We aimed to observe the combined effects of Gaussian graphical model (GGM)-derived dietary patterns and the gastric microbiome on the risk of gastric cancer (GC) in a Korean population. The study included 268 patients with GC and 288 healthy controls. Food intake was assessed using a 106-item semiquantitative food frequency questionnaire. GGMs were applied to derive dietary pattern networks. 16S rRNA gene sequencing was performed using DNA extracted from gastric biopsy samples. The fruit pattern network was inversely associated with the risk of GC for the highest vs. lowest tertiles in the total population (odds ratio (OR): 0.47; 95% confidence interval (CI): 0.28-0.77; p for trend = 0.003) and in females (OR: 0.38; 95% CI: 0.17-0.83; p for trend = 0.021). Males who had a low microbial dysbiosis index (MDI) and high vegetable and seafood pattern score showed a significantly reduced risk of GC (OR: 0.44; 95% CI: 0.22-0.91; p-interaction = 0.021). Females who had a low MDI and high dairy pattern score showed a significantly reduced risk of GC (OR: 0.23; 95% CI: 0.07-0.76; p-interaction = 0.018). Our novel findings revealed that vegetable and seafood pattern might interact with dysbiosis to attenuate the risk of GC in males, whereas the dairy pattern might interact with dysbiosis to reduce the GC risk in females.
Keywords: Gaussian graphical models; dietary patterns; gastric cancer; gastric microbiome; microbial dysbiosis.