Results of a recent study of spatial working memory are presented in support of the claim by Jenkins and her colleagues [1] that secondary tasks produce larger interference effects in individuals with higher simple spans than in individuals with lower simple spans.

Because spans and interference effects were assessed independently, this study refutes the claim by Oberauer and SiiB [2] that the relation between span and interference effect size is merely an artifact of regression to the mean.

In contrast with the present findings, Oberauer and SiiB did not find evidence of larger interference effects in higher span individuals, but the reason for this may be straightforward: The secondary tasks that they used did not produce significant interference.

Recent findings by Logie and his colleagues indicate that phonological similarity and word length effects are larger in individuals with higher word spans [3].

These results, those of Jenkins, Myerson, et al. [ 1], and the results reported here suggest that, across individuals, the absolute effect size for many manipulations that decrease memory span is an increasing function of simple span.


When the stock market goes down, individuals' losses are principally determined by two factors.

One factor obviously is which stocks they own, but if the decline is sufficiently broad-based, the major factor may be simply how much money they have invested or how many shares they own.

For any given stock, the more shares they hold, the greater their loss is likely to be.

The results of a recent meta-analysis [1] indicate that, similarly, the more items individuals hold in working memory, the more items they are likely to lose when they perform a secondary task.

This suggests that the representation of each item may be at risk when secondary task responses either interrupt rehearsal or introduce new information, and for span-length lists the degree of risk is independent of the number of other items.

The result, simply put, is that "the more you have, the more you have to lose."


When Jenkins and her colleagues [1] examined the effects of secondary tasks on working memory across the life span, they found little evidence of a systematic developmental trend in interference effects (i.e., the difference between span without a secondary task, or simple span, and span with a secondary task, or complex span).

Within each age group, moreover, individuals who had larger simple spans tended to show larger interference effects than did individuals who had smaller simple spans.

These results are not easily explained by current working memory theories, particularly those that emphasize the role of inhibition and susceptibility to interference in explaining individual and age differences [4][5][6].

However, it is to be hoped that the simple stock market analogy just outlined will make these findings seem less counterintuitive.


Oberauer and SuB [2], in their recent commentary, did not challenge Jenkins, Myerson, et al. 's [1] conclusions regarding the lack of systematic age trends in interference effects, but they did object to Jenkins, Myerson, et al. 's conclusions regarding individual differences in interference effects on both methodological and conceptual grounds.

From a methodological (or statistical) perspective, Oberauer and SuB claimed that Jenkins, Myerson, et al. 's results with respect to individual differences were an artifact of regression to the mean, resulting from the unreliability of memory span measures.

From a conceptual (or theoretical) perspective, Oberauer and SuB claimed that regardless of the results, such data are not relevant to an interference/inhibition account of working memory, because simple and complex span tasks measure different constructs, and only complex spans involve inhibition.

As a result, they contended, an interference/inhibition account does not predict smaller interference effects in individuals with larger simple spans.


We take issue with the latter position, but our argument would probably be moot if Jenkins, Myerson, et al. 's [1] original conclusions were shown to be false.

Therefore, we shall begin by presenting evidence that deals with Oberauer and SuB's [2] methodological objection and that supports Jenkins, Myerson, et al. 's conclusion that high-span individuals are more affected than low-span individuals by interference from secondary tasks.


Do Higher Span Individuals Show Larger Interference Effects?


Psychologists have long known that measurement error attenuates the slope of a regression line [7].

Oberauer and SiiB's [2] account of this problem, however, is only partially correct and appears to confuse correlation with regression.

Specifically, it is not true, as Oberauer and SiiB assert, that "the slope of the regression line decreases with decreasing reliability of the variables involved" (pp. 727-728).

This statement is only strictly true in the case of correlation in which the variables are standard scores (i.e., Z scores) and the slope is the correlation between the variables.

In the case of least squares regression more generally, the expected value of the slope estimate is unaffected by the reliability of the criterion (Y) variable, although the reliability of the slope estimate is obviously affected [8].


In contrast to the lack of effect of criterion reliability, the reliability of the predictor (X) variable does affect the expected value of the slope of the regression line [9].

Specifically, measurement error in the predictor attenuates the slope—this being the well-studied problem of error in X [10]—resulting in the phenomenon known as regression to the mean.

The issue of attenuation of regression slopes is relevant, because the pattern of individual differences reported by Jenkins, Myerson, et al. [ 1] was revealed by the regression of complex span on simple span.


For all age groups, the regression slopes were considerably less than 1.0 for both digit and location spans, indicating that the difference between simple and complex spans (i.e., the size of the interference effect) was an increasing function of simple span.

The substantive question, however, is not whether the slopes reported by Jenkins, Myerson, et al. were underestimated (they were, as Jenkins, Myerson, et al. pointed out in their article), but whether that underestimation led Jenkins, Myerson et al. to an incorrect conclusion.


Do individuals with larger simple spans really suffer greater interference from secondary tasks, as Jenkins, Myerson, et al. [ 1] concluded? Jenkins and her colleagues recently conducted an experiment that focused directly on this issue and that was specifically designed to deal with the problem of regression to the mean.

The data obtained from the first group of 60 young adults in this study were reported at the Psychonomic Society meeting in the year before publication of the meta-analysis [11].

At the following meeting [12], these results were compared with those from a second group of 50 older adults.

The findings of this experiment that are most relevant to the concerns raised by Oberauer and SiiB [2] are summarized briefly below, but for a more detailed description of all of the conditions and their results, we refer readers to Jenkins, Myerson, and Hale [13].


This latest experiment by Jenkins and her colleagues focused specifically on spatial working memory [11][12].

The primary memory task was similar to the original spatial span task described in Jenkins, Myerson, et al. [ 1], but the new procedure added three important features.

First, participants recalled locations by touching cells in a grid displayed on a touch-screen monitor, and second, memory span was measured using a psychophysical staircase procedure [14].

Third, and most importantly, performance on the primary span task alone (i.e., with no secondary task) was assessed twice.


Participants were tested on a battery of speeded visuospatial processing tasks as well as a variety of working memory task conditions that assessed their memory span for spatial locations, and the time that it took them to perform secondary tasks was also measured.

For our present purposes, we will focus on the simple span conditions and the two complex span conditions that involved spatial secondary tasks, because these conditions most closely parallel those analyzed in Jenkins, Myerson, et al. [ 1].

In both of these complex span conditions, the secondary tasks required participants to use information from the stimuli for the primary memory task.

These stimuli consisted of series of colored Xs displayed one at a time in the cells of a grid.


In the single-touch complex span condition, the spatial secondary task required participants to touch the circle (one of six differently colored circles displayed to the right of the grid) that was the same color as the X (see Figure 1 in Jenkins, Myerson, et al. [ 1]).

In the double-touch complex span condition, the spatial secondary task had an additional requirement.

After participants touched the circle whose color matched the X, the border of the cell containing the X changed color, and participants then had to touch another circle (one of a second set of four circles to the right of the grid) whose color matched the cell border.

These procedures were designed to be formally analogous to complex span procedures such as the reading span task, a paradigmatic working memory measure [15].

In both cases, a series of stimuli (Xs in a grid or sentences) are presented, and participants must respond to aspects of these stimuli (the colors of the Xs or words other than the final words) that are irrelevant to the primary memory tasks.


At the group level, not surprisingly, both spatial secondary tasks decreased memory span relative to that when there was no secondary task, and the double-touch task produced more interference than did the single-touch task.

For the present purposes, however, the more relevant results concern how individual differences in span modulated these effects.

Because of the way in which this experiment was designed, it was possible to assess differences in the amount of interference suffered by lower and higher span individuals by using two quite different analytic approaches.

The first approach paralleled that used in the Jenkins, Myerson, et al. [ 1] meta-analysis, but because there were two assessments of simple span in the present experiment, it was possible to compare the regression of complex span on simple span with an estimate of the effect of regression to the mean (as provided by the slope of the relation between the two simple span assessments).

In the second approach, the participants were divided into subgroups based on the first assessment of simple span in order to compare interference effects, calculated on the basis of the second assessment of simple span, for lower and higher span subgroups.

Importantly, the two approaches provided converging evidence consistent with the conclusion originally reached by Jenkins, Myerson, et al. [ 1] that higher span individuals suffer greater interference than lower span individuals do.


Consider the results obtained with the use of the first approach, shown in Figure 1, in which simple spans from the first assessment were used to predict the simple spans from the second assessment as well as the complex spans from the single- and double-touch conditions.

Because all three regressions use the same predictor, the error in X is held constant across regressions.

The effect of this error is to attenuate the slope (i.e., the slope estimate is some proportion of the true slope), but because the error in X is held constant, the degree of attenuation is the same for all three regressions [9].

Thus, the slopes for the regression of the two complex spans on the first simple span (0.597 and 0.385 for the single- and double-touch conditions, respectively) can be compared directly with the effect of regression to the mean as indexed by the slope for the regression of the second simple span on the first simple span (0.842).


Multiple regression analyses revealed that the slopes for the complex spans were both significantly less than that predicted by regression to the mean, indicating that the size of the interference effect produced by secondary tasks was an increasing function of an individual's simple span: for single-touch, t(216) = 2.57, p = .011; for double-touch, t(216) = 5.25, p < .001.

Moreover, the slope for the double-touch condition was significantly less than the slope for the single-touch condition, indicating that the effect of an increase in secondary task difficulty on complex span also depended on an individual's simple span [t(216) = 2.44, p = .016].

Thus, the size of the secondary task effect was an increasing function of simple span in all three cases, replicating the results reported by Jenkins, Myerson, et al. [ 1].

There were no significant differences between the regression parameters for young and older adult groups.


Our second analytic approach involved dividing participants into lower span and higher span subgroups and comparing how the subgroups are affected by secondary tasks.

If subgroups are determined on the basis of one assessment and interference effects are calculated on the basis of a second assessment, then measurement error will decrease the power of the comparison but will not systematically bias that comparison.

Accordingly, we performed a median split of each age group based on the first assessment of simple span and calculated interference effects based on the difference between complex span and the second simple span assessment.


Of the various analyses of variance (ANOVAs) conducted on the data, perhaps the most relevant is a planned comparison of the interference effects in the single- and double-touch conditions for higher and lower span young adults and higher and lower span older adults.

