Identification of Dietary Pattern Networks Associated with Gastric Cancer Using Gaussian Graphical Models: A Case-Control Study

Abstract

Gaussian graphical models (GGMs) are novel approaches to deriving dietary patterns that assess how foods are consumed in relation to one another. We aimed to apply GGMs to identify dietary patterns and to investigate the associations between dietary patterns and gastric cancer (GC) risk in a Korean population. In this case-control study of 415 GC cases and 830 controls, food intake was assessed using a 106-item semiquantitative food frequency questionnaire that captured 33 food groups. The dietary pattern networks corresponding to the total population contained a main network and four subnetworks. For the vegetable and seafood network, those who were in the highest tertile of the network-specific score showed a significantly reduced risk of GC both in the total population (OR = 0.66, 95% CI = 0.47–0.93, p for trend = 0.018) and in males (OR = 0.55, 95% CI = 0.34–0.89, p for trend = 0.012). Most importantly, the fruit pattern network was inversely associated with the risk of GC for the highest tertile (OR = 0.56, 95% CI = 0.38–0.81, p for trend = 0.002). The identified vegetable and seafood network and the fruit network showed a protective effect against GC development in Koreans.