Development of a standardized measure to assess food quality: a proof of concept – Nutrition Journal

This study aimed to develop a new unit of measure that accounts for both the caloric and micronutrient density of foods and to demonstrate the usability of this novel measure in assessing food quality. The qCaln was the standardized unit developed based on the caloric density and the amount and distribution of micronutrients per 100 g in a particular food. Results showed that food items in any food group can be analyzed based on their qCaln values. In addition, through calculating a qCaln ratio for each food item, the caloric and micronutrient contents of a food were compared with the food’s energy density (caloric value) in one single measure. Using the qCaln ratio, higher quality foods could be differentiated easily from lower quality foods. More specifically, the qCaln ratios of food items within each of the food groups, including vegetables, fruits/fruit juices, milk/dairy products, breads/cereals, and meats/meat alternatives, were compared to each other in this paper.

The advantages of the proposed approach over other existing food quality measures and nutrient profiling approaches lies in simplifying complex nutrient composition data into a singular based score that reflects both nutrient and energy density. The definition of nutrient dense foods have expanded over the years due to numerous attempts by researchers, regulatory agencies, and food companies to identify adequate criteria for ranking foods based on their nutrient composition. Some of the models highlighted the importance of limiting certain nutrients, such as total fat, saturated fat, sugar, or sodium, that were associated with adverse health outcomes, when defining nutrient-density [34, 35]. Other approaches emphasized recommended or desirable nutrients that may not be adequately consumed by population groups yet have favorable health benefits, including vitamins, minerals, and fiber [36], or included a combination of both, nutrients to be promoted and others that need to be limited [4, 6]. Despite the importance of these attempts in advancing the field of nutrient profiling, the majority of the approaches fell short of identifying a single measure to encompass the complexity of food and its nutritional value.

The selection of the 10 micronutrients that were included in the calculation of the qCaln accounted for two groups of nutrients: those that are not consumed in sufficient amounts and contribute, in part, to micronutrient deficiencies across various population groups [13–17] and less desirable nutrients that are consumed in abundance worldwide such as saturated fat, sugar, and sodium [18–21]. In addition, the number and choice of micronutrients considered in the qCaln calculation were similar, albeit not identical, to other nutrient profiling methods such as the NNR score, [26, 28], the NQI) [27] the RRRr food scores [22] and nutritional adequacy score of individual foods and limited nutrient score (SAIN/LIM system). Furthermore, the choice of the micronutrients in the present study was not dependent on nutrients that are consumed in low or high amounts in a specific country or context, but rather included a more comprehensive and global approach. This is in line with the recommendations from Drewnowski and Fulgoni, 2008 [4], who urged that the choice of nutrients included in nutrient profiling methods should be based on diets from different countries rather than focusing on a specific country or context, such as the United States. In fact, the formula for calculating qCaln presented in this study can be easily expanded or reduced based on objective research designed to identify the optimum number of micronutrients in nutrient profiling techniques.

It is worth noting that the percent daily value (%DV) of each micronutrient was not included in the calculation for qCaln within the present study for a number of reasons. First, Eqs. 3, 4, 5 and 6 described in the methodology ensure that the amount of a given micronutrient in a given food is compared to the normal distribution of that micronutrient relative to all foods within its food group. Therefore, dividing these contents by the %DV would lead to the same distribution and “S-score” (Eq. 6) used in the qCaln calculation. Second, the use of %DV relative to qCaln values of a particular food when compared to a food group would be better used for overall meal and diet construction, allowing for comparisons of foods between food groups. By accounting for the %DV in future calculations of the qCaln, this measure could be used for meal construction to educate consumers on making healthier dietary choices in an attempt to meet their daily dietary needs.

Applications of the qCaln measure: nutrition education and dietary counseling

The proposed qCaln allows for a better interpretation of the food quality not only by researchers but also consumers. The qCaln can be linked to food-based dietary guidelines depicted visually through the MyPlate, MyPyramid, and the Mediterranean Diet Pyramid among other education models used by health care professionals to educate consumers about healthy dietary choices. For example, promoting the consumption of low-fat and low-sodium dairy products that have higher qCaln ratios (i.e. higher nutrient density with lower fat and sodium content as well as higher calcium and vitamin D), can assist consumers in meeting their beneficial nutrient requirements while avoiding less desirable nutrients. Similarly, promoting the consumption of fresh fruits like oranges rather than orange juice from frozen concentrate or sweetened fruit juices can help in making smarter food choices.

