Related Experiment Video
Updated: Aug 1, 2025

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
Published on: September 18, 2020
Investigating the association between glycaemic traits and colorectal cancer in the Japanese population using
Akiko Hanyuda1,2, Atsushi Goto3,4, Ryoko Katagiri1
1Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
Abstract:
Observational studies suggest that abnormal glucose metabolism and insulin resistance contribute to colorectal cancer; however, the causal association remains unknown, particularly in Asian populations. A two-sample Mendelian randomisation analysis was performed to determine the causal association between genetic variants associated with elevated fasting glucose, haemoglobin A1c (HbA1c), and fasting C-peptide and colorectal cancer risk. In the single nucleotide polymorphism (SNP)-exposure analysis, we meta-analysed study-level genome-wide associations of fasting glucose (~ 17,289 individuals), HbA1c (~ 52,802 individuals), and fasting C-peptide (1,666 individuals) levels from the Japanese Consortium of Genetic Epidemiology studies. The odds ratios of colorectal cancer were 1.01 (95% confidence interval [CI], 0.99-1.04, P = 0.34) for fasting glucose (per 1 mg/dL increment), 1.02 (95% CI, 0.60-1.73, P = 0.95) for HbA1c (per 1% increment), and 1.47 (95% CI, 0.97-2.24, P = 0.06) for fasting C-peptide (per 1 log increment). Sensitivity analyses, including Mendelian randomisation-Egger and weighted-median approaches, revealed no significant association between glycaemic characteristics and colorectal cancer (P > 0.20). In this study, genetically predicted glycaemic characteristics were not significantly related to colorectal cancer risk. The potential association between insulin resistance and colorectal cancer should be validated in further studies.
Related Concept Videos
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Confounding in Epidemiological Studies
Cancer Survival Analysis
Bias in Epidemiological Studies
Dihybrid Crosses
Polygenic Traits

