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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

Abstract

Purpose

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.

Methods

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

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.

Conclusion

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

Keywords

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

Abbreviations

GGM

Gaussian graphical models

EPIC

European Prospective Investigation into Cancer and Nutrition

PCA

Principal component analysis

HFD

High-fat dairy pattern

T2D

Type 2 diabetes

MI

Myocardial infarction

CVD

Cardiovascular diseases

HbA1c

Glycated hemoglobin

HDL-C

High-density lipoprotein cholesterol

LDL-C

Low-density lipoprotein cholesterol

TG

Triglyceride

CRP

C-reactive protein

Notes

Acknowledgements

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.

Funding

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)

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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

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