Abstract
Purpose of Review
Because nutrition plays a crucial role in the development of chronic diseases, ensuring nutrition security is important for promoting population health. Nutrition security is defined as having consistent and equitable access to healthy, safe, affordable foods essential to optimal health and well-being. Distinguished from food security, nutrition security consists of two constructs: healthy diets and nutritional status. The study aimed to identify population measures that reflect the important constructs of nutrition security (i.e., healthy diets and nutritional status) to inform U.S. nutrition security assessment and monitoring.
Recent Findings
Through a narrative review conducted across multiple databases, associations between subconstructs of healthy diets and nutritional status were identified. Of the six subconstructs that constitute healthy diets, nutrient adequacy and moderation were most often used to assess and monitor healthfulness of U.S. population diets and were associated with health outcomes. There is little evidence of an association between health outcomes and macronutrient balance or diversity in the U.S. Thirteen instruments were identified as potentially suitable for measuring at least one subconstruct of healthy diet in the population.
Summary
This review highlights the importance of nutrition security in addressing population health challenges. It emphasizes the potential use of multiple instruments and measures to comprehensively monitor population nutrition security and inform intervention strategies. Identifying feasible and practical measures for assessing and monitoring nutrition security is imperative for advancing population health and mitigating the burden of chronic diseases.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Nutrition plays an important role in the etiology of several chronic diseases, including obesity, cardiovascular disease (CVD), type 2 diabetes (T2DM), and certain cancers [1]. A healthy diet consists of nutrient-dense foods across all food groups and can help minimize diet-related chronic disease risk [2]. Conversely, less healthy diets consist of high intakes of added sugars, saturated fat, and sodium, and are responsible for almost half of cardiometabolic deaths from heart disease, stroke, and T2DM in the United States (U.S.) [3]. Over 70% of U.S. adults are either overweight or obese [4] and nearly $173 billion a year is spent on health care for obesity [5]. One third of deaths in the U.S. are caused by heart disease or stroke, and the diseases cost our health care system $216 billion per year [6]. More than 37 million Americans have diabetes, and the American Diabetes Association reported the annual economic cost of diabetes in 2022 was $412.9 billion [7].
Recognizing nutrition’s role in the perpetuation of non-communicable diseases has led to the identification of nutrition security as key to addressing U.S. population health. Nutrition security is an emerging concept that places improvement of nutrition and health status within a broad population framework. It has been defined as encompassing consistent and equitable access to healthy, safe, affordable foods essential to optimal health and well-being [8]. The White House Conference on Hunger, Nutrition, and Health identified monitoring the role of nutrition to prevent and manage diet-related chronic conditions (i.e. measuring nutrition security) as a priority [9].
Nutrition security assessment and monitoring relies on availability of relevant nutritional data at the population level that highlight the dietary intake and nutritional status of individuals. Appropriate measures and indicators are essential to track the U.S.’s commitment to healthy diets in the population, advocate for healthfulness of diets, and design policies and programs to achieve these objectives. Having regularly available data for monitoring nutrition requires a coordinated system that provides information on diet, nutrition, and related health outcomes using suitable measures and indicators [10].
Nutrition security complements, but is not the same as, food security [11]. Both include a focus on food access across the population to meet energy needs [8], but nutrition security entails that nutritional needs are met alongside food access [8, 11]. Two constructs–healthy diets and nutritional status–form the basis of nutrition security. Six subconstructs support healthy diets: nutrient adequacy, nutrient density, macronutrient balance, diversity, moderation, and safety [11, 12]. The subconstructs of nutritional status are having optimal amounts in body tissues of energy, protein, essential fats, vitamins, and minerals [11]. While this framework defines constructs and subconstructs of nutrition security, we have not yet identified measures and indicators that are recognized to be suitable to assess and monitor nutrition security.
To begin addressing this gap, we must understand the extent to which existing measures and indicators are suitable for population assessment and monitoring of nutrition security generally and among vulnerable sub-populations. To be suitable, measures and indicators for assessing and monitoring nutrition security should meet the following criteria: 1) reflect important subconstructs of nutrition security; 2) associated with health outcomes; and 3) feasible for population assessment and monitoring (i.e., easy to collect and analyze within a short period of time).
The study’s aim was to identify population measures that reflect the fundamental constructs of nutrition security (i.e., healthy diets and nutritional status) to inform U.S. nutrition security assessment and monitoring. Our analysis focused on four research questions:
-
1.
What is the association of subconstructs of healthy diets with subconstructs of nutritional status?
-
2.
What is the association of subconstructs of healthy diets and nutritional status with food security?
-
3.
What is the association of subconstructs of healthy diets and nutritional status with health outcomes?
-
4.
Which simple measures and indicators of nutrition security, and combinations of them, are feasible for population monitoring?
Methods
Search Strategy
We conducted a narrative review by searching electronic databases and selecting literature to ensure a comprehensive coverage of relevant information [13]. Between October 2022 and June 2023, we reviewed peer-reviewed original research, systematic reviews, and grey literature in PubMed and Google Scholar to obtain answers to the four research questions. We developed search strategies for research questions 1–3 (Online Resource 1). For research question 4, we searched the National Cancer Institute (NCI) register of validated brief dietary assessment instruments [14].
Inclusion and Exclusion Criteria
Articles were included for the first three research questions if they met the following criteria: 1) focused on the U.S. population, 2) measured or focused on some aspect of nutrition security as conceptualized by Seligman et al. [11] (Table 1), 3) described or assessed at least one health outcome or food security, and 4) were published between 2012 and 2023. Review articles were included. Articles were excluded if they focused on food safety, a subconstruct of healthy diets, as food safety does not generally pose a widespread threat to population health in the U.S. Articles were also excluded if they focused on the subconstruct of density because it is related to the subconstruct of nutrient adequacy, and therefore it may be assessed instead of or in addition to adequacy [12]. Brief instruments were included if they were developed or validated among adults in the U.S. Brief instruments that focused only on a sub-population of U.S. adults, e.g., racial minorities, were excluded.
Data Screening
Two co-authors (E.K. & V.O.A.) screened titles and abstracts of articles independently for eligibility. Duplicates were removed. Those that remained eligible went through full-text screening.
Data Charting
We developed a data-charting table for data extraction for each research question. For research question 1, we reviewed articles reporting the relationship between the subconstructs of healthy diets and subconstructs of nutritional status. Articles were categorized by population, subconstruct of healthy diet, instrument used to measure the subconstruct of healthy diet, subconstruct of nutritional status, and associations between subconstructs of healthy diets and subconstructs of nutritional status.
For research question 2, we reviewed articles examining the relationships among food security, healthy diet, and nutritional status. The information from each article was extracted and categorized by population, subconstruct, food security measure (e.g., U.S. Adult Food Security Survey Module), and associations between subconstructs of nutrition security and food security.
