Aims. Deciphering the genetic relationships between major depressive disorder (MDD) and osteoarthritis (OA) may facilitate an understanding of their biological mechanisms, as well as inform more effective treatment regimens. We aim to investigate the mechanisms underlying relationships between MDD and OA in the context of common genetic variations. Methods. Linkage disequilibrium score regression was used to test the genetic correlation between MDD and OA. Polygenic analysis was performed to estimate shared genetic variations between the two diseases. Two-sample bidirectional Mendelian randomization analysis was used to investigate causal relationships between MDD and OA. Genomic loci shared between MDD and OA were identified using cross-trait meta-analysis. Fine-mapping of transcriptome-wide associations was used to prioritize putatively causal genes for the two diseases. Results. MDD has a significant genetic correlation with OA (r. g. = 0.29) and the two diseases share a considerable proportion of causal variants. Mendelian randomization analysis indicates that genetic liability to MDD has a causal effect on OA (b. xy. = 0.24) and genetic liability to OA conferred a causal effect on MDD (b. xy. = 0.20). Cross-trait meta-analyses identified 29 shared genomic loci between MDD and OA. Together with fine-mapping of transcriptome-wide association signals, our results suggest that Estrogen Receptor 1 (ESR1), SRY-Box Transcription Factor 5 (SOX5), and Glutathione Peroxidase 1 (GPX1) may have therapeutic implications for both MDD and OA. Conclusion. The study reveals substantial shared genetic liability between MDD and OA, which may confer risk for one another. Our findings provide a novel insight into phenotypic relationships between MDD and OA. Cite this article: Bone Joint Res 2022;11(1):12–22

Aims. Mendelian randomization (MR) is considered to overcome the bias of observational studies, but there is no current meta-analysis of MR studies on rheumatoid arthritis (RA). The purpose of this study was to summarize the relationship between potential pathogenic factors and RA risk based on existing MR studies. Methods. PubMed, Web of Science, and Embase were searched for MR studies on influencing factors in relation to RA up to October 2022. Meta-analyses of MR studies assessing correlations between various potential pathogenic factors and RA were conducted. Random-effect and fixed-effect models were used to synthesize the odds ratios of various pathogenic factors and RA. The quality of the study was assessed using the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines. Results. A total of 517 potentially relevant articles were screened, 35 studies were included in the systematic review, and 19 studies were eligible to be included in the meta-analysis. Pooled estimates of 19 included studies (causality between 15 different risk factors and RA) revealed that obesity, smoking, coffee intake, lower education attainment, and Graves’ disease (GD) were related to the increased risk of RA. In contrast, the causality contribution from serum mineral levels (calcium, iron, copper, zinc, magnesium, selenium), alcohol intake, and chronic periodontitis to RA is not significant. Conclusion. Obesity, smoking, education attainment, and GD have real causal effects on the occurrence and development of RA. These results may provide insights into the genetic susceptibility and potential biological pathways of RA. Cite this article: Bone Joint Res 2023;12(9):601–614

Aims. The aim of this study was to explore the genetic correlation and causal relationship between blood plasma proteins and rheumatoid arthritis (RA). Methods. Based on the genome-wide association studies (GWAS) summary statistics of RA from European descent and the GWAS summary datasets of 3,622 plasma proteins, we explored the relationship between RA and plasma proteins from three aspects. First, linkage disequilibrium score regression (LD score regression) was applied to detect the genetic correlation between RA and plasma proteins. Mendelian randomization (MR) analysis was then used to evaluate the causal association between RA and plasma proteins. Finally, GEO2R was used to screen the differentially expressed genes (DEGs) between patients with RA and healthy controls. Results. We found that seven kinds of plasma proteins had genetic correlations with RA, such as Soluble Receptor for Advanced Glycation End Products (sRAGE) (correlation coefficient = 0.2582, p = 0.049), vesicle transport protein USE1 (correlation coefficient = 0.1337, p = 0.018), and spermatogenesis-associated protein 20 (correlation coefficient = 0.3706, p = 0.018). There was a significant causal relationship between sRAGE and RA. By comparing the genes encoding seven plasma proteins, we found that only USE1 was a DEG associated with RA. Conclusion. Our study identified a set of candidate plasma proteins that showed signals correlated with RA. Since the results of this study need further experimental verification, they should be interpreted with caution. However, we hope that this paper will provide new insights for the discovery of pathogenic genes and RA pathogenesis in the future. Cite this article: Bone Joint Res 2022;11(2):134–142