This comparison directly addresses the issue of how age and ability affect the amount of interference produced by secondary tasks.


The ANOVA revealed significant effects of age (young vs. old) and ability (lower vs. higher span) [F(1,106) = 13.37, p < .001, and F(1,106) = 8.17, p < .01, respectively].

These main effects reflect the fact that the interference effects of young adults were larger than those of older adults, and the interference effects of higher span individuals were larger than those of lower span individuals.

The difference between the interference effects of higher and lower span individuals was observed in each age group, and there was no age X ability interaction [F(1,106) < 1.0].

There were, however, significant interactions between secondary task (single- versus double-touch) and both age and ability [F(1,106) = 21.71, p < .001, and F(1,106) = 11.14, p = .001, respectively].

There was no three-way interaction [F(1,106) < 1.0].


The interactions with secondary task reflect the fact that the difference between young adults' single- and double-touch interference effects was greater than the difference between older adults' single- and double-touch interference effects, and that the difference between higher span individuals' single- and double-touch interference effects was greater than the difference between lower span individuals' single- and double-touch interference effects.

These interactions are consistent with what would be expected from the regression analyses and inspection of Figure 1.

The increasing difference between the single- and double-touch regression lines with increases in simple span in that figure, for example, corresponds to the ability X secondary task interaction in the ANOVA.


The converging evidence from the ANOVA and regression analyses contradicts Oberauer and SuB's [2] suggestion that our earlier conclusions merely reflect an artifact of regression to the mean.

Why did we find evidence of larger interference effects in higher span individuals, whereas Oberauer and SuB did not? The reason may be straightforward: The secondary tasks used by Jenkins and her colleagues produced significant decreases in memory span, whereas the secondary tasks that Oberauer and SuB used did not.

Given the fact that Oberauer and SuB found no difference between simple and complex spans, it would appear to be impossible to draw any conclusions about interference effects from their data.

In contrast, the preceding results provide converging evidence for Jenkins, Myerson, et al. 's [1] contention that the effects of secondary spatial tasks on spatial memory spans are larger for individuals with larger spans than they are for individuals with smaller spans.


Implications for Theories of Working Memory


Oberauer and SuB [2] questioned not just the validity of the findings reported by Jenkins, Myerson, et al. [ 1], but also the relevance of these findings for theories of working memory.

Oberauer and SuB argued that the relation between interference effect size and simple span is irrelevant to working memory theories because simple and complex memory span tasks do not measure the same constructs.

It is only complex span, they claimed, that is of concern to theories that emphasize the role of interference and inhibition in working memory, because "of course, it is only the complex span, not the simple span variant, in which the issue of inhibiting irrelevant information arises at all" (p. 731).

According to Oberauer and SuB, only complex span tasks require that memory representations be suppressed when no longer necessary.

It is for this reason, they argued, that working memory theories that emphasize the role of interference and inhibition do not predict that a person's simple span will be related to his or her susceptibility to interference.


We certainly would agree that if simple span tasks did not require inhibition, then a working memory theory emphasizing differences in inhibitory ability would not predict that people with larger spans are less susceptible to interference.

However, many researchers, including some whose theoretical framework emphasizes the role of inhibition in working memory, would argue that simple span tasks do require inhibition.

May, Hasher, and Kane [16], for example, recently pointed out that span tasks, because they involve repeated trials, require people to suppress items from previous lists.

They argued that, as a consequence, proactive interference should be particularly noticeable in people who have a reduced ability to inhibit no-longer-relevant information.

Thus, in their view, the very nature of span measures (including both simple and complex spans) means such measures will reflect inhibitory ability and not just capacity.


This view is also widely held in neuropsychology.

For example, Berg [17], in his chapter on screening tests in The Neuropsychology Handbook, categorized the WAIS forward digit span task [18] as a test of attention because it involves both the maintenance of relevant information and the simultaneous suppression of irrelevant or no longer-relevant information.

Other researchers appear to be working from similar assumptions.

For example, Jonides and his colleagues [19][20] have used simple short-term memory tasks to identify neural structures involved in suppressing no-longer-relevant information precisely because they believe that inhibition on such tasks is relevant to working memory performance.


Thus, we are not alone in arguing that, although simple span tasks may place less demand on inhibitory abilities than complex span tasks, they clearly require participants to ignore irrelevant information, most noticeably that from previous trials but perhaps information from other events, both external and internal, as well.

Moreover, because inhibition theorists have not advocated the use of relative measures, the implication (by default) is that interference should be measured in absolute terms.

Accordingly, an inhibition-based theory of individual differences should predict that those with less susceptibility to interference will tend to have higher simple spans and will suffer less interference on complex spans.

Notably, the present data revealed the opposite pattern: Specifically, the absolute amount of interference produced by secondary tasks was larger for those with higher simple spans than for those with lower simple spans.


Interestingly, the effects of other manipulations that interfere with memory span also vary with simple span size.

Logie et al. [ 3] examined the relationship between verbal memory span and the size of phonological similarity and word length effects.

Importantly, the conditions that Logie et al. used to measure the effect sizes were not the same as the conditions they used to measure the memory spans in order to avoid the possibility of regression to the mean.

Their findings were similar to the present results in that individuals with larger simple spans showed larger effects.


May et al. [ 16] pointed to the phonological similarity effect as evidence that interference, in this case interference between similar items, plays a role in memory span tasks, and Nairne, Neath, and Serra [21] have suggested that proactive interference may underlie the word length effect.

Logie et al. [ 3] suggested that the extent of both effects was determined by two factors.

One of these was a tendency for individuals with larger spans to use different strategies than individuals with smaller spans, strategies that make them more vulnerable to various manipulations that decrease span.

In addition to the effect of strategy choice, however, they noted that there is also an independent effect of memory span.

This effect is captured by the stock market analogy presented earlier and does not depend on assumptions regarding either inhibition or strategies: Manipulations that decrease span simply may affect the representations of all items in working memory with approximately equal probability, so that the more items that are represented, the greater the number of items that will be lost.


Jenkins, Myerson, et al. [ 1] were concerned, of course, not just with individual differences in susceptibility to interference, but also with age differences in susceptibility to interference and how individual differences interact with age differences.

The results of the Logie et al. [ 3] study and those of Jenkins and her colleagues [11][12][1] shed new light on these interactions.

For example, the participants in Experiment 1 of the Logie et al. study ranged in age from 18 to 70 years, but there was no relationship between age and the relative size of phonological similarity and word-length effects (i.e., effect size expressed as a percentage of simple word span).

This suggests that, regardless of their age, individuals with the same simple span tended to show the same effect size.


Age differences in the size of interference effects in the present study [11][12] also appear to be attributable to age differences in simple span.

As noted above, there were no significant differences between the regression parameters for young and older adults.

The implications of this finding are highlighted in comparisons of lower and higher span subgroups.

For example, consider the lower span young adults and the higher span older adults.

Based on their first assessment, these subgroups did not differ in their simple spans (mean spans = 5.8 and 5.7 items, respectively), and accordingly there were no significant differences between the two subgroups in their single- and double-touch interference effects.


In contrast, the interference effects for the higher span young group were approximately twice those for the lower span old group, groups whose simple spans also turned out to differ by a factor of approximately two (mean spans = 8.1 and 4.2 items, respectively).

This result, depicted in Figure 2, is exactly the opposite of what might have been expected if higher span individuals had a greater ability to inhibit irrelevant information and older adults had an inhibition deficit, but it is obviously consistent with the idea that, regardless of age, the amount of interference produced by secondary tasks increases as simple span increases.


Conclusions


Oberauer and SiiB [2] challenged Jenkins, Myerson, et al. 's [1] report of a relationship between individual differences in simple memory span and the amount of interference produced by a secondary task, claiming that this relationship was an artifact produced by regression to the mean.

Although regression to the mean may have played a role in the Jenkins, Myerson, et al. findings, as Jenkins and her colleagues acknowledged in their original report, the data and analyses presented here demonstrate that their conclusion was correct: Individuals who have lower memory spans in the absence of a secondary task tend to be more affected (i.e., they show a larger decrease in span) by the addition of a secondary task than those with higher spans.

The present results demonstrate further that, at least with the procedures used by Jenkins and her colleagues, there are no age differences in interference effects beyond those attributable to differences in simple memory span.

Although the present analyses were confined to spatial memory span tasks and spatial secondary tasks, similar findings have been reported in the verbal domain [3].


The mechanisms underlying the observed relationship between interference effect size and simple span as well as the lack of age difference in interference effects when simple span is equivalent may ultimately prove to have nothing to do with individual and age differences in inhibitory ability.

As suggested previously, for example, manipulations that decrease span may affect the representations of all items in working memory with approximately equal probability, so that the more items that are represented, the greater the number of items that will be lost.

If this interpretation is correct, then in one sense, individuals with larger spans are not more susceptible to interference than those with smaller spans, they are just more affected by interference.

That is, if the number of items being lost is the measure of susceptibility to interference, then susceptibility increases with span.

If the probability of an item being lost from working memory is the measure of susceptibility to interference, however, then susceptibility is independent of span.


This interpretation of interference effects may help resolve the apparent conflict between an inhibition-based theoretical framework and the observed relation between span and the size of interference effects.

It does so, of course, by assuming that the absolute size of interference effects does not measure inhibitory ability, but this does not make the present findings irrelevant to theories of working memory (cf.

Oberauer & SiiB [2]).

Even if one believes that inhibition plays an important role in working memory, it would not seem to follow that only phenomena that directly reflect inhibition are relevant.

To the extent that working memory theories purport to be theories of the whole working memory system, we believe that they will have to accommodate the present results and other, similar findings [3], whatever the underlying mechanisms turn out to be.


Stage 1: Deciding for what purpose the model is to be used Criteria for descriptions of foods such as 'low in fat', 'less healthy', etc. may need to be different depending on the purpose for which they are required.

Sometimes simple definitions will be necessary; for example, if they are to be used as rules of thumb by a consumer trying to interpret the nutrition labelling panel on a food packet.

In other cases they may need to be complex; for example, where the definitions are to be used by health professionals or the food industry to categorise foods and where a wide range of nutrients and other food components are involved.


The descriptor 'high in fat', sugar or salt' is commonly used as a synonym for 'a less healthy food' and there are many purposes for which a clear definition of a 'less healthy food' would be useful.

