Development and evaluation of a nutrient-based method of classifying moderation foods

Development of a nutrient-based method for classifying foods to be consumed in moderation | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Development of a nutrient-based method for classifying foods to be consumed in moderation View ORCID Profile Amara Channell , View ORCID Profile Leah M. Lipsky , View ORCID Profile Allison Choe , View ORCID Profile Tonja R. Nansel doi: https://doi.org/10.1101/2025.03.20.25324317 Amara Channell 1 NICHD DoigPhD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Amara Channell For correspondence: amara.channelldoig{at}nih.gov Leah M. Lipsky 1 NICHD PhD, MHS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leah M. Lipsky Allison Choe 1 NICHD BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Allison Choe Tonja R. Nansel 1 NICHD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tonja R. Nansel Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background While the Dietary Guidelines for Americans recommend limiting foods high in added sugars, saturated fat, refined grains, and sodium, they do not provide criteria for classifying foods high in these components. Objective To develop a nutrient-based method to classify moderation foods based on the 2020-2025 DGA. Design The 2017-2018 Food and Nutrient Database for Dietary Studies (FNDDS, n=6909 foods) was used to evaluate content validity, by examining the proportion of moderation foods within recommended and non-recommended food groups, and convergent validity, by comparing nutrient density in moderation versus non-moderation foods. Dietary recall data from the cross-sectional, nationally representative National Health and Nutrition Examination Survey (2017-2018) were used to assess convergent validity, by investigating correlations of moderation food intake (% kcal) with overall diet quality. Participants Participants aged 2+ years (n=6180) were included. Main outcome measures The Nutrient Rich Foods 9.3 index (NRF) measured food-level nutrient density; Healthy Eating Index-2020 scores (HEI-2020) measured person-level diet quality. Statistical Analyses T-tests and ANOVA evaluated differences in nutrient density by moderation food classification. Pearson’s correlations estimated associations of moderation food intake (% kcal) with HEI-2020. Results More non-recommended (e.g., 97% of snacks, 97% of sweets) than recommended (e.g., 19% of vegetables, 23% of fruits) food groups were classified as moderation foods. Mean nutrient density was significantly lower in moderation foods than non-moderation foods (mean diff in NRF scores = -74.8, 95%CI:-70.6 to -78.9). Moderation food intake (% kcal) was negatively correlated with HEI-2020 (r=-0.72), HEI-moderation (r=-0.56), and HEI-adequacy (r=-0.67). Conclusions The moderation food classification method demonstrated strong content and convergent validity, and may facilitate diet quality assessment, surveillance, and public health interventions. Funding This research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development Intramural Research Program. The funding body had no role in the study design; collection, analysis, and interpretation of data; or writing of the manuscript. Introduction The 2020 – 2025 Dietary Guidelines for Americans (DGA) recommend limiting intake of foods and beverages high in added sugars, saturated fat, refined grains, and sodium, along with limiting alcoholic beverages 1 due to associations with adverse health outcomes. 2 – 12 These foods are often energy-dense and may displace intake of nutrient-rich foods needed to meet nutritional requirements (“core foods”). 1 However, while the DGA specify recommended overall intake limits for these nutrients, they do not provide criteria for identifying foods high in nutrients to limit. Given that people select and consume foods, not nutrients, establishing evidence-based criteria for classifying foods that are high in nutrients to limit would facilitate accessible, food-based dietary guidance and nutrition surveillance, improve research on diet and health outcomes, and inform intervention targets to improve diet quality. 13 Existing a priori metrics for assessing diet or food quality, such as diet quality indices and nutrient profiling methods, do not provide a standardized food-level approach to identify foods high in DGA nutrients to limit. For example, the Healthy Eating Index (HEI) – 2020, a measure of alignment of overall diet quality with the DGA, includes four moderation components for refined grains, sodium, added sugar, and saturated fat, which are scored at the overall intake level (per 1,000 kcal or as a percentage of total energy). 