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Cross-Sectional Associations of Diet and Insulin-Like Growth Factor Levels in 7- to 8-Year-Old Children

¹ Unit of Paediatric and Perinatal Epidemiology, Division of Community Medicine, ² Department of Social Medicine, and ³ Division of Surgery, University of Bristol, Bristol; and ⁴ Department of Paediatrics, University of Cambridge, Addenbrookes Hospital, Cambridge, United Kingdom

Requests for reprints: Imogen Rogers, Unit of Paediatric and Perinatal Epidemiology, Division of Community Medicine, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom. Phone: 44-117-331-6727; Fax: 44-117-928-5010. E-mail: Imogen.Rogers{at}bristol.ac.uk

	Abstract

Top Abstract Introduction Materials and Methods Results Discussion Appendix 1. Correlation matrix... References

 
Background: The insulin-like growth factors (IGF) are polypeptide hormones which are associated with several adult diseases including cancer and coronary heart disease. The dietary determinants of circulating levels of components of the IGF system are of interest, as these may mediate some of the effects of diet on later health. However, few studies have examined the relationship between diet and IGF levels in children.

Objective: To investigate associations between diet and IGF-I and IGFBP-3 levels in 7- to 8-year-old children.

Methods: This study used subjects participating in the Avon Longitudinal Study of Parents and Children. Diet was assessed using a 3-day unweighed food diary. Confounding variables considered were maternal education, housing tenure, birthweight, and body mass index.

Results: Complete information on dietary intakes, IGF levels, and all confounding variables were available for 521 children (287 boys). IGF-I was positively associated with intakes of protein, magnesium, zinc, calcium, potassium, and phosphorus, and IGFBP-3 was positively associated with energy. The IGF-I/IGFBP-3 ratio was positively associated with intakes of protein, zinc, and phosphorus. There was some evidence that the dietary determinants of the IGF system differed between the sexes. None of the foods examined were strongly associated with IGF levels, in particular, there was no association with red meat or vegetable intake.

Conclusion: These data suggest that the IGF axis in children is affected by diet. This may provide a mechanism whereby childhood diet could have a long-term effect on risk of chronic disease.

Key Words: Diet • protein • minerals • fat • IGF-I • IGFBP-3 • children

	Introduction

Top Abstract Introduction Materials and Methods Results Discussion Appendix 1. Correlation matrix... References

 
The insulin-like growth factors (IGF) are polypeptide hormones that stimulate cell proliferation and inhibit apoptosis. The IGF system has several potentially opposing roles in relation to overall health. Higher plasma concentrations of IGF-I are associated with a reduced risk of osteoporosis (1), diabetes (2), and possibly heart disease (3), however, several studies suggest that higher concentrations of IGF-I are also associated with increased risk of cancers including prostate (4, 5), breast (6), and colon cancer (7). Some studies have found inverse associations between concentrations of IGFBP-3 and cancer risk (8, 9), but a recent metaanalysis has suggested a positive association (10). An understanding of the determinants of IGF bioactivity is clearly of importance. The role of diet in regulating the IGF system has attracted interest, as most of the IGF-related cancers are also associated with dietary patterns. These associations include increased risk with higher red meat, animal fat, and dairy product consumption, and reduced risk with higher vegetable consumption, and in particular with diets rich in tomatoes, a source of the carotenoid lycopene.

There is good evidence that diet is an important regulator of the IGF system. Severe energy or protein restriction has been shown to lower IGF concentrations considerably (11). In addition, several studies suggest that even within the reference range, nutrient intakes are associated with variation in IGF concentrations. Most of these studies of determinants of IGF concentrations have taken place in middle-aged and elderly men (7, 12-15), although there have been some that included women and/or younger adults (16-20). Some of the more consistent associations to emerge are positive associations with dairy products and with protein and mineral intakes, including zinc, potassium, calcium, phosphorus, and magnesium. These have been observed in two large cohort studies of men and women in the US (13, 16), and in a study of female meat eaters, vegetarians, and vegans (17). It is unclear whether it is total protein or protein high in essential amino acids (such as animal and soya protein) that is the most important factor.

Although IGF-I concentrations have been shown to be reduced in malnourished children (21), and one study in growth-retarded Vietnamese children has shown zinc supplementation to increase IGF-I levels (22), we are not aware of any studies of the dietary determinants of IGF concentrations in normal children. Studies in children are potentially important as it is possible that IGF concentrations in childhood may have a long-term influence on the risk of later disease, as indicated by the associations between adult height and risk of diseases, such as cancer (23), heart disease (24), and diabetes (24). In an earlier analysis, we investigated the relationship between growth, dairy product intake, and the IGF system among a group of 7- to 8-year-old children in southwest England.⁵ The aim of this study was to examine how other aspects of their diet related to IGF concentrations.

