ArticleEuropean journal of nutrition2019
Gaussian graphical models identified food intake networks and risk of type 2 diabetes, CVD, and cancer in the EPIC-Potsdam study.
Article in European journal of nutrition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Nutritional Determinants of Type 2 Diabetes Mellitus in the European Union: A Systematic Review.Nutrients · 2025Pooled it
- Intake of dietary fats and fatty acids and the incidence of type 2 diabetes: A systematic review and dose-response meta-analysis of prospective observational studies.PLoS medicine · 2020Pooled it
- Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research.Nutrients · 2025Article
- Dietary patterns derived by Gaussian graphical models and metabolic profiles among overweight and obese individuals.Scientific reports · 2025Article
- Association betweenJournal of nutritional science · 2023Article
- Association between dietary intake networks identified through a Gaussian graphical model and the risk of cancer: a prospective cohort study.European journal of nutrition · 2022Article
- The Relationship between Dietary Patterns and High Blood Glucose among Adults Based on Structural Equation Modelling.Nutrients · 2022Article
- Saturated fats network identified using Gaussian graphical models is associated with metabolic syndrome in a sample of Iranian adults.Diabetology & metabolic syndrome · 2022Article
- Nutrition-wide association study of microbiome diversity and composition in colorectal cancer patients.BMC cancer · 2022Article
- Dietary networks identified by Gaussian graphical model and general and abdominal obesity in adults.Nutrition journal · 2021Article
- Article
- Effect of the Interaction between Dietary Patterns and the Gastric Microbiome on the Risk of Gastric Cancer.Nutrients · 2021Article
- Comorbidity Risk Score in Association with Cancer Incidence: Results from a Cancer Screenee Cohort.Cancers · 2020Article
- Article
- Meal and habitual dietary networks identified through Semiparametric Gaussian Copula Graphical Models in a German adult population.PloS one · 2018Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeThe aim of the study was to investigate the association between the previously identified Gaussian graphical models' (GGM) food intake networks and risk of major chronic diseases as well as intermediate biomarkers in the European Prospective Investigation into Cancer and nutrition (EPIC)-Potsdam cohort.
methodsIn this cohort analysis of 10,880 men and 13,340 women, adherence to the previously identified sex-specific GGM networks as well as principal component analysis identified patterns was investigated in relation to risk of major chronic diseases, using Cox-proportional hazard models. Associations of the patterns with intermediate biomarkers were cross-sectionally analyzed using multiple linear regressions.
resultsResults showed that higher adherence to the GGM Western-type pattern was associated with increased risk (Hazard Ratio: 1.55; 95% CI 1.13-2.15; P trend = 0.004) of type 2 diabetes (T2D) in women, whereas adherence to a high-fat dairy (HFD) pattern was associated with lower risk of T2D both in men (0.69; 95% CI 0.54-0.89; P trend < 0.001) and women (0.71; 95% CI: 0.53, 0.96; P trend = 0.09). Among PCA patterns, HFD pattern was associated with lower risk of T2D (0.74; 95% CI 0.58-0.95; P trend < 0.001) in men and bread and sausage pattern was associated with higher risk of T2D (1.79; 95% CI 1.29-2.48; P trend < 0.001) in women. Moreover, The GGM-HFD pattern was positively associated with HDL-C in men and inversely associated with C-reactive protein in women.
conclusionOverall, these results show that GGM-identified networks reflect dietary patterns, which could also be related to risk of chronic diseases.
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