14 However, the HEI lacks food-level thresholds for moderation components, limiting the ability to identify foods that should be consumed in moderation, i.e., “moderation foods”. Similarly, nutrient profiling methods, such as the Nutrient Rich Food Index (NRF) 9.3 15 and the Food Compass score, 16 , 17 calculate nutrient-based scores for the purpose of ranking individual foods by overall nutritional value, but these have been based on heterogeneous nutrient criteria, reference amounts, and weighting schemes, and not all methods designate cut-offs for categorizing foods. Several approaches for categorizing foods by nutrients to limit have been developed to assist charitable food organizations in procuring healthy foods; 18 – 21 however, since they were developed for this specific purpose, several characteristics (e.g., different criteria for each food group, some foods omitted, and required label reading or ingredient lists) limit their application to dietary intake data. Additionally, regulatory and policy frameworks, including the U.S. Food and Drug Administration (FDA) “healthy” or “high in” labeling criteria, 22 , 23 address most nutrients to limit (excluding refined grains and alcohol), but were developed to regulate food manufacturers’ labeling practices and do not apply to the full range of food groups, non-packaged foods, or mixed foods consumed by the U.S. population. Additionally, FDA thresholds are based on the FDA-defined serving size and 20% daily values for a fixed 2,000-kcal reference intake level, rather than food-specific nutritional profiles, 22 , 23 which may obscure nutrient density and pose challenges for informing a consistent method to identify foods high in nutrients to limit. For example, according to the FDA definition, plain sugar would not be considered high in added sugar since a serving size of 1 tsp contains 16 kcal, which is less than 20% of the daily value for a 2,000-kcal diet. Front-of-package labeling thresholds developed in other countries are informed by country-specific dietary guidance and priorities and are not intended to align with DGA-based recommendations. 24 , 25 A standardized, evidence-based approach for identifying moderation foods that is applicable to food- and dietary intake databases could enable consistent application across studies and facilitate direct translation of dietary guidance into actionable intervention targets and public health initiatives to improve diet quality in the US. Therefore, this study aimed to (1) develop a quantitative, nutrient-based method for classifying moderation foods consistent with the 2020 DGA that can be calculated using existing food and dietary intake databases for research, and (2) evaluate its ability to differentiate foods according to nutrient density and distinguish between diets of high versus low quality. Methods Process for developing the Moderation Food Classification Method (MFCM) Three guiding principles were used for developing the MFCM, including (1) alignment with the nutrient recommendations identified in the DGA 2020 – 2025, (2) alignment with food group categorizations in the DGA and peer-reviewed literature, (3) parsimony and applicability to the FNDDS food codes and dietary intake data collected in the What We Eat in America (WWEIA) National Health and Examination Survey (NHANES) dietary recalls or using the National Cancer Institute Automated Self-Administered 24-hour dietary recall application (ASA24). The first principle was operationalized by aiming to use DGA-based criteria to identify foods containing high amounts of dietary components to limit in the DGA 1 and referred to as moderation components in the HEI – 2020 14 (i.e., added sugars, saturated fat, sodium, and refined grains). The second principle was operationalized by aiming to classify as moderation foods those that are consistently described in the scientific literature as moderation, discretionary, junk, or extra (e.g., sweets, salty snacks, sugar sweetened beverages, high sugar cereal, processed meats, fried potatoes, high-fat fast food, as outlined in Figure 1 ), 26 – 41 while not classifying as moderation those foods described as nutrient-dense (e.g., “vegetables, fruits, whole grains, seafood, eggs, beans, peas, and lentils, unsalted nuts and seeds, fat-free and low-fat dairy products, and lean meats and poultry—when prepared with no or little added sugars, saturated fat, and sodium”). 