ABSTRACT
OBJECTIVE: The novel MRI-based vertebral bone quality (VBQ) score has been described as an opportunistic screening tool for osteoporosis, but the stability and practical value of this score deserve further investigation. The purpose of this study was to assess whether preoperative VBQ scores could assist in identifying reduced bone mineral density (BMD) or osteoporosis and evaluating the consistency between MRI systems with different field strengths. METHODS: The VBQ scores of the patients who underwent surgery for lumbar disc herniation and the single-level VBQ scores of each L1-4 vertebral body were measured and calculated with preoperative lumbar MRI noncontrast T1-weighted phases. The VBQ scores were evaluated for correlation analysis using dual-energy x-ray absorptiometry (DEXA) T-scores. The receiver operating characteristic (ROC) curve was used to evaluate the ability of the VBQ scores to identify patients with reduced BMD and with osteoporosis. Differences in CSF measurements at different levels of L1-4 were compared. Twenty-four patients who had been examined using another MRI machine were used as controls to test the interdevice agreement of the VBQ scores. RESULTS: The study included 100 patients with mean VBQ scores of 2.81 ± 0.28 (normal BMD), 3.06 ± 0.36 (osteopenia), and 3.43 ± 0.37 (osteoporosis). VBQ scores differed significantly between BMD subgroups (p < 0.001). The Pearson correlation coefficient showed a moderate negative linear correlation between novel VBQ scores and the lowest DEXA T-scores (r = -0.524). ROC analysis showed good discrimination of VBQ scores in patients with reduced BMD (area under the curve [AUC] 0.793) and with osteoporosis (AUC 0.810). The diagnostic thresholds of reduced BMD and osteoporosis according to the maximum Youden index were 3.06 (sensitivity 0.636, specificity 0.870, positive predictive value [PPV] 0.942, negative predictive value [NPV] 0.417) and 3.05 (sensitivity 0.875, specificity 0.618, PPV 0.519, NPV 0.913), respectively. CSF measurements at the L2, L3, and L4 levels were essentially identical and did not significantly affect the final VBQ scores (p > 0.05), whereas CSF measurements at the L1 level were found to be heterogeneous (p < 0.001). No significant differences were found in VBQ scores between the same brand of MRI machines at different field strengths (1.5 and 3.0 T, p = 0.107). CONCLUSIONS: The new VBQ score provides an additional screening opportunity for preoperative BMD assessment. A VBQ score < 3.05 essentially excludes osteoporosis, whereas a VBQ score ≥ 3.05 (especially ≥ 3.06) suggests the need for further examination. The VBQ score is comparable between different MRI systems.
ABSTRACT
Osteoarthritis (OA) and Parkinson's disease (PD) are on the rise and greatly impact the quality of individuals' lives. Although accumulating evidence indicates a relationship between OA and PD, the particular interactions connecting the two diseases have not been thoroughly examined. Therefore, this study explored the association through genetic characterization and functional enrichment. Four datasets (GSE55235, GSE12021, GSE7621, and GSE42966) were chosen for assessment and validation from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was implemented to determine the most relevant genes for clinical features. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to explore the biological processes of common genes, and to display the interrelationships between common genes, the STRING database and the application Molecular Complex Detection Algorithm (MCODE) of Cytoscape software were leveraged to get hub genes. By intersecting the common genes with the differentially expressed genes (DEGs) acquired from GSE12021 and GSE42966, the hub genes were identified. Finally, we validated the diagnostic efficacy of hub genes and explored their correlation with 22 immune infiltrating cells. As a consequence, we discovered 71 common genes, most of which were functionally enriched in antigen processing and presentation, mitochondrial translation, the mRNA surveillance pathway, and nucleocytoplasmic transport. Furthermore, WDR43 was found by intersecting eight hub genes with 28 DEGs from the two validation datasets. Receiver Operating Characteristic (ROC) implied the diagnostic role of WDR43 in OA and PD. Immune infiltration research revealed that T-cell regulatory (Tregs), monocytes, and mast cells resting were associated with the pathogenesis of OA and PD. WDR43 may provide key insights into the relationship between OA and PD.
ABSTRACT
Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease. Pigmented villonodular synovitis (PVNS) is a tenosynovial giant cell tumor that can involve joints. The mechanisms of co-morbidity between the two diseases have not been thoroughly explored. Therefore, this study focused on investigating the functions, immunological differences, and potential therapeutic targets of common genes between RA and PVNS. Methods: Through the dataset GSE3698 obtained from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) were screened by R software, and weighted gene coexpression network analysis (WGCNA) was performed to discover the modules most relevant to the clinical features. The common genes between the two diseases were identified. The molecular functions and biological processes of the common genes were analyzed. The protein-protein interaction (PPI) network was constructed using the STRING database, and the results were visualized in Cytoscape software. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) logistic regression and random forest (RF) were utilized to identify hub genes and predict the diagnostic efficiency of hub genes as well as the correlation between immune infiltrating cells. Results: We obtained a total of 107 DEGs, a module (containing 250 genes) with the highest correlation with clinical characteristics, and 36 common genes after taking the intersection. Moreover, using two machine learning algorithms, we identified three hub genes (PLIN, PPAP2A, and TYROBP) between RA and PVNS and demonstrated good diagnostic performance using ROC curve and nomogram plots. Single sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the biological functions in which three genes were mostly engaged. Finally, three hub genes showed a substantial association with 28 immune infiltrating cells. Conclusion: PLIN, PPAP2A, and TYROBP may influence RA and PVNS by modulating immunity and contribute to the diagnosis and therapy of the two diseases.