However, not all foods that might be defined as 'high in fat, sugar or salt' would necessarily be defined as 'less healthy foods'; for example, fruit is high in sugar.

So definitions of 'less healthy foods' need to take account of other characteristics of the food: whether the sugars are intrinsic or extrinsic sugars, the levels of minerals and vitamins, etc.


In this paper we consider the development of a simple model to highlight foods which are 'high in fat, sugar or salt', regardless of their other characteristics, for the purpose of a theoretical magazine article dealing with the health benefits of different breakfast cereals.

The development of this model will reflect this purpose, and the choices made at each stage of the process may not be relevant for other purposes (such as a system for simplifying nutrition labelling).


Stage 2: Deciding what group or population the purpose is relevant to Descriptors such as 'low in fat', 'healthy', etc. need different definitions depending on the age and other characteristics of the population for whom the purpose is relevant.

For example, breast milk is 'healthy' for a newborn baby but it contains a level of fat that is not optimal and therefore not 'healthy' for an adult.

The same argument applies when drawing up definitions of descriptors such as 'low in fat' or 'healthy' for other specific subgroups of the population such as adolescents, women planning pregnancy and older people.


In developing a nutrient profile model for 'high in fat, sugar or salt' for use in an article about breakfast cereals, we imagined that the target group would be the general adult population.


Descriptors defined by nutrient profile models can be absolute, or relative to other foods.

In regulating nutrition claims such as 'low in fat' it has come to be assumed that such claims mean that the food is low in fat relative to all other foods - and not just similar foods or foods of the same food category.

For example, the European Commis- sion currently proposes that the criterion for the claim 'low in fat' should be that the food has less than 3 g of fat per 100 g of food regardless of the type of food (i.e. 'across- the-board').

Conversely, in regulating the claim 'lower in fat' or 'reduced fat', it has come to be assumed that such claims mean that the food is low in fat relative to foods of the same category (i.e. 'category-specific') 3 .


With criteria for 'healthy' and 'healthier' - and their synonyms - there is less agreement and a good deal of confusion.

Most of the schemes that have been developed by public health organisations and the food industry to label foods as 'healthy' or 'healthier' tend to use food-category-specific criteria.

One of the best known of such schemes is the Tick Programme run by the National Heart Foundation of Australia.

This has different criteria for over 60 categories of foods 8 .

The symbol sometimes signifies 'healthy' per se and sometimes 'healthier' than other foods within a category.

This may not be clear to consumers.

There are, on the other hand, a few nutrient profiling models which use across-the-board criteria to classify foods as 'healthy' or 'healthier' 9 - 11 .

The definitions of 'healthier' and of 'less healthy' foods that were developed for use in relation to regulating the promotion of food to children involve across-the-board criteria 4 .


If a food labelling scheme is designed to encourage consumers to switch to healthier foods within a food category, then category-specific criteria are appropriate. But if it is designed to encourage consumers to switch to healthier food categories, for example from confectionery to fruit, across-the-board criteria will be necessary.


If category-specific criteria are required then food categories need to be defined, which is far from straightforward.

There are no universally accepted food categories and many different systems are used even Developing nutrient profile models 331 https://doi.org/10.1017/S1368980007223870 Published online by Cambridge University Press under similar circumstances (e.g. the Australian Tick Programme 8 and the Swedish Green Keyhole 12 scheme are similar category-specific labelling schemes but use very different food categories).

In addition, new compo- site foods involving foods from more than one category are constantly being developed.


In developing a nutrient profile model for 'high in fat, sugar or salt', for use in an article about breakfast cereals, across-the-board criteria are probably the most appro- priate.

This is because it is now conventional to define nutrition claims such as 'low in fat' or 'low in sugar' as low in those nutrients relative to all foods.

For a definition of 'higher in fat, sugar or salt' it would probably be more appropriate to use category-specific criteria, since the term 'higher' implies a comparison with similar foods.

Furthermore, in developing a nutrient profile model for 'high in fat, sugar or salt' we considered that it would be useful for consumers to be able to compare breakfast cereals with other foods eaten at breakfast; for example, toast, croissant, bacon and fruit.

Using across-the-board criteria allows this.


Stage 4: Deciding which nutrients


Nutrient profiling models can sometimes involve com- ponents which technically are not nutrients, such as energy or water, and even ingredients such as 'fruit and vegetables', or food additives such as preservatives.

Hereafter for simplicity we will call all the possible components of nutrient profiling 'nutrients'.


The nutrients selected for nutrient profile models depend on the definition required.

The choice is clear when the descriptor is 'low/lower in x' or even 'low/lower in x, y or z' where x, y and z are named nutrients.

It is less clear when the descriptor is 'healthy' or 'less healthy'.

In that case, the number of different combinations of nutrients and food components that could possibly be used for nutrient profiling is considerable.

A recent World Health Organization report lists 37 nutrients and other food components linked with chronic disease 1 .

The EU directive on nutrition labelling lists 31 nutrients which it is permitted to include within the nutrition labelling panel 13 .


Nutrient profiling necessarily involves prioritisation of nutrients.

In framing the EU legislation for nutrition labelling, the legislators chose four nutrients that must be presented if nutrition labelling is provided: energy, protein, fat and carbohydrate.

These nutrients, in effect, represent the 'priority nutrients' that the legislators considered were most important for consumers to know about for health-related purposes.


Nutrients are normally prioritised because levels in the population or population subgroup are important for public health.

Thus most definitions of 'healthy' or 'healthier' involve criteria for fat, saturated fat and sodium.

But nutrients may also be prioritised because they are markers of foods, the consumption of which is considered to be important for public health.

So, for example, in developing definitions of 'less healthy' and 'healthier' food for the purpose of regulating the promotion of food to children 4 it was agreed that the 'healthier' category should have a significant number of foods from the 'meat and alternatives' group of the UK's national food guide (The Balance of Good Health 14 ); initially iron and subsequently protein were incorporated into the models to ensure this.


In selecting nutrients for a nutrient profiling model there will also be practical considerations that need to be taken into account - e.g. for food labelling purposes definitions of 'healthy' or 'healthier' may need to be verifiable by analysis, limiting the choice of nutrients to those which can be measured in a laboratory.


The choice of nutrients for a definition for 'high in fat, sugar or salt' would - on the face of it - seem largely predetermined.

However, even here there are some remaining choices to be made.

In developing the model we have chosen to use sodium levels rather than salt levels because it is the sodium in salt which is of public health concern.

We have also opted for non-milk extrinsic sugars (NMES) since these are the subject of officially agreed population dietary goals rather than added sugar or total sugar 15 .


Theoretically, the amount of nutrient in a food can be expressed in many different ways (bases).

However, in practice, only per 100 g, per 100 kJ or per serving, or combinations of these, are practical.

There are advantages and disadvantages to all three of these bases. ' Per 100 g' is the base used most often for nutrient profiling, probably because this is the base used in food composition tables and in the required format for nutrition labelling in the EU.

However, a food that is high in a nutrient on a 'per 100 g' basis may supply little of that nutrient to the diet because it is eaten in small amounts and/or infrequently (e.g. fat in mustard).

Conversely, a food that may be low in a nutrient 'per 100 g' may supply a lot of that nutrient to the diet because it is eaten in large amounts and/or frequently (e.g. NMES in soft drinks).

One of the main reasons for this is that foods vary widely in their water content and foods that contain high levels of water tend to be eaten in large amounts (e.g. beverages, stews, etc.).

Of course, there are other determinants of amount and/or frequency of consumption besides the water content of foods, such as intensity of taste (e.g. yeast extract).


Using a 'per 100 kJ' basis means that the water content of foods is discounted.

The amount of food that people eat is, in general, governed by their energy needs (weight-stable individuals will consume enough food to satisfy their energy requirements; those seeking to lose weight will generally aim to consume less food in order to reduce their energy intake; and those increasing in weight will normally be consuming slightly more food than they P Scarborough et al. 332 https://doi.org/10.1017/S1368980007223870 Published online by Cambridge University Press need to supply them with energy).

One of the consequences of this is that there is stronger correlation between nutrient 'per serving' and nutrient 'per 100 kJ' than between nutrient 'per serving' and nutrient 'per 100 g'.* Using an energy basis therefore partially takes account of the amount of food people generally eat.


However, there are still some energy-dense foods - such as mustard - which appear high in a nutrient when measured 'per 100 kJ', yet supply little of that nutrient to the diet because they are eaten in small servings and/or infrequently.

Foods with very high water content, such as some fruits and vegetables, pose a further problem.

These foods tend to supply very little energy and so can appear high in a nutrient on a 'per 100 kJ basis' (e.g. raw celery contains 200 mg of sodium 'per 100 kJ', enough to be categorised as 'high sodium' by schemes which use a 'per 100 kJ' basis 5 ).


The use of a 'per serving' basis for nutrient profiles also partially takes account of the way people eat foods because serving size, together with frequency of consumption, are the main determinants of how much of a nutrient a food supplies to the diet.

However, serving size for many foods varies considerably.

For example, the serving size of milk depends on whether it is drunk by itself or with breakfast cereal or with coffee.

Also, serving sizes partly depend on the energy needs of the consumer (e.g. young children tend to eat smaller serving sizes than adults).

Serving sizes for some foods are therefore difficult to define.

In addition, for some foods the serving size bears little relation to how much of a nutrient it contributes to the diet.

For example, the serving size for margarine is small, but servings of margarine may be eaten on many occasions in a single day and so may contribute a substantial amount of fat to a diet.


The choice of which base is used for nutrient profiles is crucial, as different bases will rank foods in different orders (e.g. spaghetti bolognaise has less fat 'per 100 g' than mayonnaise, but more fat 'per serving').

However, the choice of which base to use becomes less important if 'food-category-specific' criteria are used because foods within categories tend to vary less in their water content or serving size than the whole range of foods.


In developing nutrient profile models for categorising foods there has been considerable confusion over which base to use.

The proposed EU regulation on health and nutrition claims sets criteria for nutrition claims on a 'per 100 g' basis 3 whereas the US Nutrition Labelling and Education Act sets criteria for nutrition claims on a 'per serving' basis 17 .

There are some - albeit rare - examples of the use of 'per 100 kJ' base 5 .


It is possible to combine two bases for nutrient profiles so as to gain the advantages of both bases, and this can and has been used for nutrient profiling.

