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Identification Sheet
Title: The effects of micronutrient interactions on iron status using the NDNS survey of
children (AN0848)
Project Leader:
Dr Barrie Margetts
Institute of Human Nutrition
University of Southampton
Biomedical Sciences Building
Bassett Crescent East
Southampton
SO16 7PX
Project duration:
1 April 1998 to 31 March 1999
Total Project costs: £51 453
Staff Input:
Dr Rachel Thompson RA2A.15
CLE4
1.0
0.2
year
Scientific Objectives:
To investigate the effects of micronutrient interactions on iron status using data from National
Diet and Nutrition Survey of children1 ½ to 4 ½ years of age.
To assess the effect of under-reporting on the interactions investigated above.
Specifically:
1)
2)
3)
4)
5)
6)
Is dietary iron related to iron status only in children with low iron status?
Do the food sources of iron, and riboflavin, vitamin A and zinc, together with the
meal patterns, affect iron status.
Does marginal vitamin A, zinc and riboflavin status influence iron status?
Do children with a higher infectious load have poorer iron status?
Assess the impact under-reporting has on objectives 1-4
Publish results in peer reviewed journals
Primary milestones:
01
02
03
04
April-June
July-October
November
March
Clean, organise and clarify extent of under-reporting
Analysis and development of model
Assess the impact of under-reporting on model
Final analysis, preparation of papers, submission of papers for
consideration for publication
1
Executive Summary
Iron deficiency and iron deficiency anaemia are widely prevalent and thought to underlie much
morbidity and impaired development. Chemically iron is highly reactive and its metabolism is closely
regulated. Functionally, a limitation in the availability of iron at the point of its metabolic use might be
due to limited intake, metabolic sequestration, or the limited availability of another nutrient required for
its effective utilisation. As the body content of iron is regulated by absorption, an apparent iron
deficiency might be accounted for by a dietary deficiency, interactions with other components in the
diet, limitation in the availability of other nutrients, or other pathological processes associated with
infection or an inflammatory response. The present analysis sought to clarify the effects of the
interactions of markers of iron status with lifestyle factors, diet, biochemical markers for other nutrients
and markers of inflammation. Three markers were used to indicate iron status: haemoglobin as a
functional marker of the ability to use iron in the longer term; ferritin as a marker for iron in storage;
and zinc protoporphyrin (ZPP) as a marker of the availability of iron at the site of haemoglobin
formation.
Specifically we sought to address the following questions:
1. What are the main factors affecting iron status in healthy children?
2. Is dietary iron related to iron status only in children with low iron status?
3. Do the food sources of iron, and riboflavin, vitamin A and zinc, together with the meal patterns,
affect iron status?
4. Does marginal vitamin A, zinc and riboflavin status influence iron status?
5. Do children with evidence of current infection have poorer iron status?
6. Might under-reporting of dietary intakes impact on the interpretation of the above?
From our analyses we found that:
 When considered on their own, food patterns and nutrient intakes were associated with measures
of iron status, but when they were included in multiple regression models with included
biochemical, social and anthropometric measures, statistically they became less important in well
children.
 For haemoglobin, vitamin C (positively) and n-6 polyunsaturated fats (negatively) were the only
dietary measures which were included in an explanatory model, with biochemical measures of
retinol, zinc, vitamin D, and body weight being more strongly associated than dietary measures.

For ferritin, children with a diet of poorer quality (more cakes, and sugary drinks) were more likely
to have lower levels of plasma ferritin, and there was a stronger statistical association with plasma
folate.

For ZPP, there was a negative association with the consumption of diet soft drinks and with
plasma zinc.
 In the children with low haemoglobin levels (8%), dietary iron intake was associated with
haemoglobin.
 A higher body weight was associated with better measures of iron status, but within the present
data it is not possible to determine the extent to which this potentially important association is
causal in one direction or the other.
 Evidence of current infection was not consistently associated with each of the measures of iron
status, suggesting a more complex interaction than exposed in previous analyses.
 Dietary under-reporting, although present, had little effect on associations reported.
These data show important interactions amongst indices of micronutrient status and markers
of iron status. There has been a tendency to draw a direct relationship between dietary iron and iron
status, which has been translated into dietary fortification or supplementation programmes. The
implications of these data are that during childhood more complex interactions amongst nutrients
might be of equal or greater importance, as differences in blood concentrations of retinol, zinc, and
folate appeared to be more important influences on haemoglobin than simply dietary iron intake.
When different markers for iron status, haemoglobin, ferritin, and ZPP were used, the pattern of
associations were different and the implications of these observations need to be explored more fully.
They suggest that the metabolic handling of iron is affected by other nutrients, and that factors that
determine bioavailability within the gastrointestinal tract differ from those that influence either the
storage of iron or its use in specific metabolic pathways.
The associations found between anthropometric measures, markers of inflammation and
measures of iron storage appear of particular importance and the nature of the causal relationships
need to be determined.
2
Scientific Objectives:
The objective of this study was to use the data obtained in the National Dietary and
Nutritional Survey of Children between 1½ and 4½ years of age (Gregory et al, 1995) to
investigate the effects of micronutrient interactions on iron status.
Specifically, the following questions were addressed:
1)
2)
3)
4)
5)
5)
What are the main factors affecting iron status in healthy children?
Is dietary iron related to iron status only in children with low iron status?
Do the food sources of iron, and riboflavin, vitamin A and zinc, together with the
meal patterns, affect iron status.
Does marginal vitamin A, zinc and riboflavin status influence iron status?
Do children with evidence of current infection have poorer iron status?
Might under-reporting impact on the interpretation of the above?
Methods
Between 1992 and 1993 a national dietary and nutritional survey was carried out in a
nationally representative sample of 2102 children between 1½ and 4½ years of age (Gregory
et al, 1995). The survey included an interview with the parent on the food habits and lifestyle
consideration of the child, a weighed dietary record of all food and drink consumed over a
period of four days, anthropometry, and a blood sample. For the present analysis, three
markers of iron status were identified: haemoglobin concentration, a functional measure of
iron utilisation; ferritin, a measure of stored iron; and zinc protoporphyrin, a measure of the
metabolic availability of iron at the site of haemoglobin formation. Inflammation is known to
influence iron absorption and metabolism. A number of biochemical indices might be used
as measures of an inflammatory response, -1 antichymotrypsin (ACT) is the only measure
which is not directly, or indirectly, related to other aspects of iron metabolism or nutrient
availability (eg iron for ferritin and copper for caeruloplasmin). Amongst the range of
complex metabolic interactions, the interactions of vitamin A and riboflavin with the
availability and utilisation of iron within the body have been well described. Under-reporting
of the food consumed is always a concern in dietary studies, and in the present analysis the
potential effect this might have on the interpretations was assessed. It was assumed that if
under-reporting were present for any individual, the reported energy consumption would be
less than 1.2 times the estimated basal metabolic rate.
General
The data were cleaned and checked before analysis began to ensure that the variables and data
we used were the same as those reported. In the published report it stated that 74 children
were excluded for quality control reasons, but we could only account for 72 such children.
Although only a small discrepancy, it took us a considerable time to track these cases down
and clarify the anomalies. The normality of the distribution of all continuous variables was
assessed. Distributions that were skewed were transformed to approximate more closely to
normal. Distributions where the skewness statistic was greater than or equal to 1 were
defined, for this purpose, as being skewed, and requiring transformation. The most common
transformation was base10 logarithmic and square root transformations.
3
1. Is dietary iron intake related to iron stores only in children with low iron status?
Spearman rank correlation coefficients were used to assess the relationship between dietary
iron intake (from food sources and all sources) and iron status (haemoglobin, ferritin and zinc
protoporphyrin (ZPP)). The main outcome measure for this analysis was haemoglobin.
Ferritin was used as a proxy measure for iron stores. ZPP was used as an indictor of
inadequate iron supply at the site of haem formation. The relationship between dietary intake
and iron status measures was assessed in children with haemoglobin above and below 11g/dl
(the level used by the World Health Organisation for defining anaemia). A cut-off 10µg/l
was used for ferritin. To assess the relationship between different levels of iron status and
dietary iron intake without using a pre-defined cut-off the distributions for haemoglobin,
ferritin and ZPP were divided into thirds and the relationship between iron status and iron
intake assessed for each third separately.
2. Do the food sources of iron and the meal patterns affect iron status?
In order to compute one variable describing education, income and occupation of the head of
the household a cluster analysis was performed on highest qualification achieved, income (in
thirds) and occupation (manual or non-manual or never worked). To identify food patterns, a
principal component analysis was carried out. The analysis was set so that only components
with an Eigen factor of greater than one were extracted. A varimax with Kaiser normalisation
rotation method was used. The original food groups were combined to give 25 food groups
(appendix A). Food groups were chosen à priori to cover the whole diet but also to include
food groups that may affect iron absorption such as tea and coffee and fruit juices. Individual
food groups and nutrients were correlated with haemoglobin and ferritin and those with a
statistically significant Spearman correlation coefficient were selected for further analysis
(appendix B). As correlations were higher for nutrient intakes from food sources rather than
all sources, values excluding supplements were used. A stepwise multiple regression analysis
was carried out with individual foods, food patterns, nutrients and a number of other
important variables as independent variables and an iron status measure (haemoglobin,
ferritin or ZPP) as the dependent variable. The other important variables were the social
cluster variable, age, gender, body weight, birth weight, height and whether the child was
given vitamin supplements.
