Office of Dietary Supplements, US National Institutes of Health. Micronutrient Information Center, Linus Pauling Institute, Oregon State University. Retrieved June 1, Department of Agriculture and U.

Department of Health and Human Services. December Washington, D. Retrieved June 9, Dietary Reference Intakes for Vitamin A, Vitamin K, Arsenic, Boron, Chromium, Copper, Iodine, Iron, Manganese, Molybdenum, Nickel, Silicon, Vanadium and Zinc.

National Academy Press. ISBN — via www. Community Nutrition Mapping Project. USDA Agricultural Research Service. Retrieved 6 Nov Categories : Nutrition Vitamins. Hidden categories: CS1 errors: periodical ignored Webarchive template wayback links Articles with short description Short description is different from Wikidata All articles with unsourced statements Articles with unsourced statements from March Pages displaying short descriptions of redirect targets via Module:Annotated link.

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legumes , grain products, nuts and seeds [15]. Received: April 27, ; Accepted: July 2, ; Published: August 3, Copyright: © Verger et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

No additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: This research was partially supported by Danone Research. EOV, BAH, and DP are employed by Danone Research. There are no patents, products in development or marketed products to declare. Nutritional epidemiology typically involves the analysis of associations between a specific nutrient, food or food category, and health-related outcomes.

Such an approach fails to consider the complexity of the diet as a whole, which includes multiple correlations between foods and nutrients. Dietary patterns are complementary to classical analyses because they can tackle the diet complexity using a holistic approach [1]. There are two main approaches for characterizing dietary patterns in a population.

The overall adherence or proximity to these dietary patterns is used to build indices of diet quality. The majority of existing indices are based on the traditional Mediterranean diet or national food-based dietary guidelines. One practical drawback of the food-based dietary guidelines approach is that indices can rarely be applied to populations with different dietary practices and therefore must be adapted [7] — [9] or developed [10].

Another drawback is the paucity of nutritional evidence used to construct a food-based index. In contrast, there is a large body of evidence regarding nutrient intakes including recommended dietary intakes and lower and upper intake levels that has not often been used to estimate the overall quality of the diet.

Nutrient-based diet quality indices are robust and adaptable to different populations and countries. For example the Mean Adequacy Ratio index is used as an indicator of nutritional quality [11] , [12] and the Mean Probability Adequacy index provides a composite measure of adequacy of several nutrients [13] , [14].

However these indices do not take into account the upper levels of intake and therefore cannot be used to estimate the overall quality of the diet. Lastly, it has been reported that current diet quality indices present many theoretical and methodological limitations [4] — [6] , [15] , including a lack of evaluation or validation.

This is due partly to a lack of criterion for estimating diet quality and a lack of amenability to classical criterion validation. Nevertheless, some studies have proposed strategies to evaluate the validity of diet quality indices [10] , [16] or a nutrient profile model [17] based on relevant methodologies developed in the psychometric sciences for questionnaire scales [18] , [19].

The aim of this study was therefore to develop a new diet quality index based on the intake of all nutrients, using a probabilistic approach for estimating the adequacy of nutrient intake [20] , and to carry out an evaluation of its validity using French and US national survey data.

Data used in this study came from the French Nutrition and Health Survey Etude nationale nutrition santé - ENNS, — and the US National Health and Nutrition Examination Survey NHANES, — The design, methodology and results of ENNS have been described in full elsewhere [21]. Briefly, the ENNS survey was a multistage stratified descriptive cross-sectional survey undertaken on a randomly selected sample of non-institutionalized 18—74 years olds living in mainland France.

Dietary data were collected using three hour recalls one of which was on the weekend randomly selected within a 2-week period. Dietary recalls were conducted over the telephone by trained dieticians.

Nutritional values for energy and nutrients came from a previously published nutrient database [22] , updated to include recently marketed foods and recipes. Blood samples were collected for determination of plasma folate using competitive immunoassay with direct chemiluminescence and for determination of alpha- and beta-carotene using HPLC.

The design, methodology and results of NHANES has also been described in full elsewhere [23]. Briefly, the NHANES survey was a multistage stratified descriptive cross-sectional survey on a randomly selected sample of the civilian non-institutionalized population of the US, 20 to 80 years old.

Subjects completed two hour recalls, the first of which was collected in-person by trained dieticians and the second was collected over the telephone between 3 and 10 days later. Blood samples were collected for determination of plasma folate using the microbiologic assay.

Carotenoid data were not collected. In both surveys, mean individual intakes of food in grams and nutrients were calculated, including a weighting for the day of the week weekday or weekend day. Nutrient intakes were expressed as absolute values and as a percentage of total energy intake, excluding energy from alcohol.

These food categories are principally the same for the two databases however some minor discrepancies exist due to differences in data collection and coding procedures. bodyweight , iii were pregnant or lactating and iv were identified as over- or under-reporters based on the method proposed by Black and colleagues [24].