For example, the Codex Alimentarius definition for 'low in saturated fat' is that a food should have less than 1.5 g of saturated fat 'per 100 g' and less than 10 kJ of saturated fat 'per 100 kJ' 18 .


In developing a nutrient profile model for 'high in fat, sugar or salt' we decided that it would be most useful if it helped people compare the quantity of fat, salt and sugar supplied to the diet by different foods, rather than compare the nutritional quality of these foods.

This is best achieved using a 'per serving' base.


Nutrient profile models vary considerably in their design but fundamentally they adopt one of two approaches: categorical or continuous.

Categorical models divide foods into two or more categories but beyond this categorisation foods can no longer be compared (e.g. a categorical model may categorise two foods as 'high in salt', but will not indicate which food contains more salt).

Continuous models provide a ranking of foods and so are intrinsically more complex than categorical models and therefore impractical for some purposes.


Categorical models are also called threshold models because they operate by considering whether or not a food has a nutrient content higher or lower than a specified threshold or thresholds.

Categorical models are the most common type of nutrient profile model.

Clearly criteria for nutrition claims such as 'low in fat' have to be categorical.

The criteria for the schemes that have been developed by public health organisations and the food industry to label foods as 'healthy' or 'healthier' tend to be categorical models where foods are categorised according to whether the nutrient levels are higher or lower than a number of specified thresholds.


Continuous models are often called scoring systems, as they operate by calculating a score for each food which can then be used to rank foods.

Continuous models can be converted into categorical models simply by setting score threshold criteria.

For example, a food might be defined as 'healthy' if it scores a certain number of points or more.

Continuous models are more flexible than categorical models since different score thresholds can be set for different food categories or for different purposes.


With continuous models any small change in the nutritional value of a food will result in a small change to the score for that food.

There are several examples of such continuous models 9 - 11 .

The nutrient profile model developed for use in relation to regulating the promotion of food to children 4 is a semi-continuous model.

For this model the final score for a food is always a whole number, and is determined by considering a number of score bands for each nutrient.

Hence, the scores are categorical and a small change in the nutritional value of a food may not result in a change to the score of the food.

However, the 333 https://doi.org/10.1017/S1368980007223870 Published online by Cambridge University Press other advantages of continuous models (flexibility, direct comparability) are retained.


The primary advantage of continuous models over categorical models is their ability to differentiate between foods that are high in a number of nutrients and foods that are high in only one nutrient.

For example, two biscuits - one of which is high in saturated fat and high in sodium, the other high in saturated fat but low in sodium - are indistinguishable using a categorical model used to define foods that are high in saturated fat or sodium.

However, a continuous model used for the same definition would rank the two biscuits appropriately, thereby providing more information to the user of the model.


In developing a nutrient profile model for 'high in fat, sugar or salt' we have chosen to use a continuous approach so that the model will provide a ranking of foods.


The levels set for the thresholds in categorical models or the numbers of points scored for a particular nutrient level in continuous models can be selected in a number of ways.

For example, they can be chosen pragmatically, taken from respected sources or linked to public health recommendations.


In many cases the thresholds in existing categorical models appear to have been chosen pragmatically so that certain foods are included within a definition and/or so that that the levels coincide with food producers' views of what are attainable.

In other cases the thresholds are taken from other respected sources (e.g. the proposed EU definition for a 'low fat' claim is consciously taken from the Codex Alimentarius definition, which in turn is a modification of the US definition (EU and Codex: ,3 g per 100 g; US: , 3 g per serving 3,17,18 )).

Such thresholds might be justifiable on the basis that they have been tried, and to some degree tested, elsewhere but they do not have any transparent or logical relationship to dietary recommendations.


In developing nutrient profile models it is possible to ensure that threshold levels or points scored are explicitly and logically linked to public health recommendations.

This can be done in various ways.

One way of doing this with across-the-board models is to use standard pro- portions of what have come to be known as Guideline Daily Amounts (GDAs) 19 .

This was the approach taken in developing FSA advice on what counts as a lot or a little of various nutrients 18 and in developing definitions of 'less healthy' foods for use in relation to the promotion of food to children 4 .

Another way of linking to public health recommendations, which is particularly suitable for food- category-specific criteria, is by modelling what needs to be achieved to reach desirable intake levels from current intake levels 20 .


We had decided that the purpose of our nutrient profile model for 'high in fat, sugar or salt' was to help adults compare the nutrient content of different foods eaten at breakfast; therefore we decided that the numbers chosen for the points scored at different levels of fat, sodium and NMES should be related, in a standard way, to adult GDAs (sexes combined) of 82.3 g for fat, 2.3 g for sodium and 59.9 g for NMES.

These GDAs were calculated in a way described previously 19 and are similar to GDAs recently proposed by the Institute of Grocery Distribution 21 .


Using the seven-stage decision-making process to nutrient profiling that we have outlined above generates a simple nutrient profiling model for the purpose of highlighting breakfast cereals that are 'high in fat, sugar or salt'.

The model has the following equation: The equation was used to calculate scores for a selection of breakfast cereals and other foods eaten at breakfast, using food composition data from the McCance and Widdowson database 16 and serving size data published by the former Ministry of Agriculture, Fisheries and Food 22 . ( The data for the NMES content were derived from the total sugars data by a method described previously 4 .) The foods and their scores are presented in Table 1.


One possible definition of a food that is 'high in fat, sugar or salt' would be one that has a lot of fat, sugar or salt compared with the FSA advice on what constitutes 'a lot' 19 .

This would be a simple categorical model.

The data presented in Table 1 show that using this definition would mean that Sugar Puffs, Frosties, white bread toasted, wholemeal bread toasted, streaky bacon and scrambled egg count as such foods.


The continuous model developed for this paper ranks foods as in Table 1, split into 'breakfast cereals' and 'other breakfast food' categories.

This continuous model can be converted to a categorical model by setting an arbitrary threshold score above which foods are defined as 'high in fat, sugar or salt'.

If this threshold score is set at 325 then Frosties and streaky bacon would be defined as 'high in fat, sugar or salt', but Sugar Puffs, white bread toasted, wholemeal bread toasted and scrambled egg would not.

In addition, Bran Flakes, Cheerios and Sultana Bran would be defined as 'high in fat, sugar or salt'.


In our view, the continuous model is preferable to the categorical model because it takes account of medium levels of fat, salt and sugar and not just very high levels of these nutrients when categorising the foods. But we accept that this judgement should be subject to empirical verification.

Methods of validating and comparing different nutrient profiling models will be dealt with elsewhere (Scarborough P, Boxer A, Rayner M and Stockley L, Testing P Scarborough et al. 334 https://doi.org/10.1017/S1368980007223870 Published online by Cambridge University Press and validating nutrient profile models using data from a survey of nutrition professionals, submitted).


The results of developing a nutrient profiling model to define a food as 'high in fat, sugar or salt' illustrate the particular advantage of continuous and across-the-board models, namely that foods can be compared within and outside particular food categories.

The nutrient profiling scores shown in Table 1 clearly indicate which breakfast cereals are higher in fat, sugar or salt.

But they also show how individual breakfast cereals compare with other foods eaten at breakfast.

They show, for example, that individuals concerned only with reducing fat, NMES and sodium levels in their diet should regard all the breakfast cereals to be a better choice than scrambled egg and bacon, and most (but not all) would be a better choice than a croissant.


The systematic approach to nutrient profiling we propose has the capacity to generate many different nutrient profile models even for the same purpose.

Having generated different models they need to be tested to ascertain whether they are appropriate to the purpose.

The issue of testing and validating nutrient profile models has not been raised here, but is covered in previous publications 4,6 and a parallel paper (Scarborough P, Boxer A, Rayner M and Stockley L, Testing and validating nutrient profile models using data from a survey of nutrition professionals, submitted).


Nutrient profiling has, in the past, generally been carried out in a haphazard fashion.

The approach we propose here could help all those involved in nutrient profiling to systematise their methods.


The term ‘nutrient density’ for foods/beverages has been used loosely to promote the Dietary Guidelines for Americans.

The 2010 Dietary Guidelines for Americans defined ‘all vegetables, fruits, whole grains, fat-free or low-fat milk and milk products, seafood, lean meats and poultry, eggs, beans and peas (legumes), and nuts and seeds that are prepared without added solid fats, added sugars, and sodium’ as nutrient dense.

The 2010 Dietary Guidelines for Americans further states that nutrient-dense foods and beverages provide vitamins, minerals and other substances that may have positive health effects with relatively few (kilo)calories or kilojoules.

Finally, the definition states nutrients and other beneficial substances have not been ‘diluted’ by the addition of energy from added solid fats, added sugars or by the solid fats naturally present in the food.


However, the Dietary Guidelines Advisory Committee and other scientists have failed to clearly define ‘nutrient density’ or to provide criteria or indices that specify cut-offs for foods that are nutrient dense.

Today, ‘nutrient density’ is a ubiquitous term used in the scientific literature, policy documents, marketing strategies and consumer messaging.

However, the term remains ambiguous without a definitive or universal definition.

Classifying or ranking foods according to their nutritional content is known as nutrient profiling.

The goal of the present commentary is to address the research gaps that still exist before there can be a consensus on how best to define nutrient density, highlight the situation in the USA and relate this to wider, international efforts in nutrient profiling.

‘ Nutrient-dense foods’ are widely recommended; however, there is no consistent definition of what distinguishes a nutrient-dense food.

There is also little understanding of how individual nutrient-dense foods fit into healthful dietary patterns.

The purpose of this commentary is to discuss how nutrient-dense foods have been defined and address some of the caveats of existing definitions.

The commentary also relates the challenges of defining nutrient density to recent efforts at nutrient profiling.


The 2005 Dietary Guidelines Advisory Committee placed a major emphasis on the selection of nutrient-dense foods, which were defined as ‘those foods that provide substantial amounts of vitamins and minerals and relatively few calories’ [1].

Conversely, non-nutrient-dense foods were defined as supplying energy but relatively small amounts of micronutrients.

Consumption of nutrient-dense foods was endorsed by the 2005 [2] and 2010 [3] Dietary Guidelines for Americans (DGA).

