Objective: Ankylosing spondylitis (AS) is associated with a variety of gut microbiotas. We aim to analyze the causal relationship between the two at the genetic level. Methods: Mendelian randomization (MR) is a type of instrumental variables (IVs) analysis; MR follows the Mendelian genetic rule of “parental alleles are randomly assigned to offspring” and takes genetic variation as IVs to infer the causal association between exposure factors and study outcome in observational studies. Genome-wide association study (GWAS) summary data of AS were from the FinnGen consortium, and the gut microbiota (Bacteroides, Streptococcus, Proteobacteria, Lachnospiraceae) were from the MiBioGen consortium. The TwoSampleMR and MRPRESSO packages of the R were used to perform a two-sample MR study. Random-effects inverse variance weighted (IVW) was the main analysis method, and MR Egger, weighted median, simple mode, and weighted mode were used as supplementary methods. We examined heterogeneity and horizontal pleiotropy, and examined whether the analysis results were influenced by a single SNP. We applied radial variants of the IVW and MR-Egger model for the improved visualization of the causal estimate. We further examined the causal relationship between AS and gut microbiota, and the robustness of the analysis results. Finally, we performed maximum likelihood, penalized weighted median, and IVW (fixed effects) to further identify the potential causal association. Results: The random-effects IVW results showed that Bacteroides (p = 0.965, OR 95% confidence interval [CI] = 0.990 [0.621–1.579]), Streptococcus (p = 0.591, OR 95% CI = 1.120 [0.741–1.692]), Proteobacteria (p = 0.522, OR 95% CI = 1.160 [0.737–1.826]), and Lachnospiraceae (p = 0.717, OR 95% CI = 1.073 [0.732–1.574]) have no genetic causal relationship with AS. There was no heterogeneity, horizontal pleiotropy or outliers, and results were normally distributed. The MR analysis results were not driven by a single SNP. Conclusions: This study showed that Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae, four common gut microbiotas associated with AS, had no causal relationship with AS at the genetic level. This study makes a positive contribution to the genetics of AS, but the insufficient number of gut microbiota included is a limitation.

Osteoarthritis (OA) is mainly caused by ageing, strain, trauma, and congenital joint abnormalities, resulting in articular cartilage degeneration. During the pathogenesis of OA, the changes in subchondral bone (SB) are not only secondary manifestations of OA, but also an active part of the disease, and are closely associated with the severity of OA. In different stages of OA, there were microstructural changes in SB. Osteocytes, osteoblasts, and osteoclasts in SB are important in the pathogenesis of OA. The signal transduction mechanism in SB is necessary to maintain the balance of a stable phenotype, extracellular matrix (ECM) synthesis, and bone remodelling between articular cartilage and SB. An imbalance in signal transduction can lead to reduced cartilage quality and SB thickening, which leads to the progression of OA. By understanding changes in SB in OA, researchers are exploring drugs that can regulate these changes, which will help to provide new ideas for the treatment of OA.Cite this article: Bone Joint Res 2023;12(9):536–545.

Background: Rheumatoid arthritis (RA) increases the risk of depression. However, studies on the effects of RA on the dose of depression medications are limited. Therefore, in this study, we used two-sample Mendelian randomization (MR) to explore whether RA increases the dose of depression medications and gain a more comprehensive understanding of the relationship between RA and depression. Methods: Two-sample MR was used to evaluate the causal effect of RA on the dose of depression medications. The aggregated data on RA originated from extensive genome-wide association studies (GWASs) of European descent (14,361 cases and 42,923 controls). The summary GWAS data for the dose of depression medications were derived from the FinnGen consortium (58,842 cases and 59,827 controls). Random effects inverse-variance weighted (IVW), MR-Egger regression, weighted median, and fixed effects IVW methods were used for the MR analysis. Random effects IVW was the primary method. The heterogeneity of the MR results was detected using the IVW Cochran’s Q test. The pleiotropy of the MR results was detected using MR-Egger regression and the MR pleiotropy residual sum and outlier (MR-PRESSO) test. Finally, a leave-one-out analysis was performed to determine whether the MR results were affected by a specific single-nucleotide polymorphism (SNP). Results: The primary method, random effects IVW, revealed that genetically predicted RA had a positive causal association with the dose of depression medications (Beta, 0.035; 95% confidence interval (CI), 0.007–0.064; p = 0.015). The IVW Cochran’s Q test results revealed no heterogeneity in the MR analysis (p > 0.05). The MR-Egger regression and MR-PRESSO tests revealed that there was no pleiotropy in our MR analysis. The leave-one-out analysis confirmed that a single SNP did not affect the MR results, indicating the study’s robustness. Conclusion: Using MR techniques, we discovered that having RA increases the dose of depression medications; however, the exact mechanisms and pathways still need to be further explored.

