ObjectiveTo assess the causal relationship between cervical vertebra related disorders and essential hypertension using a bidirectional two-sample Mendelian randomization study approach. MethodsThe research data comes from the genome-wide association study dataset. Four types of cervical vertebra related disorders: cervicalgia, cervical disc disorders, cervical root disorders, injury of nerves and spinal cord at neck level, as well as data on essential hypertension, were selected for the study. Relevant single nucleotide polymorphisms were selected as instrumental variables to assess the causal relationship between cervical vertebra related disorders and essential hypertension mainly by inverse variance weighted model ratio. Cochran's Q test was used to detect heterogeneity, MR-Egger intercept term and MR-PRESSO was used to detect multiplicity, and leave-one-out method was used for sensitivity analysis. ResultsCervicalgia had a positive causal relationship with the essential hypertension (OR=1.01, 95%CI 1.00 to1.02, P=0.019). Essential hypertension had a positive causal relationship with the cervical disc disorders (OR=4.08, 95%CI 1.57 to10.61, P=0.004). There was no significant causal relationship between cervical root disorders, injury of nerves and spinal cord at neck level and essential hypertension. Reliability assessment indicates that the study results were reliable. ConclusionCervicalgia is a risk factor for essential hypertension; Essential hypertension is a risk factor for cervical disc lesions; There is no correlation between cervical root disorders, injury of nerves and spinal cord at neck level and essential hypertension.
ObjectiveTo provide method references for data visualization of multiple linear regression analysis.MethodsAfter importing data to R Studio, this paper conducted general descriptive statistics analysis, then constructed a linear model between independent variables and the target. After checking independence of observations, the normality of the target, and the linearity between variables, this paper estimated coefficients of independent variables, dealt with multicollinearity, tested significance of estimates and performed residual analysis to guarantee that the regression met its assumptions, and eventually used the fitted model for prediction.ResultsThe multiple linear regression analysis implemented by R Studio software had better visualization functions and easier operation than traditional R language software.ConclusionsR Studio software has good application value in realizing multiple linear regression analysis data visualization.