In this study, we utilized the Mendelian Randomization (MR) method to estimate the relationship between the exposure and the outcome of interest using known genetic variants related to the exposure. The MR method operates under the following assumptions: (i) the selected genetic instruments are associated with the exposure of interest; (ii) they are not associated with any confounding factors in the relationship between the exposure and the outcome; (iii) the association between the genetic instruments and the outcome is solely through the exposure of interest. To ensure rigorous selection of genetic instruments, we systematically obtained instrumental variables for fasting glucose, HbA1c, and fasting C-peptide through previously published GWASs. We identified 34, 43, and 17 instruments for fasting glucose, HbA1c, and fasting C-peptide, respectively, using a threshold of P < 5 × 10–8 and minor allele frequency > 0.01 in the East Asians of the 1000 Genomes Project.
To enhance the validity of our approach, we conducted two-sample MR analyses utilizing two independent study samples to estimate the single nucleotide polymorphism (SNP)-risk factors (fasting glucose, HbA1c, and fasting C-peptide) and SNP-outcome (colorectal cancer) associations. Additionally, we employed MR-Egger regression as a sensitivity analysis to examine whether selected SNPs, such as body mass index (BMI), smoking, alcohol intake, and physical inactivity, could be confounders of the association between glycaemic metabolism and colorectal cancer.
We also calculated the proportion of explained variance and F-statistics, and performed power calculations for MR analyses. Finally, we obtained data for colorectal cancer from both individual-level GWAS and summary-level GWAS data.
Overall, our study utilized rigorous methodologies to estimate the relationship between the exposure and outcome of interest using the MR method, ultimately providing valuable insights into the relationship between glycaemic metabolism and colorectal cancer.