However, neither the 2005 [2] nor the 2010 DGA [3] actually defined the terms ‘nutrient dense’ or ‘nutrient density’; rather, both listed food groups which they deemed ‘nutrient dense’: whole grains, fruit, vegetables, fat-free or low-fat milk and milk products, seafood, lean meats and poultry, eggs, beans and peas, and nuts and seeds, prepared without added fats or sugars [3].

The implication was that these foods were healthful and others were not.

Neither the 2005 [2] nor the 2010 DGA [3] made an effort to use a more useful method of categorizing the nutrients that foods contributed at the expense of their SFA, added sugars or Na content.


Nutrient profiling of foods is the science of ranking foods based on nutrient composition.

The algorithms used to profile foods generate a numerical score which can then be used to help consumers make healthier choices [3–7], influence regulatory issues [8,9], help consumers rate foods using front-of-package labels or labels on store shelves [10,11], define healthy foods that can be advertised to children [12–15] and motivate industry to develop more healthful foods [15].

Table 1 shows a sample of the variety of nutrient density algorithms that have been used.

The continuous systems that generate scores calculate and assign a score to each food item, which can then be used to rank and compare foods.

There is no score or scoring system that identifies an individual food as ‘nutrient dense’; foods are only nutrient dense in comparison with other foods.

The criteria used in nutrient profiling systems are not currently regulated and not all algorithms have been validated.

There are many unanswered questions and challenges relating to the development of a scientific definition of nutrient density and the application of this term [16–18].

Many of the questions surrounding the definitions of nutrient density were identified in a Practice Paper of the Academy of Nutrition and Dietetics [17] and by Scarborough et al. [ 18].

The DGA’s all-encompassing definition of nutrient-dense foods [3] also adds to these challenges/questions.

Using these published references, herein we focus on eight of the important challenges related to defining nutrient density.

We expand and explain these important challenges; highlight the situation in the USA; and relate the concept of defining nutrient density and its challenges to wider international efforts in nutrient profiling.


In a fascinating article by Nicklas and colleagues [1], the authors tackle the problem of imprecise definitions of the nutritional quality of foods.

In particular, they are concerned with the term ‘nutrient density’, used in the 2010 Dietary Guidelines for Americans (DGA) [2], which is used to guide food selections in order to ensure a healthy diet.

‘ Nutrient dense’ foods are described in the 2010 DGA as ‘those foods that provide substantial amounts of vitamins and minerals and relatively few calories’ and, more rigorously (but possibly contradictorily), as ‘all vegetables, fruits, whole grains, fat-free or low-fat milk and milk products, seafood, lean meats and poultry, eggs, beans and peas (legumes), and nuts and seeds, that are prepared without added solid fats, added sugars, and sodium’ [2].

Nicklas et al. discuss the inherent difficulties in rigorously defining the term ‘nutrient density’ for the purpose of dietary advice, with reference to the growing literature on nutrient profiling, and in doing so they provide a very useful commentary on the challenges that surround nutrient profiling.

In the present commentary, we also tackle the difficulties introduced by vague definitions of the nutritional qualities of foods.

We then provide a description of what a nutrient profile model is, before describing situations where the precise measures provided by a nutrient profile model can be helpful.

We conclude by arguing that a nutrient profile model that robustly defines ‘nutrient density’ would not be useful for provision of dietary advice.


Difficulties in describing the nutritional quality of foods


The 2010 DGA uses the term ‘nutrient dense’ to indicate foods that Americans should eat in order to achieve a healthy diet (e.g. the definition of ‘nutrient density’ concludes by stating ‘eating recommended amounts from each food group in nutrient-dense forms is the best approach to achieving … a healthy eating pattern’) [2].

In this context, the term ‘nutrient density’ seems to be a synonym for ‘healthy’ or ‘healthful’.

There are various terms that are used by academics, nutritionists, dietitians, public health policy makers and the lay public to describe the nutritional quality of foods, including healthy/unhealthy, healthful/unhealthful, healthier/less healthy, nutrient dense/energy dense and good/junk.

These terms are rarely defined explicitly and if they are the definitions are frequently contested.

For example, as described in the paper by Nicklas et al. [ 1], the 2010 DGA definition of nutrient density includes vegetables, but not vegetables with added salt, despite the fact that adding a small amount of salt to a vegetable has a negligible impact on the amount of beneficial nutrients per unit of food.

This example suggests that the term ‘nutrient dense’ is being used as a synonym for ‘healthier’, where the concept of ‘healthier-ness’ is based on providing beneficial nutrients to the diet while limiting the consumption of added solid fat, sugar and/or salt.

We think the DGA’s term nutrient dense is closer to ‘healthier’ rather than ‘healthy’ or ‘healthful’ as the DGA’s definition of nutrient density seems to be implicitly based on comparison – by most standards a radish with a small amount of salt added is a healthful food, but it is not as healthful as a radish without salt added, and it is this comparison that excludes vegetables with added salt from the DGA definition of ‘nutrient dense’.


A different conception of the nutritional quality of foods, and the language that can be used to describe it, is presented below.

Note that this conception is not by any means the only one possible (indeed, the fact that there are many different ideas about what is meant by a nutrient dense/healthy/healthful food contributes to the confusion surrounding the terms).

Our conception places all foods onto a continuous spectrum ranging from ‘most healthful’ to ‘least healthful’ (or ‘most unhealthful’).

Within this spectrum, it is possible to compare any two foods, or any two sets of foods.

Figure 1 provides an illustration of this concept and shows how ‘stewed apple with sugar’ is considered less healthful than ‘stewed apple’, and how ‘high-fat beef sausage’ is considered less healthful than ‘low-fat beef sausage’, but both sausages are considered less healthful than both stewed apples with or without sugar.

Note that the mechanisms used to measure the healthfulness of each food (e.g. the nutrients to include, the unit of measurement to use) are not yet relevant – it is the concept of the continuum of healthfulness that is important here.


We prefer the word ‘healthful’ (and ‘healthfulness’) rather than ‘healthy’ (and ‘healthiness’) as foods do not develop disease so it does not make sense to describe a food as healthy.

We acknowledge that diets also do not develop disease, so it is anomalous to describe diets as healthy – we do so here to avoid conflicting terms.

The ‘correct’ order of the foods on the spectrum is dependent on how the spectrum is to be used.

For this illustration, we are defining a healthful food as one that, when consumed, makes the total diet more healthy (and the greater the contribution to healthiness of the diet the more healthful the food is).

For this reason, we have placed water in the middle of the spectrum representing that it makes neither a positive nor a negative contribution to the healthiness of the diet (note that for different spectrums water will be placed in different places – if the spectrum is based purely on the presence of beneficial nutrients then water will not be considered healthful.

If based purely on the absence of nutrients to be limited then water will be considered healthful).


Note our concept of a healthful food is not a food that is likely to contribute to a healthy diet.

Foods are eaten in patterns and combinations that can result in apparent anomalies in the comparison of healthful foods with healthy diets.

For example, as Nicklas et al. point out [1], high-sugar breakfast cereals are often found to be components of a healthy diet [3], and they therefore argue that breakfast cereals should be defined as ‘nutrient dense’.

We disagree.

There are many foods that are likely to be found to be a part of a healthy diet that have little to do with their nutritional quality but are due to the combinations in which they are eaten.

For example, salad dressing is often a part of a healthy diet because it is eaten with salad rather than because it is healthful in and of itself.

In our conception, high-sugar breakfast cereals are not defined as healthful as when consumed they make a (generally healthy) diet less healthy.


A couple of important points should be made explicit here.

First, whether or not high-sugar breakfast cereals make total diets more or less healthy is entirely dependent upon the weight placed on beneficial nutrients and nutrients to be limited, whether fortificants are included in the calculations, etc.

This is all well covered in the article by Nicklas and colleagues [1].

Second, high-sugar cereals may be more healthful than some breakfast alternatives (e.g. a croissant and chocolate spread), but still not healthful per se.

This is allowed by our conception of a continuum of healthfulness, but may not be allowed by other conceptions that are based on comparisons of foods.


What is (and isn’t) nutrient profiling?


Once a robust conception of ‘healthfulness’ is agreed, the next stage is to operationalise this conception.

One way of doing this is to group foods into categories and to distribute the categories along the continuum with (for example) fruits more healthful than chocolates.

While this may be good enough for many purposes it is an unsatisfactory representation of the conception that we have built, as there is no room for differences within food categories and the positioning of food categories along the spectrum is arbitrary, leading to a ranking of food categories rather than a continuous scale.

We need a process that unambiguously places each food at a single point on the continuum based on some predefined criteria, and this is one of the things that a nutrient profile model can do.


A nutrient profile model is defined by the WHO as a set of equations or algorithms that ‘categorise foods for the purpose of health on the basis of their nutritional properties’ [4].

The key points here that define nutrient profile models are that they must only use information about a food in isolation (i.e. they do not take account of how often the food is consumed, in what context or in combination with what other foods) and they are specifically concerned with health.

Therefore a model that aims to regulate health claims on high-fat, -salt or -sugar products by using a score for all foods on the basis of their fat, sugar and salt levels is a nutrient profile model; a model that estimates a ‘sustainability score’ on the basis of their carbon, water and land footprint is not a nutrient profile model; and a model that generates a score for a food based on the probability that the food is consumed in a sub-sample of a population that consumes a healthy diet is not a nutrient profile model.


There are at least two questions relating to this definition of a nutrient profile model.

First, does the definition allow for nutrient profile models that measure nutrients on a per serving basis?

This depends on whether ‘serving size’ is considered to be a property of a food.

This is hard to justify since so many foods are eaten in very different serving sizes depending on context (e.g. milk as a drink has a large serving size but as an addition to coffee has a small serving size).

A strict reading of the definition would therefore suggest that nutrient profile models should not use a per serving basis, but in practice many nutrient profile models use this measurement and it is generally accepted.

Indeed, Nicklas et al. state that ‘[a] challenge [for defining nutrient density] is to identify a standard unit that reflects a reasonable serving for some foods and is a concept that consumers can understand’ [1].

We think that nutrient profile models are best suited to comparing the nutritional quality of foods (irrespective of the serving size they are consumed in) and considerations of how important the nutrient profile scores are for foods that are eaten in large or small quantities should be left to the users of the nutrient profile model.


Second, how should ‘a food’ be defined? In most cases this is unproblematic, but in some cases foods are rarely if ever eaten in the form that they are sold (e.g. flour, dried pasta, instant drinking chocolate, breakfast cereal).

