A methodological framework for deriving the German food-based dietary guidelines 2024: Food groups, nutrient goals,...

Area of Science:

• Nutritional science and public health
• Computational modeling in dietetics
• Food policy and guideline development

Background:

• Diet optimization is increasingly used for developing food-based dietary guidelines (FBDGs) that balance health and sustainability.
• Systematic study and reporting of optimization model parameters are lacking, hindering consistent application.

Purpose of the Study:

• To establish a framework for diet optimization models in FBDG development.
• To define decision variables using hierarchical food classification.
• To explore mathematical forms for objective functions and nutrient goal incorporation.

Main Methods:

• Utilized FoodEx2 food groups (levels 3-7) as decision variables for German adult dietary data (n=10,419).
• Developed twelve models varying objective function types (linear/squared, relative/absolute deviation) and nutrient goal lists (allNUT-DRV, modNUT-DRV, modNUT-AR).
• Incorporated acceptability constraints and minimized deviation from average observed intakes.

Main Results:

• FoodEx2 food groups are suitable decision variables for diet optimization.
• Objective function type significantly impacted the number of modified food groups, with squared-absolute showing the largest changes (248 groups).
• All models met nutrient goals, but linear-relative models had the highest number of binding nutrient constraints.

Conclusions:

• The study provides a foundational framework for operationalizing dietary aspects within optimization models for FBDGs.
• Demonstrates the impact of methodological choices on diet optimization outcomes.
• Offers insights for refining diet optimization approaches in public health nutrition.