume. The predictor is a spatio-temporal HGCN network (ST_HGCN), following the sequence-to-sequence learning scheme. Concretely, it transforms sequences of knee representations at the historical stage into sequences of KOA predictions at the prediction stage. The predictor includes spatial HGCN convolutions, attention-based temporal fusion of feature embeddings at multiple layers, and a transformer module that generates longitudinal predictions at follow-up times. We present comprehensive experiments on the Osteoarthritis Initiative (OAI) cohort to evaluate the performance of our methodology for various tasks, including node classification, longitudinal KL grading, and progression. The basic finding of the experiments is that the larger the depth of the historical stage, the higher the accuracy of the obtained predictions in all tasks. For the maximum historic depth of four years, our method yielded an average balanced accuracy (BA) of 85.94% in KOA grading, and accuracies of 91.89% (+1), 88.11% (+2), 84.35% (+3), and 79.41% (+4) for the four consecutive follow-up visits. Under the same setting, we also achieved an average value of Area Under Curve (AUC) of 0.94 for the prediction of progression incidence, and follow-up AUC values of 0.81 (+1), 0.77 (+2), 0.73 (+3), and 0.68 (+4), respectively. Full article

Background/Objectives: In 2020, 595 million world citizens had osteoarthritis, and the largest growth in OA morbidity refers to the hip joint. Effective OA therapies have been sought for years. Assessing the treatment effectiveness and QoL improvement in hip OA after intra-articular administration of fresh peripheral blood hematopoietic CD34+ stem cells. Methods: The study comprised 49 adults (median age: 63). The SCs were injected into hip joints and straight to the bone. Hip manipulation was conducted. Patients were subjected to a standardized rehabilitation protocol. Hip degeneration was graded by Kellgren–Lawrence. Multi-factor statistical analyses, with replications, were performed. The study was an R&D project, co-financed by the E.U. Results: Patient-reported outcomes (HOOS, SF-36) ameliorated remarkably over 24 months (p < 0.0001). Ranges of movement improved significantly (p < 0.0001). The most noticeable improvement manifested 6 months after the SC administration. Its furtherance was maintained. Conclusions: Intra-articular administration of CD34+ cells significantly reduces pain and improves hip joint function, regardless of the severity of OA, according to K-L, over a 24-month follow-up period. The combination of CD34+ cell therapy with joint mobilization and rehabilitation allows for the postponement of hip arthroplasty by significantly improving patients’ QoL over the 24-month follow-up period. Full article

Objective. To investigate the relationships among neuropathic pain (NP), pain catastrophizing (PC), and central sensitization (CS) in relation to functional status and radiological damage in patients with knee osteoarthritis (OA). Methods. This cross-sectional study included knee OA patients derived from an observational cohort. The Spearman correlation test was used to analyze the relationship between the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the PainDetect Questionnaire (PDQ), Central Sensitization Inventory (CSI), and Pain Catastrophizing Scale (PCS). The Kruskal–Wallis test was employed to compare WOMAC scores according to CSI categories. A multivariate analysis was conducted to identify predictors of functional ability, with the WOMAC score as the dependent variable and the independent variables including pain-related indices such as PCS, PDQ, and CSI, along with Kellgren–Lawrence (K-L) grading and demographic characteristics. Results. This study included 149 patients (76.5% female; mean age 71.5 years; mean duration of pain 8.1 years). In total, 23.5% exhibited NP, 30.9% showed PC, and 33.6% had CS. Higher mean values of WOMAC were correlated with CSI categories (p < 0.0001). WOMAC showed a significant relationship with CSI (rho = 0.791; p < 0.0001), PDQ (rho = 0.766; p < 0.0001), and PCS (rho = 0.536; p < 0.0001). In the multiple regression analysis, WOMAC was independently associated with CSI (p < 0.0001), PDQ (p < 0.0001), and PC (p = 0.0001). No association was observed between the K-L grading and the other variables. Conclusions. A reduced functional capacity in patients with knee OA is correlated with the presence of NP, PC and CS, without being significantly associated with radiological damage. Full article