European Journal of Nutrition

, Volume 58, Issue 4, pp 1673–1686 | Cite as

Gaussian graphical models identified food intake networks and risk of type 2 diabetes, CVD, and cancer in the EPIC-Potsdam study

  • Khalid IqbalEmail author
  • Lukas Schwingshackl
  • Anna Floegel
  • Carolina Schwedhelm
  • Marta Stelmach-Mardas
  • Clemens Wittenbecher
  • Cecilia Galbete
  • Sven Knüppel
  • Matthias B. Schulze
  • Heiner Boeing
Original Contribution



The 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.


In 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.


Results 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.


Overall, these results show that GGM-identified networks reflect dietary patterns, which could also be related to risk of chronic diseases.


Gaussian graphical models Dietary patterns Networks Western-type Type 2 diabetes Chronic diseases 



Gaussian graphical models


European Prospective Investigation into Cancer and Nutrition


Principal component analysis


High-fat dairy pattern


Type 2 diabetes


Myocardial infarction


Cardiovascular diseases


Glycated hemoglobin


High-density lipoprotein cholesterol


Low-density lipoprotein cholesterol




C-reactive protein



We thank Dr. Manuela Bergmann, Head of the Human Study Centre of the German Institute of Human Nutrition Potsdam-Rehbrücke for the contribution to the study design and leading the underlying processes of data generation.

Author contributions

The authors’ responsibilities were as follows: (1) KI and HB conceived the idea and designed analysis plan; (2) KI conducted statistical analysis and wrote manuscript (3) SK provided support in statistical analysis and LS provided support in manuscript writing); (4) KI and HB had primary responsibility for final content; (5) AF, CS, MS, CW, CG, SK, and MBS contributed to data interpretation and provided critical comments on the manuscript. All authors read and approved the final manuscript.


EPIC-Potsdam cohort supported by the German Federal Ministry of Science (01 EA 9401), the European Union (SOC 95201408 05F02), and the current study supported by Higher Education Commission of Pakistan and German Academic Exchange Program, Germany (no. 50015451 to KI).

Compliance with ethical standards

Conflict of interest

Authors declare no conflict of interest.

Supplementary material

394_2018_1714_MOESM1_ESM.docx (850 kb)
Supplementary material 1 (DOCX 849 KB)