For research question 3, we reviewed articles looking at the relationship between healthy diets, nutritional status, and health outcomes, extracting information separately for subconstructs of healthy diets and subconstructs of nutritional status. For healthy diets, we reviewed relevant articles and extracted information from the following categories: subconstructs measured, health outcomes measured, and associations between the subconstructs of healthy diets and health outcomes. For nutritional status, we extracted information by subconstructs, biomarker (used to measure the subconstruct), health outcome, and associations between the subconstructs of nutritional status and health outcomes.
For research question 4, we reviewed articles on brief dietary assessment instruments to determine how well they measure healthy diets (or diet quality) and their accuracy in assessing dietary data. The criteria for describing and comparing instruments were adopted from the report by the Institut de Recherche pour le Développement on healthy diet metrics [15] and encompasses the following aspects: 1) instrument, 2) assessment of healthy diet subconstruct, 3) length of measure, 4) predictive validity for health outcomes, 5) how well the instrument assesses dietary data, and 6) ease of computation.
Results
Associations between Subconstructs of Healthy Diets and Subconstructs of Nutritional Status
Using different dietary assessment instruments, studies among U.S. adults investigated the relationship between some of the subconstructs of healthy diets (diet diversity, moderation, nutrient adequacy), and nutritional status captured via anthropometry (e.g., body mass index, waist circumference, percent body fat); and the subconstructs of healthy diets and specific biomarkers (e.g., serum cholesterol). Healthy diet subconstructs were assessed via different instruments including the Healthy Food Diversity (HFD) index, Healthy Eating Index (HEI) (1995, 2010 and 2015 versions), and Sustainable Diet Index (SDI)-US. Higher scores on the HFD index (which captures dietary diversity) were associated with lower risk of adiposity or obesity [16]. Studies using the HFD index found lower odds of obesity, android-to-gynoid ratio, and fat mass index (FMI), and lower odds of elevated waist circumference among men and women with the highest quality diet compared to the lowest [16, 17]. Higher scores on the HEI (which captures nutrient adequacy and moderation) were associated with reduced risk of abdominal and central obesity, lower percentage body fat, and lower fat mass index [18,19,20]. A higher SDI score captured nutrient adequacy and was also associated with lower odds of obesity (Table 2). These findings indicate an association between the subconstructs of healthy diets and nutritional status as higher scores on indices of healthy diets are associated with lower risk of adiposity (i.e., obesity and percentage of body fat).
Associations Between Food Security and Nutrition Security
In the U.S., both food-secure and food-insecure individuals have poor diets [22, 23]. For the subconstructs of healthy diets, macronutrient intakes did not differ when comparing food-secure and food-insecure individuals, but food-insecure individuals were at higher risk of inadequacy for some micronutrients. Among food-insecure men and women, lower intake was found for magnesium, potassium, vitamins B6, C, and D, when compared to food-secure men and women [24]. A systematic review of food insecurity and diet quality found no evidence of an association between food insecurity and intake of carbohydrates or protein and limited evidence of adverse associations with total fat or fiber, with mixed evidence concerning intake of saturated fat [25]. There were, however, associations across samples of varying age and sex between food insecurity and inadequate intakes of calcium, magnesium, and zinc (Table 3).
The literature provides limited evidence regarding associations between the subconstructs of nutritional status and food security. In some studies, U.S. adults show positive associations between food insecurity and obesity, with stronger associations observed in women [26, 28, 34]. Researchers investigating the connection between food insecurity and inflammation found that food insecurity was linked to higher levels of C-reactive protein with an adjusted odds ratio of 1.21 [29]. Furthermore, a study focusing on the association of food insecurity with nutritional biomarkers in U.S. children revealed no difference in vitamin D, zinc, and iron status between food-secure and food-insecure children [30]. The associations between the subconstructs of nutritional status and food security have not been extensively studied in U.S. adults.
Associations Between Subconstructs of Healthy Diets and Nutritional Status with Health Outcomes
Healthy Diets
The adequacy and moderation subconstructs of healthy diet are associated with health outcomes (Table 4). There is less evidence of an association between macronutrient balance or diversity and health outcomes. Most U.S. studies relied on the HEI and Alternate Healthy Eating Index (AHEI) to measure the healthfulness of diets. These indices, along with several others, are primarily constructed to assess adherence to a particular diet (e.g., Dietary Approaches to Stop Hypertension (DASH) score) or dietary guidelines (e.g., HEI). They also encompass measures of adequacy, moderation, and density. The AHEI was developed to better predict health outcomes when compared to the HEI [35,36,37], but there were no important differences between the associations from the HEI and AHEI with health outcomes [38, 39]. Both the HEI and AHEI are correlated with chronic diseases. Higher scores on both indices are associated with reduced risk of all-cause mortality, cancer, CVD, T2DM, and other chronic diseases (Table 4). The Dietary Patterns Methods Project synthesized that higher diet quality as measured via the HEI, AHEI, alternate Mediterranean diet score, and the DASH score was associated with an 11–28% reduced risk of death due to all-cause, CVD, and cancer when compared to the lower diet quality [40].
Nutritional Status
Energy, a subconstruct of nutritional status, is associated with health outcomes. Excess energy has been linked with adverse health outcomes including obesity, CVD, T2DM, cancer, osteoporosis, and osteoarthritis [54]. Most adults obtain sufficient protein through their diets. There is insufficient evidence to suggest that excess protein intake leads to adverse health effects [55]. There is evidence, however, that long-term consumption of red meat and processed meat is associated with an increased risk of total mortality, CVD, colorectal cancer, and T2DM [56, 57]. Diets high in saturated fats are linked to higher mortality rates from all causes, CVD, and cancer. In contrast, diets rich in polyunsaturated and monounsaturated fats are associated with lower all-cause mortality [58].
The Dietary Guidelines for Americans identified nutrients or vitamins and minerals of public health concern for individuals aged one year and older. The nutrients of concern include low intakes of fiber, calcium (for individuals aged 2 years and older), vitamin D, and potassium. Excessive nutrient intake is a concern for sodium, added sugars, and saturated fats (for individuals aged 2 years and older). These nutrients have been associated with adverse health consequences [59]. For sodium, high intakes above recommendations have been associated with higher risks of hypertension and cardiovascular diseases [60, 61]. Both suboptimal and excessive intakes of nutrients such as calcium, iron, vitamin D, and potassium are associated with health outcomes like hypertension and coronary heart diseases [62,63,64] (Table 5). For instance, suboptimal vitamin D concentrations are associated with higher risks of CVD, hypertension, cancer, T2DM [63]; while excessive intake of calcium supplements is associated with increased risk of cardiovascular diseases, including coronary heart disease and myocardial infarction [64, 65].
Summary of Instruments
Twenty-four-hour dietary recall is a standard method to obtain dietary data but is difficult to collect and analyze for timely population-level monitoring. Thus, instruments that measure the relevant subconstructs of healthy diets and nutritional status and are valid and reliable on a population basis are needed for timely population-level assessment and monitoring of nutrition security. The following is a list of potential instruments that may be used to assess and monitor the subconstructs of nutrition security.
