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European Journal of Epidemiology

, Volume 26, Issue 2, pp 145–156 | Cite as

Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics

  • Elisabeth Altmaier
  • Gabi Kastenmüller
  • Werner Römisch-Margl
  • Barbara Thorand
  • Klaus M. Weinberger
  • Thomas Illig
  • Jerzy Adamski
  • Angela Döring
  • Karsten Suhre
Nutritional Epidemiology

Abstract

Nutrition plays an important role in human metabolism and health. However, it is unclear in how far self-reported nutrition intake reflects de facto differences in body metabolite composition. To investigate this question on an epidemiological scale we conducted a metabolomics study analyzing the association of self-reported nutrition habits with 363 metabolites quantified in blood serum of 284 male participants of the KORA population study, aged between 55 and 79 years. Using data from an 18-item food frequency questionnaire, the consumption of 18 different food groups as well as four derived nutrition indices summarizing these food groups by their nutrient content were analyzed for association with the measured metabolites. The self-reported nutrition intake index “polyunsaturated fatty acids” associates with a decrease in saturation of the fatty acid chains of glycero-phosphatidylcholines analyzed in serum samples. Using a principal component analysis dietary patterns highly associating with serum metabolite concentrations could be identified. The first principal component, which was interpreted as a healthy nutrition lifestyle, associates with a decrease in the degree of saturation of the fatty acid moieties of different glycero-phosphatidylcholines. In summary, this analysis shows that on a population level metabolomics provides the possibility to link self-reported nutrition habits to changes in human metabolic profiles and that the associating metabolites reflect the self-reported nutritional intake. Moreover, we could show that the strength of association increases when composed nutrition indices are used. Metabolomics may, thus, facilitate evaluating questionnaires and improving future questionnaire-based epidemiological studies on human health.

Keywords

Dietary pattern Metabolomics Mass spectrometry Nutrition habits Food questionnaires Epidemiology 

Abbreviations

KORA

Cooperative Health Research in the Region of Augsburg

MONICA

Monitoring of Trends and Determinants in Cardiovascular Disease

DP

Dietary pattern

Notes

Acknowledgments

The KORA research platform and the MONICA Augsburg studies were initiated and financed by the Helmholtz Zentrum München—National Research Center for Environmental Health, which is funded by the German Federal Ministry of Education, Science, Research and Technology and by the State of Bavaria. The KORA study group consists of H.-E. Wichmann (speaker), R. Holle, J. John, T. Illig, C. Meisinger, A. Peters, and their coworkers, who are responsible for the design and conduct of the KORA studies. We gratefully acknowledge the contribution of all members of field staffs who were involved in planning and conducting the MONICA/KORA Augsburg studies. Finally, we express our appreciation to all study participants for donating their blood and time. This work was partially funded by the Federal Ministry of Education and Research within the SysMBo project (project number: 0315494A), by the research consortium GANI_MED (project number: 03IS2061A) and by the Deutsches Zentrum für Diabetesforschung e.V.

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Elisabeth Altmaier
    • 1
    • 2
  • Gabi Kastenmüller
    • 1
  • Werner Römisch-Margl
    • 1
  • Barbara Thorand
    • 3
  • Klaus M. Weinberger
    • 4
  • Thomas Illig
    • 3
  • Jerzy Adamski
    • 5
    • 6
  • Angela Döring
    • 3
  • Karsten Suhre
    • 1
    • 2
  1. 1.Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthNeuherbergGermany
  2. 2.Faculty of BiologyLudwig-Maximilians-UniversitätPlanegg-MartinsriedGermany
  3. 3.Institute of Epidemiology, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthNeuherbergGermany
  4. 4.Biocrates Life Sciences AGInnsbruckAustria
  5. 5.Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthNeuherbergGermany
  6. 6.Institute of Experimental Genetics, Life and Food Science Center WeihenstephanTechnische Universität MünchenFreising-WeihenstephanGermany

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