Abstract
BackgroundIt is known in some studies that higher the LDL-C, the greater the risk of developing cardiovascular disease. However, studies of the causal effects between LDL-C and hypertension are limited by their observational study design, and genetic epidemiology studies of associations between LDL-C and hypertension are lacking, as are studies using data for Koreans. In this study, we confirmed the causal effect of LDL-C on hypertension using Korean chip data.MethodThe epidemiology and genotype data were collected from the Korean Genome and Epidemiology Study conducted by the Korea National Institute of Health and covered 20,701 subjects. Single-nucleotide polymorphisms associated with LDL-C were selected (p-value < 5 × 10− 8) from the Global Lipids Genetics Consortium database, and Mendelian randomization analysis (MRA) was performed with counted genetic risk scores and weighted genetic risk scores (WGRSs) for 24 single-nucleotide polymorphisms.ResultThe assumptions for MRA were statistically confirmed, and WGRSs showed a strong association with LDL-C. Interestingly, while the relationship between LDL-C and hypertension was not statistically significant in the observational study, MRA study demonstrated that the risk of hypertension increased as LDL-C increased in both men and women.ConclusionsThe results of this study confirmed that the relationship between LDL-C and hypertension is greatly influenced by genetic information.
Highlights
It is known in some studies that higher the LDL-C, the greater the risk of developing cardiovascular disease
The results of this study confirmed that the relationship between LDL-C and hypertension is greatly influenced by genetic information
Study population This study evaluated participants included in a ruralbased, cardiovascular disease association study (CAVA S) among individuals of the Korean Genome Epidemiology Study (KoGES) conducted by the Korea Centers for Disease Control and Prevention
Summary
It is known in some studies that higher the LDL-C, the greater the risk of developing cardiovascular disease. In accordance with Mendel’s second law, genetic factors can indirectly affect disease incidence through various risk factors, making it necessary to identify causal associations through Mendelian randomization analysis (MRA). To determine the genetic basis of a phenotype or to characterize gene function, conventional studies in genetic epidemiology seek to document associations between genetic and phenotype variations within a population. In such studies, genetic variations are assessed using markers, often single nucleotide polymorphisms (SNPs), and markers are considered informative if they show sufficient variation within a population and are of high enough prevalence to allow for meaningful comparisons. It is possible to exploit the random assignment of genes as a means of reducing confounding when examining exposure–disease associations: this is Mendelian randomization in the epidemiological context [7]
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