Healthy Diets
We identified 13 brief dietary instruments out of 63 brief dietary instruments in the National Cancer Institute registry that measured at least one subconstruct of healthy diets and can be used for population assessment and monitoring (Table 6). Three brief dietary instruments developed and validated for global use were also included.
These brief instruments have between six and 26 items administered as a short food frequency questionnaire, food checklist, or behavioral questionnaire about dietary practices. Most instruments measure frequency of consumption of specific foods or food groups in the past month [14]. An exception is PrimeScreen, an instrument that captures the frequency of consumption of certain food groups over the previous year using five categories of frequency of consumption: less than once per week, once per week, two to four times per week, nearly daily or daily, or twice or more per day [97, 98].
While some brief instruments were inclusive of food groups or dietary components [14], others were specific to fruits and vegetables [94]. For example, the Dietary Screener Questionnaire (DSQ) is a 26-item instrument to capture intakes of fruits and vegetables, dairy/calcium, added sugars, whole grains/fiber, red meat, and processed meat. The Behavioral Risk Factor Surveillance System (BRFSS) fruit and vegetable brief instrument, however, has just 6 items on fruit and vegetable intake frequency.
Some brief instruments (e.g., DSQ) have been used in national surveys like the National Health and Nutrition Examination Survey (NHANES) and the National Health Interview Survey (NHIS) [14]. The Prime Diet Quality Score (PDQS), Global Diet Quality Score (GDQS), and Global Dietary Recommendation (GDR) score have been used for global assessment and to distinguish between healthy and unhealthy components using difference scores (i.e., scores that combine both positive (healthy) and negative (unhealthy) components of an instrument to create a total score). Others (e.g., Starting the Conversation or Rapid Eating and Activity Assessment for Participants-Short) were designed and validated for clinical settings [101, 103].
Most brief instruments were validated against standard food frequency questionnaires and/or 24-h recalls to determine how well they measured dietary data (Table 6). Correlations with the standard instruments were reported. While some instruments had modest correlations with standard instruments [94, 97], others underreported some dietary components compared to standard instruments [14]. Few of the identified brief instruments have been examined for predictive validity for health outcomes [96].
The three brief instruments developed for global use measure more healthy diet subconstructs compared to other brief instruments. The GDQS measures nutrient adequacy, diversity, and moderation [42], whereas the GDR score measures nutrient adequacy, nutrient density, diversity, and moderation [105]. The GDQS can be collected using a full 24-h recall but does not require quantifying and converting foods into nutrient values, making data cleaning and processing simpler. The instrument can also be used via an electronic app that provides standard, easy-to-use methods for collecting low-cost, time-relevant data on diet quality. For the ease of data collection, the GDR score relies on a questionnaire that gathers simple information on consumption (yes/no) of a limited number of food groups (or sentinel foods representing those food groups), which allows a short interview time (from 3 to 6 min) and does not require interviewers to have specific training [15].
Nutritional Status
There are a range of methods for measuring the subconstructs of nutritional status, including anthropometric, body composition, clinical, and biomarker measures. These include several different techniques for measuring body composition, including anthropometry, dual-energy X-ray absorptiometry (DEXA), and bioelectrical impedance analysis (BIA). DEXA and BIA are valid measures of body composition at the population level, but not feasible for population data collection [54, 66]. Body mass index (BMI) is a widely used anthropometric measure related to body composition that predicts mortality of adults at the population level, but BMI does not differentiate between body lean mass and body fat mass [107,108,109]. Potential biomarkers for assessment of red meat are 13C, 15N, Creatinine, Taurine, 1-methylhistidine, and 3-methylhistidine [56, 57]. For fats, it may be useful to measure polyunsaturated fats, monounsaturated fats, and saturated fats via red blood cell, plasma phospholipids, and cholesterol esters [57, 58].
Calcium levels in the blood are responsive to hormonal regulation rather than dietary intake. Therefore, monitoring serum calcium levels will not accurately capture changes in dietary calcium intake. Dietary calcium intake is related to bone health. Measuring biomarkers of bone remodeling (e.g., tartrate-resistant acid phosphatase, procollagen type, osteocalcin) should determine the role of dietary intake of calcium on bone health at the population level [81]. It may also be appropriate to assess dietary intake of calcium to assess adequacy of calcium intake. For vitamin D, serum 25-hydroxy vitamin D level is an appropriate biomarker to assess vitamin D levels in the blood, as the levels are reflective of dietary intake of vitamin D and sun exposure. This biomarker is not appropriate to assess bone loss due to deficient vitamin D intake [81, 110]. For potassium, blood (serum) potassium concentrations and hypokalaemia are not reliable measures of usual potassium intake or status in the healthy population. Therefore, assessing dietary intake of potassium is more appropriate to determine adequacy of potassium intake [111]. Assessing urinary excretion of sodium, especially over a 24-h period, is an appropriate biomarker for dietary sodium intake at the population level [112]. Lastly, higher consumption of heme iron is associated with higher levels of serum ferritin and hemoglobin levels. Serum ferritin as a biomarker reflects adequate dietary intake of iron, especially heme iron, and is appropriate to assess adequacy of iron intake at the population level [62].
Discussion
Nutrition plays a pivotal role in the prevention and management of many chronic diseases, prompting the emergence of nutrition security as an essential component in improving population health. Distinguished from food security, nutrition security encapsulates not just access to food but ensuring that food accessed and consumed meets nutritional needs, which is the basis for the subconstructs of healthy diets and nutritional status [11]. To inform U.S. nutrition security measurement needs, our study aimed to identify measures at the population level that reflect important subconstructs of healthy diets and nutritional status. To assess key subconstructs for population monitoring, we reviewed associations between healthy diet and nutritional status, as well as their associations with food security and health outcomes. These findings then laid the foundation for identifying feasible measures and indicators crucial for assessing and monitoring nutrition security.
Some biomarkers might be useful in monitoring nutrition security. For some nutrients, like iron, assessing only subconstructs of healthy diets is sufficient because biomarkers (e.g., serum ferritin) are associated with dietary intake. For other nutrients such as calcium, biomarkers (e.g., serum calcium) are not associated with dietary intake; nevertheless, it may be useful to monitor some biomarkers such as those of bone remodeling that are related to calcium functions in the body, specifically bone health and potentially predictive of health outcomes such as osteoporosis. Measuring all the subconstructs of healthy diets and nutritional status may be ideal but is not practical for population-level monitoring due to time and financial constraints.
Food security is associated with some, but not all subconstructs of nutrition security. For example, in the U.S., limited evidence links food insecurity to specific macronutrient intake differences, although studies highlight associations between food insecurity and lower intakes of calcium, magnesium, and zinc across various age groups and genders. Research in U.S. adults indicates positive association between food insecurity and obesity in some sub-populations and with higher levels of C-reactive protein. While there are associations between food security and certain subconstructs of nutrition security, monitoring both will be important. Nutrition security offers insights that food security alone cannot provide.
