Association between meniscal volume and development of knee osteoarthritis

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches

BACKGROUND

Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted therapies remain limited.

METHODS

In this study, we analyzed the GSE48556 dataset to identify differentially expressed ion channel-related genes (DEGs) in OA and normal controls. We employed machine learning algorithms, least absolute shrinkage and selection operator(LASSO), and support vector machine recursive feature elimination(SVM-RFE) to select potential diagnostic markers. Then the gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential diagnostic markers' involvement in biological pathways. Finally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with OA.

RESULTS

We identified a total of 47 DEGs, with the majority involved in transient receptor potential (TRP) pathways. Seven genes (CHRNA4, GABRE, HTR3B, KCNG2, KCNJ2, LRRC8C, and TRPM5) were identified as the best characteristic genes for distinguishing OA from healthy samples. We performed clustering analysis and identified two distinct subtypes of OA, C1, and C2, with differential gene expression and immune cell infiltration profiles. Then we identified three key genes (PPP1R3D, ZNF101, and LOC651309) associated with OA. We constructed a prediction model using these genes and validated it using the GSE46750 dataset, demonstrating reasonable accuracy and specificity.

CONCLUSIONS

Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer potential diagnostic markers and therapeutic targets for the treatment of OA.

Predictive models of radiographic progression and pain progression in patients with knee osteoarthritis: data from the FNIH OA biomarkers consortium project

OBJECTIVES

The progression of knee osteoarthritis (OA) can be defined as either radiographic progression or pain progression. This study aimed to construct models to predict radiographic progression and pain progression in patients with knee OA.

METHODS

We retrieved data from the FNIH OA Biomarkers Consortium project, a nested case-control study. A total of 600 subjects with mild to moderate OA (Kellgren-Lawrence grade of 1, 2, or 3) in one target knee were enrolled. The patients were classified as radiographic progressors (n = 297), non-radiographic progressors (n = 303), pain progressors (n = 297), or non-pain progressors (n = 303) according to the change in the minimum joint space width of the medial compartment and the WOMAC pain score during the follow-up period of 24-48 months. Initially, 376 variables concerning demographics, clinical questionnaires, imaging measurements, and biochemical markers were included. We developed predictive models based on multivariate logistic regression analysis and visualized the models with nomograms. We also tested whether adding changes in predictors from baseline to 24 months would improve the predictive efficacy of the models.

RESULTS

The predictive models of radiographic progression and pain progression consisted of 8 and 10 variables, respectively, with area under curve (AUC) values of 0.77 and 0.76, respectively. Incorporating the change in the WOMAC pain score from baseline to 24 months into the pain progression predictive model significantly improved the predictive effectiveness (AUC = 0.86).

CONCLUSIONS

We identified risk factors for imaging progression and pain progression in patients with knee OA over a 2- to 4-year period, and provided effective predictive models, which could help identify patients at high risk of progression.

Extrusion of the anterior segment of the medial meniscus extrusion initiates knee osteoarthritis: evaluation using magnetic resonance imaging

PURPOSE

Meniscus extrusion contributes to the progression of knee osteoarthritis (OA). It is not clear which site of the medial meniscus (MM) extrusion (MME) is greatest. Moreover, the relationship between sites of MME and progression of OA has not yet been elucidated. The purpose of this study was to determine which sites of MME that showed the greatest extrusion and to investigate the relationship between the presence of MM tears and MME, the relationship between the progression of OA and MME.

METHODS

A cohort of 111 patients were studied retrospectively. The OA grade was classified using the Kellgren-Lawrence (K-L) grade. MME was measured at 13 positions from the anterior to the posterior segment using magnetic resonance imaging (MRI) with slices perpendicular to the MM (radial MRI). The relationship between the K-L grade and the site of the MME was investigated. The patients were grouped as follows: The patients over 40-years-old were grouped as follows: patients with the K-L grade ≤1 and without a MM tear (Group En (early, no meniscus tear)); patients with the K-L grade ≤1 with a MM tear (Group Ep (early, positive meniscus tear)); patients with the K-L grade ≥2 and without a MM tear (Group An (advanced, no meniscal tear)); patients over-40 years-old with the K-L grade ≥2 and with a MM tear (Group Ap (advanced, positive meniscus tear)). And patients between 15 and 39-years-old with no abnormal findings on MRI were defined as control group (Group C).

RESULTS

In the Groups En and Ep, MME was greatest in the anterior segment, and was greater in Group Ep than in Group En. In Groups Ap and Group C, extrusion was greatest in the middle segment.

