ObjectiveTo conduct a two-sample Mendelian randomization (MR) study to assess the bidirectional causal relationship between multiple sclerosis and inflammatory bowel disease. MethodsWe performed two-sample bidirectional MR analysis using publicly available genome-wide association study (GWAS) data. The primary analysis method used was the inverse variance weighted (IVW) method, with MR-Egger weighted median as a supplementary analysis. Sensitivity analyses were conducted. ResultsIVW, weighted median, and weighted mode all supported a causal relationship between multiple sclerosis and an increased risk of ulcerative colitis (OR=1.07, 95%CI 1.01 to 1.13, P=0.018), while no association was found between multiple sclerosis and Crohn's disease. Sensitivity analyses suggested that the study results were not affected by pleiotropy. ConclusionGenetic predisposition to multiple sclerosis is associated with an elevated risk of developing ulcerative colitis but not Crohn’s disease.
ObjectiveA two-sample Mendelian randomization analysis was used to explore the causal associations between four basic body indices (basal metabolic rate, body fat percentage, BMI and hip circumference) and myasthenia gravis (MG). MethodsPooled gene-wide association study (GWAS) data were obtained from large publicly searchable databases, and four basic body indices were selected as the exposure factors and myasthenia gravis as the outcome factors, and single nucleotide polymorphisms (SNPs), which were strongly correlated with the phenotype of the exposure factors, were screened as the instrumental variables, and two-sample Mendelian randomization analyses were performed in order to assess the potential causal relationship between the exposure and the disease. ResultsInverse variance weighting (IVW) analysis showed that increased basal metabolic rate (OR=1.39, 95%CI 1.00 to 1.93, P=0.047), body fat percentage (OR=1.61, 95%CI 1.06 to 2.44, P=0.024), and hip circumference (OR=1.67, 95%CI 1.29 to 2.17, P<0.001) increased the risk of MG. But there was no significant causal relationship between BMI and MG. ConclusionBasal metabolic rate, body fat percentage and hip circumference have a positive causal relationship with MG, while BMI does not have a significant causal relationship with MG.
Conducting real-world studies inevitably faces the challenge of bias. Researchers need to employ appropriate bias analysis methods to determine whether the strength of causal associations derived from the study is distorted by potential biases. Traditional qualitative assessments and statistical methods often struggle to effectively quantify the impact of unknown or unmeasured residual biases on study conclusions. Quantitative bias analysis can address this by constructing quantitative models to quantify sources of bias (such as misclassification or unmeasured confounding) and systematically evaluate the direction and magnitude of the effect of residual biases on effect estimates after conventional analysis, thereby confirming the robustness of the estimates. This method has gained widespread international recognition for addressing key questions such as, "How much uncertainty might potential biases introduce?" and "How credible are the study conclusions in the presence of bias?" However, there is still a lack of systematic introduction to this method domestically. Therefore, this article will begin with the practical challenges of bias analysis in real-world studies and sequentially elaborate on the development, theoretical framework, implementation cases, and application of quantitative bias analysis in real-world research. It aims to systematically introduce the methodological characteristics of quantitative bias analysis, providing researchers with a methodological reference for addressing bias analysis issues.