Of the six subconstructs identified to constitute healthy diets [11], our review found that nutrient adequacy and moderation were most often used to assess and monitor healthfulness of U.S. population diets and were associated with health outcomes. There is little evidence of an association between health outcomes and macronutrient balance or diversity in the U.S. since most research relies on HEI/AHEI and neither HEI nor AHEI captures these two subconstructs. Further research is needed to relate macronutrient balance and diversity to U.S. population health outcomes.
Various data systems and several instruments are used for dietary assessment. Data systems include the NHANES, the NHIS, and the BRFSS. Indices that are used frequently in the U.S. are the HEI and the AHEI. While HEI is obtained through NHANES data, and frequently utilized in research, data from NHANES has been released to the public every other year, and there can be a substantial lag before that data is publicly available. It may be more opportune to utilize HEI data in combination with a brief instrument or instruments that could be incorporated into other surveys (e.g., NHIS), where data may be released more promptly. Possible existing instruments include the GDQS, GDR score, PDQS, and the BRFSS fruit and vegetable instrument. The GDQS, GDR score, and PDQS also measure healthy and unhealthy food consumption. This may inform policies and programs to promote healthier foods and curb unhealthy food consumption. Further research is required to understand the predictive validity of these brief instruments in relation to health outcomes. Additionally, it is essential to investigate possible differences in the association of unhealthy foods (moderation) and health outcomes vs. healthy foods (adequacy) and health outcomes (i.e., risk reduction).
An ideal nutrition security system for population-level assessment and monitoring should encompass several elements to ensure comprehensive and effective tracking and intervention strategies. The first is regular data collection. There is a need for regular and standardized data collection mechanisms to monitor nutrition security across different demographic groups and geographic regions. This may require multiple data sources, including surveys (e.g., NHANES, NHIS), health facilities, school records, and community health workers to gather comprehensive data. There may be value in linking multiple data sources, or at least making use of multiple different sources, as the 1990 National Nutrition Monitoring and Related Research Act did in bringing together multiple sources of data on nutrition monitoring from federal agencies and state and local governments [10]. Rapid data collection and reporting that leverages the use of technology, can help enable timely reporting and interventions.
The next element is the measure of subconstructs of healthy diets and, if appropriate and possible, nutritional status, as well as monitoring health outcomes of importance, including obesity, CVD, T2DM, and other related health conditions. Nutrition-related chronic diseases often have associated biomarkers that are distinct from traditional measures of nutritional status. For instance, blood pressure, a biomarker for hypertension, does not inherently reflect nutritional status or dietary intake. Similarly, biomarkers like blood glucose levels and HbA1C play roles in diagnosing and monitoring diabetes but do not inherently reflect nutritional status or dietary intake. Measures like these may be helpful in monitoring nutrition security because they tell us about important consequences of nutrition-related chronic conditions.
For measures of healthy diets, adequacy and moderation independently and potentially differentially affect health outcomes. Adequacy is focused on having enough important nutrients in the diet, which may decrease risk of particular health outcomes, whereas moderation is focused on moderating the amounts of unhealthy constituents in the diet, which can increase risk of poor health outcomes. It will be important to track metrics for each of these two subconstructs separately rather than using the difference between the two metrics. Using difference scores in dietary data is not ideal due to overestimation of total scores, ambiguous interpretation (i.e., midrange scores that can have various contributing components resulting in different dietary patterns), low reliability, discarded theoretical information, and discriminant validity issues [39, 113, 114].
Another essential element for a nutrition security system is data dissemination and transparency. Nutrition security data and assessment results should be publicly available to encourage transparency and accountability, and will include effective communication of findings to policymakers, healthcare providers, and the public [115, 116]. The last element is regular evaluation and adaptation of the nutrition security system. Incorporating feedback from stakeholders and communities to improve the system over time, along with continuous evaluation of the system’s effectiveness and adaptability to changing circumstances will be necessary to ensure useful assessment and monitoring. By incorporating these elements into a nutrition security system, policymakers and stakeholders can better understand the nutritional needs of the population and implement targeted interventions and policies to improve nutrition outcomes at the population level.
Several lines of research would be particularly helpful in further developing nutrition security monitoring. First among these is how measures and indicators can capture and reflect disparities in nutrition security across diverse populations. Understanding how various factors such as socio-economic status, geographical region, and cultural background intersect with nutrition security is paramount in developing tailored interventions and policies that can effectively target vulnerable sub-populations. Another priority for research will be the ongoing validating and updating of measures to reflect changes in nutrition security over time. It is essential to consider whether these updates should coincide with guidelines, like the Dietary Guidelines for Americans, to ensure that our measures align with the latest nutritional recommendations. Finally, the question of how these measures should be communicated and utilized remains central. Effective communication and utilization are critical in informing policy decisions, interventions, and resource allocation. In this context, the journey to understanding and improving nutrition security is dynamic and evolving and will require continued attention, research, collaboration, and innovation.
Conclusion
This analysis outlined the relationship between subconstructs of healthy diets, subconstructs of nutritional status, food security, and health outcomes. The interplay among these highlights the complex understanding of nutrition and its impact on population well-being. The pursuit of feasible and practical measures and indicators for nutrition security monitoring and assessment is a critical challenge. These metrics serve as the foundation for evidence-based policy development and program design, enabling monitoring of progress towards ensuring the attainment of healthful diets for all. The complexity of these associations necessitates an ongoing commitment to research, monitoring, and the implementation of data-driven strategies that can ultimately enhance the health and well-being of our communities.
Data Availability
No datasets were generated or analysed during the current study.
References
Poor Nutrition | CDC [Internet]. 2023 [cited 2023 Oct 13]. Available from: https://www.cdc.gov/chronicdisease/resources/publications/factsheets/nutrition.htm.
Dietary Guidelines for Americans, 2020–2025 and Online Materials | Dietary Guidelines for Americans [Internet]. [cited 2023 Mar 3]. Available from: https://www.dietaryguidelines.gov/resources/2020-2025-dietary-guidelines-online-materials.
Micha R, Peñalvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D. Association Between Dietary Factors and Mortality From Heart Disease, Stroke, and Type 2 Diabetes in the United States. JAMA. 2017;317:912–24.
Overweight & Obesity Statistics - NIDDK [Internet]. Natl. Inst. Diabetes Dig. Kidney Dis. [cited 2023 Sep 26]. Available from: https://www.niddk.nih.gov/health-information/health-statistics/overweight-obesity.
Ward ZJ, Bleich SN, Long MW, Gortmaker SL. Association of body mass index with health care expenditures in the United States by age and sex. PLoS ONE. 2021;16: e0247307.
Health and Economic Costs of Chronic Diseases | CDC [Internet]. 2023 [cited 2024 Jan 4]. Available from: https://www.cdc.gov/chronicdisease/about/costs/index.htm.