The PANDiet aims to measure the overall diet quality of an individual through the probability of having an adequate nutrient intake. We selected 24 nutrients for inclusion in the PANDiet: protein, total carbohydrate, fibre, total fat, saturated and polyunsaturated fatty acids, cholesterol, thiamin, riboflavin, niacin, folate, vitamins A, B-6, B, C, D and E, calcium, magnesium, zinc, phosphorus, potassium, iron and sodium.

This selection was based on the available current national nutritional recommendations for French [25] — [30] and US adults [31] — [38] , and the availability of data in ENNS and NHANES food composition databases.

We used the probabilistic approach developed by the Institute of Medicine [20] to estimate, for each individual, if the usual intake of a nutrient was adequate. The calculation of the probability takes into account the number of days of dietary data, the mean intake and the day-to-day variability of intake, the nutrient reference value and the interindividual variability Figure 1.

For each nutrient, adequate intake was assumed to be the level likely to satisfy the nutrient requirements and unlikely to be excessive and elicit adverse health effects.

Therefore, we assessed separately the probability that the intake was adequate inasmuch as it satisfied the requirement, on one hand, and the probability that it was not excessive, on the other hand.

Consequently, the PANDiet was constructed based on two sub-scores - the Adequacy sub-score and the Moderation sub-score. The Adequacy sub-score was calculated as the average of the probability of adequacy for items for which the usual intake should be above a reference value, multiplied by According to the nutrient reference values, the probability was determined as follows:.

The Moderation sub-score was calculated as the average of the probability of adequacy for items for which the usual intake should not exceed a reference value and penalty values, multiplied by For other vitamins and minerals with available upper tolerable limits but where the risk of excessive intake is low, we used a penalty value system: a value equal to 0 was generated when the average intake of a nutrient exceeded the upper tolerable limit of intake.

The PANDiet score is the average of the Adequacy and Moderation sub-scores. In principle, the score ranges from 0 to ; the higher the score, the better the diet quality. A French implementation of the PANDiet Figure 2 was developed based on the French nutritional recommendations for adults [25] — [27] , including European Community values when specific French recommendations did not exist [28] — [30].

A US implementation of the PANDiet Figure 3 was developed based on the US nutritional recommendations for adults [31] — [38]. Although the structure of these two implementations is almost identical, it should be noted that the differences in reference values renders cross-national comparisons of PANDiet scores meaningless.

The Adequacy sub-score is composed of 21 items and the Moderation sub-score is composed of 6 items plus 12 potential penalty values.

EIEA, Energy Intake Excluding Alcohol. The Adequacy sub-score is composed of 21 items and the Moderation sub-score is composed of 5 items plus 9 potential penalty values. The French and US implementations of the PANDiet were evaluated by assessing their content and construct validity.

Content validity consists of a judgment as to whether or not the index samples all the relevant or important content or domains [18] , [19].

The correlations between the individual items and the PANDiet and the relationship between the PANDiet score and energy intake were investigated.

The latter was checked in order to verify if a higher score would be automatically attributed to a higher energy diet. Construct validity is an on-going process which involves three steps: 1 explicitly spelling out a set of theoretical concepts and how they are related 2 developing scales to measure these theoretical constructs and 3 testing the relationships among the constructs [18] , [19].

We selected specific traits supported by literature in both France and the US:. In addition, given that fruit and vegetable intakes are main contributors to intakes of folate [47] and carotenoids [48] , we hypothesised that higher plasma folate and carotenoids concentrations would reflect diet quality.

Accordingly, participants with a higher PANDiet score should be more likely to have a higher plasma folate, alpha and beta-carotene concentrations. All analyses were performed using SAS version 9. Weighting schemes proposed by ENNS and NHANES were used to account for the complex survey designs and were adapted to the population samples analyzed.

To describe the distribution of the PANDiet, elemental statistics mean, standard error of the mean and quartiles were used. Continuous variables are presented as mean ± SEM. Associations between the PANDiet dependent variable and sex, age, smoking status, total energy density of the diet, food intakes, plasma folate, and alpha- and beta-carotene independent variables were assessed in simple linear models and in a multivariate model after adjusting for age, sex and smoking status where appropriate.

The mean PANDiet score was The correlations between the PANDiet score and PANDiet items were as expected, except for PUFA, zinc, vitamin A, vitamin B and vitamin D Table 1.

Figure 4 presents the results for the PANDiet score according to 10 food groups identified as likely to indicate diet quality, important in terms of nutrition policies and with a robust number of consumers. Full results for all food groups are shown in Table S2.

Participants with a higher PANDiet score had a diet higher in the intake of milk, other dairy products e. Relative mean intake of each quartile shown as a percentage compared to the highest observed mean intake across the quartiles for selected foods among the French sample.

ENNS — The correlations between the PANDiet score and PANDiet items were as expected, except for PUFA, vitamin B and vitamin E Table 3. As shown in Figure 5 , participants with a higher PANDiet score had a diet higher in the intake of milk, other dairy products e.

