Use of Mathematical Optimization Models to Derive Healthy and Safe Fish Intake

Area of Science:

• Nutrition science
• Dietary recommendations
• Mathematical modeling

Background:

• Observed fish consumption in Denmark significantly differs from recommended levels, with only ~15% of the population meeting guidelines.
• Individual fish intake varies widely, making population-average recommendations insufficient for personalized dietary advice.

Purpose of the Study:

• To develop a method for personalized dietary recommendations based on individual body weight and current fish intake.
• To establish specific fish intake levels that meet recommendations for essential fatty acids (eicosapentaenoic acid, docosahexaenoic acid) and vitamin D.
• To ensure personalized recommendations do not exceed limits for contaminants like methyl mercury, dioxins, and polychlorinated biphenyls.

Main Methods:

• Developed two quadratic programming mathematical optimization models.
• Modeled personalized recommended fish intake to meet nutrient criteria and contaminant limits while minimizing deviation from individual observed intake.
• Generated recommended intake levels for 8 fish species for 3016 Danish adults based on national dietary survey data.

Main Results:

• Successfully modeled personalized dietary recommendations for fish intake.
• For women, proposed average weekly increases were 14g lean fish and 63g fatty fish.
• For men, proposed average weekly increases were 12g lean fish and 55g fatty fish.

Conclusions:

• Demonstrated the utility of quadratic programming for optimizing individual diets by balancing nutritional benefits and risks.
• This personalized approach has the potential to improve compliance with dietary guidelines by minimizing the need for drastic behavioral changes.