BACKGROUND: Nutrient profiling of foods is the science of ranking or classifying foods based on their nutrient composition. Most profiling systems use similar weighting factors across nutrients due to lack of scientific evidence to assign levels of importance to nutrients. OBJECTIVE: Our aim was to use a statistical approach to determine the nutrients that best explain variation in Healthy Eating Index (HEI) scores and to obtain β-coefficients for the nutrients for use as weighting factors for a nutrient-profiling algorithm. DESIGN: We used a cross-sectional analysis of nutrient intakes and HEI scores. PARTICIPANTS: Our subjects included 16,587 individuals from the National Health and Nutrition Examination Survey 2005-2008 who were 2 years of age or older and not pregnant. MAIN OUTCOME MEASURE: Our main outcome measure was variation (R(2)) in HEI scores. STATISTICAL ANALYSES: Linear regression analyses were conducted with HEI scores as the dependent variable and all possible combinations of 16 nutrients of interest as independent variables, with covariates age, sex, and ethnicity. The analyses identified the best 1-nutrient variable model (with the highest R(2)), the best 2-nutrient variable model, and up to the best 16-nutrient variable model. RESULTS: The model with 8 nutrients explained 65% of the variance in HEI scores, similar to the models with 9 to 16 nutrients, but substantially higher than previous algorithms reported in the literature. The model contained five nutrients with positive β-coefficients (ie, protein, fiber, calcium, unsaturated fat, and vitamin C) and three nutrients with negative coefficients (ie, saturated fat, sodium, and added sugar). β-coefficients from the model were used as weighting factors to create an algorithm that generated a weighted nutrient density score representing the overall nutritional quality of a food. CONCLUSIONS: The weighted nutrient density score can be easily calculated and is useful for describing the overall nutrient quality of both foods and diets.
BACKGROUND: Nutrient profiling of foods is the science of ranking or classifying foods based on their nutrient composition. Most profiling systems use similar weighting factors across nutrients due to lack of scientific evidence to assign levels of importance to nutrients. OBJECTIVE: Our aim was to use a statistical approach to determine the nutrients that best explain variation in Healthy Eating Index (HEI) scores and to obtain β-coefficients for the nutrients for use as weighting factors for a nutrient-profiling algorithm. DESIGN: We used a cross-sectional analysis of nutrient intakes and HEI scores. PARTICIPANTS: Our subjects included 16,587 individuals from the National Health and Nutrition Examination Survey 2005-2008 who were 2 years of age or older and not pregnant. MAIN OUTCOME MEASURE: Our main outcome measure was variation (R(2)) in HEI scores. STATISTICAL ANALYSES: Linear regression analyses were conducted with HEI scores as the dependent variable and all possible combinations of 16 nutrients of interest as independent variables, with covariates age, sex, and ethnicity. The analyses identified the best 1-nutrient variable model (with the highest R(2)), the best 2-nutrient variable model, and up to the best 16-nutrient variable model. RESULTS: The model with 8 nutrients explained 65% of the variance in HEI scores, similar to the models with 9 to 16 nutrients, but substantially higher than previous algorithms reported in the literature. The model contained five nutrients with positive β-coefficients (ie, protein, fiber, calcium, unsaturated fat, and vitamin C) and three nutrients with negative coefficients (ie, saturated fat, sodium, and added sugar). β-coefficients from the model were used as weighting factors to create an algorithm that generated a weighted nutrient density score representing the overall nutritional quality of a food. CONCLUSIONS: The weighted nutrient density score can be easily calculated and is useful for describing the overall nutrient quality of both foods and diets.