3. Does marginal vitamin A, zinc and riboflavin status influence iron status?
Lower haemoglobin concentrations may be a result of poor intakes of other nutrients such as
vitamin A, riboflavin and zinc. Vitamin A and riboflavin both affect iron absorption and
utilisation and zinc is important in the synthesis of haemoglobin and affects the
bioavailability of iron. The main dietary source of vitamin A, riboflavin and zinc is milk. To
determine whether children with low milk consumption also had low intakes of vitamin A,
zinc and riboflavin, nutrient intakes for each fifth of milk consumption were computed. The
association between dietary and blood measures of vitamin A, riboflavin and zinc was
assessed using Spearman rank correlation coefficients. To assess whether blood or dietary
variables were more strongly related to iron status, Spearman rank correlation coefficients
were computed for dietary and blood measures (vitamin A, zinc and riboflavin) with
measures of iron status (haemoglobin, ferritin and ZPP). To assess whether children with
lower intakes and blood levels of vitamin A, zinc and riboflavin also had a poorer iron status
independently of their iron intake an analysis of variance was carried out. Dietary and blood
measures of zinc, vitamin A and riboflavin were divided into fifths and then grouped into the
4
bottom fifth and remaining fifths. The model also included age, gender, social cluster, birth
weight, current body weight and iron intake.
4. Do children currently experiencing an inflammatory response load have poorer iron
status?
Infections elicit metabolic responses, including the inflammatory response, with widespread,
co-ordinated alterations in metabolism, which includes a change in the pattern of proteins
secreted by the liver. There is an increase in the synthesis and secretion of “acute phase
reactants” such as ACT, and the magnitude of the increase in circulating concentration might
be used as a crude measure of the infective load. Infection may be associated with a lower
haemoglobin. In addition, data are available for ACT, albumin, caeruloplasmin, and ferritin.
As ACT is the only acute phase reactant not directly or indirectly related to iron metabolism,
this has been used as the independent indicator of the presence of infection. The association
between the iron status measures and measures of inflammation was assessed using Spearman
rank correlation coefficients for ACT, albumin, caeruloplasmin (both as continuous variables
and in thirds), and iron status measures (haemoglobin, ferritin and ZPP). To determine which
factors accounted for the variation in iron status a stepwise multiple regression analysis was
carried out with iron status as the dependent variable. The independent variables included
foods, food components, nutrients, social cluster variable, age, gender, body weight, birth
weight, height and whether child took vitamin supplements, blood measures of retinol, zinc,
vitamin B12, folate, vitamin C,  tocopherol, vitamin D and inflammation markers, ACT,
albumin, caeruloplasmin and ferritin.
5. Assess the impact of under-reporting
The Schofield equations for the relevant age groups (Schofield et al 1985) were used to
calculate basal metabolic rate. In order to assess the effect of under-reporting a cut-off of 1.2
for energy intake to basal metabolic rate ratio (EI/BMR) was used. It is recognised that this is
an arbitrary cut-off and that under-reporting is possible across the whole range of intakes
therefore the distribution of EI/BMR was also divided into thirds.
To determine whether there was differential under-reporting in the toddlers the social and
demographic characteristics of the toddlers above and below the 1.2 cut-off for EI/BMR and
across thirds of the distribution were compared. The effect of under reporting on nutrient and
food intakes was assessed by comparing mean intakes in children above and below the cutoff. To assess the effect of under-reporting and being unwell on the final results the multiple
regression analysis as used in 4) was re-run on sub-groups of the population. Firstly those
children with biochemical evidence of infection (ACT > 0.65) were excluded. For the
remainder of children beta coefficients were computed separately for those who were well
during the diary, those above the 1.2 cut-off for EI/BMR and those who were both well and
above the 1.2 cut-off. The final group was considered to be better reporters of dietary habits
and were well at the time of the diary and thus were considered to be the group of the most
representative of normal health. Complete data on all variables were not available for all
children. To assess the generalisability of the results the social and demographic variables of
those toddlers with complete dietary and blood data were compared to children without
complete data.
5
Results
1. Is dietary iron related to iron status only in children with low iron status?
Table 1 shows the correlations for haemoglobin and ferritin with dietary iron. For all subjects
small but statistically significant correlations were found. In general correlations were
stronger for food sources of iron rather than all sources which included dietary supplements.
When children were divided into thirds of the distribution for ferritin only those children
below the cut-off and in the lowest third of the distribution showed a statistically significant
association with iron intake. The same was true for haemoglobin and ZPP. When the 11g/dl
cut-off for haemoglobin was used statistically significant correlations were seen for children
above the cut-off although the correlation between iron intake and haemoglobin was stronger
for those below the cut-off. This may be because few children had haemoglobin levels below
11g/dl. Table 2 shows similar results for ZPP and shows statistically significant negative
correlations with iron from food sources for all subjects and those in the highest third. As
single measures of iron status may not be good indicators of iron deficiency those children
with haemoglobin below 11g/dl and ferritin less than 10g/l were selected. Only 31(3%)
children had low values for both haemoglobin and ferritin. Correlations in this group were
0.47 for haemoglobin with iron from food sources and 0.37 with iron from all sources (both
were statistically significant). Correlations with ferritin were lower and did not reach
statistical significance (0.23 for food sources and 0.25 for all sources). Respective
correlations with ZPP were –0.21 and –0.03.
Table 1: Spearman rank correlation coefficients for dietary iron intake with iron status for all
subjects and by cut-offs for iron status
Haemoglobin (Hb)
Ferritin (Ferr)
Iron
Iron
Iron
Iron
(food
(all sources)
(food
(all sources)
sources)
sources)
All (n=923)
0.092*
0.085*
All (n=904)
0.070*
0.073*
Hb < 11g/dl
0.141
0.040
0.192**
Ferr < 10g/l 0.208**
(n=72)
(n=185)
0.069*
0.071*
-0.012
Hb  11g/dl
Ferr  10g/l -0.018
(n=851)
(n=719)
Divided into thirds
Divided into thirds
Hb (n=300)
0.125*
0.101
Ferr (n=290) 0.181**
0.174**
(7.5-11.7g/dl)
(4-12g/l)
Hb (n= 329)
0.022
0.006
Ferr (n=307) -0.012
-0.016
(11.8-12.5g/dl)
(13-25g/l)
Hb (n=294)
-0.048
-0.069
Ferr (n=307) -0.036
-0.005
(12.6-17.3g/dl)
(26-139g/l)
Range of values for iron status measures divided into thirds given in brackets.
* P <0.05, ** P <0.01
6
Table 2: Spearman rank correlation coefficients for dietary iron intake with zinc
protoporphyrin (ZPP) for all subjects and divided into thirds
Iron (food sources)
Iron (all sources)
All (n=922)
-0.075*
-0.048
ZPP(n = 314)
-0.029
-0.022
(1-45mol/mol haem)
ZPP(n = 306)
0.040
0.035
(46-56 mol/mol haem)
ZPP(n = 302)
-0.143*
-0.100
(57-245 mol/mol haem)
Range of values for iron status measures divided into thirds given in brackets.
* P <0.05, ** P <0.01
Summary
Dietary iron intake from food sources was only related to iron status in those children with the
lowest iron status. The strongest association between iron intake and status was in children
with haemoglobin < 11g/dl and ferritin < 10g/l.
2. Do the food sources of iron and the meal patterns affect iron status.
In order to summarise key socio-economic characteristics a cluster analysis was performed
with income, education and occupation. The results are shown in table 3. Parents of those
children in cluster 1 compared with cluster 2 had a lower income, poorer education and were
more likely to have a manual occupation. There were no statistically significant differences
in mean levels of haemoglobin between cluster 1 and cluster 2 (cluster 1-12.09, cluster 212.23), ferritin (cluster 1-23.30, cluster 2- 23.69) or ZPP (cluster 1-54.69, cluster 2-54.07).
Table 3: Percent distribution of income, education and occupation by social cluster
Cluster 1 (%) Cluster 2 (%)
Income
Lowest third
66.0
0.0
Middle third
28.7
25.8
Highest third
5.3
74.2
Head of
‘A’ levels or higher
8.7
66.8
Household’s highest
‘O’ levels or other
46.4
30.8
Qualification
No formal
44.9
2.4
Head of household work Non-manual
18.4
71.1
Manual
76.2
26.9
Other, never worked
5.3
2.0
As diets are made up of a range of foods it can be difficult to obtain a summary statement of
different patterns of food consumption. A principal component analysis is one way to
characterise food patterns rather than individual foods. Each component is identified as a
continuous variable. Nine separate dietary patterns of consumption, or principal
components, with Eigen factors greater than one were identified accounting for 52% of the
total variance of all components (see appendix A for more details). Table 4 shows the foods
that were positively and negatively associated with each dietary pattern component. A
descriptive term has been used to attempt to characterise the overall dietary pattern of each
component and draws out contrasts between the positively and negatively weighted factors.
For ease of presentation the positively weighted factors are described first in the descriptive
7
term. The description in Table 4 is used below to assess the relationship between dietary
patterns and iron status. Hence, a direct association indicates a preponderance of positive
over negative factors within the principal component and an inverse association a
preponderance of negative over positive. Table 4 shows that the greatest contrast in food
patterns was between children eating either more crisps, chips and burgers or more fruit juice,
milk or yoghurt. The third component highlighted the contrast between children eating more
bread, potatoes and vegetables and more sugar, tea and coffee.
Table 4: Description of components for principal component analysis. Only food groups with
a value of at least 0.20 are shown, listed in descending order of importance for each
component.