  1. 1.
    Lim SS, Vos T, Flaxman AD, Danaei G et al (2012) A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380(9859):2224–2260. CrossRefGoogle Scholar
  2. 2.
    Hu FB (2002) Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 13(1):3–9CrossRefGoogle Scholar
  3. 3.
    Iqbal K, Buijsse B, Wirth J, Schulze MB et al (2016) Gaussian graphical models identify networks of dietary Intake in a German adult population. J Nutr 146(3):646–652. CrossRefGoogle Scholar
  4. 4.
    Schulze MB, Hoffmann K, Kroke A, Boeing H (2001) Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. Br J Nutr 85(3):363–373CrossRefGoogle Scholar
  5. 5.
    Riboli E, Kaaks R (1997) The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition. Int J Epidemiol 26(Suppl 1):S6-14Google Scholar
  6. 6.
    Boeing H, Korfmann A, Bergmann MM (1999) Recruitment procedures of EPIC-Germany. European Investigation into Cancer and Nutrition. Ann Nutr Metab 43(4):205–215. doi:12787CrossRefGoogle Scholar
  7. 7.
    Kroke A, Klipstein-Grobusch K, Voss S, Moseneder J et al (1999) Validation of a self-administered food-frequency questionnaire administered in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study: comparison of energy, protein, and macronutrient intakes estimated with the doubly labeled water, urinary nitrogen, and repeated 24-h dietary recall methods. Am J Clin Nutr 70(4):439–447CrossRefGoogle Scholar
  8. 8.
    Stefan N, Fritsche A, Weikert C, Boeing H et al (2008) Plasma fetuin-A levels and the risk of type 2 diabetes. Diabetes 57(10):2762–2767. CrossRefGoogle Scholar
  9. 9.
    Jacobs S, Kroger J, Floegel A, Boeing H et al (2014) Evaluation of various biomarkers as potential mediators of the association between coffee consumption and incident type 2 diabetes in the EPIC-Potsdam Study. Am J Clin Nutr 100(3):891–900. CrossRefGoogle Scholar
  10. 10.
    Mendez MA, Popkin BM, Buckland G, Schroder H et al (2011) Alternative methods of accounting for underreporting and overreporting when measuring dietary intake-obesity relations. Am J Epidemiol 173(4):448–458. CrossRefGoogle Scholar
  11. 11.
    Gottschald M, Knuppel S, Boeing H, Buijsse B (2016) The influence of adjustment for energy misreporting on relations of cake and cookie intake with cardiometabolic disease risk factors. Eur J Clin Nutr 70(11):1318–1324. CrossRefGoogle Scholar
  12. 12.
    Huang TT, Roberts SB, Howarth NC, McCrory MA (2005) Effect of screening out implausible energy intake reports on relationships between diet and BMI. Obesity Res 13(7):1205–1217. CrossRefGoogle Scholar
  13. 13.
    David CR (1972) Regression models and life tables (with discussion). J R Stat Soc 34:187–220Google Scholar
  14. 14.
    Willett WC, Howe GR, Kushi LH (1997) Adjustment for total energy intake in epidemiologic studies. The Am J Clin Nutr 65 (4 Suppl):1220S–1228S (discussion 1229S-1231S)CrossRefGoogle Scholar
  15. 15.
    Greenland S (1995) Dose-response and trend analysis in epidemiology: alternatives to categorical analysis. Epidemiology (Cambridge Mass) 6(4):356–365CrossRefGoogle Scholar
  16. 16.
    Schwedhelm C, Iqbal K, Knuppel S, Schwingshackl L et al (2018) Contribution to the understanding of how principal component analysis-derived dietary patterns emerge from habitual data on food consumption. Am J Clin Nutr 107(2):227–235. CrossRefGoogle Scholar
  17. 17.
    Jannasch F, Kroger J, Schulze MB (2017) Dietary patterns and type 2 diabetes: a systematic literature review and meta-analysis of prospective studies. J Nutr. Google Scholar
  18. 18.
    McEvoy CT, Cardwell CR, Woodside JV, Young IS et al (2014) A posteriori dietary patterns are related to risk of type 2 diabetes: findings from a systematic review and meta-analysis. J Acad Nutr Diet 114(11):1759–1775 e1754. CrossRefGoogle Scholar
  19. 19.
    Fung TT, Schulze M, Manson JE, Willett WC et al (2004) Dietary patterns, meat intake, and the risk of type 2 diabetes in women. Archiv Intern Med 164(20):2235–2240. CrossRefGoogle Scholar
  20. 20.
    Pastorino S, Richards M, Pierce M, Ambrosini GL (2016) A high-fat, high-glycaemic index, low-fibre dietary pattern is prospectively associated with type 2 diabetes in a British birth cohort. Br J Nutr 115(9):1632–1642. CrossRefGoogle Scholar
  21. 21.
    van Dam RM, Rimm EB, Willett WC, Stampfer MJ et al (2002) Dietary patterns and risk for type 2 diabetes mellitus in U.S. men. Ann Intern Med 136(3):201–209CrossRefGoogle Scholar
  22. 22.
    Montonen J, Knekt P, Härkänen T, Järvinen R et al (2005) Dietary Patterns and the Incidence of Type 2 Diabetes. Am J Epidemiol 161(3):219–227. CrossRefGoogle Scholar
  23. 23.
    Schwingshackl L, Hoffmann G, Lampousi AM, Knuppel S et al (2017) Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol. Google Scholar
  24. 24.
    Micha R, Wallace SK, Mozaffarian D (2010) Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus. A Syst Rev Meta Anal 121(21):2271–2283. Google Scholar
  25. 25.
    Halton TL, Willett WC, Liu S, Manson JE et al (2006) Potato and french fry consumption and risk of type 2 diabetes in women. Am J Clinl Nutr 83(2):284–290CrossRefGoogle Scholar
