The aim of this study was to elucidate the complex interrelationships among dietary intake, demographics, and the risk of comorbidities. We applied a Gaussian graphical model to calculate the dietary scores of the participants. The network structure of dietary intake, demographics, and comorbidities was estimated in a mixed graphical model. The centrality indices of the nodes (strength (S), closeness (C), and betweenness (B)) were measured to identify the central node. Multinomial logistic regression was used to examine the association between the factors and comorbidities. Among 7423 participants, the strongest pairwise interactions were found between sex and smoking (1.56), sex and employment (0.66), sex and marital status (0.58), marital status and income (0.65), and age and employment (0.58). Among the factors in the network, sex played a central role (S = 4.63, C = 0.014, B = 41), followed by age (S = 2.81, C = 0.013, B = 18), smoking (S = 2.72, C = 0.013, B = 0), and employment (S = 2.17, C = 0.014, B = 22). While the odds of hypertension and diabetes were significantly higher among females than males, an inverse association was observed between high cholesterol and moderate chronic kidney disease. Among these factors, dietary intake was not a strongly interacting factor in the network, whereas age was consistently associated with the comorbidities of hypertension, high cholesterol, diabetes, and chronic kidney disease.
Keywords: Gaussian graphical model; mixed graphical model; network analysis.