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The causal relationship between depression and frozen shoulder: A two-sample Mendelian randomization

To investigate the causal relationship between depression and frozen shoulder using a Mendelian randomization (MR) approach. Pooled data from a large-scale genome-wide association study were used. Genetic loci that were independent of each other and associated with depression and frozen shoulder in...

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Detalles Bibliográficos
Autores principales: Deng, Guang-hua, Wei, Yong-kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627603/
https://www.ncbi.nlm.nih.gov/pubmed/37933078
http://dx.doi.org/10.1097/MD.0000000000035556
Descripción
Sumario:To investigate the causal relationship between depression and frozen shoulder using a Mendelian randomization (MR) approach. Pooled data from a large-scale genome-wide association study were used. Genetic loci that were independent of each other and associated with depression and frozen shoulder in populations of European ancestry were selected as instrumental variables. Inverse variance weighting was used as the primary analysis method. Weighted median and MR-Egger were used as complementary analysis methods to assess causal effects. To explore the causal relationship between depression and frozen shoulder. Sensitivity test analysis was performed using heterogeneity test, multiple validity test, and leave-one-out analysis to explore the robustness of the results. Inverse variance weighting results showed an odds ratio (95% confidence interval) of 1.18 (0.91–1.53), P = .204, indicating that depression was not causally related to the development of frozen shoulder. And the test revealed no heterogeneity and pleiotropy, and the sensitivity analysis also showed robust results. In this study, genetic data were analyzed and explored using a two-sample MR analysis, and the results showed no causal relationship between depression and the occurrence of frozen shoulder, requiring the inclusion of a larger sample for the study.