Christina C. Dahm

Food substitution models for nutritional epidemiology

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

DOI

  • Daniel B Ibsen
  • Anne Sofie D Laursen
  • Anne Mette L Würtz
  • Christina C Dahm
  • Eric B Rimm, Departments of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health., Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
  • Erik T Parner
  • Kim Overvad
  • Marianne U Jakobsen, Division for Diet, Disease Prevention and Toxicology, National Food Institute, Technical University of Denmark, Søborg, Denmark.

The advantage of using specified substitution analysis in nutritional epidemiology has been clearly demonstrated in studies of macronutrient intake and disease risk. However, the method has not been widely applied in studies of food intake. The aim of this article is to describe and compare the interpretation and application of different food substitution models in epidemiologic studies on diet and disease development. Both theoretically and in the context of a specific example, we discuss methodologic issues to be considered, including modeling of food substitutions using diet at a single time point or at multiple time points (focusing on dietary changes), choice of substitution unit, adjustment for total energy intake, and adjustment for confounding. We argue that specified food substitution analyses can be used to identify optimal food composition of the diet and that these analyses are thus highly relevant to inform public health policy decision makers.

Original languageEnglish
Article numbernqaa315
JournalThe American Journal of Clinical Nutrition
Volume113
Issue2
Pages (from-to)294-303
Number of pages10
ISSN0002-9165
DOIs
Publication statusPublished - Feb 2021

    Research areas

  • nutritional epidemiology, habitual diet, dietary change, substitution analysis, cohort studies, replacement, compositional data, methodology

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