Full results are shown in Table S2. Relative mean intake of each quartile shown as a percentage compared to the highest observed mean intake across the quartiles for selected foods among US sample. NHANES — The present study describes the development of a new diet quality index, the PANDiet.

This index provides a measure of overall diet quality and each PANDiet item assesses the probability of adequate nutrient intake according to a specific nutritional reference.

We report the strategy used to evaluate the validity of this index, and the ensuing validity elements based on the application of the PANDiet to data from two different populations.

The correlation between the PANDiet score and PANDiet items reflect the contribution of the variation of each item to the variation of the PANDiet score. In both implementations, we found that the items related to total carbohydrates lower bound , total fat upper bound , SFA, fibre and vitamin C had the most important influence on the PANDiet score and thus, satisfying the recommendations for these nutrients were the most important factors in discriminating the diet quality of the population samples analyzed.

Conversely, low correlations reflected that some nutritional recommendations were not discriminating factors and the related items did not influence the PANDiet score e. vitamin D. Nevertheless, such items still provide important information and need to be taken into account in an overall assessment of diet quality.

Recent publications have emphasized that diet quality indices developed to date present several unresolved methodological issues that may reduce their diagnostic capacity [4] — [6] , [15].

One issue concerns the existence of high inter-correlations between index items that may lead to an undesirable over-contribution of some items to the score. The inter-correlations between items of the PANDiet reflect the complexity of the diet and interactions between dietary and nutrient intakes.

These inter-correlations do not point to a problem of assessing similar aspects of the diet with different items. Because of the lack of a science-based rationale to develop a weighting system for the nutrients, we used an equal weighting for nutrients within each sub-score of the PANDiet.

It should be noted that using two sub-scores and averaging their scores to provide the final PANDiet score designates a higher weight to the items of the Moderation sub-score than to the items of the Adequacy sub-score since the former includes fewer items than the latter.

Like very few other diet quality indices [10] , [16] , the validity of the PANDiet was evaluated through a strategy based on methodologies developed in the psychometric sciences [18] , [19]. The choice of groups is based on differences in nutrient requirements related to speed of growth, change in endocrine status, such as in puberty, and differences in nutrient absorption or body functions, such as renal function.

Specific needs during pregnancy and lactation are also considered. Hence, DRVs are developed for different life-stages and gender groups, and for different age ranges. For each nutrient, the target populations are set on a case-by-case basis, depending on the available data. For infants below 6 months, nutritional requirements are generally considered to be equal to the supply from breast milk, so no DRVs have been set by EFSA for this group.

However, infants in their first half year may have specific nutritional requirements, which are addressed by national public health policies.

DRVs should not be viewed as recommendations for individuals. Rather, DRVs are scientific references for professionals, who use them when setting nutrient goals for populations or recommendations for individuals.

Nutrient goals and recommendations are tailored to national contexts e. public health priorities, nutritional status, dietary patterns, composition of available foods and may therefore differ from country to country.

ULs for vitamins and minerals published before have been compiled in a single report. Scientific opinions on ULs issued since then are published in the EFSA Journal.

An overview table of all UL values is available for quick reference. The full scientific opinions on DRVs for energy, water, macronutrients and dietary fibre, and for micronutrients excepts ULs have been compiled in a special issue of the EFSA Journal.

An official EU website. An official website of the European Union. Other sites EFSA Open EFSA EFSA Journal Connect.

Dietary reference values. Last reviewed date :. Dietary reference values finder. The draft opinion states that excessive preformed Vitamin A intake may cause defects in the developing fetus.

Lower values have been calculated for younger populations groups. European populations are unlikely to exceed the UL for preformed vitamin A if consumption of liver and other offal is limited to once per month or less.

Women who are planning to become pregnant or who are pregnant are advised not to consume liver products.

Lung cancer risk was selected as the critical effect The adverse effect seen at the lowest dose when a vulnerable population is exposed to a substance such as an environmental or food toxin.

This can relate to humans as well as to other species such as animals, plants or microbes. of excess supplemental β-carotene.

The data does not allow the setting of a UL. The experts concluded however that there is no indication that β-carotene intake from the background diet leads to adverse health effects.

Smokers should avoid consuming food supplements containing β-carotene. The general population should limit supplemental β-carotene intake to the purpose of meeting vitamin A requirements.

EFSA's role EFSA gives independent scientific advice on nutrient intakes to EU risk managers and policy makers. FAQ 1. What are DRVs used for? What is the methodology for setting DRVs? Why are there different types of value to express nutrient requirements?

Are DRVs applicable to individuals suffering from diseases? How are target populations defined? Why are DRVs not set for infants in their first half year of life? What is the difference between DRVs and nutrient goals and recommendations? All DRVs values, including ULs, can also be easily retrieved through the DRVs Finder.

Published on this topic Full list. External Scientific Report. Preparatory work for the update of the tolerable upper intake levels for vitamin A. Published :. Scientific Opinion. Scientific opinion on the tolerable upper intake level for manganese. Scientific opinion on the tolerable upper intake level for folate.

Preparatory work for the update of the tolerable upper intake levels for manganese.