Here it is necessary for each nutrient profile model to explicitly state whether the model should be applied to foods as sold or foods as consumed, and if the latter then how these consumed foods should be constructed.


Nutrient profile models can provide a precise score representing the healthfulness of all foods, but this precision can be deceptive.

What do the nutrient profile scores mean? They are based on many decisions and assumptions; for example, which nutrients should be included in the model, what weighting should be placed on each nutrient.

These decisions have been detailed elsewhere [1, 5].

It has been shown that different nutrient profile models sometimes agree and sometimes do not agree with each other [6, 7].

This may be because the nutrient profile models are based on different conceptions of ‘healthfulness’ (e.g. one based on the presence of health-promoting micronutrients such as the Nutrient Value Score (NVS) [8] and one that balances positive and negative nutrients such as the SAIN:LIM score [9]), or because they have been developed in very different ways.

While nutrient profile models can be useful tools for public health regulations, their inherent limitations should not be overlooked.


It is important to separate the nutrient profile model from its application.

For example, the UK Food Standards Agency/Ofcom model that provides a healthfulness score for all foods is used to support the regulation of broadcast advertising of foods to children in the UK [10].

The regulation itself is clearly not a nutrient profile model.

Similarly, the Choices International logo, applied to many food products internationally to identify healthy choices, is supported by a set of nutritional criteria and equations to identify healthy foods [11].

The criteria and equations are a nutrient profile model, whereas the logo itself is not.

This distinction may seem simple, but the confusion between nutrient profile model and application is rife (e.g. Townsend [12]).

In the following section, we discuss applications where nutrient profile models would be helpful and those where they would not.


When nutrient profiling can (and cannot) be useful


A public health intervention could potentially benefit from a nutrient profile model if: (i) it is concerned with improving nutrition; and (ii) it operates on the food level (i.e. it does not take into account information about the diets or health status of individuals).

Some examples of interventions that meet these criteria include: (i) regulation of health claims; (ii) restriction of foods supplied in school vending machines; (iii) criteria for front-of-pack nutrition labelling and health symbols; (iv) health-related food taxes and subsidies; and (v) restriction of marketing of unhealthy foods.


The second criterion here is crucial in uniting these interventions – they are all applied in contexts where individual foods must be considered independent of the context in which they are consumed.

This is not the case for dietary advice, where the aim is to provide advice about foods in the context of how they can combine to create a healthy diet.

In this situation, the context provides more information which a nutrient profile model does not use in its calculations; for example, the combination in which foods are consumed, the need for dietary variety, the context-specific serving size in which foods are consumed, etc.

Nicklas et al. [ 1] describe some unintended consequences associated with using a ‘nutrient density’ approach for dietary advice, including that nutrient-dense foods are often eaten in combination with energy-dense foods, and suggest that to demonise one could affect consumption of the other. They argue that ‘the value of a food should be determined within the context of the whole diet’.

Where this is possible – as in the case of dietary advice – we agree, and given the definition of nutrient profiling provided above, this precludes the use of a nutrient profile model.


Conclusion


There is confusion among policy makers and nutrition academics regarding the role of nutrient profile models.

Much of this confusion is due to the vague definitions of ‘healthy’, ‘healthful’ and ‘nutrient dense’ that are often applied to foods without a complete conception about what is meant by these terms.

Therefore, when a nutrient profile model (which must have an explicit basis for the categorisations or scores that it produces) is compared against these vague definitions, many apparent ‘anomalies’ are found, which may be due to poorly fitting nutrient algorithms but could also be due to the mismatch between different conceptions of ‘healthfulness’.

Nutrient profile models can be useful tools for supporting a large range of public health interventions where only information on the nutritional quality of the food is available.

Where additional information about the role of foods in the overall diet is available, nutrient profile models are less useful.


Although the precise cause of multiple sclerosis remains unknown, various factors have been discovered that de- termine an individual's risk to develop this chronic demyelinating disease of the CNS.

A majority of risk al- leles identified by genome-wide association studies relate to immune functions (Baranzini and Oksenberg, 2017), supporting an autoimmune aetiology for the CNS inflam- mation, demyelination and neurodegeneration that is characteristic of multiple sclerosis.


Environmental factors, such as a symptomatic Epstein-Barr virus (EBV) infection at a vulnerable age (Ascherio and Munger, 2010), smok- ing (Wingerchuk, 2012) as well as lack of sunlight expos- ure (Bjornevik et al., 2014) and low levels of vitamin D (Munger et al., 2006; Ascherio et al., 2010) may also enhance the risk of developing multiple sclerosis.

The latter two factors could be interdependent, as the primary form of vitamin D, cholecalciferol (vitamin D3) is gener- ated in the skin upon ultraviolet (UV) radiation; alterna- tively, vitamin D can be ingested as part of the diet.


While diet is generally considered the minor source of vitamin D (Holick, 2004), it may become essential when UVB exposure is limited (Holick, 1987).

Because vitamin D levels can be effectively and rapidly raised by diet, vita- min D supplementation has gained interest over recent years in multiple sclerosis as well as other chronic conditions.


In general, patients with multiple sclerosis have relatively low levels of vitamin D, which may refer to genetic and metabolic alterations associated with multiple sclerosis itself (Nieves et al., 1994; Laursen et al., 2015; Mokry et al., 2015; Bhargava et al., 2017).

Lower serum concentrations of vitamin D increase the likelihood of both relapses and early onset of disability progression (Runia et al., 2012), whereas levels above 70 nmol/l are associated with a decreased risk for attacks (Smolders et al., 2008).

While these findings clearly highlight low vitamin D levels as a negative predictor in multiple sclerosis, it is currently un- clear whether this association is causal and accordingly, whether therapeutically raising the level of vitamin D in patients with multiple sclerosis is of clinical benefit (Stein et al., 2011; Dorr et al., 2012; Bhargava et al., 2014).


Primarily based on the perception of its harmlessness it is nevertheless current practice to broadly supplement vitamin D in patients with multiple sclerosis, often at excessively high doses.

In light of the current lack of evidence for a clear therapeutic benefit in multiple sclerosis we revisited this concept and investigated the clinical and immunolo- gical effect of chronic vitamin D exposure in the preclinical multiple sclerosis model, murine experimental autoimmune encephalomyelitis (EAE).


Peripheral blood mononuclear cells (PBMCs) were obtained after informed consent.

The protocol was approved by the Ethics committee of the University Medicine of Gö ttingen, ap- proval number 3/4/14.


Wild-type C57BL/6 mice were purchased from Charles River.

MOG p35-55 TCR transgenic 2D2 mice were kindly provided by Dr Kuchroo (Boston, USA).

All animal experiments were carried out in accordance with the guidelines of the Central Department for Animal Experiments, University Medical Center, Gö ttingen and approved by the Office for Consumer Protection and Food Safety of the State of Lower Saxony (protocol number 33.9-42502-04-13/1201, 33.9-42502-04-15/ 1804 and 33.9-42502-04-17/2615).


Female wild-type mice were immunized subcutaneously with 50 mg MOG peptide (p)35-55 MEVGWYRSPFSRVVHLYRNGK (Auspep) emulsified in complete Freund's adjuvant (Sigma- Aldrich) containing 250 mg killed Mycobacterium tuberculosis H37 Ra (BD Bioscience) followed by intraperitoneal injections of 200 ng of Bordetella pertussis toxin (Sigma-Aldrich) on the day of immunization and 2 days thereafter.

EAE severity was assessed daily and scored on a scale from 0 to 5 as follows: 0 = no clinical signs; 1.0 = tail paralysis; 2.0 = hindlimb paresis; 3.0 = severe hindlimb paresis; 4.0 = paralysis of both hindlimbs; 4.5 = hindlimb paralysis and beginning forelimb paresis; and 5.0 = moribund/death.


Mice were fed with a diet containing either low (55 IU/kg food), standard (1500 IU/kg food) or high (75 000 IU/kg food) vitamin D3 concentrations (ssniff Spezialdiaeten) for at least 8 weeks.

These doses were chosen after a dose titration, as they generated serum vitamin D levels reflective of vitamin D defi- ciency (530 nmol/l), physiological vitamin D levels (100 nmol/ l) and continuous high-dose supplementation (250 nmol/l) in patients (Vieth, 1999; Burton et al., 2010; Smolders et al., 2010).

All three diets contained identical calcium (1%) and phosphate (0.7%) concentrations (Supplementary Fig. 1).

Hypercalcaemia in mice was induced by daily intraperitoneal Downloaded from https://academic.oup.com/brain/article/142/9/2737/5532100 by Institut Armand-Frappier user on 16 May 2024 injection of calcium gluconate (2000 mg/kg/day; Sigma- Aldrich) starting 3 days before immunization.


Blood was collected by vein facialis puncture and serum was isolated by centrifugation using gel columns (Sarstedt).

25-OH- vitamin D3 concentrations in serum were measured with liquid chromatography-tandem mass spectrometry using a MassChrom Õ 25-OH-vitamin D3/D2 LC-MS/MS kit (Chromsystems) on an AB Sciex API 4000 LC/MS/MS system (AB Sciex).

Total calcium in serum and culture medium was determined using the Arsenazo III dye binding method on an ARCHITECT c16000 analyser (Abbott Diagnostics).

Ionized calcium in culture medium was measured on a blood gas analyser GEM Premier 4000 (Instrumentation Laboratory).

Total inorganic phosphate in serum was quanti- fied based on Molybdenum blue colorimetric method on an ARCHITECT c16000 analyser (Abbott Diagnostics).

Total sodium in serum and total chloride in culture medium were measured using ion-selective electrodes on an ARCHITECT c16000 analyser (Abbott Diagnostics).


Human PBMCs from healthy donors were isolated after Ficoll gradient centrifugation (Biochrom).

Human T cells were pur- ified from PBMCs by negative magnetic-activated cell sorting (MACS) separation using MojoSort TM human T cell isolation kit (BioLegend).

Single cell suspensions of murine lymphoid tissues were generated by gentle dissection and passing through 70 mm cell strainer (Greiner Bio-One).

Murine CNS mononu- clear cells were isolated by digestion of perfused CNS tissue using collagenase D (Roche) and DNase I (Roche) at 37 C for 45 min.


