An Alternative Nutrient Rich Food Index (NRF-ai) Incorporating Prevalence of Inadequate and Excessive Nutrient Intake

Area of Science:

• Nutrition Science
• Public Health
• Dietary Assessment

Background:

• Existing nutrient profiling models often assign equal importance to all nutrients, regardless of their prevalence.
• The selection of nutrients in current models can be arbitrary, potentially overlooking critical dietary gaps.
• There is a need for a more nuanced approach to nutrient profiling that reflects population-level intake patterns.

Purpose of the Study:

• To develop an alternative Nutrient Rich Food index (NRF-ai) for Australian adults that incorporates the prevalence of inadequate and excessive nutrient intake.
• To create nutrient weighting factors based on a distance-to-target method using national health survey data.
• To apply the NRF-ai model to various food groups and explore its utility in addressing specific nutrient deficiencies and sustainability aspects.

Main Methods:

• Developed the NRF-ai index using data from the Australian Health Survey.
• Calculated nutrient weighting factors based on the proportion of the population with usual intake below Estimated Average Requirements (EARs).
• Created separate models for males and females, and specific age groups, to account for differing nutrient needs.

Main Results:

• The NRF-ai model successfully identified foods rich in nutrients with low population intake.
• Separate models for different demographic groups highlighted variations in nutrient requirements and intake.
• The model's application demonstrated its potential to address nutrient gaps in subgroups, such as older adults.

Conclusions:

• The NRF-ai index offers a more targeted approach to nutrient profiling by focusing on population nutrient inadequacies.
• This method can guide food selection to improve public health nutrition and address specific dietary shortfalls.
• The NRF-ai model can be extended to evaluate food sustainability, including affordability and environmental impact.