1 The third principle was operationalized by aiming to develop thresholds that can be calculated from variables currently available in the FNDDS and ASA24 data and applied across all food groups within these data sources, deviating from established thresholds or creating food-group specific thresholds only if required to ensure consistency with the first two principles. Download figure Open in new tab Figure 1. Foods defined as foods to limit or nutrient dense for measurement development and content validity. Threshold development Aligned with the first principle, initial thresholds were selected in accordance with values of nutrients of to limit referred to in the DGA 1 and the HEI-2020 14 scoring algorithm. In alignment with the second principle, initial thresholds were evaluated with respect to their accuracy in classifying foods to limit and nutrient-dense foods within each food group. Since the HEI – 2020 and the 2020-2025 DGA 1 were developed for evaluating overall dietary intake, the initial thresholds based on these guidelines resulted in some misclassifications at the individual food level. The initial thresholds were then modified accordingly, by incorporating food-group specific thresholds or additional established criteria, where appropriate, in accordance with the third principle. Each of the thresholds tested and a summary of the findings are presented in Figure 2 . Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Figure 2. Moderation Food Classification Method thresholds tested, classification findings, and decisions. Added Sugars Since individual foods that modestly exceed the DGA limit for individual nutrients may still contribute to a healthful diet in balance with overall intake, the initial threshold for added sugars in an individual food was set to ≥20% of kcal, which is twice the maximum recommended daily intake of added sugars for the whole diet (10% of total daily energy intake). 1 This threshold correctly classified the majority of the high sugar sweets and sugar sweetened beverages and resulted in no false positives, so was unchanged. Fats Using the same rationale as for added sugars, the initial threshold for saturated fat was set to ≥20% of kcal, which is twice the DGA maximum recommended daily intake of saturated fat for the overall diet. 1 Evaluation of this threshold resulted in misclassifications in the vegetables and snacks and sweets food groups based on the 2 nd guiding principle, since several vegetables cooked with a small amount of butter (e.g. broccoli, collards, or carrots cooked with butter) exceeded the saturated fat threshold (false positive) due to the inherently low energy density of vegetables, whereas fried vegetables (e.g., fries, onion rings, vegetable fritters) and several high-fat snacks and sweets (e.g., potato chips, doughnuts) were not classified as moderation foods since they contained primarily unsaturated fat (false negative). Although current dietary guidelines do not limit unsaturated fat at the dietary level, the addition of oils to these foods substantially reduces nutrient density and increases energy density; these foods are also consistently referred to in the literature as discretionary, junk, extra, or moderation. 26 – 40 To address these misclassifications, we evaluated multiple alternative approaches to incorporate both saturated and unsaturated fat for the vegetable and snacks/sweets food groups only ( Figure 2 ). The final thresholds that resulted in the strongest alignment with the guiding principles included a ≥9% cut-off for total fat by weight, applied only to these food groups; and a threshold of ≥20% kcal from saturated fat applied to all food groups except vegetables. Sodium An initial threshold of ≥2.0 grams of sodium per 1,000 kcal was evaluated, based on the value corresponding to the minimum score for the HEI-2020 sodium component. 42 However, because sodium has no energy value, using an energy density-based cut point developed for the whole diet resulted in misclassifications (false positives) when applied to individual foods. For example, several vegetables cooked with a small amount of salt exceeded this threshold due to their low energy content (e.g. raw spinach, fresh kale, carrots, green beans, or broccoli cooked with no added fat). After examining multiple alternative thresholds using energy or weight as the referent value, the threshold was revised to align with the FDA definition of a high-sodium food (460 mg per serving), which corresponds to 20% of the recommended maximum daily intake for a 2,000-kcal diet (2300 mg). 