Description of each
Positively weighted factors
Negatively weighted
principal component
factors
1. Crisps, processed and
sugary foods versus fruit
and, milk & yoghurt
2. Tea & coffee, meat and
vegetables versus soft
drinks, sweets and crisps
3. Traditional diet versus
sugar, tea & coffee
4. Ordinary fat spread and
sugary drinks versus low
fat spreads and diet soft
drinks
Crisps; chips; burgers, sausages & meat pies; sweets
& chocolate; bread; tea & coffee; soft drinks; sugar;
low fat spread; low fibre breakfast cereal
Tea & coffee; sugar; potatoes; milk & yoghurt;
ordinary fat spread; vegetables; beef, lamb, pork &
liver; bread
Bread; potatoes; vegetables; fruit; cheese & eggs;
ordinary fat spread; bacon; low fat spread; diet soft
drinks; beef, lamb, pork & liver; cakes, buns,
puddings & biscuits;
Ordinary fat spread; soft drinks; high fibre breakfast
cereals; fruit juice; cheese & eggs
5. Soft drinks, vegetables,
sugary foods and low fat
spread versus ordinary fat
spread and diet soft drinks
6. Meat, low fibre
breakfast cereals and
sugary foods versus low
fat spread, bread and fruit
juice
7. Cereals, sugary foods
and meat versus milk,
processed meats and chips
Soft drinks; vegetables; cakes, buns, puddings &
biscuits; low fat spread; potatoes; high fibre
breakfast cereals; chips
8. Sugary drinks, low fibre
breakfast cereals versus
diet soft drinks and high
fibre breakfast cereals
Soft drinks; cheese & eggs; pasta & rice; low fibre
breakfast cereals
9. Chicken, fish, chips,
and rice & pasta
Chicken; fish; chips; pasta & rice
Fruit juice; milk & yoghurt;
fruit; high fibre breakfast
cereals
Sweets & chocolate; soft
drinks; crisps; diet soft
drinks
Sugar; tea & coffee; milk
and yoghurt
Low fat spread; diet soft
drinks; low fibre breakfast
cereals; beef, lamb, pork &
liver; milk & yoghurt;
potatoes
Ordinary fat spread; diet
soft drinks; bread; low fibre
breakfast cereals
Beef, lamb, pork & liver; low fibre breakfast
cereals; sweets & chocolates; cakes, buns, puddings
& biscuits; ordinary fat spread; soft drinks;
vegetables
Low fat spread; bread; fruit
juice
Pasta & rice; high fibre breakfast cereals; cakes,
buns, puddings & biscuits; diet soft drinks; beef,
lamb, pork & liver, sugar
Milk & yoghurt; burgers,
sausages & meat pies; low
fibre breakfast cereals;
chips
Diet soft drinks; high fibre
breakfast cereals; cakes,
buns, puddings & biscuits;
chips; burgers, sausages &
meat pies
Table 5 presents the multiple regression analyses of individual foods, the principal component
derived dietary patterns (nine components), nutrients, social, demographic and
anthropometric variables on the iron status measures. For haemoglobin, children who had a
dietary pattern characterised by the consumption of more soft drinks, vegetables, sugary foods
and low fat spreads compared to more ordinary fat spreads and diet soft drinks, (as derived
from principal component analysis) were more likely to have higher haemoglobin levels.
8
Haem iron was positively and sucrose (either as a nutrient or as sugar) was negatively related
to haemoglobin and ferritin. Vitamin C was positively related to ferritin. Anthropometric
variables were more statistically significant than the dietary variables. Body weight was
positively, and birth weight negatively, related to haemoglobin whereas height was positively
related to ferritin. For ferritn the dietary pattern contrasting more low fat spreads and diet soft
drinks compared with ordinary fat spreads and soft drinks was associated with a higher
ferritin. For ZPP, the dietary pattern contrasting diet soft drinks and high fibre breakfast
cereals compared with sugary drinks and low fibre breakfast cereals were associated with
higher levels.
Table 5: Multiple regression analysis with individual foods, dietary patterns (from principal
components analysis), nutrients (food sources), social, demographic and anthropometric
variables in the model
i) Haemoglobin
R-squared: 8.8% F-ratio (df): 13.66 (852) P-value P < 0.001
Variable
Standardised T
Significance
coefficient 
Sucrose (food)*
-0.089
-2.625
0.009
Soft drinks, vegetables, sugary foods
0.087
2.584
0.010
and low fat spread versus ordinary fat
spread and diet soft drinks
Haem iron†
0.096
2.869
0.004
Body weight*
0.233
6.865
<0.001
Birth weight
-0.157
-4.627
<0.001
Social cluster
0.085
2.490
0.013
Transformations * log, † square-root
ii) Ferritin
R-squared: 6.1% F-ratio (df): 9.1 (836) P-value P < 0.001
Variable
Standardised T
coefficient 
Ordinary fat spread and sugary drinks
-0.139
-3.615
versus low fat spreads and diet soft
drinks *
Haem iron †
0.107
2.914
Sugar (nutrient)
-0.124
-3.138
Vitamin C*
0.115
3.077
Gender
0.071
2.079
Height
0.159
4.583
Significance
<0.001
0.004
0.002
0.002
0.038
<0.001
Transformations * log, † square-root
iii) Zinc protoporphyrin
R-squared: 4.7% F-ratio (df): 10.33 (851) P-value P <0.001
Variable
Standardised
coefficient 
Sugary drinks, low fibre breakfast cereals versus -0.116
diet soft drinks and high fibre breakfast cereals
Glucose*
-0.166
Calcium*
0.071
Age group
-0.079
T
-3.180
Significance
0.002
-4.523 <0.001
2.103
0.036
-2.298 0.022
Transformations * log
Summary
9
Less than 10% of the variation in iron status measures was explained by dietary, social,
demographic and anthropometric measures. Measures of overall diet such as food patterns
and nutrients were weakly related to iron status. Anthropometric measures were more
strongly related to iron status than dietary variables for haemoglobin and ferritin.
3. Does marginal vitamin A, zinc and riboflavin status influence iron status?
In terms of nutrient interactions, vitamin A, riboflavin and are known to interact with iron. It
was postulated that children with low milk consumption would have low intakes of vitamin
A, zinc and riboflavin, and these relationships are shown in Table 6. With increasing level of
milk consumption there was increased intake of each of the three nutrients.
Table 6: Mean daily nutrient intakes from food sources (SD) by fifths of milk consumption
Fifth of milk
consumption
Range of milk consumption in
each fifth (g/day)
Vitamin A (g)
Riboflavin (mg)
Zinc (mg)
1
2
3
4
5
0-121.0
121.2-207.0
207.5-310.3
310.4-436.9
437.1-1278
386 (1301)
446 (634)
534 (804)
513 (409)
665 (704)
0.76 (0.29)
1.01 (0.26)
1.14 (0.28)
1.31 (0.24)
1.68 (0.40)
3.5 (1.2)
4.0 (1.1)
4.3 (1.2)
4.8 (1.3)
5.5 (1.4)
Spearman rank correlation coefficients were used to assess the relationship between dietary
and blood measures for vitamin A, riboflavin and zinc. This was –0.099 for dietary zinc with
plasma zinc, 0.058 for dietary vitamin A with plasma retinol and –0.287 for dietary
riboflavin with erythrocyte glutathione reductase activation coefficient (EGRAC).
Table 7: Spearman rank correlation coefficients for dietary (food sources) and blood variables
with iron status measures (number of subjects given in brackets)
Haemoglobin
Ferritin
ZPP
Dietary iron
0.092* (923)
0.070* (904)
-0.075* (922)
Dietary vitamin A
0.007 (923)
-0.108** (904)
0.028 (922)
Plasma retinol
0.186** (801)
-0.047 (792)
-0.052 (800)
Dietary riboflavin
0.001 (923)
-0.088** (904)
0.069* (922)
EGRAC †
0.002 (812)
0.013
(802)
0.013 (811)
Dietary zinc
0.052 (923)
-0.034 (904)
0.026 (922)
Plasma zinc
0.110** (594)
-0.116** (588)
-0.021 (594)
*P < 0.05, ** P<0.01, † EGRAC
With haemoglobin, there were higher correlations for the blood measures than for dietary
measures for vitamin A and zinc. There was no statistically significant association between
riboflavin or EGRAC and haemoglobin. With ferritin, dietary variables correlated better than
blood measures for vitamin A and riboflavin, and blood measures were more strongly
correlated for zinc. With ZPP, dietary riboflavin produced a statistically significant
correlation.
To assess whether children with lower intakes and blood levels of vitamin A, zinc and
riboflavin had a poorer iron status independently of their iron intake those children in the
bottom fifth for nutrient status were compared with children in the remaining fifths. The
results of the analysis of variance are shown in Table 8. Lower haemoglobin levels were
observed for children in the lowest fifth for plasma zinc and plasma retinol. There was no
10
association with the dietary variables for haemoglobin. Higher ferritin levels were observed
in children in the lowest fifth for plasma zinc and dietary riboflavin.