CONCLUSION

The results suggest that MME predominantly occurred in the anterior segment with increasing age, after that, MM extruded at the middle segment with progression of OA and MM tear.

LEVEL OF EVIDENCE IV

Factors associated with meniscus volume in knees free of degenerative features

OBJECTIVES

To explore factors that were associated with meniscus volume in knees free of radiographic osteoarthritis (OA) features and symptoms of OA.

METHODS

In the third Rotterdam Study cohort, clinical, radiographic, and magnetic resonance data were obtained at baseline (BL) and after 5 years of follow-up. Meniscus volumes and their change over time were calculated after semi-automatic segmentation on Magnetic Resonance Imaging. Knees with radiographic OA features (Kellgren and Lawrence>0) or clinical diagnosis of OA (American College of Rheumatology) at BL were excluded. Ten OA risk factors were adjusted in the multivariable analysis (generalized estimating equations), treating two knees within subjects as repeated measurements.

RESULTS

From 1065 knees (570 subjects), the average (standard deviation) age and Body mass index (BMI) of included subjects were 54.3 (3.7) years and 26.5 (4.4) kg/m2. At BL, nine factors (varus alignment, higher BMI, meniscus pathologies, meniscus extrusion, cartilage lesions, injury, greater physical activity level, quadriceps muscle strength, and higher age) were significantly associated with greater meniscus volume. Five factors (injury, meniscus pathologies, meniscus extrusion, higher age, and change of BMI) were significantly associated with meniscus volume loss.

CONCLUSIONS

Modifiable factors (varus alignment, BMI, physical activity level, and quadriceps muscle strength) and non-modifiable factors (higher age, injury, meniscus pathologies, meniscus extrusion, and cartilage lesions) were all associated with meniscus volume or meniscus volume loss over time.

Imaging of early-stage osteoarthritis: the needs and challenges for diagnosis and classification

In an effort to boost the development of new management strategies for OA, there is currently a shift in focus towards the diagnosis and treatment of early-stage OA. It is important to distinguish diagnosis from classification of early-stage OA. Diagnosis takes place in clinical practice, whereas classification is a process to stratify participants with OA in clinical research. For both purposes, there is an important opportunity for imaging, especially with MRI. The needs and challenges differ for early-stage OA diagnosis versus classification. Although it fulfils the need of high sensitivity and specificity for making a correct diagnosis, implementation of MRI in clinical practice is challenged by long acquisition times and high costs. For classification in clinical research, more advanced MRI protocols can be applied, such as quantitative, contrast-enhanced, or hybrid techniques, as well as advanced image analysis methods including 3D morphometric assessments of joint tissues and artificial intelligence approaches. It is necessary to follow a step-wise and structured approach that comprises, technical validation, biological validation, clinical validation, qualification, and cost-effectiveness, before new imaging biomarkers can be implemented in clinical practice or clinical research.

The influence of meniscal pathology in the incidence of knee osteoarthritis: a review

IMPORTANCE

Knee osteoarthritis (OA) is a common cause of pain and disability in older persons, affecting approximately 14 million individuals in the USA. Meniscal damage is also common in this age group with a prevalence of 35% in a middle-aged and older community sample and 82% in persons with evidence of radiographic knee osteoarthritis. This paper systematically reviews evidence on the association of meniscal pathology and incident radiographic knee OA.

OBSERVATIONS

We included 15 articles, published between 2013 and 2021, assessing the relationship between meniscal pathology and OA incidence (Fig. 1). The menisci are crucial load-bearing structures, and the resulting increase in biomechanical stress due to meniscal damage increases the risk for OA development. While some discrepancies are present in the literature, a clinically meaningful association has been generally established between the presence of a meniscal tear or meniscal extrusion and subsequent development of incident OA. Of note, larger radial tears as well as complex and more severe tears exhibit the strongest association with the development of incident OA. The relationship between other features of meniscal morphology-such as meniscal volume and meniscal coverage-and incident OA is less clearly documented.

CONCLUSIONS AND RELEVANCE

The early detection of meniscal pathology can be used to trigger preventative and therapeutic strategies designed to avert or delay knee OA in this at-risk population.

Physical Activity and Features of Knee Osteoarthritis on Magnetic Resonance Imaging in Individuals Without Osteoarthritis: A Systematic Review

OBJECTIVE

To systematically review all studies that have evaluated the association between physical activity (PA) levels and features of knee osteoarthritis (OA) on magnetic resonance imaging (MRI) for subjects without OA.

