A recent study has utilized bidirectional two-sample Mendelian randomization (MR) analysis to investigate the association between genetically predicted serum remnant cholesterol (RC) levels and the risk of cardiovascular diseases (CVDs). The analysis incorporated data from a genome-wide association study (GWAS) involving 115,078 individuals, identifying over 12 million single nucleotide polymorphisms (SNPs).
The research was conducted using data from the IEU OpenGWAS project and the FinnGen study, which included various CVD conditions such as ischemic heart disease and heart failure. The findings suggest a significant relationship between serum RC levels and CVD risk, highlighting the potential for genetic factors to influence cardiovascular health.
To validate these results, the study also applied MR analysis using data from the Coronary ARtery DIsease Genome wide Replication And Meta-analysis (CARDIoGRAM), which included nearly 87,000 participants. The MR analyses employed multiple methods, including inverse-variance-weighted and MR-Egger regression, to ensure robustness and reliability.
The research identified candidate instrumental variables (IVs) based on genome-wide significance, evaluating the causal connections with various CVD outcomes. The study concluded that serum RC levels may play a crucial role in the development of cardiovascular diseases, warranting further investigation into genetic predispositions.