43 , 44 Refined Grains The initial threshold of ≥50% of total grains from refined grains, based on the DGA recommendation that half of grains consumed should be whole grains, 1 was selected for classifying foods as moderation. However, this threshold resulted in misclassification of sprouted grain bread, corn tortillas, and other foods made from masa as moderation foods (false positives). While these foods are labeled as refined grains in the USDA Food Patterns Equivalents Database (FPED), this contradicts guidance from WIC, 45 the National School Lunch Program, 46 and the Child and Adult Care Food Program 47 , which refers to these foods as whole grains. To address this, the final refined grains threshold incorporated the American Heart Association whole grain threshold 48 (carbohydrate to fiber ratio of < 10:1) in addition to the initial DGA-based threshold of ≥50% total grains from refined grains. To avoid misclassifying mixed dishes containing only a small amount of refined grains (e.g., soup thickened with cornstarch), the grain criteria were applied only to foods containing at least 0.5 ounce-equivalents of total grains. Alcohol Based on the DGA guidance to limit alcohol intake, 1 all alcoholic beverages were classified as moderation. Final Criteria The final criteria for classifying moderation foods are presented in Figure 3 . Any food meeting at least one component criterion is classified as a moderation food. In addition to the dichotomous moderation food classification system, an ordinal classification value was created by summing the number of component thresholds met. Download figure Open in new tab Figure 3. Final criteria for the Moderation Food Classification Method. Foods are classified as moderation if they meet any of the criteria. Finally, in alignment with the first two guiding principles, it was determined that no whole plant foods (i.e. whole fruit, vegetables, whole grains, legumes, nuts, or seeds) should be classified as a moderation food. 1 Only three food items (Brazil nuts, fresh coconut, and avocado [raw or for use on a sandwich]) were misclassified (due to their naturally occurring fat) using the established criteria, so these were recoded manually as non-moderation foods. Evaluation of the MFCM The MFCM was evaluated by assessing its content and construct validity ( Figure 4 ). Using FNDDS, content validity was examined by evaluating the accuracy of the method for classifying foods to limit versus foods to encourage as described in the literature, and convergent validity was examined by evaluating the difference in mean nutrients to limit and nutrient density (NRF 9.3 score) of foods classified as moderation versus those classified as non-moderation. Internal construct validity was assessed at the diet level by descriptively examining the proportion of total daily intake of nutrients to limit from moderation foods. Convergent validity was also assessed by examining the strength of the relationship of percent of daily energy intake from moderation foods with overall diet quality measured using the HEI-2020. Download figure Open in new tab Figure 4. Approaches used to evaluate the Moderation Food Classification Method. Data sources Food-level nutrient data The 2017-2018 United States Department of Agriculture (USDA) Food and Nutrient Database for Dietary Studies (FNDDS) 49 was used to evaluate the MFCM at the food level. FNDDS is released for each cycle of NHANES and provides nutrient values and gram amounts for foods and beverages reported in the WWEIA dietary intake assessment. The accompanying Food Patterns Equivalents Database (FPED) 50 converts the foods and beverages to their corresponding USDA food patterns components (i.e., teaspoon equivalents of added sugars, gram equivalents of saturated fat, and cup equivalents of grains). To facilitate analysis of food and beverage intake, items in these databases are organized into WWEIA food categories based on typical usage and nutrient content. 51 The 2017-2018 FNDDS contains nutrient values for 7083 individual foods and beverages consumed in the United States. However, since the moderation food criteria are based on DGA dietary recommendations for individuals over age two years, infant formula and human milk were excluded from these analyses (n=6909 foods and beverages). The food weight for “quantity not specified” in the FNDDS Portions and Weights dataset, which reflects the most common portion size 49 (similar in concept to the FDA reference amount customarily consumed), was used to indicate serving size. For foods with multiple subcodes, serving size was defined as the mean of the portion weights for all subcodes. Nationally Representative Dietary Data The 2017-2018 National Health and Nutrition Examination Survey (NHANES) 52 , 53 was used to evaluate percent energy intake from moderation foods in the US population. NHANES is a cross-sectional, nationally representative survey of health and nutrition. Estimates based on weighted samples from each NHANES cycle are representative of the US civilian noninstitutionalized resident population, 54 and analyses of recalls are suitable for estimating mean population level intakes from foods and beverages. 