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

Survival analysis on subchondral bone length for total knee replacement

OBJECTIVE

Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR).

METHODS

We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk.

RESULTS

The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827.

CONCLUSION

SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.

Overexpression of lncRNA LINC00665 inhibits the proliferation and chondroblast differentiation of bone marrow mesenchymal stem cells by targeting miR-214-3p

BACKGROUND

Osteoarthritis is a chronic disease mainly involving the damage of articular cartilage and the whole articular tissue, which is the main cause of disability in the elderly. To explore more effective treatment measures, this study analyzed the regulatory role and molecular mechanism of lncRNA LINC00665 (LINC00665) in the chondrogenic differentiation of bone marrow mesenchymal stem cells (BMSCs), providing a valuable theoretical basis for the pathogenesis and patient treatment of osteoarthritis.

METHODS

Osteoarthritis tissues and healthy tissues were obtained from 52 patients with osteoarthritis and 34 amputated patients without osteoarthritis, and the levels of LINC00665 and miR-214-3p were assessed by RT-qPCR. BMSCs were cultured and induced chondrogenic differentiation. The proliferation ability of BMSCs was detected by CCK-8 method, and the apoptosis level of BMSCs was evaluated by flow cytometry. The content of proteoglycan-glycosaminoglycan (GAG) in cartilage matrix was determined by Alcian blue staining. In addition, the binding relationship between LINC00665 and miR-214-3p was verified by luciferase reporter assay, and the molecular mechanism was further analyzed.

RESULTS

In osteoarthritis tissues, LINC00665 was elevated and miR-214-3p was down-regulated. With the chondrogenic differentiation of BMSCs, the level of GAG increased, and LINC00665 expression gradually decreased, while miR-214-3p level was on the contrary. After transfection of pcDNA3.1-LINC00665 in BMSCs, cell proliferation capacity was decreased, apoptosis rate was increased, and GAG content was reduced. Moreover, LINC00665 sponged miR-214-3p and negatively regulate its expression. Transfection of pcDNA3.1-LINC00665-miR-214-3p mimic changed the regulation of pcDNA3.1-LINC00665 on the viability and chondrogenic differentiation of BMSCs.

CONCLUSIONS

Overexpression of lncRNA LINC00665 inhibited the proliferation and chondrogenic differentiation of BMSCs by targeting miR-214-3p. The LINC00665/miR-214-3p axis may improve joint damage and alleviate the progression of osteoarthritis.

Emerging roles of RNA binding proteins in intervertebral disc degeneration and osteoarthritis

The etiology of intervertebral disc degeneration (IDD) and osteoarthritis (OA) is complex and multifactorial. Both predisposing genes and environmental factors are involved in the pathogenesis of IDD and OA. Moreover, epigenetic modifications affect the development of IDD and OA. Dysregulated phenotypes of nucleus pulposus (NP) cells and OA chondrocytes, including apoptosis, extracellular matrix disruption, inflammation, and angiogenesis, are involved at all developmental stages of IDD and OA. RNA binding proteins (RBPs) have recently been recognized as essential post-transcriptional regulators of gene expression. RBPs are implicated in many cellular processes, such as proliferation, differentiation, and apoptosis. Recently, several RBPs have been reported to be associated with the pathogenesis of IDD and OA. This review briefly summarizes the current knowledge on the RNA-regulatory networks controlled by RBPs and their potential roles in the pathogenesis of IDD and OA. These initial findings support the idea that specific modulation of RBPs represents a promising approach for managing IDD and OA.

Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome.

Bibliometric insights from publications on subchondral bone research in osteoarthritis

Background: The role of subchondral bone in the pathogenesis of osteoarthritis has received continuous attention worldwide. To date, no comprehensive bibliometric analysis of this topic has been carried out. The purpose of this study was to investigate the knowledge landscape, hot spots, and research trends in subchondral bone research through bibliometrics. Methods: Web of Science Core Collection database was used to collect articles and reviews on subchondral bone in osteoarthritis published between 2003 and 2022. CiteSpace, VOSviewer, Scimago Graphica, and a bibliometric online analysis platform (http://bibliometric.com/) were used to visualize the knowledge network of countries, institutions, authors, references, and keywords in this field. Both curve fitting and statistical plotting were performed using OriginPro, while correlation analysis was done using SPSS. Results: A total of 3,545 articles and reviews were included. The number of publications on subchondral bone showed an exponential growth trend. The US produced the most (980), followed by China (862) and the United Kingdom (364). Scientific output and gross domestic product were significantly correlated (r = .948, p < .001). The University of California System and Professor Pelletier Jean-Pierre were the most prolific institutions and influential authors, respectively. The most active and influential journal for subchondral bone research was Osteoarthritis and Cartilage. The majority of papers were financed by NSFC (474, 13.4%), followed by HHS (445, 12.6%), and NIH (438, 12.4%). In recent years, hot keywords have focused on the research of pathomechanisms (e.g., inflammation, apoptosis, pathogenesis, cartilage degeneration/repair, angiogenesis, TGF beta) and therapeutics (e.g., regeneration, stromal cell, mesenchymal stem cell). Conclusion: Subchondral bone research in osteoarthritis is flourishing. Current topics and next research trends would be centered on the pathomechanisms of cellular and molecular interactions in the subchondral bone microenvironment in the development of osteoarthritis and the exploration of targeted treatment medicines for the altered subchondral bone microenvironment.