Differences in Dietary Patterns Identified by the Gaussian Graphical Model in Korean Adults With and Without a Self-Reported Cancer Diagnosis

Abstract

BackgroundThe synergistic effect of food groups on health outcomes is better captured by examining dietary patterns (DPs) than single food groups. Regarding this issue, a Gaussian graphical model (GGM) can identify pairwise correlations between food groups and adjust for the remaining items. However, the application of GGMs in the nutritional field has not been widely investigated, especially in Korean adults. ObjectiveThe aim of this study was to identify the major DPs of Korean adults by using a GGM and to examine the associations between the DP scores and prevalence of self-reported cancer. DesignThis cross-sectional study used baseline data from the 2007-2019 Cancer Screenee Cohort of the National Cancer Center, Korea. Participants/settingIn total, 10,777 Korean adults who completed a questionnaire regarding their general medical history, including clinical test results, and a validated food frequency questionnaire were included. Main outcome measuresThe main outcome measure was the prevalence of self-reported cancer at baseline. Statistical analysisDP networks were identified using a GGM. The GGM-identified networks were scored and categorized into tertiles, and their association with the prevalence of self-reported cancer was investigated using a multivariable logistic regression model. ResultsThe GGM identified the following 4 DP networks: principal, oil-sweet, meat, and fruit. After adjusting for covariates, the odds of moderate and high consumption of foods in the oil-sweet DP for participants who self-reported cancer were 25% and 34% lower than those for participants who did not report a cancer diagnosis (odds ratio [OR] = 0.75, 95% confidence interval [CI] = 0.62-0.90 and OR = 0.66, 95% CI = 0.53-0.81, respectively). Additionally, the odds of meat DP consumption in the self-reported cancer group was 29% lower than in participants who did not report a cancer diagnosis (OR = 0.71 and 95% CI = 0.57-0.88). In contrast, an increase in the odds of fruit DP consumption was observed for self-reported cancer participants (OR = 1.34 and 95% CI = 1.09-1.65). Similar results were observed among the female but not the male subjects. ConclusionsGGM is a novel method that can distinguish the direct pairwise correlation of food groups and control for the indirect effect of other foods. Future large-scale longitudinal population-based studies are needed to build on these findings in general populations.