METHODS

The inclusion criteria for prospective studies were as follows: 1) subjects without OA; 2) average age 35-80 years; and 3) any self-reported PA or objective measurement of PA. The eligible MRI outcomes were OA-related measures of intraarticular knee joint structures. Exclusion criteria were evaluations of instant associations with transient structural changes after PA.

RESULTS

Two randomized controlled trials and 16 observational studies were included. One of 11 studies found that PA was harmfully related to cartilage volume or thickness, but 4 studies found a significant protective association. Four of 10 studies found that PA was harmfully related to cartilage defects, while others showed no significant associations. Two of 3 studies reported a significantly increased cartilage T2 value in individuals with more PA. All 3 studies reported no significant association between PA and bone marrow lesions. Two studies assessed the association between PA and meniscus pathology, in which only occupational PA involving knee bending was associated with a greater risk of progression.

CONCLUSION

Within the sparse and diverse evidence available, no strong evidence was found for the presence or absence of an association between PA and the presence or progression of features of OA on MRI among subjects without OA. Therefore, more research is required before PA in general and also specific forms of PA can be deemed safe for knee joint structures.

CNN-based fully automatic wrist cartilage volume quantification in MR images: A comparative analysis between different CNN architectures

PURPOSE

Automatic measurement of wrist cartilage volume in MR images.

METHODS

We assessed the performance of four manually optimized variants of the U-Net architecture, nnU-Net and Mask R-CNN frameworks for the segmentation of wrist cartilage. The results were compared to those from a patch-based convolutional neural network (CNN) we previously designed. The segmentation quality was assessed on the basis of a comparative analysis with manual segmentation. The best networks were compared using a cross-validation approach on a dataset of 33 3D VIBE images of mostly healthy volunteers. Influence of some image parameters on the segmentation reproducibility was assessed.

RESULTS

The U-Net-based networks outperformed the patch-based CNN in terms of segmentation homogeneity and quality, while Mask R-CNN did not show an acceptable performance. The median 3D DSC value computed with the U-Net_AL (0.817) was significantly larger than DSC values computed with the other networks. In addition, the U-Net_AL provided the lowest mean volume error (17%) and the highest Pearson correlation coefficient (0.765) with respect to the ground truth values. Of interest, the reproducibility computed using U-Net_AL was larger than the reproducibility of the manual segmentation. Moreover, the results indicate that the MRI-based wrist cartilage volume is strongly affected by the image resolution.

CONCLUSIONS

U-Net CNN with attention layers provided the best wrist cartilage segmentation performance. In order to be used in clinical conditions, the trained network can be fine-tuned on a dataset representing a group of specific patients. The error of cartilage volume measurement should be assessed independently using a non-MRI method.

Enhancing autophagy and energy metabolism in the meniscus can delay the occurrence of PTOA in ACLT rat

Osteoarthritis (OA) is a progressive degenerative joint disease characterized by the destruction of the articular cartilage, meniscus and the like. Autophagy and cellular energy metabolism are the mechanisms by which cells maintain homeostasis. However, little is known about the effects of autophagy and cellular energy metabolism on meniscus degeneration, and the pathogenesis of posttraumatic osteoarthritis (PTOA) after the meniscal injury is rarely reported. Therefore, this study aimed to investigate the relationship between changes in autophagy and cellular energy metabolism in the meniscus following anterior cruciate ligament transection (ACLT) and PTOA induced by subsequent articular cartilage injury. In this study, we use a combination of cell experiments in vitro and animal experiments in vivo. On the one hand, cell experiment results show that inhibiting the mTORC1 signaling pathway by inhibiting the phosphorylation of S6K and AKT proteins in meniscal cells will lead to the increase of Beclin1, LC-3B, ATG12, ULK1, P62, and activate autophagy-related signaling pathways, which in turn protects the extracellular matrix component COL1 of meniscal cells from degradation by catabolic factor MMP13. In addition, it increased the generation of mitochondrial membrane potential in meniscal cells, increased the expression of anti-apoptotic factor BCL-XL, decreased the expression of pro-apoptotic factors BAD and BAX, and reduced the apoptosis of meniscal cells. More importantly, under the stimulation of inflammatory factor IL-1β, the secretion of meniscus cells can reduce the elevated levels of MMP13 and Adamts5 caused by chondrocytes affected by IL-1β. On the other hand, the results of animal experiments in vivo further proved the validity of the results of the cell experiments, and also proved that the meniscus injury did prior to the articular cartilage degeneration after ACLT. In conclusion, this study suggests that the meniscus prior to articular cartilage damage during the development of PTOA after ACLT, and that promoting autophagy and energy metabolism of meniscal cells may be a potential therapeutic target for delaying PTOA.

