ObjectivesTo investigate the longitudinal association between MRI-detected osteophyte scores and progression of knee symptoms, and whether the association was modified in the presence of bone marrow lesions (BMLs) or effusion-synovitis.MethodsData from Vitamin D Effects on Osteoarthritis (VIDEO) study, a randomized, double-blinded and placebo-controlled clinical trial in symptomatic knee osteoarthritis (OA) patients, were analyzed as an exploratory study. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was used to assess knee symptoms. Osteophytes, BMLs and effusion-synovitis were measured using MRI.Results334 participants with MRI information and WOMAC score (baseline and follow-up) were included in the analyses, with 24.3% of them having knee pain increased 2 years later. Statistically significant interactions were found between MRI-detected osteophytes and BMLs or effusion-synovitis on increased knee symptoms. In participants with BMLs, higher baseline scores of MRI-detected osteophytes in most compartments were significantly associated with increased total knee pain, weight-bearing pain, stiffness, and physical dysfunction, after adjustment for age, sex, body mass index, intervention and effusion-synovitis. In participants with effusion-synovitis, higher baseline scores of MRI-detected osteophytes in almost all the compartments were significantly associated with increased total knee pain, weight-bearing pain, stiffness, and physical dysfunction, after adjustment for age, sex, body mass index, intervention and BMLs. In contrast, MRI-detected osteophyte scores were generally not associated with knee symptom progression in participants without baseline BMLs or effusion-synovitis.ConclusionsMRI-detected OPs are associated with increased total knee pain, weight-bearing knee pain, stiffness and physical dysfunction in participants presenting BMLs or effusion-synovitis, but not in participants lacking BMLs or effusion-synovitis. This suggests they could interact with bone or synovial abnormalities to induce symptoms in knee OA.
Background: The roles of microRNAs (miRNAs) in osteogenic differentiation of human periodontal ligament stem cells (hPDLSCs) remain largely unexplored. In this study, the underlying molecular mechanism of osteogenic differentiation in hPDLSCs is investigated using miRNA profiling. Methods: The miRNA expression profile during osteogenic differentiation was analyzed using a microarray. Target genes of miRNAs with at least two‐fold change in expression ( P <0.05) were predicted by bioinformatics. Six miRNAs with osteogenesis‐related target genes were validated by quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR). Results: Expression of 116 miRNAs was found to be altered after osteoinduction, with 30 upregulated and 86 downregulated. Thirty‐one of these miRNAs (26.7%) had osteogenesis‐related target genes. Changes in expression levels of six of the 31 miRNAs ( miR‐654‐3p , miR‐4288 , miR‐34c‐5p , miR‐218‐5p , miR‐663a , and miR‐874‐3p ) were validated by qRT‐PCR. Conclusions: Significant alterations in miRNA expression profiles were observed during osteogenic differentiation of hPDLSCs. These results imply that miRNAs may have regulatory effects on this process by targeting osteogenesis‐related genes.
Objectives To develop and validate a nomogram to detect improved knee pain in osteoarthritis (OA) by integrating magnetic resonance imaging (MRI) radiomics signature of subchondral bone and clinical characteristics. Methods Participants were selected from the Vitamin D Effects on Osteoarthritis (VIDEO) study. The primary outcome was 20% improvement of knee pain score over 2 years in participants administrated either vitamin D or placebo. Radiomics features of subchondral bone and clinical characteristics from 216 participants were extracted and analyzed. The participants were randomly split into the training and validation cohorts at a ratio of 8:2. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate radiomics signatures. The optimal radiomics signature and clinical indicators were fitted into a nomogram using multivariable logistic regression model. Results The nomogram showed favorable discrimination performance [AUCtraining, 0.79 (95% CI: 0.72–0.79), AUCvalidation, 0.83 (95% CI: 0.70–0.96)] as well as a good calibration. Additional contributing value of fusion radiomics signature to the nomogram was statistically significant (NRI, 0.23; IDI, 0.14, P < 0.001 in training cohort and NRI, 0.29; IDI, 0.18, P < 0.05 in validating cohort). Decision curve analysis confirmed the clinical usefulness of nomogram. Conclusion The radiomics-based nomogram comprising the MR radiomics signature and clinical variables achieves a favorable predictive efficacy and accuracy in differentiating improvement in knee pain among OA patients. This proof-of-concept study provides a promising way to predict clinically meaningful outcomes.
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