Table 8: Analysis of variance for iron status measure (bottom fifth compared with other
fifths) for dietary and blood variables
Haemoglobin (g/dl)
Ferritin(g/l)*
Bottom fifth Remaining
Bottom
Remaining
nutrient
fifths for
fifth
fifths for
status
nutrient
nutrient
nutrient
status
status
status
Mean SE
Mean
SE
Mean SE Mean
SE
Dietary zinc (mg/d)
12.23 0.07 12.15
0.03 19.6
1.3 17.6
0.5
11.98 0.08 12.19† 0.04 20.1
1.5 16.3†
0.6
Plasma zinc (mol/l)
Dietary Vitamin A
12.21 0.07 12.17
0.03 19.5
1.2 17.5
0.5
(g/d)
11.91 0.07 12.27† 0.04 19.0
1.2 16.9
0.5
Plasma retinol (mol/l)
Dietary riboflavin (mg/d) 12.26 0.07 12.15
0.03 20.6
1.3 17.3†
0.5
EGRAC
12.15 0.07 12.19
0.04 16.0
1.0 17.7
0.6
Model includes age, gender, social cluster (cluster analysis performed with education, income and occupation of
parents), birth weight, current body weight and iron intake
* Log transformed
† Statistically significantly different between bottom fifth and remaining fifths, P<0.05
‡ Top fifth compared with remaining fifths as higher values of EGRAC reflects a lower saturation with riboflavin
Summary
Children with the lowest milk consumption had the lowest dietary intakes of vitamin A,
riboflavin and zinc. Lower plasma zinc and retinol status was associated with a poorer
haemoglobin level. Lower plasma zinc and dietary riboflavin were associated with a higher
ferritin level.
4. Do children currently experiencing an inflammatory response have poorer iron
status?
Infection impairs iron absorption and metabolic availability of iron. Children with evidence of
an inflammatory response may be expected to have a poorer iron status. Of those proteins
measured which, might mark an inflammatory response (ACT, caeruloplasmin, albumin and
ferritin), only ACT is not directly responsive to changes in iron or nutrient status. Spearman
rank correlation coefficients were used to assess the association between acute phase proteins
and iron status. Table 9 shows that ACT was not associated with haemoglobin, but was
directly associated with ferritin, making it difficult to interpret the extent to which ferritin,
caeruloplasmin and albumin might be markers for nutrient status or for inflammation. During
inflammation, the plasma concentration of caeruloplasmin may increase, but it also acts as a
ferroxidase making transport iron available to cellular metabolism. Albumin was positively
correlated and caeruloplasmin and ferritin negatively correlated with ZPP.
11
Table 9: Spearman rank correlation coefficients between acute phase proteins and iron status
Haemoglobin
Ferritin
ZPP
Albumin
0.162**
-0.028
0.139**
Caeruloplasmin
-0.171**
0.031
-0.080*
Ferritin
0.127**
1.000
-0.114**
ACT
-0.060
0.253**
-0.012
Table 10 compares levels of iron status across thirds of the distribution for markers of
infection. The lowest values of haemoglobin were found in the bottom third for albumin and
top third for caeruloplasmin. The lowest ferritin was observed for children in the lowest third
for ACT.
Table 10: Iron status measures for thirds of the distribution of acute phase proteins
Third of
Haemoglobin Ferritin
ZPP
distribution (g/dl)
(g/l)
(mol/mol haem)
(range)
Albumin(g/l)
1 (28-42)
11.93 (1.00)*
23.6 (19.0) 54.4 (25.7)
2 (42-45)
12.19 (0.83)
22.2 (16.8) 52.4 (16.6)
3 (46-65)
12.29 (0.89)
22.2 (17.9) 56.5 (23.4)
Caeruloplasmi
n
(g/l)
1 (0.12-0.25)
12.33 (0.83)*
20.7 (14.4)
54.1 (17.7)
2 (0.26-0.31)
3 (0.32-0.62)
12.25 (0.92)
11.90 (0.93)
23.4 (17.6)
23.5 (20.6)
54.7 (21.9)
54.2 (25.4)
1 (0.13-0.40)
2 (0.41-0.49)
3 (0.50-1.18)
* One way anova P<0.05
12.16 (1.04)
12.21 (0.77)
12.13 (0.92)
17.8 (12.8)*
20.6 (15.3)
28.6 (21.5)
56.1 (26.7)
52.6 (16.4)
54.6 (21.6)
ACT (g/l)
When a cut-off of 0.65 was used for ACT, mean values above and below the cut-off were
12.1 and 12.2g/dl for haemoglobin; 27.2 and 16.2 (P<0.001 for ferritin) and 52.0 and 51.4 for
ZPP.
Table 11 shows the multiple regression analysis when markers of infection and blood levels
of nutrients were added to the previous model. The variation explained by the model had
increased to around 20% for haemoglobin and ferritin, and 11% for ZPP. For haemoglobin
body weight was again the most important factor. As shown in section 3) Plasma zinc and
retinol were positively related to haemoglobin. Of the markers of infection both ferritin and
caeruploplasmin were included in the model, however albumin despite being correlated at a
univariate level was not. Of the dietary factors haem iron was not included in the model,
however vitamin C, copper and polyunsaturated fats were included.
12
Table 11: Multiple regression analysis with foods, dietary patterns( from principal
component analysis), nutrients, social, demographic and anthropometric variables, bloods and
infection markers included in the model.
i) Haemoglobin
R-squared: 16.4% F-ratio (df): 8.42 (440) P-value P < 0.001
Variable
Standardised
T
Significance
coefficient 
Sugary drinks, low fibre breakfast
-0.087
-1.933
0.054
cereals versus diet soft drinks and
high fibre breakfast cereals
Dietary vitamin C*
0.107
2.356
0.019
Dietary copper*
0.155
2.885
0.004
Dietary n-6 Pufa*
-0.125
-2.352
0.019
Birth weight
-0.148
-3.216
0.001
Body weight*
0.171
3.521
<0.001
Plasma zinc
0.122
2.704
0.007
Plasma retinol*
0.145
3.179
0.002
Ferritin*
0.124
2.720
0.007
Caeruloplasmin
-0.145
-3.209
0.001
Transformation * log
ii) Ferritin
R-squared: 23.4% F-ratio (df): 13.17 (440) P-value P < 0.001
Variable
Standardised
T
coefficient 
Biscuits†
-0.127
-2.930
Processed meat*
0.137
2.914
Ordinary fat spread and sugary
-0.157
-3.665
drinks versus low fat spreads and
diet soft drinks *
Meat, low fibre breakfast cereals, -0.103
-2.194
sugary foods versus low fat
spread, bread and fruit juice
Dietary n-6 polyunsaturated fat*
0.151
3.329
Age group
0.164
3.710
Plasma folate
0.116
2.667
Plasma zinc
-0.098
-2.292
Caeruloplasmin
-0.142
-3.010
ACT*
0.362
7.540
Significance
0.004
0.004
<0.001
0.029
0.001
<0.001
0.008
0.022
0.003
<0.001
Transformation * log, † square-root
In contrast to the model shown in table 5ii, individual foods were associated with ferritin.
Biscuits were negatively related and processed meat positively related to ferritin. The same
food patterns component ‘Ordinary fat spread and sugary drinks versus low fat spreads and
diet soft drinks’ was negatively related to ferritin. In contrast to the findings for haemoglobin
polyunsaturated fats were positively associated with ferritin. Age group but not body weight
was included in the model. Plasma zinc was negatively related to ferritin as shown in table 8,
whereas folate was positively related. Of the acute phase proteins both caeruloplasmin and
ACT were associated with ferritin.
13
iii) ZPP
R-squared: 10.6% F-ratio (df): 10.28 (440) P-value P < 0.001
Variable
Standardised T
Significance
coefficient 
Diet soft drinks*
-0.120
-2.626
0.009
Soft drinks, vegetables,
-0.107
-2.321
0.021
sugary foods and low fat
spread versus ordinary
fat spread and diet soft
drinks
Fructose*
-0.185
-3.992
<0.001
Plasma zinc
-0.118
-2.573
0.010
Ferritin*
-0.188
-4.110
<0.001
Transformation * log
For ZPP foods, both dietary patterns and nutrients were important. The variables most
strongly associated were fructose and ferritin, both negatively related.
Summary
When all factors were taken into account acute phase proteins (markers of infection) were
important in determining iron status. Biochemical measures of nutrient status were more
strongly associated with iron status than dietary measures for retinol, zinc and folate.
Inclusion of the blood variables altered the dietary variables of importance. In particular
haem iron and sugar were no longer included in the model. The key dietary variables for
haemoglobin were vitamin C and copper both positively associated and polyunsaturated fats
negatively associated. For ferritin individual foods were included in the model and
polyunsaturated fat was negatively related.
5. Assess the impact of under-reporting
Seventeen percent of the children were classified as under-reporters (EI/BMR < 1.2). We
assessed whether there was any differential bias by demographic and social characteristics in
the percent of children who were below compared with above the EI/BMR. The results can
be seen in appendix C. The key characteristics of under-reporters were; children where head
of the household was unemployed, a father with no qualifications, child receiving prescribed
medicines, poor compliance with items not being weighed for the diet diary, child unwell
with eating being affected, a father who smokes and child being in the top fifth for BMI.
The effect of under-reporting on mean nutrient intakes is shown in table 12. Children with an
EI/BMR < 1.2 had lower intakes of all nutrients and energy than those children with EI/BMR
 1.2. Children who were well during the weighed record had higher macronutrient intakes
than those who were unwell. Those who were well had higher intakes of vitamin A and
riboflavin than those who were unwell and those who reported their eating was affected.
Those who were well had higher intakes of zinc and iron than those who were unwell.
