Abstract

In the past, food-based dietary guidelines (FBDGs) were derived nearly exclusively by using systematic reviews on diet-health relationships and translating dietary reference values for nutrient intake into foods. This approach neglects many other implications that dietary recommendations have on society, the economy and environment. In view of pressing challenges, such as climate change and the rising burden of diet-related diseases, the simultaneous integration of evidence-based findings from different dimensions into FBDGs is required. Consequently, mathematical methods and data processing are evolving as powerful tools in nutritional sciences. The possibilities and reasons for the derivation of FBDGs via mathematical approaches were the subject of a joint workshop hosted by the German Nutrition Society (DGE) and the Federation of European Nutrition Societies (FENS) in September 2019 in Bonn, Germany. European scientists were invited to discuss and exchange on the topics of mathematical optimisation for the development of FBDGs and different approaches to integrate various dimensions into FBDGs. We concluded that mathematical optimisation is a suitable tool to formulate FBDGs finding trade-offs between conflicting goals and taking several dimensions into account. We identified a lack of evidence for the extent to which constraints and weights for different dimensions are set and the challenge to compile diverse data that suit the demands of optimisation models. We also found that individualisation via mathematical optimisation is one perspective of FBDGs to increase consumer acceptance, but the application of mathematical optimisation for population-based and individual FBDGs requires more experience and evaluation for further improvements.

Highlights

  • Food-based dietary guidelines (FBDGs) provide guidance for individuals, health professionals and policy makers on which foods should preferably be consumed in what amounts to maintain good health

  • The approaches from the Netherlands and France show that it seems to be a suitable method to derive FBDGs that are based on scientific evidence from several dimensions with conflicting as well as aligning goals

  • The use of mathematical optimisation can support the objectivity and transparency of approaches to develop FBDGs, when the decisions made in the optimisation models, the list of constraints and the equation of the objective function are clearly stated and described, helping to create comprehensive background material on the development of FBDGs

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Summary

Decision variables

Selection of the optimal combination of foods (e.g. food list and food quantities), Foods available: + Additional information per decision variable: which answers your question and is in Yes compliance with your requirements:.

Objective function
Workshop conclusions
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