Using Essential Nutrients to Identify Nutrient Dense Foods for the
Mexican-American Population of Maricopa County
There are many persistent health disparities in underserved communities. There, patients have traditionally received a lower standard of care than more prosperous areas of the country. As a SOMA student, you can directly influence the correction of the disparities and ensure culturally-competent medical care is provided in the areas that need it most.
Working on a Community Campus, you will gain a greater understanding of the healthcare deficiencies among patients from different cultural groups and locations. This is an eye-opening experience that can provide knowledge and insight, which will inform your medical career moving forward. You can engage in seeing how the healthcare system is lacking, and how practitioners of osteopathic medicine can make positive changes for the future in helping these patients.
Our mission to serve is not just to provide quality, affordable healthcare – but to influence long-term change and systematic solutions.
Jesse Miller B.S.E, Joshua Nead B.S., and Joy H. Lewis, DO, PhD
TNutrient Density is an emerging term in public health. The concept has gained popularity and aids in developing nutrient dense, cost effective diets, especially for demographics at-risk for obesity and other diet-related morbidities. This study proposes an objective nutrient density definition based on essential nutritional dietary requirements.
This study defines nutrient density (ND) as the total essential nutrient daily value (DV%) per 100g and per Reference Amounts Customarily Consumed (RACC). The equation calculates the DV% for all 35 essential nutrients, using the Institute of Medicine (IOM) Recommended Daily Intake for 18-30 year. In the United States Department of Agriculture’s Food and Nutrient Database for Dietary Studies version 4.1, nutrient densities were calculated for 2,566 whole foods consumed by the Mexican-American population in Maricopa County. Scores were ranked and compared to energy density (ED). Food group ND means were calculated to compare and rank food groups.
The ND/RACC calculation was used to compare foods. The top 5 were: fish, dark leafy greens, corn, guava, and beans. Using the ND/ED ratio, the top 5 foods were: Cilantro, dark leafy greens, chili peppers, fish, non-starchy vegetables. Food groups were analyzed against each other using the ND/RACC value and ranked as follows: meat and protein; nuts and seeds; breads, cereals, and grains; dairy; vegetables; fruits; fats and oils; herbs and spices. Food groups ranked using ND/ED were as follows: herbs and spices; vegetables; meat and proteins; dairy; fruits; nuts and seeds; breads, cereals, and grains; fats and oils.
Other nutrient density definitions select and weight nutrients using expert and personal opinion. The proposed definition provides objective standards, without bias, for constructing nutritional guidance based on nutrient density. With dietary costs and food preferences, community health centers (CHC) can use ND and ED to create cost conscientious dietary guidance. This model provides a tool for optimizing a CHC’s population’s ability to meet essential nutritional requirements within their caloric needs by helping identify foods highest in essential nutrients, while remaining flexible enough to apply to diverse sub-populations.
We have identified the following areas presenting limitations to our study.
The study was designed using recommended daily intake (RDI) values for 19-30 years old males. This selection limits our study relevance to males within this age range.
Nutrients considered essential to human metabolism were selected as the independent variables. This selection limits our study to these 35 nutrients. Macronutrients are not accounted for, along with other non-essential nutrients, which may or may not have beneficial effects on health. Our decision was to limit the nutrients to those required in the human diet for optimal metabolic functioning.
After developing the nutrient density (ND) algorithm, we considered the following limitations of our calculation. The original algorithm calculated the ΣDV%/100g. Using a calculation per 100g limits comparison of foods consumed in different quantities. The reference amount of 100g was selected for simplicity in comparing to energy density. Energy density (ED) is defined as kcal/100g.
To compare foods, ND was calculated per ED and reference amounts customarily consumed (RACC). This allowed for comparison based on serving size and nutrients per calorie. The limitations of these calculations lie in their ability to measure distribution of nutrients.
Distribution of Nutrients
As mentioned previously, the algorithm and score does not account for distribution across essential nutrients or essential amino acids. The algorithm currently cannot identify foods with a large amount of one nutrient, food with even distribution of many nutrients, or foods with complete proteins.
Foods are classified into 19 food groups in the United States Department of Agriculture’s Food and Nutrient Database for Dietary Studies version 4.1. While filtering the foods list, we identified diverse foods, in various preparations, categorized in the same food group. While some food groups contained more diverse foods, such as vegetables and fruits, others, such as beef, varied little. To account for this limitation we propose further differentiating food groups for better analysis.
To identify foods commonly consumed in the Mexican-American population in Maricopa County, we used a source looking at all of Maricopa County. To account for the variation in Adelante demographics, a food frequency questionnaire (FFQ) should be administered to the Adelante population based.
The main limitation to our study is in validating the algorithm. Other studies attempting to create nutrient density indices used USDA nutritional guidelines to construct their algorithm and validated their score against those guidelines. Our interest is in creating an objective nutrient density score that is validated against objective standards. To overcome this limitation, we will meet with a nutritionist to develop an algorithm validation, followed by application and validation at the CHC level.