14
Table 12: The effect of under-reporting on mean (SD) energy and nutrient intakes
Nutrient / day
EIBMR
EI/BMR < 1.2
(n=272)
EI/BMR  1.2
(n=1319)
Energy (kJ)
Protein (g)
Fat (g)
Carbohydrate (g)
Vitamin A (g)
Riboflavin (mg)
Zinc (mg)
Iron (mg)
3540 (652)
28.0 (7.6)
32.6 (8.2)
116.3 (25.4)
344 (397)
0.90 (0.34)
3.3 (1.1)
4.2 (1.4)
5061 (962)
38.6 (9.8)
48.4 (12.1)
163.6 (36.5)
545 (908)
1.24 (0.42)
4.6 (1.4)
5.6 (1.7)
Reported health status during diary
Unwell eating Unwell
Well
affected
eating not
(n=1219)
(n=266)
affected
(n=190)
4298 (1173)
4637 (995)
4933 (1046)
33.7 (11.1)
35.8 (9.8)
37.6 (10.2)
41.0 (13.5)
44.0 (12.0)
47.0 (12.8)
138.1 (40.5)
150.2 (35.0)
160.0 (38.5)
493 (440)
594 (775)
594 (939)
1.12 (0.44)
1.20 (0.47)
1.23 (0.47)
4.2 (1.6)
4.3 (1.3)
4.5 (1.4)
5.2 (2.9)
5.3 (1.9)
5.7 (2.4)
All results for EIBMR P<0.05; all results for unwell eating affected and well P<0.05; all results for unwell eating not
affected and well P<0.05 except for vitamin A and riboflavin.
Table 13 shows that those children who had EI/BMR < 1.2 had lower intakes of all food
groups than those with EI/BMR  1.2. Those who were well had higher intakes of cereals and
vegetables than those who were unwell and reported their eating was affected and higher
intakes of meat than those who were unwell. There was no difference for milk.
Table13: Mean (SE) intakes of food groups by level of reporting and whether well
EI/BMR
Reported health status during diary
Food group EI/BMR <
Unwell,
Unwell,
Well
EI/BMR 
g/week
1.2
eating
eating
not
(n=1219)
1.2
(n=272)
affected
affected
(n=1319)
(n=266)
(n=190)
Cereals
730(26)
1042(12)
851(27) ab
996(32) a
1013(13)b
Milk*
1478(68)
2191(38)
1997(83)
2066(100)
2072(40)
Meat*
220(13)
330(7)
285(15) a
264(17) b
323(7) ab
Vegetables* 492(19)
650(10)
538(21) ab
606(26) b
644(10) a
*Geometric means (all square-root transformed)
All results for EIBMR P<0.05; for reported health status during diary values with same superscript statistically significantly
different (P<0.05)
When the means were adjusted for energy intake many of the differences for individual foods
disappeared (table 14). The differences persisted for milk; children with EI/BMR <1.2 had a
lower milk consumption than those with EI/BMR 1.2. For meat, those who were unwell and
whose eating was affected, had higher intakes than those who were well and who were unwell
and whose eating was not affected.
Table 14: Mean (SE) intakes of food groups by level of reporting and whether well, adjusted
for energy intake
EI/BMR
Reported health status during diary
Food group
g/week
EI/BMR < 1.2
(n=272)
EI/BMR  1.2
(n=1319)
Unwell, eating
affected
(n=266)
Unwell, eating
not affected
(n=190)
Well
(n=1219)
Cereals
Milk*
Meat*
Vegetables*
989 (27)
1731(84) a
315 (17)
649 (24)
988 (11)
2130 (38) a
309 (7)
616 (10)
951 (24)
2158 (85)
324 (15) a
597 (21)
998 (28)
2118 (98)
276 (16) a b
626 (25)
986 (11)
2028 (38)
312 (7) b
629 (10)
* Geometric means (all square-root transformed) Values with same superscript statistically significantly different (P<0.05)
with each variable
15
The effects of excluding children with infections, those who were unwell and those below the
1.2 cut-off for EI/BMR on measures of iron status are shown in Table 15(only the
standardised coefficients are shown, with % variation explained). The data for all children is
the same as that presented in table 11. For any dietary survey a proportion of the population
will tend to under-report their dietary intake and it is important to try to assess the likely
impact of this on the overall interpretation. Table 15, shows the effect of only including those
who were well and in whom the data are likely to be more complete compared with data on
all well children on the final model. For haemoglobin, the model accounted for 17% of the
variability for all well children, which increased to 21% of the variability when those with
less complete records were excluded. This was in part due to stronger influences of dietary
vitamin C, caeruloplasmin and body weight and an effect of vitamin D. Dietary copper,
ferritin and birth weight no longer exerted any effect. For ferritin, the variation explained
decreased from 23 to 17%. There was a loss of effect of n-6 polyunstaturated fatty acids, age,
and plasma zinc, and less influence of ACT, with body weight appearing as an important
variable and a stronger effect exerted by plasma folate. Patterns of dietary intake remained
important overall for explaining differences in ferritin, although the relative importance of
individual items and clusters was changed. For ZPP, the percent of variation explained
increased from 11 to 13%. Fructose was lost as an explanatory variable, with glucose being
included, together with plasma zinc and ferritin.
In any representative sample of the population a proportion will have an infection or be
recovering from an infection at the time of the survey. This is particularly important for a
group of young children. The effect of including children who either report being unwell or
have a high level of ACT compared with data on all children is shown in Table 15. For
haemoglobin, including unwell children decreased the variation explained by the model from
17 to 16%. Food patterns were now included, together with n-6 polyunsaturated fats, dietary
copper and ferritin. Plasma tocopherol and vitamin D were excluded in the model together
with ACT. For ferritin, including the unwell children increased the variation explained from
17 to 23%. Dietary factors, such as processed meat was included in the model along with the
‘meat, low fibre breakfast cereals and sugary foods’ food patterns. Plasma folate and zinc
were now included in the model. For ZPP, the variability explained decreased from 15 to
11%. The explanatory power of dietary fructose, plasma zinc and ferritin decreased, and
biscuits, the food component ‘crisps and some processed sugary foods’ lost their importance,
whilst the ‘soft drinks, vegetables and low fat spreads’ food pattern appeared important.
To assess the generalisability of the results a comparison of the characteristics of children
with a haemoglobin measure and those with no haemoglobin is necessary (appendix D).
Those with haemoglobin and dietary data compared to those without complete data were
more likely to be non-manual, parents have ‘A’ levels or higher qualifications, mum left
school over 16 years and child took vitamin supplements. Dietary intakes were also compared
and showed that those with a haemoglobin measure consumed more energy, carbohydrate,
vitamin A and iron (appendix D).
Also when the multiple regression was carried out not all children had complete measures of
all blood data. A comparison between children in the final model with those with complete
dietary and haemoglobin measures showed that those with complete data for all blood
measures tended to be older; more likely to live in Scotland and less likely to live in London;
less likely to live in rented property from local authority; their father more likely to have ‘A’
levels or higher; parents were less likely to be on benefits and father was less likely to smoke.
16
Table 15: Effect of under-reporting and excluding children with ACT >0.65 on the final
multiple regression analysis model.
i) Haemoglobin
Well and excludes
Well , includes
ALL
Standardised coefficient 
under-reporters
under-reporters children
(ACT >0.65, reported being
well whilst keeping diary
and EI/BMR  1.2)
Cereals, sugary foods and meat versus
milk, processed meat and chips
Dietary vitamin C*
Dietary copper*
Dietary n-6 polyunsaturated fat*
Birth weight
Body weight*
Plasma retinol*
Plasma vitamin D
Plasma alpha tocopherol
Plasma zinc
Ferritin*
ACT*
Caeruloplasmin
% explained
N
-0.087
0.179
0.174
-0.124
0.214
0.158
0.125
0.142
0.157
0.115
0.132
0.142
0.156
-0.263
20.5
268
0.165
-0.277
17.4
311
ii) Ferritin
Standardised coefficient 
Biscuits†
Processed meat*
Buns, cakes & puddings*
Ordinary fat spread and sugary drinks versus
low fat spread and diet soft drinks*
Meat, low fibre breakfast cereals and sugary
foods versus low fat spread, bread and fruit
juice
Sugary drinks, low fibre breakfast cereals
versus diet soft drinks and high fibre
breakfast cereals
Chicken, fish, chips, pasta and rice*
Dietary n-6 polyunsaturated fat*
Body weight*
Age group
Plasma folate
Plasma zinc
ACT*
Caeruloplasmin
% explained
N
(ACT >0.65, reported
being well during diary)
Well, excludes
under-reporters
-0.139
-0.185
0.107
0.155
-0.125
-0.148
0.171
0.145
0.122
0.124
-0.145
16.4
441
Well, includes
underreporters
-0.138
ALL
children
-0.159
-0.157
-0.127
0.137
-0.103
0.150
0.187
0.151
0.171
0.164
0.116
-0.098
0.362
-0.142
23.4
441
0.218
0.146
0.172
-0.157
17.6
268
0.266
-0.198
16.9
311
17
iii) ZPP
Standardised coefficient 
Biscuits
Diet soft drinks*
Soft drinks, vegetables, sugary foods and
low fat spread versus ordinary fat spread
and diet soft drinks
Crisps, processed and sugary foods versus
fruit and milk & yoghurt
Fructose*
Glucose
Plasma zinc
Ferritin*
% explained
N
Well, excludes
under-reporters
-0.127
Well, includes
underreporters
-0.125
-0.150
ALL
children
-0.120
-0.107
-0.136
-0.248
-0.141
-0.213
13.2
268
-0.243
-0.185
-0.165
-0.216
14.7
311
-0.118
-0.188
10.6
441
Summary
Excluding children with infections altered the model for haemoglobin and in particular
reduced the importance of the dietary variables. Excluding children who reported being
unwell and those who were under-reporters increased the importance of some variables for
haemoglobin (vitamin C and body weight) and allowed body weight into the model for
ferritin
Discussion
Iron plays a fundamental role in metabolism, being central to the body’s ability to engage in
aerobic respiration and efficiently capture energy for internal and external work. The
metabolism and availability of iron is tightly regulated. There are important dietary factors
that might influence the extent to which iron in the diet might be captured for the body’s use
in the processes of digestion and absorption. In addition, the extent to which iron is moved
around the body, or sequestered in one form or another is related to the metabolic
requirements of the body from time to time. The mass of tissue to be supported (represented
by the shape and size of the body), might be one variable of importance. The presence or
absence of an infection and the associated re-ordering of metabolic processes with an
inflammatory response is known to exert a considerable influence. For pathways of
metabolism to be open requires that all co-factors are present in adequate amounts, and
limitations of other specific nutrients might themselves exert a modulatory effect on the
movement of iron through the body. Here we have considered vitamin A, riboflavin and zinc
as being of special relevance in this respect. Iron is highly reactive, and can under suitable
circumstances enhance the unbalanced generation of free radicals, with the danger of
oxidative damage to molecules, cells and tissues. Hence, the ability to contain free iron in a
non-damaging form may be as important to the system as having sufficient present for any
particular process.
