Navigating the Future of Dietary Guidelines

Food-based dietary guidelines (FBDGs) help people understand how to eat healthily. They offer advice on food choices to help meet dietary reference values (DRVs) and cut down the risk of diseases linked to poor diets. Recently, these guidelines have started to include environmental aspects, making it more difficult to define what the ideal food intake should be for different food groups.

#Diet Optimization: A Complex Task

Creating a good diet plan that takes into account many factors requires diet optimization. This process looks for the best mix of food groups that can meet both health and personal needs. It tries to balance food groups, which can have conflicting or supportive roles, while also following certain rules to either minimize or maximize some objectives. Diet optimization tools have been used effectively in countries like Australia, France, and the Netherlands for developing their FBDGs.

#The Role of FoodEx2 in Dietary Guidelines

The success of diet optimization often depends on the food groups chosen for the analysis. The European Food Safety Authority (EFSA) has created a detailed system called FoodEx2 to classify food. FoodEx2 offers a hierarchy that makes it easy to analyze food intake in European countries. However, how well these food classifications work in diet optimization models is still a question.

The tool used for optimization often looks at how well the observed diet matches with the standard diet expected from the guidelines. The goal is to make the optimized diet acceptable to the population's current eating habits. There are also constraints in place to ensure the recommendations remain realistic, based on what people usually consume.

#Decision Variables in FBDGs

In diet optimization, the decision variables are the food groups targeted for improvement based on consumption patterns. For the German guidelines, FoodEx2 provided food intake data that could be compared internationally. The system contains a hierarchy with various levels, starting with general categories and moving to more specific ones. The most detailed level provides the most precise data, which can often come from national surveys.

However, FoodEx2 does not always distinguish clearly between whole grain and refined grain products, which can lead to confusion in identifying these types of foods. To streamline the focus on whole grains, some categories were modified for clarity. This also happened for processed meats, where a new food group was created to separate processed from unprocessed meats.

#Reporting Diets Using Food Groups

When dealing with many food groups, stating the results of diet optimization can become complicated. Therefore, a simplified list of food groups was agreed upon for reporting, making it easier to communicate the recommendations. The main food groups included water, coffee, tea, vegetables, fruits, legumes, nuts, dairy, and various types of meat.

Some guidelines aim to limit so-called "discretionary foods," which are foods high in sugars, fats, and empty calories. These often contribute significantly to daily energy intake but offer little nutritional value. To manage these, the report will also summarize the energy share from these discretionary foods.

#Choosing the Right Level in the FoodEx2 System

When deciding what level of the FoodEx2 system to use for the optimization model, it’s important to have enough detail for the food groups that will be reported. For instance, if vegetable oils are discussed in the guidelines, they need to be clearly distinguished from other forms of fats. The analysis showed that using level 3 of FoodEx2 provided enough detail for effective reporting.

#Linking Food Choices to Nutrient Data

The nutrient content of different food groups is available through the EFSA Comprehensive European Food Consumption Database. This data helps to establish what people are eating in Germany based on surveys. It considers demographics and socioeconomic factors to accurately reflect the eating habits of the German population.

Nutritional information is gathered from national databases, which also classify foods differently than FoodEx2. To align these two sources, a matching process was conducted. This also included adjusting for any missing data on specific nutrients.

#Acceptability Constraints in Diet Optimization

To ensure that the optimized diet is realistic, certain limits are set based on observed eating habits. These limits, known as acceptability constraints, help define a space where the model can propose changes. Any optimized diet must stay within the boundaries set by the 5th and 95th percentiles of food intake, keeping the recommendations grounded in what people actually consume.

Special exceptions for discretionary food categories were made, recognizing their complex nutrient profiles, which can offer varying nutritional benefits.

#Objective Functions and Nutrient Goals

In diet optimization, different mathematical approaches can be employed to minimize the difference between the observed diet and the optimized one. Four methods were examined that change how the model looks at these differences, either relative to the observed diet or in absolute quantities.

Different nutrient goals were also evaluated, based on recommended daily values for adults in Germany. These nutrient goals can vary in terms of what is considered acceptable. For example, some components may have upper limits, while others have lower limits to ensure a balanced nutrient intake.

#Impacts of Food Choices on Diet Optimization

When optimizing diets, the mathematical approach and nutrient goals have a direct effect on the outcomes. For example, shifting to a more restrictive set of nutrient goals usually leads to less deviation from the expected diet but can also introduce challenges.

Models that applied all nutrient goals often showed a greater discrepancy from the observed diet, while those that modified the nutrient goals tended to yield results that better matched typical consumption patterns. Keeping flexibility in the model by excluding certain nutrient goals could lead to more practical recommendations.

#Conclusion: Building Effective Dietary Guidelines

The study of diet optimization has revealed various strategies to build successful food-based dietary guidelines. It emphasizes the importance of using clear food classifications and well-defined parameters while also considering the typical consumption patterns of the population.

The hierarchical food classification system like FoodEx2 is useful for ensuring consistency and transparency in dietary recommendations. Understanding how to best implement these systems can support healthier dietary choices while also addressing sustainability and public health needs.

#Future Directions

Further research will focus on expanding the optimization model to include additional dimensions, such as health impacts and environmental considerations. This would help create FBDGs that reflect not just dietary needs but also broader societal goals.

Overall, the findings provide valuable insights for policymakers and nutritionists. They show that careful consideration of food classifications, mathematical models, and nutrient goals can significantly improve dietary guidelines' effectiveness.