  26. 26.
    de Munter JSL, Hu FB, Spiegelman D, Franz M et al (2007) Whole grain, bran, and germ intake and risk of type 2 diabetes: a prospective cohort study and systematic review. PLoS Med 4(8):e261. CrossRefGoogle Scholar
  27. 27.
    Aune D, Norat T, Romundstad P, Vatten LJ (2013) Whole grain and refined grain consumption and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of cohort studies. Eur J Epidemiol 28(11):845–858. CrossRefGoogle Scholar
  28. 28.
    Feskens EJ, Sluik D, van Woudenbergh GJ (2013) Meat consumption, diabetes, and its complications. Curr Diabetes Rep 13(2):298–306. CrossRefGoogle Scholar
  29. 29.
    Bechthold A, Boeing H, Schwedhelm C, Hoffmann G et al. (2017) Food groups and risk of coronary heart disease, stroke and heart failure: a systematic review and dose-response meta-analysis of prospective studies. Crit Rev Food Sci Nutr, pp 1–20.
  30. 30.
    Schwingshackl L, Schwedhelm C, Hoffmann G, Knuppel S et al (2017) Food groups and risk of hypertension: a systematic review and dose-response meta-analysis of prospective studies. Adv Nutr (Bethesda MD) 8(6):793–803. CrossRefGoogle Scholar
  31. 31.
    Wang X, Bao W, Liu J, Ouyang YY et al (2013) Inflammatory markers and risk of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care 36(1):166–175. CrossRefGoogle Scholar
  32. 32.
    Iqbal K, Schwingshackl L, Gottschald M, Knuppel S et al (2017) Breakfast quality and cardiometabolic risk profiles in an upper middle-aged German population. Eur J Clin Nutr 71(11):1312–1320. CrossRefGoogle Scholar
  33. 33.
    Ericson U, Hellstrand S, Brunkwall L, Schulz CA et al (2015) Food sources of fat may clarify the inconsistent role of dietary fat intake for incidence of type 2 diabetes. Am J Clin Nutr 101(5):1065–1080. CrossRefGoogle Scholar
  34. 34.
    Diaz-Lopez A, Bullo M, Martinez-Gonzalez MA, Corella D et al (2016) Dairy product consumption and risk of type 2 diabetes in an elderly Spanish Mediterranean population at high cardiovascular risk. Eur J Nutr 55(1):349–360. CrossRefGoogle Scholar
  35. 35.
    Kirii K, Mizoue T, Iso H, Takahashi Y et al (2009) Calcium, vitamin D and dairy intake in relation to type 2 diabetes risk in a Japanese cohort. Diabetologia 52(12):2542–2550. CrossRefGoogle Scholar
  36. 36.
    Eussen SJPM, van Dongen MCJM, Wijckmans N, den Biggelaar L et al (2016) Consumption of dairy foods in relation to impaired glucose metabolism and type 2 diabetes mellitus: the Maastricht Study. Br J Nutr 115(8):1453–1461. CrossRefGoogle Scholar
  37. 37.
    Tong X, Dong JY, Wu ZW, Li W et al (2011) Dairy consumption and risk of type 2 diabetes mellitus: a meta-analysis of cohort studies. Eur J Clin Nutr 65(9):1027–1031. CrossRefGoogle Scholar
  38. 38.
    Aune D, Norat T, Romundstad P, Vatten LJ (2013) Dairy products and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of cohort studies. Am J Clin Nutr 98(4):1066–1083. CrossRefGoogle Scholar
  39. 39.
    Forouhi NG (2015) Association between consumption of dairy products and incident type 2 diabetes—insights from the European Prospective Investigation into Cancer study. Nutr Rev 73(Suppl 1):15–22. CrossRefGoogle Scholar
  40. 40.
    Kroger J, Zietemann V, Enzenbach C, Weikert C et al (2011) Erythrocyte membrane phospholipid fatty acids, desaturase activity, and dietary fatty acids in relation to risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Am J Clin Nutr 93(1):127–142. CrossRefGoogle Scholar
  41. 41.
    Yakoob MY, Shi P, Willett WC, Rexrode KM et al (2016) Circulating biomarkers of dairy fat and risk of incident diabetes mellitus among men and women in the united states in two large prospective cohorts. Circulation 133(17):1645–1654. CrossRefGoogle Scholar
  42. 42.
    Forouhi NG, Koulman A, Sharp SJ, Imamura F et al (2014) Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study. Lancet Diabetes Endocrinol 2(10):810–818. CrossRefGoogle Scholar
  43. 43.
    Schulze MB, Hoffmann K, Kroke A, Boeing H (2003) An approach to construct simplified measures of dietary patterns from exploratory factor analysis. Br J Nutr 89(3):409–419. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Khalid Iqbal
    • 1
    Email author
  • Lukas Schwingshackl
    • 1
    • 2
  • Anna Floegel
    • 1
    • 3
  • Carolina Schwedhelm
    • 1
    • 2
  • Marta Stelmach-Mardas
    • 1
    • 4
  • Clemens Wittenbecher
    • 5
  • Cecilia Galbete
    • 2
    • 5
  • Sven Knüppel
    • 1
  • Matthias B. Schulze
    • 2
    • 5
  • Heiner Boeing
    • 1
    • 2
  1. 1.Department of EpidemiologyGerman Institute of Human Nutrition Potsdam-RehbrueckeNuthetalGermany
  2. 2.NutriAct-Competence Cluster Nutrition Research Berlin-PotsdamPotsdamGermany
  3. 3.Leibniz Institute for Prevention Research and Epidemiology-BIPSBremenGermany
  4. 4.Department of Gastroenterology and Metabolic DiseasesPoznan University of Medical SciencesPoznanPoland
  5. 5.Department of Molecular EpidemiologyGerman Institute of Human Nutrition Potsda-RehbrueckeNuthetalGermany

Personalised recommendations