Training consumers to select foods with higher qCaln values, where appropriate, can help improve diet quality and meet dietary recommendations. Currently, dietary guidelines are presented to the public as recommendations in terms of serving sizes of food groups that are promoted to be consumed, such as whole grains, low fat dairy products, and foods to avoid including those high in sugar and fat. These recommendations are usually based on studies highlighting the daily optimal intakes of energy, and macro- as well as micro-nutrients among humans (acceptable macronutrient distribution ranges and DRIs, respectively). Despite the extensive efforts exerted by nutrition professionals to relay dietary guidelines in simple and interactive manners, consumers still face challenges in interpreting these recommendations. Difficulties arise when consumers are asked to make healthy dietary choices from a wide array of food products available on the market and with various nutrition facts labeling systems. In addition, deciphering the labeling systems, which include the amount of calories, macronutrient, and micronutrient content of foods, requires health and nutrition literacy at the consumer end, which may be lacking by various population groups and in numerous contexts. This is further complicated with the diversity of units, serving sizes, and nutrients listed on nutrition facts labels in differing countries, in addition to front-of-package labels along with other competing marketing slogans and techniques. Furthermore, fresh produce and certain types of meats and dairy products lack nutrition labels and the choice among the myriad of produce on the market leaves consumers confused as to which of the food items within each of the food groups are the most nutritious. The qCaln measure can merge the multiple nutritional measures including, caloric content of food, and the macro- and micronutrient content of food into one simple, standardized unit. In addition, the qCaln can be linked to optimal daily nutrient intakes of individuals and allows for daily meal construction. This linkage will allow consumers to make food purchases with conscious dietary knowledge rather than just preference, taste, convenience, and cost. The qCaln, if deemed valid in various contexts, could be used in addition to food labeling either on the front or the side of products, and on menus at various points of purchase with the potential for expanding nutrition education and supporting nutrition labeling systems that are already in place. Future research should evaluate the experiences of consumers towards the use of the qCaln and their success in attempting to make healthier dietary choices at different points of sales (supermarkets, restaurants, worksite cafeterias, schools, etc.).

Using the qCaln, as a composite score that takes into consideration energy and nutrient density, is not only valuable for consumers but also researchers and health professionals who seek to assess the diet quality of individuals and population groups. The proposed measure can provide a simple method to assess nutrient profiles of individual food items within the same food category while also providing investigators with a relatively simple tool to assess the overall nutrient profiles of individuals (for example, the average qCaln ratios of consumers over a specific period of time such as week or month). The latter measure can be then linked to other global measures of diet quality, such as the HEI and diet diversity scores.

Applications of the qCaln measure: linking nutrition to agriculture

One of the main benefits and applications of the qCaln is to help individuals make better food item choices and improve their overall dietary intake. In fact, nutrition programs that attempt to increase awareness and proper consumption of micronutrients achieve some of the highest benefit-cost ratios, even in the short term [37]. However, improving dietary intake is not dependent only on the knowledge of the consumers regarding quality of foods through targeted nutrition awareness programs but also on the quantity and quality of agricultural produce available in the market and accessible to consumers. Nevertheless, the link between the evidence provided by these nutrition programs and agricultural practices is, at best, limited. In addition, there is a dearth in robust monitoring and evaluation tools to assess the impact of agriculture on nutrition outcomes [38]. Thus, it is important to generate easy-to-understand measures that can be compatible for both fields to best inform how to grow healthy food in a sustainable manner while respecting individuals and populations’ food choices, preferences, and traditions [39]. Researchers, public health professionals, farmers, and consumers can be trained to interpret the qCaln measure, as a relatively easier tool to use compared to other more complex agricultural and nutrition indicators. The qCaln could then be used to bridge the gap between nutrition and agriculture by tracking quality energy produced to quality energy consumed through the qCaln. However, the applicability of qCaln in linking agricultural management practices aimed at sustainable crop production with nutritional outcomes is worth more thorough explorations in future studies.

Limitations

Findings from this paper need to be interpreted in light of a number of limitations. First, equal weights were assumed for all included micronutrients in the calculation for the qCaln. Although the use of equal-weighted scores has been used extensively by other researchers when developing nutrient profiling methods [4], targeted differential weightings for micronutrients have been suggested for context-specific cases such as biological quality of nutrients in food, bioavailability, and distribution of nutrients in the food supply [4]. Also, researchers have been showing the importance of altering the relative weightings of micronutrients in profiling to fit populations [40]. Thus, the formula for the qCaln can be further improved through reflecting different dietary needs and increasing or limiting intakes of specific nutrients for various segments of the population, such as increasing folate and vitamin B12 for pregnant women or limiting sodium and saturated fats for individuals at risk of hypertension or cardiovascular diseases.

Another limitation of the qCaln calculations in the present study is the lack of inclusion of bioavailability data and the effect of food preparation on micronutrient content of foods. For example, animal-based protein sources are more readily digested and absorbed allowing for higher bioavailability of essential micronutrients compared to plant-based sources, and cooked vegetables that are prepared through steaming have different micronutrient contents compared to boiled or baked vegetables [41]. To address these limitations, bioavailability and food preparation factors could be added to the proposed qCaln equation in future studies to allow for a better depiction of the quality of the foods and diets consumed by individuals. Additionally, the validity of this measure needs to be tested against other diet quality indices, including the HEI and diet diversity scores, and a number of nutrient profiling methods, such as the Nutrient-Rich Food Index (NRF) [6], and the SAIN/LIM scoring system [24]. The measure could also be tested against other objective health outcomes and biomarkers such as obesity, hypertension, and diabetes; and using more sophisticated validation techniques including ‘goodness of fit’ models (Drewnowski and Fulgoni 2008). Finally, further attention is needed to consider other attributes of food. For example, the qCaln could be compared to the Nutrient Rich Foods Index [7] to compare foods of varying food quality relative to cost. The qCaln, as a single number unit of measure, would allow for easy comparison of food quality to cost.