In this study we have used the data collected in the National Diet and Nutritional Survey of
Children 1½ to 4½ years of age to explore factors, which might contribute to explaining the
variability in measures of iron status. Haemoglobin, ferritin and ZPP have been used as
markers for iron status and by the use of statistical models, we have identified those variables
18
which relate to these within the population with a normal iron status and those with evidence
of anaemia and iron deficiency (low haemoglobin and low ferritin). In the first instance we
have sought relationships with dietary factors and the consumption of specific nutrients. We
have explored the extent to which anthropometric variables and biochemical measures of
nutritional status might interact to exert an influence on iron status, and how the presence of
inflammation might modify these relations. Some of the inter-relationships we have
identified were expected and fit into standard perceptions of how iron is handled in the diet
and by the body. Others invite new interpretations and raise important questions for further
investigation.
We identified a sub-group of the study population who appeared well at the time of study,
who had no evidence of infection, in whom the dietary data appeared representative or
adequate, and who had a normal iron status. We considered this group to be representative of
normal healthy children. Around 20% of the variation in haemoglobin and ferritin was
explained by the final model and the variables most strongly associated with iron status
included dietary, anthropometric and biochemical measures. Haemoglobin was positively
associated with vitamin C and negatively associated with n-6 polyunsaturated fats.
Haemoglobin was positively associated with body weight and plasma measures of retinol,
vitamin D and zinc. ACT was positively and caeruloplasmin negatively related to
haemoglobin. Ferritin was associated with lower intakes of buns, cakes and puddings and the
‘low fat spread and diet soft drinks’ food pattern, but positively associated with the ‘sugary
drinks, low fibre breakfast cereal’ food pattern. Body weight was positively associated with
ferritin. For plasma nutrients, only plasma folate was positively associated with ferritin and
both ACT and caeruloplasmin were included in the model. ZPP was negatively associated
with plasma zinc, ferritin and glucose as well as diet soft drinks. In interpreting these data we
have presumed that the dietary intake of nutrients represents what is potentially available to
the body for its use, whereas biochemical markers, or plasma concentrations, are a better
indication of nutrient status. On this basis it would not be surprising that in more complex
models, biochemical markers showed stronger associations than measures of dietary intake,
which tended to be displaced. Body weight was positively associated with haemoglobin and
may reflect good nutrition and growth. It may be that adequate iron status is necessary for
adequate growth or alternatively poor growth (possibly as a result of poor nutrition) may lead
to a poorer iron status. It has been suggested that red cell mass is regulated in relation to lean
body mass, as the demand for oxygen would be less with a smaller lean body mass. It would
be of importance to know whether normal values for haemoglobin vary with an individual’s
size or resting metabolic rate. The finding that vitamin C remained an important explanatory
variable fits with the evidence showing that consumption of vitamin C enhances the
absorption of iron. The relevance of n-6 polyunsaturated fatty acids is less clear. Of the
possible theoretical interactions, an effect on membrane composition and iron absorption
appears a possibility. Retinol is known to exert an influence on the availability of iron to
metabolism, but generally this effect has been observed in those groups deficient or marginal
for vitamin A. The identification of an interaction in individuals with no evidence of
deficiency implies a more subtle modulation of iron metabolism by retinol than appreciated
previously. Zinc is essential for all aspects of protein synthesis, and the possibility that it may
modulate haemoglobin status is of interest. The biological relevance of vitamin D and folate
are not immediately apparent. For Asian toddlers in Birmingham a low vitamin D
concentration was associated with lower concentrations of haemoglobin. The authors
suggested that this was unlikely to represent overall poor nutrition as there was no evidence
of energy protein malnutrition or zinc deficiency (Grindulis et al 1986). The dietary patterns
associated with poorer measures of iron status suggests that even within a group who are
19
otherwise normal, good dietary practices may be associated with a greater margin of safety in
terms of iron status.
The prevalence of low haemoglobin (< 11g/dl) was 8% and low ferritin (< 10g/l) was 20%
in the present study. However only 3% had both low haemoglobin and ferritin, and could be
confidently identified as having an iron deficient anaemia. There was a stronger relationship
between dietary iron and haemoglobin in children with poorer iron status, indicating that
dietary iron was more likely to be limiting in these individuals. The relative importance of
different factors in determining iron status are thus likely to vary depending on the degree of
iron deficiency in the population as well as other nutrient deficiencies. Iron absorption is
likely to be increased under conditions of iron deficiency. A review of studies in Argentina
showed that in regions of the country where the prevalence of low haemoglobin (<11 g/dl)
was 49%, a multivariate model with social, demographic, anthropometric and dietary
measures explained 24% of the variation. In contrast, in a region where the prevalence of low
haemoglobin was 24% the same model only explained 6% of the variation, and here age
(being below 18 months) and haem iron intake were statistically associated with haemoglobin
levels (O’Donnell et al, 1997).
The interactions amongst nutrients may be complex. For iron there are a range of dietary and
metabolic interactions of potential importance. We were interested to explore the effect on
iron status, or the extent of interactions, for differences in the dietary intake, or nutrient status
of, vitamin A, riboflavin or zinc. Each has been shown either to have metabolic interactions
with iron, or for a deficiency to limit the apparent absorption or metabolic utilisation of iron.
When dietary iron was included in the model, as shown in Table 8, children in the lowest fifth
for plasma zinc, haemoglobin was lower and ferritin higher than those in the remaining fifths,
suggesting that the ability to use stored iron for haemoglobin formation might have been
compromised. Similarly, ferritin was higher in those with the lowest dietary riboflavin.
Riboflavin is known to interact with iron availability. The positive association with plasma
retinol conforms with the results of other studies (Lynch 1997). Vitamin A deficiency is
known to affect the availability of iron for red blood cell production but any effect on iron
absorption is less certain. In populations marginally deficient in vitamin A, assessed as low
serum retinol levels, vitamin A supplement alone may result in increased haemoglobin.
There is a shift in the partitioning of nutrients during infection and inflammation, and blood
concentrations of nutrients are less reliable as markers of nutritional status. Thus, when the
children with obvious inflammation were excluded (those with high levels of ACT), the
relationship between markers of infection and iron status was strengthened. Those children
with the highest ACT tended to have lower haemoglobin and higher ferritin levels. The
direction of the relationship can not be determined, but for this population ferritin is at least
as likely to indicate an inflammatory process as it is to indicate iron stores. It may be that
poor iron status increases the risk of infection alternatively infections may reduce
haemoglobin and increase ferritin. For the children in this study differences in the
consumption of milk were important in accounting for differences in the intakes vitamin A,
riboflavin and zinc. These data indicate that milk might be of importance in providing an
adequate intake of a balance of nutrients, even though over-reliance on milk has been
associated with increased risk of iron deficiency. Although in univariate analysis whole milk
was inversely related to haemoglobin and ferritin, its importance amidst all other explanatory
variables was lost in subsequent analyses.
In a group of Asian children living in the UK no association was found between biochemical
iron status and dietary intake of iron or energy, nor was their an association between protein
20
energy nutritional status and iron status (Duggan et al, 1991). In a study on a Mediterranean
paediatric population a multiple regression analysis between age adjusted parameters of
biochemical iron status versus nutrient intake found only 5% of the variation was explained
by energy, vegetable fibre, ascorbic acid, haem iron and non-haem iron for ferritin
(Fernández-Ballart et al 1992). An Australian study with 10.5% with iron depletion (Ferritin
< 12 g/L) found odds ratios of 2.86 for age less than 2 years for increased chance of iron
depletion and eating meat 1-3 times a week compared with 4-6 times 2.27. There were no
differences for gender, income and amount of milk consumed (Karr et al, 1996). A French
study (Preziosi et al 1994) that included an age range from 6 months to 97 years showed a
relationship between inflammatory markers. A multiple regression analysis of haemoglobin
with age, gender dietary intake and inflammatory markers showed a positive relationship for
age, phosphorus and negative for gender calcium and orosomucoid. The same model for
ferritin was positive for age, haem iron, non-haem iron and C reactive protein and
orosomucoid but negative for gender and calcium. The percent explained by the model was
greater than for our analysis at 28% for haemoglobin and 38% for ferritin.