Parker ED, Lin J, Mahoney T, Ume N, Yang G, Gabbay RA, et al. Economic Costs of Diabetes in the U.S. in 2022. Diabetes Care. 2024;47:26–43.
Food and Nutrition Security [Internet]. [cited 2023 Mar 7]. Available from: https://www.usda.gov/nutrition-security.
EXECUTIVE SUMMARY: Biden-Harris Administration National Strategy on Hunger, Nutrition, and Health [Internet]. White House. 2022 [cited 2023 Oct 13]. Available from: https://www.whitehouse.gov/briefing-room/statements-releases/2022/09/27/executive-summary-biden-harris-administration-national-strategy-on-hunger-nutrition-and-health/.
Briefel RR, McDowell MA. Nutrition Monitoring in the United States. Present Knowl Nutr [Internet]. John Wiley & Sons, Ltd; 2012 [cited 2023 Dec 8]. p. 1082–109. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119946045.ch64
Seligman HK, Levi R, Adebiyi VO, Coleman-Jensen A, Guthrie JF, Frongillo EA. Assessing and monitoring nutrition security to promote healthy dietary intake and outcomes in the United States. Annu Rev Nutr. 2023;43:409–29. This article provides a conceptual framework and constructs for nutrition security aimed to understand how it arises, how it differs from food security, why assessment and monitoring is important, and guidance for future research.
Arimond M, Deitchler M. Measuring diet quality for women of reproductive age in low- and middle-income countries: Towards new metrics for changing diets. [Internet]. Washington, D.C.: Intake - Center for Dietary Assessment/FHI 360; 2019 [cited 2023 Sep 26]. Available from: https://www.intake.org/sites/default/files/2019-09/IntakeMeasuringDietQuality_Jan%202019.pdf.
Ferrari R. Writing narrative style literature reviews. Med Writ. 2015;24:230–5.
Register of Validated Short Dietary Assessment Instruments | EGRP/DCCPS/NCI/NIH [Internet]. 2021 [cited 2023 Sep 26]. Available from: https://epi.grants.cancer.gov/diet/shortreg/.
Verger EO, Savy M, Martin-Préve Y, Coates J, Frongillo E, Neufeld L, et al. Healthy diet metrics: a suitability assessment of indicators for global and national monitoring purposes [Internet]. World Health Organization; 2023 [cited 2023 Sep 26]. Available from: https://iris.who.int/handle/10665/371497. This technical report assesses the validity, usefulness, and fitness of seven healthy diet metrics, identifying four that are most suitable for global and national monitoring: Global Dietary Recommendations Score, Global Diet Quality score, Minimum Dietary Diversity for Women, and Nova UPF score.
Vadiveloo M, Dixon LB, Mijanovich T, Elbel B, Parekh N. Dietary Variety Is Inversely Associated with Body Adiposity among US Adults Using a Novel Food Diversity Index1–3. J Nutr. 2015;145:555–63.
Vadiveloo M, Parekh N, Mattei J. Greater healthful food variety as measured by the US Healthy Food Diversity index is associated with lower odds of metabolic syndrome and its components in US adults. J Nutr [Internet]. 2015 [cited 2024 Feb 2];145. Available from: https://pubmed.ncbi.nlm.nih.gov/25733473/.
Bailey BW, Perkins A, Tucker LA, LeCheminant JD, Tucker JM, Moncur B. Adherence to the 2010 dietary guidelines for americans and the relationship to adiposity in young women. J Nutr Educ Behav. 2015;47:86–93.
Yoshida Y, Scribner R, Chen L, Broyles S, Phillippi S, Tseng T-S. Diet quality and its relationship with central obesity among Mexican Americans: findings from National Health and Nutrition Examination Survey (NHANES) 1999–2012. Public Health Nutr. 2017;20:1193–202.
Xu F, Greene GW, Earp JE, Adami A, Delmonico MJ, Lofgren IE, et al. Relationships of physical activity and diet quality with body composition and fat distribution in US adults. Obesity. 2020;28:2431–40.
Jung S, Young HA, Simmens SJ, Braffett BH, Ogden CL. The cross-sectional association between a sustainable diet index and obesity among US adults. Obesity (Silver Spring). 2023;31:1962–71.
Long T, Zhang K, Chen Y, Wu C. Trends in Diet Quality Among Older US Adults From 2001 to 2018. JAMA Netw Open. 2022;5: e221880.
Tao M-H, Liu J-L. Nguyen U-SDT. Trends in diet quality by race/ethnicity among adults in the United States for 2011–2018. Nutrients. 2022;14:4178.
Cowan AE, Jun S, Tooze JA, Eicher-Miller HA, Dodd KW, Gahche JJ, et al. Total usual micronutrient intakes compared to the dietary reference Intakes among U.S. adults by food security status. Nutrients. 2020;12:38.
Hanson KL, Connor LM. Food insecurity and dietary quality in US adults and children: a systematic review1,2,3. Am J Clin Nutr. 2014;100:684–92.
Dhurandhar EJ. The food-insecurity obesity paradox: A resource scarcity hypothesis. Physiol Behav. 2016;162:88–92.
Eicher-Miller HA, Zhao Y. Evidence for the age-specific relationship of food insecurity and key dietary outcomes among US children and adolescents. Nutr Res Rev. 2018;31:98–113.
Franklin B, Jones A, Love D, Puckett S, Macklin J, White-Means S. Exploring Mediators of Food Insecurity and Obesity: A Review of Recent Literature. J Community Health. 2012;37:253–64.
Gowda C, Hadley C, Aiello AE. The Association Between Food Insecurity and Inflammation in the US Adult Population. Am J Public Health. 2012;102:1579–86.
Jun S, Cowan AE, Dodd KW, Tooze JA, Gahche JJ, Eicher-Miller HA, et al. Association of food insecurity with dietary intakes and nutritional biomarkers among US children, National Health and Nutrition Examination Survey (NHANES) 2011–2016. Am J Clin Nutr. 2021;114:1059–69.
Lee SJ, Lee KW, Cho MS. Association of food insecurity with nutrient intake and depression among Korean and US adults: data from the 2014 Korea and the 2013–2014 US national health and nutrition examination surveys. Int J Environ Res Public Health. 2021;18:506.
Shaheen M, Kibe LW, Schrode KM. Dietary quality, food security and glycemic control among adults with diabetes. Clin Nutr ESPEN. 2021;46:336.
Sharpe PA, Whitaker K, Alia KA, Wilcox S, Hutto B. Dietary intake, behaviors and psychosocial factors among women from food-secure and food-insecure households in the United States. Ethn Dis. 2016;26:139.
Brown AGM, Esposito LE, Fisher RA, Nicastro HL, Tabor DC, Walker JR. Food insecurity and obesity: research gaps, opportunities, and challenges. Transl Behav Med. 2019;9:980–7.
