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Implementing food-based dietary guidelines for - United Nations ...

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Terminology and framework <strong>for</strong> nutrient intake values<br />

both “recommended” and “reference.”<br />

In addition to an average and recommended (or<br />

reference) nutrient intake value, recommendations<br />

include a lower reference intake that defines a value<br />

that is below the requirements of most individuals in<br />

the population, an upper intake value that is the highest<br />

intake unlikely to pose any health risks, a safe intake<br />

value (or range) or adequate intake that is used when<br />

the statistical distribution of requirements is unknown,<br />

and a range of intakes <strong>for</strong> macronutrients (adequate<br />

macronutrient distribution range [AMDR]) associated<br />

with a reduced risk of chronic disease.<br />

Proposed terminology <strong>for</strong> nutrient intake<br />

values (NIVs)<br />

To avoid confusion about the meaning of acronyms<br />

and the use of multiple acronyms <strong>for</strong> the same nutrient<br />

intake standard, we propose that a common set<br />

of terms and definitions be used by all countries and<br />

regions establishing NIVs. We recognize, however,<br />

that the absolute values will probably differ among<br />

the various countries and regions, because differences<br />

in body size, the bioavailability of <strong>food</strong> sources, and<br />

environmental conditions that affect nutrient needs<br />

(e.g., sunshine exposure or altitude) will influence the<br />

statistical distribution of nutrient requirements in a<br />

particular population. Nevertheless, we believe that the<br />

same terms and definitions can be used worldwide. The<br />

following terms are recommended.<br />

Average nutrient requirement (ANR)<br />

The ANR is the average or median requirement estimated<br />

from a statistical distribution of requirements<br />

<strong>for</strong> a specific criterion and <strong>for</strong> a particular age- and<br />

sex-specific group.<br />

Individual nutrient level x (INL x )<br />

The INL x is the recommended nutrient level <strong>for</strong> all<br />

healthy individuals in a specific subpopulation. Often<br />

committees add 2 SD to ANR, which will cover the<br />

needs of most (i.e., 98%) of the population, assuming<br />

that the distribution is symmetrical. In that case, the<br />

INL x would be INL 98 . But one might choose a lower<br />

level in the distribution of requirements <strong>for</strong> the recommended<br />

intake if the current nutrient policies of the<br />

country or region conclude that it is not practical to<br />

achieve an intake equal to the 98th percentile <strong>for</strong> the<br />

entire population. This might be the case, <strong>for</strong> example,<br />

if the additional cost of providing fish rich in omega-3<br />

fatty acids is inconsistent with the <strong>food</strong> supply <strong>for</strong> a<br />

country. Then one might choose to make a recommendation<br />

at a lower level, such as 1.5 SD above the<br />

ANR, or at the 93rd percentile. In that case, the INL 93<br />

S21<br />

would meet the needs of 93% of the population. Thus,<br />

the INL x <strong>for</strong> a specific country or region should be<br />

consistent with the overall nutrition policy.<br />

Upper nutrient level (UNL)<br />

The UNL is the highest level of daily nutrient intake<br />

that is likely to pose no risk of adverse health effects<br />

<strong>for</strong> almost all individuals in a specified life-stage group.<br />

Ideally, it is <strong>based</strong> on an analysis of the statistical distribution<br />

of risk <strong>for</strong> high nutrient intakes. The UNL is<br />

usually set at a level where the risk of excessive intakes<br />

is close to zero. Also, the UNL is a level of intake that<br />

should be avoided on a chronic basis [9].<br />

General framework <strong>for</strong> estimating nutrient<br />

intake values (NIVs)<br />

Starting with the report by the <strong>United</strong> Kingdom recommending<br />

a multiple reference framework <strong>for</strong> quantitative<br />

nutrient standards [10] and the subsequent reports<br />

of the US/Canadian <strong>dietary</strong> reference intakes (DRIs)<br />

[3–7], a new statistical approach was used <strong>for</strong> making<br />

<strong>dietary</strong> recommendations. The general features of this<br />

model were adopted by a number of other countries or<br />

regions, such as the Germanic language nutrition societies<br />

[15], South Korea [13], Southeast Asia [14], and<br />

Australia and New Zealand [16], as well as WHO/FAO<br />

[23]. The framework <strong>for</strong> assessing intakes of population<br />

groups stems from the work of a subcommittee of<br />

the US Food and Nutrition Board [25] that related the<br />

probability of inadequacy to nutrient requirements.<br />

The framework has since been expanded to include<br />

both assessment and planning of nutrient intakes and<br />

can now be applied to both individuals and groups<br />

[17, 26].<br />

Determining the average nutrient requirement<br />

The first step in estimating nutrient requirements is<br />

to determine the average nutrient requirement (ANR)<br />

<strong>for</strong> a defined population from primary research data<br />

(fig. 1). Once the distribution of requirements is<br />

known, the ANR can be estimated. Often the ANR<br />

<strong>for</strong> a population is estimated from experimental data<br />

derived from a subsample of individuals in the defined<br />

group, because in practice, rarely, if ever, are the specific<br />

nutrient requirements known <strong>for</strong> each person in<br />

a group of interest. However, it is usually not possible<br />

to mirror completely the diversity of the group. For<br />

most nutrients, data on individual responses to varying<br />

levels of nutrient intake are scant, resulting in the<br />

need to make many assumptions about the attributes of<br />

the population group from data obtained from a small<br />

subsample that has been studied in depth. With each<br />

assumption, there is potential error and a decrease in

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