The Tufts Food Compass

A novel nutrient profiling system for science-based assessment of the overall healthfulness of foods

Nutrient profiling systems (NPS) aim to discriminate the healthfulness of foods for front-of-package labeling, warning labels, taxation, company ratings, and more. However, existing NPS often assess relatively few nutrients and ingredients, use inconsistent criteria across food categories, and have not incorporated the newest science.

To address these gaps, we developed the Food Compass, a comprehensive NPS incorporating an expanded assessment of nutrient and ingredient characteristics, additional food components, and processing parameters in a uniform fashion across food categories to help guide healthy food choices; industry reformulations; environmental, social and corporate governance metrics; and policy actions.

We scored 54 attributes across 9 health-relevant domains: nutrient ratios, vitamins, minerals, food ingredients, additives, processing, specific lipids, fiber and protein, and phytochemicals. Important novel features of Food Compass include:

• Equally considering healthful vs. harmful factors in foods (many existing systems focus on harmful factors);
• Incorporating cutting-edge science on nutrients, food ingredients, processing characteristics, phytochemicals, and additives (existing systems focus largely on just a few nutrients); and
• Objectively scoring all foods, beverages, and even mixed dishes and meals using one consistent score (existing systems subjectively group and score foods differently).

Our findings indicate that the Food Compass is a credible, transparent tool for science-based assessment of the overall healthfulness of diverse foods, snacks, beverages, and mixed dishes. The publicly available scoring algorithm can inform a more nuanced approach to help guide consumer behavior, food policy, scientific research, industry reformulations, and socially focused investment decisions.

Aim One

To continue to refine, test, and validate the Tufts Food Compass, including for applications in which limited nutrient data are available.

Aim Two

To assess the healthfulness of foods, beverages, and mixed dishes in expanded databases, as well as food choices, such as supermarket baskets, diets, and food company portfolios.