ObjectiveTo analyze the causal relationship between the intake of cheese or tea and the risk of gastroesophageal reflux disease (GERD). MethodsUsing a two-sample Mendelian randomization approach, single nucleotide polymorphisms (SNPs) associated with milk or tea intake were used as instrumental variables. The causal effect of milk or tea intake on the risk of GERD was investigated using the MR Egger method, the weighted median method, the inverse-variance weighted (IVW) random-effects model, and the IVW fixed-effects model. Multivariable analysis was conducted using the MR Egger method, and leave-one-out sensitivity analysis was performed to validate the reliability of the data. ResultsCheese intake could reduce the occurrence of GERD [IVW random-effects model β=–1.010, 95%CI (0.265, 0.502), P<0.05], while tea intake could lead to the occurrence of GERD [IVW random-effects model β=0.288, 95%CI (1.062, 1.673), P<0.05]. ConclusionCheese intake may have a positive causal relationship with reducing the risk of GERD occurrence, while tea intake may have a positive causal relationship with increasing the risk of GERD occurrence.
Randomized controlled trial (RCT) are considered the "gold standard" for evaluating the causal effects of interventions on outcome measures. However, due to high research costs and ethical constraints, conducting RCT in clinical practice, especially in the surgical field, faces numerous challenges such as difficulties in subject recruitment, implementation of blinding, and standardization of interventions. In such cases, using real-world data to perform causal inference under the framework of target trial emulation (TTE), based on the principles of RCT design, helps to identify and reduce biases arising from design flaws in traditional observational studies, such as immortal time bias, confounding, selection bias, or collider bias. This approach can produce high-quality evidence comparable to that of RCT, thereby enhancing the clinical guidance value of real-world data studies. However, TTE has limitations, such as the inability to completely eliminate confounding, high quality requirements for source data, and the current lack of reporting standards. Therefore, researchers should be fully aware of these limitations to avoid making incorrect causal inferences. This article intends to provide an overview of the TTE framework, implementation points, application scope, application cases, and advantages and disadvantages of the framework.