Akbaraly TN, Ferrie JE, Berr C, Brunner EJ, Head J, Marmot MG, et al. Alternative Healthy Eating Index and mortality over 18 y of follow-up: results from the Whitehall II cohort123. Am J Clin Nutr. 2011;94:247–53.
Chiuve SE, Fung TT, Rimm EB, Hu FB, McCullough ML, Wang M, et al. Alternative Dietary Indices Both Strongly Predict Risk of Chronic Disease. J Nutr. 2012;142:1009–18.
McCullough ML, Chantaprasopsuk S, Islami F, Rees-Punia E, Um CY, Wang Y, et al. Association of Socioeconomic and Geographic Factors With Diet Quality in US Adults. JAMA Netw Open. 2022;5: e2216406.
Al-Ibrahim AA, Jackson RT. Healthy eating index versus alternate healthy index in relation to diabetes status and health markers in U.S. adults: NHANES 2007–2010. Nutr J. 2019;18:26.
Aljuraiban GS, Gibson R, Oude Griep LM, Okuda N, Steffen LM, Van Horn L, et al. Perspective: The Application of A Priori Diet Quality Scores to Cardiovascular Disease Risk—A Critical Evaluation of Current Scoring Systems. Adv Nutr. 2020;11:10–24.
Liese AD, Krebs-Smith SM, Subar AF, George SM, Harmon BE, Neuhouser ML, et al. The Dietary Patterns Methods Project: Synthesis of Findings across Cohorts and Relevance to Dietary Guidance 1, 2, 3, 4. J Nutr. 2015;145:393–402.
Asghari G, Mirmiran P, Yuzbashian E, Azizi F. A systematic review of diet quality indices in relation to obesity. Br J Nutr. 2017;117:1055–65.
Bromage S, Batis C, Bhupathiraju SN, Fawzi WW, Fung TT, Li Y, et al. Development and validation of a novel food-based Global Diet Quality Score (GDQS). J Nutr. 2021;151:75S-92S. This article aimed to develop a simple metric for nutrient adequacy and diet-related non-communicable disease risk in diverse settings.
2020 Dietary Guidelines Advisory Committee and Nutrition Evidence Systematic Review Team. Dietary Patterns and Risk of Cardiovascular Disease: A Systematic Review. 2020 Dietary Guidelines Advisory Committee Project. Alexandria, VA: U.S. Department of Agriculture, Food and Nutrition Service, Center for Nutrition Policy and Promotion; 2020. Available at: https://nesr.usda.gov/2020-dietary-guidelines-advisory-committee-systematic-reviews.
Gil Á, de Victoria EM, Olza J. Indicators for the evaluation of diet quality. Nutrición Hospitalaria. 2015;31:128–44.
Harrison S, Couture P, Lamarche B. Diet quality, saturated fat and metabolic syndrome. Nutrients. 2020;12:3232.
Miller V, Webb P, Micha R, Mozaffarian D, Global Dietary Database. Defining diet quality: a synthesis of dietary quality metrics and their validity for the double burden of malnutrition. Lancet Planet Health. 2020;4:e352-70.
Onvani S, Haghighatdoost F, Surkan PJ, Larijani B, Azadbakht L. Adherence to the Healthy Eating Index and Alternative Healthy Eating Index dietary patterns and mortality from all causes, cardiovascular disease and cancer: a meta-analysis of observational studies. J Human Nutr Diet. 2017;30:216–26.
Panizza CE, Shvetsov YB, Harmon BE, Wilkens LR, Le Marchand L, Haiman C, et al. Testing the predictive validity of the healthy eating index-2015 in the multiethnic cohort: is the score associated with a reduced risk of all-cause and cause-specific mortality? Nutrients. 2018;10:452.
Petersen KS, Kris-Etherton PM. Diet quality assessment and the relationship between diet quality and cardiovascular disease risk. Nutrients. 2021;13:4305.
Savarino G, Corsello A, Corsello G. Macronutrient balance and micronutrient amounts through growth and development. Ital J Pediatr. 2021;47:109.
Schwingshackl L, Hoffmann G. Diet quality as assessed by the healthy eating index, the alternate healthy eating index, the dietary approaches to stop hypertension score, and health outcomes: a systematic review and meta-analysis of cohort studies. J Acad Nutr Diet. 2015;115:780-800.e5.
Steck SE, Guinter M, Zheng J, Thomson CA. Index-based dietary patterns and colorectal cancer risk: a systematic review. Adv Nutr. 2015;6:763–73.
Wabo TMC, Wang Y, Nyamao RM, Wang W, Zhu S. Protein-to-carbohydrate ratio is informative of diet quality and associates with all-cause mortality: Findings from the National Health and Nutrition Examination Survey (2007–2014). Front Public Health. 2022;10:1043035.
Kuriyan R. Body composition techniques. Indian J Med Res. 2018;148:648–58.
Shams-White MM, Chung M, Du M, Fu Z, Insogna KL, Karlsen MC, et al. Dietary protein and bone health: a systematic review and meta-analysis from the National Osteoporosis Foundation. Am J Clin Nutr. 2017;105:1528–43.
Richi EB, Baumer B, Conrad B, Darioli R, Schmid A, Keller U. Health risks associated with meat consumption: a review of epidemiological studies. Int J Vitam Nutr Res. 2015;85:70–8.
Hedrick VE, Dietrich AM, Estabrooks PA, Savla J, Serrano E, Davy BM. Dietary biomarkers: advances, limitations and future directions. Nutr J. 2012;11:109.
Kim Y, Je Y, Giovannucci EL. Association between dietary fat intake and mortality from all-causes, cardiovascular disease, and cancer: A systematic review and meta-analysis of prospective cohort studies. Clin Nutr Edinb Scotl. 2021;40:1060–70. This systematic review looked at the associations between dietary fat and mortality concluding that diets high in saturated and trans-fat were associated with higher mortality from all-causes and cardiovascular disease.
Bailey RL, Ard JD, Davis TA, Naimi TS, Schneeman BO, Stang JS, et al. A proposed framework for identifying nutrients and food components of public health relevance in the dietary guidelines for Americans. J Nutr. 2021;151:1197–204. This article identifies nutrients of public health concern based on the Dietary Guidelines for Americans: fiber, calcium, vitamin D, and potassium for low intakes and sodium, added sugars, and saturated fats for high intakes.
Wang Y-J, Yeh T-L, Shih M-C, Tu Y-K, Chien K-L. Dietary sodium intake and risk of cardiovascular disease: a systematic review and dose-response meta-analysis. Nutrients. 2020;12:2934.
Filippini T, Malavolti M, Whelton PK, Vinceti M. Sodium intake and risk of hypertension: a systematic review and dose-response meta-analysis of observational cohort studies. Curr Hypertens Rep. 2022;24:133–44.
Lynch S, Pfeiffer CM, Georgieff MK, Brittenham G, Fairweather-Tait S, Hurrell RF, et al. Biomarkers of nutrition for development (BOND)—iron review. J Nutr. 2018;148:1001S-1067S.