Anaemia is a characteristic of a wide range of clinical conditions, including chronic
inflammation and bacterial infection. During the inflammatory response, circulating iron is
sequestered in the liver, bound to ferritin and it can be difficult to differentiate whether an
increase in the circulating concentration of ferritin represents an increase in stored iron or
ferritin as an acute phase reactant. The present data show that higher levels of ACT were
associated with increased ferritin and therefore, at least for some of the population, higher
ferritin levels might not adequately describe iron status. Our results showed a relationship
with haemoglobin and ferritin only in the whole sample. Once children with ACT > 0.65
were excluded ferritin was no longer included in the model, however ACT was now included.
This raises important questions about the factors which determine the relative rates of
formation and degradation of individual proteins within the whole body, and whether
proteins, such as ACT, which have only been considered as acute phase reactants have more
subtle functions under normal circumstances.
It was considered that the dietary data were incomplete in 17% of the subjects, who were thus
classified as “under-reporters”. It is important to see whether these toddlers differed from
those who were considered to have adequately reported energy intake. Social and education
factors appeared to be important associates of under-reporting, however, some were unwell at
the time and others admitted that they had not remembered to weigh the foods consumed
during the study period. An analysis was carried out to determine whether foods in general,
were under-reported or whether there were differences in foods that contribute to the nutrients
of interest. Milk is of particular interest. There was no difference in the amount of milk
consumed in relation to whether they were well at the time when the diary was being
completed. Therefore, it is possible that even in children who were feeling unwell at the time
of the study, they managed to drink milk but consumed less of other foods. By and large, for
those who appeared to “under-report”, the differences in intake disappeared when the relative
contribution of the food groups were adjusted for energy intake which provides some
evidence that the overall lower intake of foods was unselective. Nevertheless, differences for
milk consumption persisted, with the low energy reporters consuming around 80% of the
milk consumed by children above the cut-off for EI/BMR.
Complete data on all measures were not available therefore it is important to compare data on
children with and without complete data. The educational level of the parents appeared
important in determining whether children had both dietary and haemoglobin data or were
21
missing either diet analysis or haemoglobin. Those who were better educated were more
likely to provide complete data. Children who took vitamin supplements and whose parents
were employed in non-manual occupations were also more likely to have complete data.
There were differences in dietary habits, and those with complete data had higher energy,
carbohydrate, vitamin A and iron intakes. Amongst those with both haemoglobin and dietary
measures not all had complete blood data. Those who did were less likely to be aged 1.5 to
2.5 years, more likely to live in Scotland but less likely to live in London and fathers were
more likely to smoke. Hence the results reflect a population of children from more
advantaged homes and thus may under estimate the prevalence of anaemia.
These data provide very interesting insights into those factors, which might be of importance
in determining the iron status of toddlers, and go beyond those risk factors that are usually
considered to be of relevance. Caution is needed to ensure that there is not overinterpretation of cross-sectional data, but there are a series of hypotheses emerging that are
amenable to formal testing. In one sense iron status might be seen as a summary statement of
general nutritional, social and infective health and wellbeing. Future advice to improve iron
status should not solely represent an emphasis on increasing iron intake but should recognise
the importance of the overall pattern of diet to promote nutritional wellbeing. The value of
consuming foods, which are rich in vitamin C deserves emphasis. An adequate supply of
micronutrients such as folate, zinc and vitamin A appears more important than generally
appreciated. Milk consumption emerges as having benefits, which are less well recognised in
terms of iron status, whereas a diet which contains a high sugar content should be avoided. It
may be that the importance of intercurrent infection, and illness for iron status are not as
widely appreciated as they might be. There seem to be important interactions with size, or
body weight, which have not received any attention, but might be of considerable importance
in setting normal standards.
References
Duggan MB, Steel G, Elwys G, Harbottle L & Noble C. Iron status, energy intake, and nutritional status of
healthy young Asian children. Archives of Disease in Childhood 1991; 66: 1386-1389.
Fernández-Ballart J, Doménech-Massons JM, Slas J, Arija V & Martí-Henneberg C. The influence of nutrient
intakes on the biochemical parameters of iron status in a healthy paediatric Mediterranean population. European
Journal of Clinical Nutrition 1992 46; 143-149.
Grindulis H, Scott PH, Belton NR & Wharton BA. Combined deficiency of iron and vitamin D in Asian
toddlers. Archives of disease in childhood 1986; 61: 843-848.
Karr M, Alperstein G, Causer J, Mira M, Lammi A & Fett MJ. Iron status and anaemia in preschool children in
Sydney. Australian and New Zealand Journal of Public Health 1996; 20: 618-622.
Lynch SR. Interaction of iron with other nutrients. Nutrition Reviews 1997; 55: 102-110.
O’Donnell AM, Carmuega ES & Durán P. Preventing iron deficiency in infants and preschool children in
Argentina. Nutrition Reviews 1997; 55: 189-194.
Preziosi P, Hercberg S, Galan P, Devanlay M, Cherouvrier & Dupin H. Iron status of a healthy French
population: factors determining biochemical markers. Annals of Nutrition and Metabolism 1994; 38: 192-202.
Schofield WN, Schofield EC, James WPT. Basal metabolic rates: review and prediction. Hum Nutr:Clin Nutr
1985;39c Suppl1: 1-96.
Publications
Abstract accepted for Nutrition Society Conference 1999.
Thompson RL, Margetts BM & Jackson AA. Does vitamin A, zinc and riboflavin status influence iron status in
children aged 1.5 to 4.5 years?
Papers in draft
The importance of dietary factors in determining iron status in children in the National Diet and Nutrition Survey
The effect of infection and under-reporting dietary intake on iron status
22
The role of nutrient interactions in determining iron status
23
Appendices
Appendix A: Food patterns – results of principal component analysis (only values of 0.20 or
more shown)
a)
Components
Food groups
1
2
3
Tea & coffee
Sugar
Soft drinks (not fruit
juice or diet soft drinks
Milk & yoghurt
Sweets & chocolate
Crisps
Bread
Bacon
Cheese & eggs
Vegetables (not
potatoes)
Potatoes (not chips)
Beef, lamb, pork, liver
Cakes, buns, puddings
& biscuits
Chips & fries
Burgers, pies sausages
Pasta & rice
Fruit
Low fat spreads
Ordinary fat spreads
High fibre breakfast
cereals
Low fibre breakfast
cereals
Diet soft drinks
Chicken & turkey
Fish
Fruit juice
0.35
0.31
0.31
0.67
0.65
-0.27
-0.26
-0.33
-0.41
0.42
0.46
0.39
0.35
-0.32
-0.24
0.21
-0.21
0.29
0.42
0.24
4
5
6
0.40
0.43
0.24
-0.22
0.36
-0.30
0.56
0.32
0.33
0.42
0.47
0.26
0.25
-0.31
-0.35
0.37
0.22
0.22
-0.20
-0.30
0.33
0.25
0.34
0.36
0.27
-0.20
0.37
0.31
0.32
0.27
-0.45
0.22
-0.59
0.27
-0.30
-0.44
0.55
0.33
0.27
-0.37
-0.28
0.30
-0.41
-0.36
-0.20
9
0.30
0.20
0.30
8
0.23
0.45
0.45
-0.32
0.29
7
0.29
0.24
0.28
-0.33
-0.20
-0.28
0.51
-0.27
-0.27
0.24
0.34
-0.39
-0.26
0.21
0.26
-0.45
0.43
0.39
0.57
0.52
-0.41
0.26
-0.22
24
Appendix B: Multiple regression analysis – spearman correlations between foods and nutrients with
haemoglobin and ferritin
Correlations between foods and nutrient and haemoglobin and ferritin – only those P<0.05 given (nutrients –
supps)
Food/nutrient
Bacon and ham
Buns, cakes, pastries & fruit
pies
Sugar
Whole milk
Biscuits
Diet soft drinks
Milks – other than whole
Processed meat
Pufa oils and marg
Calcium
Copper
Iron
Fructose
Glucose
Haem iron
Intrinsic milk sugars
Lactose
N6 pufa
Non-haem iron
Niacin
Non milk extrinsic sugars
Non starch polysaccharide
Other sugars
Retinol equivalents
Retinol
Saturated fat
Sucrose
Sugar
Trans fatty acids
Vitamin B2
Vitamin C
Phosphorus
Vitamin B12
Birth weight
Body weight
Height
Haemoglobin
0.107**
0.066*
-0.076*
-0.066*
-0.080*
Ferritin
-0.133**
0.070*
0.077*
0.066*
0.067*
-0.118** (-0.120**)
0.079* (0.082*)
0.085** (0.092**)
0.067* (0.067*)
0.073* (0.072*)
0.080* (0.080*)
0.073*
(0.070*)
0.066*
-0.115**
-0.130**
0.075*
(0.066*)
(-0.115**)
(-0.129**)
(0.075*)
0.077* (0.084*)
0.065* (0.072*)
0.095** (0.094**)
0.070* (0.070*)
0.090** (0.089*)
-0.072* (-0.108**)
-0.090** (-0.130**)
-0.110** (-0.110**)
0.071* (0.071*)
0.072* (0.072*)
-0.085* (-0.086*)
-0.095** (-0.095**)
-0.078* (-0.088**)
0.068* (0.078*)
-0.088*
-0.069*
0.185**
0.166*
(-0.088*)
(-0.067*)
0.039
0.096**
0.127**
25
Appendix C: Characteristics of low energy reporters
Demographic Variables
Range
% Low energy
N low energy reporters
reporters
Age (years)
1.5-2.5
15.1a
81
2.5-3.5
19.6 a
113
3.5-4.5
16.4
78
Gender
Male
16.8
133
Female
17.4
139
Region
Northern
14.8
59
Central, SW and Wales
16.4
88
Scotland
18.1
29
London & SE
19.3
96
Accommodation
Own/mortgage
16.2
165
Rent- Local Authority
18.2
78
Rent- Other
20.0
29
Household
Married couple + kids
17.2
225
One parent + kids
16.6
47
Employment HOH
Economically inactive
13.5 a
33
Work
17.3
205
Unemployed
21.3 a
34
HOH Social Class
Non-manual
17.0
123
Manual
16.6
135
Mum’s qualification
‘A’ levels and higher
16.1
75
‘O’ levels and other
17.1
135
None
18.2
61
Dad’s qualification
‘A’ levels and higher
15.0 a
86
‘O’ levels or other
18.7
88
None
20.8 a
54
Age when Mum left fullUnder 16 years
16.2
27
time education
16 years
17.3
131
Over 16 years
16.9
112
Age when Dad left fullUnder 16 years
19.8
33
time education
16 years
18.6
123
Over 16 years
15.1
72
Parents receiving benefits
Yes
16.9
85
No
17.2
187
Prescribed medicines
Yes
23.0 a
40
No
16.4 a
230
Child’s appetite
Good
16.2
118
Average
17.7
118
Poor
18.4
36
Take vitamin
Yes
14.8
51
supplements
No
17.8
221
Forgot to weigh items
Yes
21.2 a
82
No
15.8 a
190
Child unwell in 4d
Unwell eating affected
32.0 a,b
82
Unwell eating not affected
15.8b
28
Well
14.0 a
162
Body mass index
1 (Lowest)
14.7
42
(Fifths)
2
11.5
33
3
15.9
47
4
18.3
53
5 (Highest)
24.5c
71
Mum smokes
Yes
16.5
83
No
17.3
188
Dad smokes
Yes
21.1 a
93
No
15.5 a
135
Categories with same superscript are statistically significantly different (P <0.05) Chi squared test.