53 The National Center for Health Statistics Ethics Review Board approves all protocols for NHANES. 55 Adult participants provide written consent for their participation; for participants <18 years of age, parents or guardians provide written consent, and documented assent is obtained from participants aged 7-17 years. 53 WWEIA collects 24-hour dietary recalls from individuals of all ages using the interviewer-administered five-step USDA Automated Multiple-Pass Method. 53 , 56 Dietary recalls are proxy-reported for children 5 years and younger and proxy-assisted for children ages 6-11 years. NHANES participants also self-report sociodemographic characteristics including age, race, ethnicity, gender, income, and household size. Since the moderation food criteria are based on DGA dietary recommendations for individuals over age two years, the analytic sample for validation included all participants aged two years and older who had two reliable dietary recalls from the 2017-2018 NHANES cycle. Of 6502 participants with two reliable dietary recalls, 322 participants under two years of age were excluded, resulting in a final sample of 6180 participants. Moderation food intake was calculated as the percentage of total intake across both days. Measures Nutrient density Nutrient density for foods in the 2017-2018 FNDDS was estimated using the Nutrient Rich Foods Index (NRF 9.3), which is calculated as the sum of the percent of daily recommended values of nutrients to encourage (protein, fiber, vitamin A, vitamin C, Vitamin E, calcium, iron, magnesium, and potassium) minus the percent of the maximum recommended daily value for nutrients to limit (saturated fat, added sugars, and sodium) per 100 kcal. Higher scores indicate higher nutrient density. 57 Diet quality Person-level diet quality was assessed in NHANES participants using the Healthy Eating Index–2020 (HEI-2020), which reflects adherence to the 2020-2025 Dietary Guidelines for Americans. 1 , 14 The HEI-2020 total score is calculated as the sum of 13 component scores, including 9 adequacy components (for which higher intake earns higher scores) and 4 moderation components (for which lower intake earns higher scores). HEI-2020 14 total scores (range 0-100), HEI-2020 adequacy scores (range 0-60), and HEI-2020 moderation scores (range 0-40) were calculated for each NHANES participant using the simple scoring algorithm. 58 The HEI-2020 adequacy score is calculated as the sum the component scores for total fruit, whole fruits, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafoods and plant proteins, and fatty acids, while the moderation score is calculated as the sum of the component scores for refined grains, sodium, added sugars, and saturated fats. Higher total, adequacy, and moderation scores represent closer alignment with the DGA. 14 Scores are calculated using density standards (e.g., per 1,000 kilocalories) to account for individual differences in energy requirements. Scores for each component are truncated, whereby maximum scores are assigned when intake meets or exceeds the recommended level for adequacy components, or when intake reaches or falls below a threshold for moderation components; a minimum score of zero is assigned when intake is at or below the minimum threshold for adequacy components, or exceeds the maximum threshold for moderation components. Statistical Analysis The statistical programming code used to implement the MFCM and calculate percent intake from moderation foods is provided at https://github.com/amaracd/Moderation-Food-Classification-Method/ . All analyses were completed in SAS 9.4, 59 and statistical significance was set at p<0.05. Design-based statistical methods for all NHANES analyses accounted for the complex, multistage sampling design 60 and were weighted using the dietary recall 2-day sample weights. Content validity To operationalize the second guiding principle, content validity at the food level was evaluated by examining how closely the MFCM food classifications aligned with literature-based characterizations of foods to limit or nutrient-dense foods ( Figure 1 ). The percentage of foods classified as moderation foods and the percentage meeting each moderation criterion were summarized overall, by food group, and by WWEIA category in FNDDS. To indicate good content validity, a high proportion of foods referred to as foods to limit in the peer-reviewed literature, including sweets, salty snacks, sugar sweetened beverages, high sugar cereal, processed meats, fried potatoes, and high-fat fast food (e.g. pizza, chicken nuggets), 26 – 41 should be classified as moderation foods. In contrast, few to none