Grant WB, Al Anouti F, Boucher BJ, Dursun E, Gezen-Ak D, Jude EB, et al. A narrative review of the evidence for variations in serum 25-hydroxyvitamin D concentration thresholds for optimal health. Nutrients. 2022;14:639.
Yuan S, Yu L, Gou W, Wang L, Sun J, Li D, et al. Health effects of high serum calcium levels: Updated phenome-wide Mendelian randomisation investigation and review of Mendelian randomisation studies. eBioMedicine [Internet]. 2022 [cited 2024 Mar 29];76. Available from: https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(22)00049-4/fulltext.
Michos ED, Cainzos-Achirica M, Heravi AS, Appel LJ. Vitamin D, calcium supplements, and implications for cardiovascular health: JACC focus seminar. J Am Coll Cardiol. 2021;77:437–49.
Marra M, Sammarco R, De Lorenzo A, Iellamo F, Siervo M, Pietrobelli A, et al. Assessment of body composition in health and disease using bioelectrical impedance analysis (BIA) and dual energy x-ray absorptiometry (DXA): a critical overview. contrast media mol imaging. 2019;2019:1–9.
Yu Z, Nan F, Wang LY, Jiang H, Chen W, Jiang Y. Effects of high-protein diet on glycemic control, insulin resistance and blood pressure in type 2 diabetes: A systematic review and meta-analysis of randomized controlled trials. Clin Nutr. 2020;39:1724–34.
Calder PC, Boobis A, Braun D, Champ CL, Dye L, Einöther S, et al. Improving selection of markers in nutrition research: evaluation of the criteria proposed by the ILSI Europe Marker Validation Initiative. Nutr Res Rev. 2017;30:73–81.
Naghshi S, Aune D, Beyene J, Mobarak S, Asadi M, Sadeghi O. Dietary intake and biomarkers of alpha linolenic acid and risk of all cause, cardiovascular, and cancer mortality: systematic review and dose-response meta-analysis of cohort studies. BMJ. 2021;375:n2213.
Tanumihardjo SA, Russell RM, Stephensen CB, Gannon BM, Craft NE, Haskell MJ, et al. Biomarkers of nutrition for development (BOND)-vitamin A review. J Nutr. 2016;146:1816S-48S.
Zabetian-Targhi F, Mahmoudi MJ, Rezaei N, Mahmoudi M. Retinol binding protein 4 in relation to diet, inflammation, immunity, and cardiovascular diseases. Adv Nutr. 2015;6:748–62.
Huang J, Weinstein SJ, Yu K, Männistö S, Albanes D. Association between serum retinol and overall and cause-specific mortality in a 30-year prospective cohort study. Nat Commun. 2021;12:6418.
U.S. Centers for Disease Control and Prevention. Second national report on biochemical indicators of diet and nutrition in the U.S. population 2012. Atlanta (GA): National Center for Environmental Health; 2012.
Sakakeeny L, Roubenoff R, Obin M, Fontes JD, Benjamin EJ, Bujanover Y, et al. Plasma pyridoxal-5-phosphate is inversely associated with systemic markers of inflammation in a population of U.S. adults. J Nutr. 2012;142:1280–5.
Obeid R, Geisel J, Nix WA. 4-pyridoxic acid/pyridoxine ratio in patients with type 2 diabetes is related to global cardiovascular risk scores. Diagnostics. 2019;9:28.
Allen LH, Miller JW, de Groot L, Rosenberg IH, Smith AD, Refsum H, et al. Biomarkers of nutrition for development (BOND): vitamin B-12 review. J Nutr. 2018;148:1995S–2027S.
Wolffenbuttel BHR, Heiner-Fokkema MR, Green R, Gans ROB. Relationship between serum B12 concentrations and mortality: experience in NHANES. BMC Med. 2020;18:307.
Kweon OJ, Lim YK, Lee M-K, Kim HR. Clinical utility of serum holotranscobalamin measurements in patients with first-ever ischemic stroke. Dis Markers. 2021;2021:e9914298.
Wang X, Li W, Xiang M. Increased serum methylmalonic acid levels were associated with the presence of cardiovascular diseases. Front Cardiovasc Med [Internet]. 2022 [cited 2024 Apr 1];9. Available from: https://www.frontiersin.org/articles/10.3389/fcvm.2022.966543.
Azzini E, Raguzzini A, Polito A. A brief review on vitamin B12 deficiency looking at some case study reports in adults. Int J Mol Sci. 2021;22:9694.
Gibson RS. Principles of Nutritional Assessment: 3rd Edition [Internet]; 2024. https://nutritionalassessment.org/.
Tian T, Shao J, Shen Z, Sun X, Liu Y, Cao L, et al. Association of serum vitamin C with all-cause and cause-specific death: Data from National Health and Nutrition Examination Survey (NHANES 2003-2006). Nutrition. 2022;101:111696.
Liu Y, Geng T, Wan Z, Lu Q, Zhang X, Qiu Z, et al. Associations of serum folate and vitamin B12 levels with cardiovascular disease mortality among patients with type 2 diabetes. JAMA Netw Open. 2022;5:e2146124.
Rohner F, Zimmermann M, Jooste P, Pandav C, Caldwell K, Raghavan R, et al. Biomarkers of nutrition for development—iodine review1234. J Nutr. 2014;144:1322S-1342S.
Inoue K, Leung AM, Sugiyama T, Tsujimoto T, Makita N, Nangaku M, et al. Urinary iodine concentration and mortality among U.S. adults. Thyroid®. 2018;28:913–20.
King JC, Brown KH, Gibson RS, Krebs NF, Lowe NM, Siekmann JH, et al. Biomarkers of Nutrition for Development (BOND)-Zinc Review. J Nutr. 2015;146:858S-885S.
Qu X, Yang H, Yu Z, Jia B, Qiao H, Zheng Y, et al. Serum zinc levels and multiple health outcomes: Implications for zinc-based biomaterials. Bioact Mater. 2020;5:410–22.
Jackson SL, Cogswell ME, Zhao L, Terry AL, Wang C-Y, Wright J, et al. Association between urinary sodium and potassium excretion and blood pressure among adults in the united states: national health and nutrition examination survey, 2014. Circulation. 2018;137:237–46.
Wu Y, Qian Y, Pan Y, Li P, Yang J, Ye X, et al. Association between dietary fiber intake and risk of coronary heart disease: A meta-analysis. Clin Nutr. 2015;34:603–11.
Jawhara M, Sørensen SB, Heitmann BL, Andersen V. Biomarkers of whole-grain and cereal-fiber intake in human studies: a systematic review of the available evidence and perspectives. Nutrients. 2019;11:2994.
National Cancer Institute. Register of validated short dietary assessment instruments |EGRP/DCCPS/NCI/NIH. 2021 [cited 2023 Sep 26]. Available from: https://epi.grants.cancer.gov/diet/shortreg/.