For BMI category denoted c is different from all other categories.
26
Characteristics by thirds of the distribution for EI/BMR
Demographic Variables
Range
Age (years)**
1.5-2.5
2.5-3.5
3.5-4.5
Gender*
Male
Female
Region
Northern
Central, SW and Wales
Scotland
London & SE
Accommodation
Own/mortgage
Rent – LA
Rent –other
Household
Married couple + kids
One parent + kids
Employment HOH
Economically inactive
Work
Unemployed
HOH Social Class*
Non-manual
Manual
Mum’s highest
‘A’ levels and higher
qualification
‘O’ levels and other
None
Dad’s qualification
‘A’ levels and higher
‘O’ levels or other
None
Age when Mum left fullUnder 16 years
time education
16 years
Over 16 years
Age when Dad left fullUnder 16 years
time education
16 years
Over 16 years
Parents receiving benefits Yes
No
Prescribed medicines**
Yes
No
Child’s appetite
Good
Average
Poor
Vitamin supplements*
Yes
No
Forgot to weigh items
Yes
No
Child unwell in 4d***
Unwell eating affected
Unwell eating not affected
Not unwell
Body mass index***
1 (Lowest)
(Fifths)
2
3
4
5(Highest)
Mum smokes*
Yes
No
Dad smokes
Yes
No
1
29
40
31
52
48
25
31
10
34
64
28
8
82
18
3
73
9
48
52
28
50
22
41
40
19
11
48
41
13
53
34
31
69
14
86
45
43
12
18
82
25
75
24
11
65
15
17
21
21
27
30
70
36
64
2
34
36
31
54
46
25
35
9
31
67
24
10
83
17
3
74
9
50
50
33
48
18
47
34
19
9
45
45
13
47
40
29
71
11
89
48
41
12
23
77
22
78
13
11
76
19
19
21
21
19
29
71
28
72
3
40
34
26
45
55
26
35
11
29
61
30
9
81
19
4
68
11
42
58
27
49
25
42
35
23
12
49
39
13
53
34
36
64
9
91
45
42
13
23
77
26
74
11
12
78
25
23
20
18
14
36
64
37
63
Test for linear trend * P < 0.05 ** P <0.01 *** P < 0.001
27
Appendix D: Social and dietary factors of children with and without measured haemoglobin
(* diet vs diet + haemoglobin P<0.05)
Demographic Variables
Range
Age (years)
1.5-2.5
2.5-3.5
3.5-4.5
Male
Female
Northern
Central, SW and Wales
Scotland
London & SE
Own/mortgage
Rent- Local Authority
Rent- Other
Married couple + kids
One parent + kids
Economically inactive
Work
Unemployed
Non-manual*
Manual
‘A’ levels and higher*
‘O’ levels and other
None*
‘A’ levels & higher**
‘O’ levels or other
None**
Under 16 years
16 years
Over 16 years**
Under 16 years
16 years
Over 16 years
Yes
No
Yes
No
Gender
Region
Accommodation
Household
Employment HOH
HOH Social Class
Mum’s qualification
Dad’s qualification
Age when Mum left fulltime education
Age when Dad left fulltime education
Parents receiving benefits
% under-reporters
Well during diary
Diet + hb
(n=923)
34
36
30
50
50
26
34
10
30
64
26
10
83
17
15
75
10
49
51
31
49
21
46
35
18
10
46
44
13
51
37
31
69
16
84
Diet only (n=752)
36
36
28
52
48
25
33
10
32
63
29
8
82
18
16
74
10
43
57
27
51
22
39
38
23
11
50
38
13
52
35
33
68
19
81
Well
74
71
Unwell eating unaffected
11
12
Unwell eating affected
15
17
Prescribed medicines
Yes
11
12
No
89
88
Child’s appetite
Good
46
46
Average
42
41
Poor
12
13
Take vitamin
Yes*
23
19
supplements
No
77
81
Forgot to weigh items
Yes
23
26
No
77
74
Mum smokes
Yes
33
31
No
67
69
Dad smokes
Yes
34
33
No
66
67
Chi squared test between diet + hb and the rest * P<0.05, **P<0.01, ***P<0.001
No diet
(n=184)
39
34
27
52
48
21
35
13
31
60
31
9
76
24
18
72
10
40
60
22
42
36
36
36
28
19
48
33
21
52
27
36
64
6
94
50
41
9
16
84
38
62
34
66
37
63
28
Comparison of dietary variables
Variable
Energy (kJ)
Protein (g)
Fat (g)
Cho (g)
NSP (g)
Vitamin A
Riboflavin
Zinc
Total iron
Diet + haemoglobin only
(n=923)
Mean
SD
4852
1107
36.9
10.4
46.0
13.6
157.8
39.1
6.2
2.3
621
1044
1.22
0.48
4.46
1.44
5.63
2.44
Diet
(n=752)
Mean
4732
36.7
45.3
152.2
6.1
525
1.19
4.35
5.45
T-test
SD
1059
10.3
12.4
39.2
2.3
554
0.44
1.40
2.38
Appendix E
Comparison of children with complete and incomplete data in the final model
Demographic
Range
Incomplete
Complete
Variables
data (482)
data (441)
Age (years)
1.5-2.5
40
27
2.5-3.5
33
40
3.5-4.5
27
34
Gender
Male
48
51
Female
52
49
Region
Northern
28
24
Central, SW and Wales
31
37
Scotland
6
14
London & SE
35
26
Accommodation
Own/mortgage
62
66
Rent- Local Authority
29
23
Rent- Other
8
11
Household
Married couple + kids
82
83
One parent + kids
18
17
Employment HOH
Economically inactive
70
73
Work
11
7
Unemployed
3
4
HOH Social Class
Non-manual
49
48
Manual
51
52
Mum’s qualification
‘A’ levels and higher
31
31
‘O’ levels and other
49
48
None
20
22
Dad’s qualification
‘A’ levels and higher
43
50
‘O’ levels or other
37
33
None
20
17
Age when Mum left
Under 16 years
10
10
full-time education
16 years
45
46
Over 16 years
45
43
Age when Dad left
Under 16 years
13
12
full-time education
16 years
50
51
Over 16 years
37
37
Parents receiving
Yes
35
28
benefits
No
65
72
Prescribed medicines
Yes
11
10
No
89
90
Child’s appetite
Good
46
46
Average
42
42
Poor
12
13
Take vitamin
Yes
21
25
supplements
No
79
75
Under-reporters
Yes
15
17
No
85
83
P
0.02
Ns
Ns
0.004
Ns
0.002
Ns
ns
0.04
P
***
*
*
Ns
Ns
Ns
***
**
Ns
*
Ns
Ns
Ns
Ns
Ns
Ns
Ns
Ns
Ns
*
Ns
Ns
Ns
Ns
Ns
Ns
Ns
Ns
*
Ns
Ns
Ns
Ns
Ns
Ns
29
Well
Forgot to weigh items
Mum smokes
Dad smokes
Unwell eating affected
Unwell eating not affected
Well
Yes
No
Yes
No
Yes
No
17
10
73
23
77
34
66
38
62
13
11
76
24
76
31
69
30
70
Ns
Ns
ns
Ns
Ns
*
Chi-squared test between well and unwell (eating affected + eating unaffected) * P <0.05, ** P < 0.01,
*** P < 0.001.
30