of the foods referred to as nutrient-dense, including vegetables, fruits, whole grains, seafood, beans, peas, and lentils, unsalted nuts and seeds, eggs, fat-free and low-fat dairy products, and lean meats and poultry that are prepared with minimal amounts of added sugars, saturated fats, and sodium, 1 should be classified as moderation foods. Construct validity Aligned with the first guiding principle and the DGA recommendation to select nutrient-dense versions of foods within each food category, convergent validity was examined using FNDDS by comparing nutrient density (NRF 9.3 score) of foods classified as moderation versus non-moderation in total and by food group (t-tests) and across foods meeting 1, 2, and ≥3 MFCM thresholds (ANOVA with Tukey’s HSD post hoc test) ( Figure 4 ). In addition, convergent validity was examined by assessing differences in mean energy density and nutrients to limit between moderation versus non-moderation foods using t-test and across foods meeting 1, 2, and ≥3 MFCM thresholds using ANOVA with Tukey’s HSD post hoc test for multiple comparisons. Internal construct validity at the diet level was evaluated using NHANES data to describe the proportion of total daily intake of nutrients to limit, and convergent validity was evaluated by estimating correlations (Pearson’s ρ) of moderation food intake (% kcal) with overall diet quality (HEI-2020, HEI-2020-moderation, and HEI-2020-adequacy scores). A lower nutrient density of moderation versus non-moderation foods and an inverse association of moderation food intake with diet quality indicators reflect stronger convergent validity. Results Food-level evaluation The MFCM classified 4555 (66%) of 6909 foods and beverages in the 2017-2018 FNDDS (excluding formula and human milk) as moderation foods (data not shown in table). Of these, 60% met one threshold, 33% met two thresholds, and 7% met three or more thresholds. Sodium was the most frequent threshold classifying moderation foods (47%), followed by refined grains (41%), added sugars (24%), saturated fat (20%), and percent fat by weight (16%). Content validity of the MFCM The number and percent of foods classified as moderation foods for each FNDDS food group and WWEIA food category description are presented in Table 1 . Vegetables contained the smallest proportion of moderation foods (19%), while snacks contained the largest proportion (97%). The fruit and beverage food groups contained the smallest proportion of foods that met two nutrient thresholds (<1%), while sweets (50%) contained the highest. Only snacks (4%), mixed dishes (6%), and sweets (28%) contained more than 2% of foods that met three or more thresholds. Within the WWEIA categories, all milkshakes, frankfurters, pizza, nachos, potato chips, cookies and brownies, doughnuts, and soft drinks were classified as moderation foods. Conversely, none of the low-fat or non-fat milk, grapes, melon, broccoli, spinach, string beans, or tomatoes were classified as moderation foods. View this table: View inline View popup Table 1. Number and percent of foods classified as moderation and meeting each Moderation Food Classification Method dietary component criterion by What We Eat in America (WWEIA) food category description. Convergent validity Mean energy density, sodium, carbohydrate to fiber ratio, and percent kcal from added sugar and saturated fat were higher in moderation foods compared to non-moderation foods, while percent grains from whole grain was lower in moderation foods than non-moderation foods (p<0.001; Table 2 ). Mean energy density, carbohydrate to fiber ratio, and percent kcal from saturated fat increased with the number of moderation thresholds met (p<0.05; Table 2 ). Mean percent kcal from added sugar was higher for 1 and 3 thresholds met but lower for 2 thresholds met (p<0.05). Mean sodium increased and mean percent grains from whole grain decreased with the number of moderation thresholds met, although there was no significant difference between those meeting two versus three thresholds. View this table: View inline View popup Download powerpoint Table 2. Summary of dietary components to limit (mean±SD) by dichotomous and ordinal categories based on the Moderation Food Classification Method (MFCM) in the 2017-2018 Food and Nutrient Database for Dietary Studies (FNDDS; n=6909). Nutrient density Mean nutrient density was significantly lower in moderation foods than non-moderation foods overall (mean diff: -74.8, 95% CI: -70.6 to -78.9) ( Figure 5A ) and for each of the individual food groups ( Supplemental Figure 6 ). Mean nutrient density decreased linearly with increasing number of moderation thresholds met, although there was no significant difference