Objective To employ Mendelian randomization (MR) to assess the potential causal relationship between 15 micronutrients in serum and male infertility, so as to provide a scientific basis for the etiological prevention of male infertility. Methods Male infertility was taken as the outcome variable, and 15 serum micronutrients was regarded as the potential influencing factors. A two-sample MR analysis was conducted using genome-wide association study with the European population. Multivariable MR analysis was employed to explore the independent effects of potential micronutrients on male infertility. Results A total of 198 single nucleotide polymorphisms across 15 groups were included as instrumental variables to assess the causal association between micronutrients and male infertility. Univariate analysis showed that increased serum iron levels were positively causally associated with a higher risk of male infertility (odds ratio=2.917, P=0.015). Besides, multivariate MR analysis suggested that this positive causal relationship between iron and male infertility remains significant (odds ratio=3.253, P=0.029). Meanwhile, this study found a negative association between elevated vitamin D levels and the risk of male infertility (odds ratio=0.403, P=0.020).Conclusion Elevated serum iron levels and increased vitamin D levels are associated with significant positive and negative causal effects on male infertility, respectively.
The level of evidence in randomized controlled studies is high. However, it cannot be widely applied due to its high cost, external authenticity, ethics and other reasons. The traditional observational studies reduce the internal authenticity due to various confounding factors, and the level of evidence is low. Regression discontinuity design (RDD) is a design that observes and compares outcome of object around the threshold under practical clinical conditions. Its capability to adjust confounding factors is second only to that of randomized control studies. It can be used in cases where the intervention (or exposure) is directly related to the value of a continuous variable. For instance, whether an HIV patient needs antiretroviral treatment mainly depends on whether the CD4 cell count is lower than 200/μL. Because the measurement of continuous variables has random error, whether intervention is given near the threshold or is close to random, the baseline of patients in the intervention group and non-intervention group near the threshold should be balanced and comparable. Based on this assumption, the causal effect of intervention (or exposure) and outcome can be estimated by comparing the outcomes of populations near the threshold. RDD is mainly applicable to the study of classification outcomes in medicine, among which two-stage least square method, likelihood ratio based estimation method and Bayesian method are more commonly used model estimation methods. However, the application conditions of RDD and the requirement of sample size limit its extensive application in medicine. With the improvement of data accessibility and the development of real world research, RDD will be more widely used in clinical research.