Thompson FE, Subar AF, Smith AF, Midthune D, Radimer KL, Kahle LL, et al. Fruit and Vegetable assessment: performance of 2 new short instruments and a food frequency questionnaire. J Am Diet Assoc. 2002;102:1764–72.
Greene GW, Resnicow K, Thompson FE, Peterson KE, Hurley TG, Hebert JR, et al. Correspondence of the NCI fruit and vegetable screener to repeat 24-h recalls and serum carotenoids in behavioral intervention trials12. J Nutr. 2008;138:200S-204S.
Alcantara I, Haardörfer R, Gazmararian JA, Hartman TJ, Greene B, Kegler MC. Relative validation of fruit and vegetable intake and fat intake among overweight and obese African-American women. Public Health Nutr. 2015;18:1932–40.
Serdula M, Coates R, Byes T, Mokdad A, Jewell S, Chavez N, et al. Evaluation of a brief telephone questionnaire to estimate fruit and vegetable consumption in diverse study populations. Epidemiology. 1993;4:455.
Adams ML, Grandpre J, Katz DL, Shenson D. The impact of key modifiable risk factors on leading chronic conditions. Prev Med. 2019;120:113–8.
England CY, Andrews RC, Jago R, Thompson JL. A systematic review of brief dietary questionnaires suitable for clinical use in the prevention and management of obesity, cardiovascular disease and type 2 diabetes. Eur J Clin Nutr. 2015;69:977–1003.
Rifas-Shiman SL, Willett WC, Lobb R, Kotch J, Dart C, Gillman MW. PrimeScreen, a brief dietary screening tool: reproducibility and comparability with both a longer food frequency questionnaire and biomarkers. Public Health Nutr. 2001;4:249–54.
Fung TT, Isanaka S, Hu FB, Willett WC. International food group–based diet quality and risk of coronary heart disease in men and women. Am J Clin Nutr. 2018;107:120–9.
Brennan S, Finlay R, Ferrari M, Grohmann T, Courtney A, Cardwell C, et al. Validity and reproducibility of the Prime Diet Quality Score (PDQS) against a four-day food diary in adults at risk of cardiovascular disease on the island of Ireland. Proc Nutr Soc. 2022;81:E97.
Segal-Isaacson CJ, Wylie-Rosett J, Gans KM. Validation of a short dietary assessment questionnaire: the rapid eating and activity assessment for participants short version (REAP-S). Diabetes Educ. 2004;30:774–81.
Murphy SP, Kaiser LL, Townsend MS, Allen LH. Evaluation of validity of items for a food behavior checklist. J Am Diet Assoc. 2001;101:751–61.
Paxton AE, Strycker LA, Toobert DJ, Ammerman AS, Glasgow RE. Starting the conversation performance of a brief dietary assessment and intervention tool for health professionals. Am J Prev Med. 2011;40:67–71.
Block G, Gillespie C, Rosenbaum EH, Jenson C. A rapid food screener to assess fat and fruit and vegetable intake. Am J Prev Med. 2000;18:284–8.
Herforth AW, Wiesmann D, Martínez-Steele E, Andrade G, Monteiro CA. Introducing a suite of low-burden diet quality indicators that reflect healthy diet patterns at population level. Curr Dev Nutr [Internet]. 2020 [cited 2023 Sep 26];4. Available from: https://cdn.nutrition.org/article/S2475-2991(22)12102-5/fulltext. This article aimed to develop and validate a suite of simple indicators that reflect adherence to global dietary recommendations.
Women’s Dietary Diversity Project (WDDP) Study Group. Development of a dichotomous indicator for population-level assessment of dietary diversity in women of reproductive age. Curr Dev Nutr. 2017;1:cdn.117.001701.
Bastien M, Poirier P, Lemieux I, Després J-P. Overview of Epidemiology and Contribution of Obesity to Cardiovascular Disease. Prog Cardiovasc Dis. 2014;56:369–81.
Nuttall FQ. Body Mass Index. Nutr Today. 2015;50:117–28.
Troiano RP, Frongillo EA, Sobal J, Levitsky DA. The relationship between body weight and mortality: a quantitative analysis of combined information from existing studies. Int J Obes Relat Metab Disord J Int Assoc Study Obes. 1996;20:63–75.
Dietary Reference Intakes for Calcium and Vitamin D [Internet]. Washington, D.C.: National Academies Press; 2011 [cited 2023 Sep 26]. Available from: http://www.nap.edu/catalog/13050.
Stallings VA, Harrison M, Oria M, editors. Dietary Reference Intakes for Sodium and Potassium [Internet]. Washington, D.C.: National Academies Press; 2019 [cited 2023 Sep 26]. Available from: https://www.nap.edu/catalog/25353.
Cogswell ME, Maalouf J, Elliott P, Loria CM, Patel S, Bowman BA. Use of urine biomarkers to assess sodium intake: challenges and opportunities. Annu Rev Nutr. 2015;35:349–87.
Jun J, Arendt SW. How do social norms affect customers’ food selections at restaurants? Investigating social norms misalignment using polynomial regression with response surface analysis. J Hosp Mark Manag. 2020;29:702–21.
Ocké MC. Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis. Proc Nutr Soc. 2013;72:191–9.
Ryan PD [R-W-1]. H.R.4174 - 115th Congress (2017-2018): Foundations for Evidence-Based Policymaking Act of 2018 [Internet]; 2019 [cited 2024 May 13]. Available from: https://www.congress.gov/bill/115th-congress/house-bill/4174.
USDA ERS - ERS Data Product Quality Standards [Internet]. [cited 2024 May 13]. Available from: https://primary.ers.usda.gov/about-ers/policies-and-standards/data-product-quality/ers-data-product-quality-standards/#transparency.
Acknowledgements
This research was supported by the US Department of Agriculture, Economic Research Service Strategic Priority Grant program though cooperative agreements 58-4000-1-0093 with the University of California San Francisco (H.K.S., Principal Investigator) and 58-4000-1-0084 and 58-4000-2-0054 with the University of South Carolina (E.A.F., Principal Investigator).
Funding
Open access funding provided by the Carolinas Consortium. Economic Research Service, United States, 58-4000-1-0084, 58-4000-1-0093.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by E.K. and V.O.A. The first draft of the manuscript was written by E.K. and V.O.A. and all authors commented on previous versions of the manuscript. A.C.-J. and E.A.F. acquired the funding. E.A.F. provided supervision. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Disclosure
The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official US Department of Agriculture or US Government determination or policy.
Conflict of Interest
The authors declare no competing interests.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Kenney, E., Adebiyi, V.O., Seligman, H.K. et al. Assessing and Monitoring Nutrition Security in the United States: A Narrative Review of Current Measures and Instruments. Curr Nutr Rep (2024). https://doi.org/10.1007/s13668-024-00547-7
Accepted:
Published:
DOI: https://doi.org/10.1007/s13668-024-00547-7