between those meeting two versus three thresholds ( Figure 5B ). Download figure Open in new tab Figure 5. A ) Nutrient density for moderation versus non-moderation foods (mean diff: -74.8, 95% CI: -70.6 to - 78.9) according to the Moderation Food Classification Method. B ) Nutrient density according to number of moderation thresholds met. Download figure Open in new tab Supplemental Figure 6. Nutrient density by moderation food classification for each food group. Diet-level evaluation The 2017-2018 NHANES sample was majority non-Hispanic White and 51% female. The mean age was 39 years (SE: 0.5) and mean IPR was 2.9 (SE:0.1) ( Table 3 ). View this table: View inline View popup Download powerpoint Table 3. Analytic Sample Characteristics (n=6180) in the 2017-2018 National Health and Nutrition Examination Survey (NHANES) Construct validity Internal construct validity Moderation foods contributed on average 77% (SE:0.6) of total daily energy intake in the sample ( Table 3 ). Foods exceeding only one moderation food threshold contributed 42% of total daily energy intake (SE: 0.3), while foods exceeding two thresholds contributed 26% of total daily energy intake (SE:0.4), and foods exceeding three or more thresholds contributed 9% of total daily energy intake (SE: 0.3). Moderation foods contributed 94% of total daily added sugar intake (SE: 0.4), 78% of total daily sodium intake (SE:0.5), 83% of total daily saturated fat (SE: 0.5), and 94% of total daily refined grains (SE:0.3). Convergent validity Intake from moderation foods was negatively correlated with HEI-2020 total, HEI-moderation, and HEI-adequacy scores ( Table 4 ). View this table: View inline View popup Download powerpoint Table 4. Pearson correlation of moderation food intake (% kcal) with Healthy Eating Index-2020 (HEI) scores in participants 2 years of age and er (n=6180) in the 2017-2018 National Health and Nutrition Examination Survey (NHANES). Discussion This study developed a nutrient-based method applying standardized criteria to classify moderation foods, i.e., foods that should be consumed in moderation due to containing high amounts of nutrients to limit, aligned with the 2020-2025 DGA. 1 The MFCM demonstrated high content validity, as evidenced by the large proportion of moderation foods in the snacks (97%), sweets (96%), and mixed dishes (87%) food groups, and the small proportion of moderation foods in the vegetable (19%) and fruit food groups (23%), consistent with the second guiding principle. The MFCM demonstrated strong convergent validity at the food level, as indicated by the significant difference in nutrient density between moderation versus non-moderation foods overall and for each food group and the negative relationship of the number of moderation thresholds met with nutrient density. The measure also demonstrated strong convergent validity at the person level, as evidenced by the negative association of moderation food intake (% kcal) with HEI-2020 scores. Food classifications from the MFCM criteria closely align with foods to limit described in prior literature, 26 – 41 and included the top food contributors to added sugar, 61 , 62 saturated fat, 62 , 63 and sodium 64 in the U.S. diet. Of note, snack foods were more often categorized as moderation foods based on the fat and refined grain thresholds than the sodium threshold. This likely reflects their relatively small portion sizes, such that the sodium content per portion did not exceed the threshold. However, this is consistent with national intake data showing that mixed and main dishes contribute more dietary sodium than snacks, the latter accounting for approximately 4% of total sodium intake. 64 Although the positive association of moderation food intake with the HEI-2020 moderation components score indicates alignment with the DGA, the magnitude of the association (r = -0.56) suggests non-overlapping variance between the two scores. Moderation food intake calculated using this food-based method may more precisely reflect intake of nutrient-poor foods than the HEI-2020 moderation score, 14 as it does not truncate intake and it accounts for some nutrients not explicitly captured in the HEI-2020 moderation score, such as unsaturated fat from chips and many sweets. Additionally, while the HEI-2020 moderation score includes nutrients to limits coming from core foods (e.g., saturated fat from low-fat milk or lean meat 65 ), these are not counted as moderation food intake using the food-based method. Future research elucidating whether moderation food intake and HEI-2020-moderation scores are differentially related to health outcomes would be informative. The MFCM was designed to classify foods that are major