Mononuclear cells were passed through a 70 mm cell strainer (Greiner Bio-One) and lymphocytes were collected by discontinuous density Percoll Õ gradient (GE Healthcare).

Murine blood was collected in PBS containing 1 mM EDTA followed by erythrocytes lysis using BD Pharm Lysing Buffer.

Murine splenic CD11b + cells were purified by negative MACS separation using a mouse lineage panel (BD Bioscience).

Murine T cells were isolated by negative MACS separation using a mouse pan T cell isolation kit II (Miltenyi).

For sep- arate stimulation of CD4 + and CD8 + T cells, murine T cells were isolated by positive MACS separation using CD4 (L3T4) and CD8a (Ly-2) MicroBeads (Miltenyi), respectively.


Mice were transcardially perfused with PBS followed by 4% paraformaldehyde (PFA) and tissue was paraffin embedded.

One-micrometre thick slices were stained with haematoxylin and eosin and Luxol fast blue/periodic acid shiff (LFB/PAS).

T cells, B cells and macrophages were detected by immunohis- tochemistry with an avidin-biotin technique using antibodies specific for CD3 (SP7; DCS Innovative Diagnostik-Systeme), CD45R/B220 (RA3-6B2; BD Biosciences) and Mac-3 (M3/ 84; BD Biosciences).

Histological sections were captured using a digital camera (DP71; Olympus Europa) mounted on a light microscope (BX51; Olympus Europa).

The percentage of demyelinated white matter was calculated using cellSens Dimension software (Olympus Europa).

Inflammatory cells were quantified at Â400 magnification using an ocular count- ing grid and are shown as cells/mm 2 .

At least eight spinal cord cross sections were taken for each analysis.


Composition of murine immune cells was analysed using the following antibodies: CD3 (145-2C11; BioLegend), CD4 (RM4-5; BioLegend), CD8 (53-6.7; BioLegend), CD45R/ B220 (RA3-6B2; BioLegend), CD11b (M1/70; BioLegend), CD11c (N418; BioLegend), F4/80 (BM8; BioLegend) and CD45 (30-F11; BioLegend).


Splenic monocyte activation/differ- entiation was determined using: CD40 (3/23; BD Bioscience), CD80 (16-10A1; BioLegend), CD86 (GL-1; BioLegend), MHCII (AF6-120.1; BioLegend), PD-L1 (MIH5; eBioscience) and LAP (TW7-16B4; BioLegend).


T-cell activation was inves- tigated using: CD25 (PC61; BioLegend), CD69 (H1.2F3; BioLegend), CD95 (Jo2; BD Bioscience) and CD44 (IM7; BioLegend).


T regulatory cell differentiation was evaluated by intracellular staining for FoxP3 (FJK-16s; eBioscience) after fixation and permeabilization using the fixation/perme- abilization kit (eBioscience).


To investigate Th1 and Th17 cell differentiation cell suspensions were stimulated with 50 ng/ml phorbol 12-myristate 13-acetate (PMA; Sigma-Aldrich) and 0.5 mg/ml ionomycin (Sigma-Aldrich) in the presence of 1 ml/ml brefeldin A (BD Bioscience) for 6 h.


Cytokine production was analysed by intracellular staining for IFN- (XMG1.2; BioLegend) and IL-17A (TC11-18H10; BD Bioscience) after fixation and permeabilization (BD Bioscience).


Fc receptors were blocked using monoclonal antibody specific for CD16/ CD32 (93; BioLegend).


Dead cells were stained with a fixable viability kit (BioLegend).


Samples were acquired on a BD LSR Fortessa (BD Bioscience).


To investigate the cytokine profile of human T cells in migration assays, cells from lower and upper chamber were stimulated with 20 ng/ml PMA and 1 mg/ml ionomycin in the presence of 1 ml/ml brefeldin A (BD Bioscience).


Dead cells were stained with LIVE/DEAD TM Fixable Aqua Dead Cell Stain Kit (Thermo Fisher Scientific).


CD3 (OKT3, Biolegend), CD8 (RPA-T8, BD Bioscience) and CD4 (SK3, BD Bioscience) were used for surface staining.


To assess cytokine production IL-17 (eBio64CAP17, eBioscience), IFN- (B27, BD Bioscience), GM-CSF (BVD2-21C11, BD Bioscience) and IL-4 (8D4-8, BD Bioscience) were used.


Samples were acquired on a BD TM LSRII cytometer (BD Bioscience).


All data evaluation was performed using FlowJo software (FlowJo LLC, Ashland, USA).


Production of IFN-, IL-17 and GM-CSF was measured using ELISA MAX TM Standard Set kits (BioLegend).

Absorbance was measured at 450 nm with subtraction of a 540 nm refer- ence wavelength on iMark TM microplate reader (Bio-Rad).


Thymocytes were gently dissected from wild-type mice and passed through a 70 mm cell strainer (Greiner Bio-One) and apoptosis was induced by 1 mM dexamethasone (Sigma- Aldrich) incubation overnight.

Apoptotic thymocytes were labelled with pHrodo Red, succinimidyl ester (pHrodo Red, SE; Thermo Fisher Scientific) and added to LPS pre-stimulated (50 ng/ml; overnight) splenic CD11b + cells.

After incubation of splenic CD11b + cells with apoptotic thymocytes at 37 C or on ice for 1 h phagocytosis was assessed by flow cytometry.


For the analysis of T-cell proliferation, T cells were stained with carboxyfluorescein succinimidyl ester (CFSE), for evalu- ation of differentiation or migration across the blood-brain barrier model, T cells remained unstained.

T cells were incu- bated in anti-CD3 (clone 145-2C11 for murine T cells; clone OKT3 for human T cells), or anti-CD28 (clone 37.51 for murine T cells; clone CD28.2 for human T cells) (BioLegend) pre-coated wells for 48-72 h (for murine T cells) or 96-120 h (for human T cells).

To study the effects of calcium, cholecal- ciferol, 25-(OH)-vitamin D and 1,25-(OH) 2 -vitamin D on T- cell proliferation and differentiation, T cells were preincubated with the respective components at 37 C for 1 h. Calcium chloride was dissolved in complete medium, cholecalciferol, 25-(OH)-vitamin D as well as 1,25-(OH) 2 -vitamin D were dis- solved in absolute ethanol and adjusted to contain equal etha- nol concentrations after dilution.

Monitoring of pH occurred with inoLab Õ pH Level 1 (WTW).


T cells were pre-incubated in complete Hanks' balanced salt solution (HBSS) medium (HBSS medium containing 1.3 mM CaCl 2 , 0.5 mM MgCl 2 and 1% foetal calf serum) with increas- ing calcium concentrations at 37 C for 1 h followed by stain- ing with 4 mg/ml Fluo-3 AM and 10 mg/ml Fura Red AM (Biomol) in complete HBSS (Thermo Fisher Scientific) contain- ing 0.02% Pluronic F-68 (Life Technologies) at 37 C for 30 min.

T cells were kept on ice and directly before flow cytome- try acquisition pre-heated to 37 C for 5 min.

After a 25-s baseline recording, human or mouse T cells were stimulated using 10 or 20 ng/ml ionomycin (Sigma-Aldrich), respectively.


T cells were preincubated at the indicated calcium concentrations for 1 h at 37 C followed by stimulation in anti-CD3/anti-CD28 pre-coated 6-well plates for 1-6 h (murine T cells) or for 3-20 h (human T cells) at 37 C. Total RNA was isolated using the RNeasy Õ mini kit (Qiagen) and transcribed into cDNA using the QuantiNova TM Reverse Transcription kit (Qiagen) according to the manufacturer's instructions.

Quantitative (q)PCR was per- formed using 500 nM per primer and qPCRBIO SyGreen (Nippon Genetics Europe) in a total volume of 10 ml on a QuantStudio TM 7 (Applied Biosystems).

Primers specific for IL- 2, GM-CSF, IFN- and beta-actin were purchased from Bio-Rad (for mouse) and Thermo Fisher Scientific (for human).

Quantitative PCR runs were performed at 95 C denaturating and 66 C or 68 C annealing temperature for mouse and human T cells, respectively.

Elongation was performed for 30 s and 40 cycles with subsequent melt-curve analysis.

Primer speci- ficity was validated by product size analysis using a 2% agarose gel containing GelRed (Biotium) and UV light illumination.

Detailed primer information are listed in the supplementary ma- terial (Supplementary Table 1).

Samples were analysed in dupli- cate or triplicate and considered valid when Ct 5 35 and standard deviation (SD) Ct 5 0.5.

Analysed cytokine expression was normalized to beta-actin expression loading control (ÁCt) in mice or human T cells, respectively.

ÁÁCt values were calculated in comparison to the 1.0 mM Ca 2 + condition and relative gene expression was calculated by 2 ÀddCt .


Blood-brain barrier-endothelial cells (BBB-ECs) were isolated and cultured from non-epileptic surgical human CNS material as pub- lished previously (Ifergan et al., 2006).

Written informed consent was obtained from every donor prior to surgery (CHUM re- search ethics committee; approval number BH07.001 and HD04.046).


In vitro transmigration assays were performed using a modified Boyden chamber as published previously (Ifergan et al., 2006; Larochelle et al., 2012, 2015).

In brief, human BBB-ECs were grown to confluence on gelatin-coated 3-mm pore size Boyden chambers (Collaborative Biomedical Products) in culture medium supplemented with 40% astrocyte- conditioned medium.


For the inflamed condition, TNF/IFN- (100 U/ml) was added in the upper chamber 24 h before the assay.

Boyden chambers were washed, and fresh medium was used for the transmigration assay.


Anti-CD3/anti-CD28-stimu- lated human T lymphocytes (10 6 ), which had been pre-incubated with the respective calcium concentrations were added to the upper chamber.


After 16 h, cells that had transmigrated across the BBB-ECs layer to the lower chamber were harvested, counted and analysed by flow cytometry.

Migration experiments were performed in triplicates for each donor, in all conditions.


Statistics were calculated using the software GraphPad Prism 5.01.

All in vivo and ex vivo comparisons were made to the standard vitamin D diet.

Serum concentrations of vitamin D3, serum and urine concentrations of calcium, phosphate and sodium as well as body weight, macrophage phagocytosis, ex vivo T-cell proliferation, cytokine concentrations are shown as mean AE standard error of the mean (SEM) and were analysed by the two-tailed t-test.