Cross-Cohort Automatic Knee MRI Segmentation With Multi-Planar U-Nets

Background

Segmentation of medical image volumes is a time‐consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings.

Purpose

To evaluate the performance of the open‐source Multi‐Planar U‐Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state‐of‐the‐art two‐dimensional (2D) U‐Net architecture on three clinical cohorts without extensive adaptation of the algorithms.

Study Type

Retrospective cohort study.

Subjects

A total of 253 subjects (146 females, 107 males, ages 57 ± 12 years) from three knee osteoarthritis (OA) studies (Center for Clinical and Basic Research [CCBR], Osteoarthritis Initiative [OAI], and Prevention of OA in Overweight Females [PROOF]) with varying demographics and OA severity (64/37/24/53/2 scans of Kellgren and Lawrence [KL] grades 0–4).

Field Strength/Sequence

0.18 T, 1.0 T/1.5 T, and 3 T sagittal three‐dimensional fast‐spin echo T1w and dual‐echo steady‐state sequences.

Assessment

All models were fit without tuning to knee magnetic resonance imaging (MRI) scans with manual segmentations from three clinical cohorts. All models were evaluated across KL grades.

Statistical Tests

Segmentation performance differences as measured by Dice coefficients were tested with paired, two‐sided Wilcoxon signed‐rank statistics with significance threshold α = 0.05.

Results

The MPUnet performed superior or equal to KIQ and 2D U‐Net on all compartments across three cohorts. Mean Dice overlap was significantly higher for MPUnet compared to KIQ and U‐Net on CCBR (0.83±0.04 vs. 0.81±0.06 and 0.82±0.05), significantly higher than KIQ and U‐Net OAI (0.86±0.03 vs. 0.84±0.04 and 0.85±0.03), and not significantly different from KIQ while significantly higher than 2D U‐Net on PROOF (0.78±0.07 vs. 0.77±0.07, P=0.10, and 0.73±0.07). The MPUnet performed significantly better on N=22 KL grade 3 CCBR scans with 0.78±0.06 vs. 0.75±0.08 for KIQ and 0.76±0.06 for 2D U‐Net.

Data Conclusion

The MPUnet matched or exceeded the performance of state‐of‐the‐art knee MRI segmentation models across cohorts of variable sequences and patient demographics. The MPUnet required no manual tuning making it both accurate and easy‐to‐use.

Level of Evidence

3

Technical Efficacy

Stage 2

Are changes in meniscus volume and extrusion associated to knee osteoarthritis development? A structural equation model

OBJECTIVE

To explore the interplay between (changes in) medial meniscus volume, meniscus extrusion and radiographic knee osteoarthritis (OA) development over 30 months follow-up (FU).

METHODS

Data from the PRevention of knee Osteoarthritis in Overweight Females study were used. This cohort included 407 middle-aged women with a body mass index ≥27 kg/m², who were free of knee OA at baseline. Demographics were collected by questionnaires at baseline. All menisci at both baseline and FU were automatically segmented from MRI scans to obtain the meniscus volume and the change over time (delta volume). Baseline and FU meniscus body extrusion was quantitatively measured on mid-coronal proton density MR images. A structural equation model was created to assess the interplay between both medial meniscus volume and central extrusion at baseline, delta volume, delta extrusion, and incident radiographic knee OA at FU.

RESULTS

The structural equation modeling yielded a fair to good fit of the data. The direct effects of both medial meniscus volume and extrusion at baseline on incident OA were statistically significant (Estimate = 0.124, p = 0.029, and Estimate = 0.194, p < 0.001, respectively). Additional indirect effects on incident radiographic OA through delta meniscus volume or delta meniscus extrusion were not statistically significant.

CONCLUSION

Baseline medial meniscus volume and extrusion were associated to incidence of radiographic knee OA at FU in middle-aged overweight and obese women, while their changes were not involved in these effects. To prevent knee OA, interventions might need to target the onset of meniscal pathologies rather than their progression.

3D MRI in Osteoarthritis

Osteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification.

A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone

Objectives

Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis.

Methods

The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC).

Results

Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73–0.87).

Conclusion

Our results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously.

Key Points

• Subchondral bone plays a role in the osteoarthritis disease processes.

• MRI radiomics is a potential method for quantifying changes in subchondral bone.

• Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07951-5.