contributors to the excess intake of nutrients of concern in the U.S. population and therefore should be consumed in moderation. Classifying foods as moderation versus not may be useful for guiding consumer behavior, product labeling, and quantifying the contribution of moderation food intake to overall dietary intake when examining diet-disease relationships. However, any binary food classification will include foods of varying nutritional value within each classification. The inverse associated of nutrient density with the number of thresholds met indicates there is variance in the nutritional value of moderation foods. Similarly, foods not classified as moderation may also vary in their nutritional value and contribution to DGA guidelines, and should be consumed within daily energy requirements across all recommended food groups. The MFCM has utility for research, practice, and policy. This evidence-based, nutrient-based method can be applied across widely varying diets and allows for comparison of findings across studies of diverse populations. The ability to classify individual moderation foods may also facilitate clinical dietary education, 13 actionable consumer guidance, and product labeling. For example, in 2016, the Chilean government mandated warning labels on foods using nutrient thresholds similar to the current moderation food classification. The policy led to decreased intakes of sugar, sodium, and saturated fat, 66 , 67 suggesting that clearly labeled foods can improve eating behaviors. In addition, the MFCM could be used to monitor and evaluate consumer purchasing behaviors and the availability of moderation foods in the food supply, such as in restaurants or schools. Strengths and limitations Strengths of this study include the use of evidence-based criteria applied to the FNDDS database, which supports the internal validity and generalizability of the classification method. The use of FNDDS for classifying moderation foods is a strength, as it is a comprehensive database for foods consumed by the U.S. population and enables implementation of the classification method with data from NHANES WWEIA or the National Cancer Institute Automated Self-Administered 24-hour dietary assessment tool (ASA24®). 68 However, as the criteria were developed for FNDSS, they will likely require adaptation for use in studies based on other nutrient databases. Another strength is the use of a large, nationally representative sample of the civilian, noninstitutionalized U.S. population to estimate associations of moderation food intake with overall diet quality. Limitations include the use of self-reported dietary intake data, which are susceptible to response bias. However, this is not expected to impact comparisons of moderation food intake with HEI-2020 scores, as misreporting similarly impact the independent and dependent variables. Food-level analyses are also limited to the foods included in FNDDS, in which items represent generic food representations instead of specific brands. Consequently, differences in recipe and preparation methods that alter nutrient content may not be fully captured. However, these issues would similarly impact any classification based on nutrient content. Additionally, there is variability in how the MFCM thresholds were established because of the difficulty of directly applying HEI guidance to individual foods. Nevertheless, the thresholds consistently identify lower nutrient-dense foods and demonstrate strong convergent validity. Conclusions These findings support the validity of the MFCM for identifying foods with lower nutrient density and for quantifying moderation food intake, which is inversely related to adherence to dietary guidelines in the U.S. population. By establishing quantitative nutrient-based criteria that effectively identify foods high in nutrients to limit, the MFCM has utility for diet quality assessment, behavioral nutrition interventions, and investigations of diet-disease relationships. Data Availability All data produced in the present study are available upon reasonable request. Footnotes The manuscript has been revised to clarify the process for developing the measure and the evaluation approach. References 1. ↵ U.S. Department of Agriculture and U.S. Department of Health and Human Services UaH . Dietary Guidelines for Americans , 2020-2025. 2020 . December 2020. Accessed 9/2023. DietaryGuidelines.gov. 2. ↵ Hu H , Zhao Y , Feng Y , et al. Consumption of whole grains and refined grains and associated risk of cardiovascular disease events and all-cause mortality: a systematic review and dose-response meta-analysis of prospective cohort studies . 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