Quantitative PCR results, in vitro T- cell viability, pH/calcium-/chloride concentrations in medium are shown as mean AE SEM and were analysed by one-way analysis of variance (ANOVA) followed by Bonferroni's mul- tiple comparison test.

Clinical scores are depicted as mean AE SEM, composition of immune cells, white matter de- myelination and infiltration, monocyte- and T-cell activation and differentiation are shown as median and were analysed using the Mann-Whitney U-test.

Calcium flux results are pre- sented as mean AE SEM and were analysed by Kruskal-Wallis test followed by Dunn's test for multiple comparisons.

T-cell migration results are shown as mean AE SEM and were ana- lysed by Friedman test followed by Dunn's test for multiple comparisons.

Statistical analysis of the in vitro T-cell


The data that support the findings of this study are available from the corresponding author, upon reasonable request.


Mice received a diet either containing a low concentration of vitamin D3 (55 IU vitamin D3/kg), representing vitamin D deficiency, a standard (1500 IU/kg) or a high (75 000 IU/kg) dose of vitamin D3 for 15 weeks.

As indicated in Supplementary Fig. 2A, the respective diet did not influence mean body weight.


In vivo, vitamin D is hydroxylated in the liver and the kidney to its biologically active form 1,25- dihydroxy (1,25-(OH) 2 ) vitamin D3.

The half-life of 1,25- (OH) 2 vitamin D is relatively short though, so that the vita- min D status is commonly assessed by measuring 25-(OH) vitamin D (Holick, 2009).

As shown in Fig. 1A, mice in the low vitamin D group showed an average serum level below 30 nmol/l, which reflects a commonly accepted definition of vitamin D deficiency in humans (Holick, 2007).

The stand- ard group contained serum levels around 100 nmol/l, which is considered an upper normal vitamin D level in humans (Holick, 2007) and facility-housed mice (Bolton et al., 2013).

The high vitamin D diet led to serum 25-(OH) vita- min D concentrations of $250 nmol/l, a level that is com- monly achieved in patients by its continuous supplementation at high doses (Vieth, 1999; Burton et al., 2010; Smolders et al., 2010).

Mice that received the high vitamin D diet showed elevated concentrations of both total as well as ionized calcium in their serum.

Urine calcium concentrations were also substantially increased, indicating that long-term treatment with high vitamin D doses regularly caused hypercalcaemia in a predictable manner (Fig. 1B-H)


Vitamin D exerts immunomodulatory effects on cells within the innate and adaptive immune system, which widely ex- press cell surface vitamin D receptors (VDRs) (Cantorna et al., 2004).

Therefore, we examined whether the respect- ive vitamin D diet may have affected the frequency of T cells, B cells and myeloid antigen-presenting cells (APCs) in blood, lymph node and spleen.

As indicated in Supplementary Fig. 3, high vitamin D was associated with an increase in frequency of T cells and fewer B cells in the blood.

Moreover, in the spleen, these mice contained an elevated frequency of CD11c + dendritic cells and F4/ 80 + macrophages.


EAE was induced after chronic vitamin D supplementa- tion.

All three treatment groups developed EAE around Day 12 (Fig. 2A).

Mice that received the stand- ard dose of vitamin D developed the lowest scores, as ex- pected based on earlier EAE studies showing that short- term oral vitamin D administration ameliorated EAE and impaired activation and CNS migration of monocytes (Nashold et al., 2000) and T cells (Mayne et al., 2011).


Surprisingly, mice supplemented with high vitamin D dose manifested a more severe EAE course.

Mice were main- tained on their respective diet throughout the entire disease course.

Recognizing nutrition as a potential confounder, we monitored weights daily.

While low and high vitamin D groups had modestly higher mean body weights within days after immunization, there were no significant differ- ences in body weights in any groups throughout the disease course (Supplementary Fig. 2B).


The unexpected clinical observation of an EAE exacerba- tion in the group of mice containing high vitamin D serum levels was further corroborated by histological analyses, revealing a significantly increased inflammatory infiltration of the CNS (Fig. 2B).

An abundance of activated CNS infiltrating myeloid cells as well as an elevated number of CNS T cells was observed in the high dose vitamin D group (Fig. 2C-E).

Enhanced cellular infiltration was associated with an increase in the extent of CNS demyelination (Fig. 2F).


CNS T cells were isolated and evaluated for their dif- ferentiation.

As indicated in Fig. 2G, CNS T cells from mice with high vitamin D levels contained a significantly higher frequency of IFN- or IL-17 producing Th1- and Th17 cells, respectively.

In contrast, the frequency of FoxP3 + regulatory T cells (Treg) within the CNS was slightly lower in the high vitamin D group (Fig. 2H).


Taken to- gether, the high dose vitamin D diet was associated with higher absolute numbers of myeloid cells and T cells and pro-inflammatory differentiation of these cells.


In light of the increased inflammatory CNS infiltration we analysed whether continuously high levels of vitamin D had Hypercalcaemia by vitamin D exacerbates EAE BRAIN 2019: 142; 2737-2755 | 2741 Downloaded from https://academic.oup.com/brain/article/142/9/2737/5532100 by Institut Armand-Frappier user on 16 May 2024 promoted peripheral immune cell activation and differenti- ation prior to CNS transmigration.


As shown in Fig. 3A, myeloid APC from mice in the vitamin D high group indeed showed an enhanced surface expression of MHC class II and co-stimulatory CD40, CD80 and CD86.


In contrast, expression of PD-L1, an inhibitory costimulatory molecule (Salama et al., 2003), or latency-associated pep- tide (LAP), a surface molecule indicative of a Treg-fostering TGF-beta production (Miyazono et al., 1991), was un- affected by the high vitamin D diet.


As a prerequirement for presentation to T cells, larger antigens must first be phagocytosed and processed by the APC.


As indicated in Fig. 3B, in vivo vitamin D treatment dose-dependently increased the phagocytic capacity of mye- loid macrophages.


While this is likely of benefit in clearance of infections (Djukic et al., 2015), enhanced uptake of auto-antigen in contrast enlarges the basis to generate an auto-reactive adaptive immune response (Kinzel et al., 2016).


In conjunction with the elevated frequency of CD11c + dendritic cells and highly differentiated F4/80 + macrophages in vitamin D- treated naïve mice (Supplementary Fig. 3) these data thus suggest that high dose vitamin D supplementation is associated with matur- ation of myeloid APC, an increased surface expression of the molecular machinery participating in phagocytosis and antigen presentation.


Standard and high vitamin D3 treatments were associated with a significant increase in expression of the early acti- vation marker CD69 on T cells.

There were also dose-de- pendent increases in the frequencies of activated CD25 + and antigen-experienced CD44 hi memory T cells (Baaten et al., 2012) (Fig. 4A).


Of interest, standard and high vita- min D supplementation was associated by an increase in Treg (Fig. 4B).


When T cells were restimulated in vitro by anti-CD3/anti-CD28, however, in vivo high dose vitamin D-exposed T cells proliferated at a significantly higher rate as assessed by dilution of an intracellular dye (Fig. 4C) and preferentially differentiated into IFN- pro- ducing Th1 and IL-17 releasing Th17 cells (Fig. 4D and E).


The possibility that pro-inflammatory Th1 and Th17 dif- ferentiation of high vitamin D was due to a direct effect on T cells, or was a secondary effect resulting from enhanced APC maturation was investigated.

Here, we used a co-cul- ture system, in which naïve T cells were activated by mye- loid APC.

Either cell type was isolated from mice fed with low, standard or high vitamin D doses.


Based on the substantial phenotypic alterations of CD11b + myeloid cells, we first focused on a possible APC-driven effect.

As indicated in Fig. 5A, myeloid APC continuously exposed to high vitamin D levels triggered a slightly higher prolifer- ation of myelin-specific T cells.


Yet, in vivo exposure of T cells to vitamin D exerted the greater contribution to enhanced T-cell proliferation; T cells isolated from mice treated with low or high vitamin D doses always prolifer- ated at a significantly higher rate independent of whether the activating myeloid APC had been treated with low, standard or high vitamin D (Fig. 5B).


Most strikingly, naïve myelin-specific T cells, which had been exposed to high vitamin D levels in vivo consistently differentiated into Th1 and Th17 cells, largely independent of the respect- ive APC origin (Fig. 5C and D); conversely, low, but even more so high vitamin D effectively prevented development of Treg ex vivo (Fig. 5E).


Of note, the standard vitamin D diet generated T cells with the greatest propensity to differ- entiate into Treg cells when activated ex vivo, which is in line with a reported fostering effect of moderate vitamin D levels on the frequency and function Treg in mice (Korf et al., 2012) and humans (Barrat et al., 2002).


Next, to confirm that high 25-(OH) vitamin D levels influenced T- cell activation and differentiation directly in an antigen- and APC-independent manner, we stimulated CD4 + or CD8 + T cells purified from naïve mice fed with the low, medium and high vitamin D diet by anti-CD3/anti-CD28 directly.


Parallel with our findings in EAE-diseased mice, high-dose vitamin D treatment accelerated proliferation of both CD4 + and CD8 + T cells (Fig. 5F and G).


In addition, T cells from high dose vitamin D-treated mice differentiated in a pro-inflammatory manner, when activated ex vivo (Supplementary Fig. 4).


Based on these findings, next, we assessed the direct effect of vitamin D and its metabolites on both human and murine T cells in vitro.

Purified T cells were pre-incubated with cholecalciferol, 25-(OH)-vitamin D or 1,25-(OH) 2 - vitamin D at increasing concentrations.

Most importantly, these concentrations reflected both the serum ranges mea- sured in our EAE study as well as low, physiological, and supra-physiological levels in humans.


None of the vitamin D concentrations affected viability of human or murine T cells in culture (Supplementary Fig. 5A-F).

As indicated in Fig. 6A-F, 25-(OH)-vitamin D or 1,25-(OH) 2 -vitamin D clearly inhibited proliferation of both human and murine CD4 + and CD8 + T cells in a dose-dependent manner, while non-hydroxylated cholecalciferol only exerted this effect on human T cells.


These results confirmed a direct regulatory effect of vitamin D itself on T cells (Mayne et al., 2011; Peelen et al., 2011) and thus failed to explain the enhanced proliferation and pro-inflammatory differen- tiation of T cells observed in high dose vitamin D-treated mice.