Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 10g/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 < 10g/l 0.208** (n=72) (n=185) 0.069* 0.071* -0.012 Hb 11g/dl Ferr 10g/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) (4-12g/l) Hb (n= 329) 0.022 0.006 Ferr (n=307) -0.012 -0.016 (11.8-12.5g/dl) (13-25g/l) Hb (n=294) -0.048 -0.069 Ferr (n=307) -0.036 -0.005 (12.6-17.3g/dl) (26-139g/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 (1-45mol/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 < 10g/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 (< 10g/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