ArticleFrontiers in endocrinology2023
Gene-diet interaction analysis using novel weighted food scores discovers the adipocytokine signaling pathway associated with the development of type 2 diabetes.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 4 citations in OpenAlex.
- The Complex Gene-Carbohydrate Interaction in Type 2 Diabetes: Between Current Knowledge and Future Perspectives.Nutrients · 2025Review
- Gene-Diet Interactions in Diabetes Mellitus: Current Insights and the Potential of Personalized Nutrition.Genes · 2025Review
- Comprehensive insights into berberine's hypoglycemic mechanisms: A focus on ileocecal microbiome in db/db mice.Heliyon · 2024Article
- Analysis of type 2 diabetes mellitus-related genes by constructing the pathway-based weighted network.IET systems biologyArticle
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Authors and funding
5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: The influence of dietary patterns measured using Recommended Food Score (RFS) with foods with high amounts of antioxidant nutrients for Type 2 diabetes (T2D) was analyzed. Our analysis aims to find associations between dietary patterns and T2D and conduct a gene-diet interaction analysis related to T2D. Methods: Data analyzed in the current study were obtained from the Korean Genome and Epidemiology Study Cohort. The dietary patterns of 46 food items were assessed using a validated food frequency questionnaire. To maximize the predictive power of the RFS, we propose two weighted food scores, namely HisCoM-RFS calculated using the novel Hierarchical Structural Component model (HisCoM) and PLSDA-RFS calculated using Partial Least Squares-Discriminant Analysis (PLS-DA) method. Results: Both RFS (OR: 1.11; 95% CI: 1.03- 1.20; P = 0.009) and PLSDA-RFS (OR: 1.10; 95% CI: 1.02-1.19, P = 0.011) were positively associated with T2D. Mapping of SNPs (P < 0.05) from the interaction analysis between SNPs and the food scores to genes and pathways yielded some 12 genes (CACNA2D3, RELN, DOCK2, SLIT3, CTNNA2, etc.) and pathways associated with T2D. The strongest association was observed with the adipocytokine signalling pathway, highlighting 32 genes (STAT3, MAPK10, MAPK8, IRS1, AKT1-3, ADIPOR2, etc.) most likely associated with T2D. Finally, the group of the subjects in low, intermediate and high using both the food scores and a polygenic risk score found an association between diet quality groups with issues at high genetic risk of T2D. Conclusion: A dietary pattern of poor amounts of antioxidant nutrients is associated with the risk of T2D, and diet affects pathway mechanisms involved in developing T2D.
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