Food co-consumption network as a new approach to dietary pattern in non-alcoholic fatty liver disease

Abstract

Abstract Dietary patterns strongly correlate with non-alcoholic fatty liver disease (NAFLD), which is a leading cause of chronic liver disease in developed societies. In this study, we introduce a new definition, the co-consumption network (CCN), which depicts the common consumption patterns of food groups through network analysis. We then examine the relationship between dietary patterns and NAFLD by analyzing this network. We selected 1500 individuals living in Tehran, Iran, cross-sectionally. They completed a food frequency questionnaire and underwent scanning via the FibroScan for liver stiffness, using the CAP score. The food items were categorized into 40 food groups. We reconstructed the CCN using the Spearman correlation-based connection. We then created healthy and unhealthy clusters using the label propagation algorithm. Participants were assigned to two clusters using the hypergeometric distribution. Finally, we classified participants into two healthy NAFLD networks, and reconstructed the gender and disease differential CCNs. We found that the sweet food group was the hub of the proposed CCN, with the largest cliques of size 5 associated with the unhealthy cluster. The unhealthy module members had a significantly higher CAP score (253.7 ± 47.8) compared to the healthy module members (218.0 ± 46.4) (P < 0.001). The disease differential CCN showed that in the case of NAFLD, processed meat had been co-consumed with mayonnaise and soft drinks, in contrast to the healthy participants, who had co-consumed fruits with green leafy and yellow vegetables. The CCN is a powerful method for presenting food groups, their consumption quantity, and their interactions efficiently. Moreover, it facilitates the examination of the relationship between dietary patterns and NAFLD.