The objective of this study was to develop a musculoskeletal model incorporated with a subject-specific knee joint to predict the tibiofemoral contact force (TFCF) during daily motions. For this purpose, 18 healthy participants were recruited to perform the motion data acquisition using synchronized motion capture and force platform systems, and motion simulation based on an improved musculoskeletal model for five daily activities, including normal walking, stair ascent, stair descent, sit-to-stand, and stand-to-sit. The proposed musculoskeletal model included subject-specific models of bones, cartilages, and meniscus, detailed knee ligaments and muscles, deformable elastic contacts, and multiple degrees of freedom (DOFs) of the knee joint. The prediction accuracy was demonstrated by the good agreements of TFCF curves between the model predictions and in vivo measurements for the five activities (RMSE: 0.216~0.311 BW, R²: 0.928~0.992, and C_E: 0.048~0.141). Based on the validated model, the TFCF on total, medial, and lateral compartments (TFCF_Total, TFCF_Medial, and TFCF_Lateral) during the five daily activities were predicted. For TFCF_Total, the peak force for stair descent or sit-to-stand was the largest, followed by stair ascent or stand-to-sit, and finally normal walking. For TFCF_Medial, stair descent had the largest peak, followed by stair ascent. There were no significant differences between the peak TFCF_Medial values of normal walking, sit-to-stand, and stand-to-sit. For TFCF_Lateral, the peak of sit-to-stand was the largest, followed by stand-to-sit or stair descent, and finally normal walking or stair ascent. This study is valuable for further understanding the biomechanics of a healthy knee joint and providing theoretical guidance for the treatment of knee osteoarthritis (KOA).

Based on previous observational studies, the causal association between circulating adiponectin (CA) levels and ankylosing spondylitis (AS) risk remains unclear. Therefore, this study aims to investigate whether CA levels are related to the risk of AS. We carried out a bidirectional two-sample Mendelian randomization (MR) analysis to examine the causal correlation between CA levels and AS via published genome-wide association study (GWAS) datasets. Single-nucleotide polymorphisms (SNPs) related to CA levels were derived from a large GWAS that included 39,883 individuals of European descent. SNPs related to AS were obtained from the FinnGen consortium (2252 cases and 227,338 controls). The random-effects inverse variance weighted (IVW) method was the primary method utilized in our research. We also used four complementary approaches to improve the dependability of this study (MR–Egger regression, Weighted median, Weighted mode, and Simple mode). Random-effects IVW (odds ratio [OR], 1.00; 95% confidence interval [CI], 0.79–1.27, p = 0.984) and four complementary methods all indicated that genetically predicted CA levels were not causally related to the risk of AS. In reverse MR analysis, there is little evidence to support the genetic causality between the risk of AS and CA levels.

Introduction: Appropriate levels of physical activity (PA) can help prevent osteoarthritis (OA) and alleviate its symptoms. This study aims to clarify the causal link between PA and OA by investigating their shared genetic factors.Material and methods: The study utilized genome-wide association analysis (GWAS) summary data to investigate the relationship between three types of PA, include moderate to vigorous physical activity (MVPA), vigorous physical activity (VPA) and strenuous sports or other exercises (SSOE), and knee osteoarthritis (KOA). A two-sample Mendelian randomization (MR) study was conducted using the TwoSampleMR and MRPRESSO packages in R. We did a sensitivity analysis, including heterogeneity, horizontal pleiotropy, outliers, and assessed for influence by a single single-nucleotide polymorphisms (SNPs) and for compliance with normal distribution.Results: The results of the MR analysis indicate that MVPA (P = 0.436, OR 95% CI = 1.814 [0.405-8.119]), VPA (P = 0.995, OR 95% CI = 1.011 [0.040-25.224]) and SSOE (P = 0.266, OR 95% CI = 0.258 [0.024-2.812]) have no significant genetic causal relationship with KOA. We did not detect any heterogeneity or horizontal pleiotropy (P > 0.05), nor were there any outliers in our MR analysis. Our MR results were not driven by a single SNP and were normally distributed (P > 0.05).Conclusions: The results of this study provide evidence that there is no genetic causal relationship between PA and KOA, thereby contributing to the understanding of their correlation. However, it cannot be excluded that a relationship may exist between the two at other levels beyond the scope of genetic factors.