Search

Purpose: Up to 30% of spine facet osteoarthritis patients with lumbar spinal stenosis (SF-OA ​+ ​LSS) have little to no improvement in their pain after surgery. Lack of meaningful improvement in pain following surgery provides a unique opportunity to identify specific predictive biomarker signatures that might be associated with the outcomes of surgical treatment. The objective of the present study was to determine whether a microRNA (miRNA) biomarker signature could be identified in presurgical blood plasma that corresponded with levels of SF-OA ​+ ​LSS patient post-surgical pain intensity one year later. Methods: RNA was extracted from baseline plasma of SF-OA ​+ ​LSS patients and prepared for miRNA sequencing. Statistical approaches were performed to identify differentially expressed miRNAs associated with reduced 1-year postsurgical pain (n ​= ​56). Using an integrated computational approach, we further created predicted gene and pathway networks for each identified miRNA. Results: We identified a panel of 4 circulating candidate miRNAs (hsa-miR-155-5p, hsa-let-7e-5p, hsa-miR-125a-5p, hsa-miR-99b-5p) with higher levels at presurgical baseline that were associated with greater changes in % NPRS20Δ, reflecting reduced pain intensity levels at one year. Genes encoding hsa-let-7e-5p, hsa-miR-125a-5p, and hsa-miR-99b-5p are part of an evolutionarily conserved miRNA cluster. Using integrated computational analyses, we showed that mammalian target of rapamycin, transforming growth factor-β1 receptor, Wnt signaling, epithelial–mesenchymal transition regulators, and cholecystokinin signaling were enriched pathways of predicted gene targets. Conclusions: Taken together, our findings suggest that 4 presurgical baseline circulating miRNAs correlate with 1-year postsurgical SF-OA ​+ ​LSS patient pain intensity and represent possible candidate biomarker signature of surgical pain response.

Objective: OsteoDIP aims to collect and provide, in a simple searchable format, curated high throughput RNA expression data related to osteoarthritis. Design: Datasets are collected annually by searching “osteoarthritis gene expression profile” in PubMed. Only publications containing patient data and a list of differentially expressed genes are considered. From 2020, the search has expanded to include non-coding RNAs. Moreover, a search in GEO for “osteoarthritis” datasets has been performed using ‘Homo sapiens' and ‘Expression profiling by array’ filters. Annotations for genes linked to osteoarthritis have been downloaded from external databases. Results: Out of 1204 curated papers, 63 have been included in OsteoDIP, while GEO curation led to the collection of 28 datasets. Literature data provides a snapshot of osteoarthritis research derived from 1924 human samples, while GEO datasets provide expression for additional 1012 patients. Similar to osteoarthritis literature, OsteoDIP data has been created mostly from studies focused on knee, and the tissue most frequently investigated is cartilage. GEO data sets were fully integrated with associated clinical data. We showcase examples and use cases applicable for translational research in osteoarthritis. Conclusions: OsteoDIP is publicly available at http://ophid.utoronto.ca/OsteoDIP. The website is easy to navigate and all the data is available for download. Data consolidation allows researchers to perform comparisons across studies and to combine data from different datasets. Our examples show how OsteoDIP can integrate with and improve osteoarthritis researchers’ pipelines.

Summary: Objective: There are suggestions that the relationship between inflammation and pain in osteoarthritis (OA) may differ by sex, yet studies have been limited. We investigated whether the relationship between knee-specific OA pain and systemic inflammatory markers differs by sex. Design: 196 patients scheduled for knee arthroplasty for OA were included. Questionnaires were completed and blood samples drawn pre-surgery. Questionnaire data: knee pain (WOMAC), sex, age, height, weight, comorbidities, depressive symptoms, and symptomatic joint count. Systemic inflammatory markers (cytokines IL-6, IL-8, IL-10, IL-1β and TNF-α) were measured by multiplex ELISA. A series of regression models with interaction terms between sex and ln-transformed inflammatory markers were estimated with pain score as the outcome. The adjusted relationship between pain and inflammatory markers, by sex, were presented graphically. Results: Mean age was 64 years (range 43–89); females comprised 58.7% of the sample. In adjusted analyses, similar relationships between knee pain and lnIL-10 (negative: β ​= ​−1.28, 95%CI (−1.97, −0.58)) and lnTNF-α (positive: β ​= ​0.92, 95%CI (0.11, 1.76)) were found for females and males. In contrast, relationships between knee pain and lnIL-1β, lnIL-6 and lnIL-8 differed in direction for females and males. Specifically, for lnIL-1β and lnIL-8 they were positive for males, negative for females. The opposite was found with lnIL-6, negative for males, positive for females. Conclusion: These findings provide some evidence of sex-specific relationships between individual inflammatory markers and knee OA pain. They expose a need for further exploration of sex-differences in this context, with potential future implications for treatment or drug development in OA.