	Materials and Methods

Top Abstract Introduction Materials and Methods Results Discussion Appendix 1. Correlation matrix... References

 
This study formed part of the Avon Longitudinal Study of Parents and Children (ALSPAC), a geographically based cohort study investigating factors influencing the health, growth and development of children. All pregnant women residing within a defined part of the former county of Avon in southwest England with an expected date of delivery between April 1991 and December 1992 were eligible. Between 80% and 90% of these enrolled (14,893 pregnancies; ref. 25).Data in ALSPAC is collected by self-completion postal questionnaires, abstracted from medical records, and from examination of the children at research clinics. Compared with the 1991 National Census data on mothers with infants under 1 year of age who were residing in Avon, the ALSPAC population showed a slight shortfall in those living in rented accommodation, those without a car, single parent families, and unmarried cohabiting couples. There were also a smaller proportion of mothers from ethnic minorities. The children in this study formed part of a randomly selected 10% subcohort of ALSPAC (n = 1,335) known as Children in Focus, this study has been described in more detail elsewhere (25). Ethical approval for the study was obtained from the three Health Trusts covering the study area, and from the ALSPAC ethical committee.

The children attended research clinics at 7 to 8 years of age, where diet was assessed, growth measurements were made, and a blood sample was taken.

Assessment of Diet
Diets were assessed using a 3-day unweighed dietary record. About a week before the first clinic appointment, three 1-day dietary diaries with an instruction leaflet were sent to the main carer of the child. The carer was instructed to complete the diaries by recording everything the child ate and drank in household measures for 2 weekdays and 1 weekend day, and to bring the completed diaries with them to the clinic. At the clinic, the carer was interviewed by a trained assistant to check for completeness and clarify any uncertainties in the diaries.

The dietary records were coded using the computer program Diet In, Data Out, which is designed for direct entry of dietary records (26). This was used in conjunction with a database consisting of the fifth edition of McCance & Widdowson's "The composition of foods" (27) and its supplements (28-34) to generate mean daily nutrient intakes and food group intakes for each child. Nutrient intakes from supplements were not assessed—9.3% of children were reported to take a vitamin or mineral supplement of some kind, and 0.7% of children took cod liver oil.

Assessment of IGF Concentrations
Serum concentrations of IGF-I were determined by RIA using a monoclonal antibody (Blood Products, Elstree, Hertfordshire, United Kingdom) and recombinant peptide (Pharmacia, Stockholm, Sweden) for standard and tracer, following iodination using the chloramine T method. Samples were analyzed following acid-acetone extraction to remove the IGF-binding proteins with an excess of IGF-II added to the extract in order to saturate any residual binding proteins (35). Serum concentrations of IGFBP-3 were determined by RIA using an in-house polyclonal antibody raised against recombinant nonglycosylated IGFBP-3. The assay was calibrated against recombinant glycosylated IGFBP-3 (Dr. C. Maack, Celtrix, Santa Clara, CA, USA). The molar ratio of IGF-I/IGFBP-3 was calculated by multiplying the concentration ratio by 5.33 [based on the molecular weights of IGF-I (7,500) and IGFBP-3 (40,000)]. The average coefficients of variation for intra-assay variability for IGF-I and IGFBP-3 were 6.7% and 3.6%, and for inter-assay variation, were 12% and 14%. IGF-I, IGFBP-3, and IGF-I/IGFBP-3 were all transformed to the natural logarithm to reduce skewness in the distribution, and adjusted for age using the residuals method.

Confounders
The following confounding variables were considered: highest maternal educational level, housing tenure, birthweight (obtained from hospital records) and body mass index (BMI) at 7 years [calculated from weight (kg)/height in meters²]. Highest maternal educational level was grouped as Certificate of Secondary Education or equivalent or no qualifications, vocational qualifications, O-level or equivalent, A-level or equivalent, university degree (Certificate of Secondary Education and O-level were, respectively, lower and higher levels of qualifications taken at around 16 years of age; A-levels were the standard qualifications taken at around 18 years of age). Housing tenure was grouped as council-rented, i.e., government housing, other rented, and owned or mortgaged. Birthweight was adjusted for gestational age using the residuals method and converted to sex-specific z scores. BMI was adjusted for age using the residuals method and converted to sex-specific z scores. Paternal social class and maternal smoking in pregnancy were also initially considered as potential confounders, but found to be minimally associated with IGF or growth once the other confounders had been considered.

Statistical Methods
Only White, singleton children were included in analyses, as preliminary analyses suggested that the growth and IGF concentrations of children from non-White ethnic groups differed from those of White children, and there were too few children from non-White ethnic groups to analyze separately.

The relationships between food and nutrient intakes and IGF-I, IGFBP-3, and IGF-I/IGFBP-3 were examined using Pearson correlation analysis and linear regression. The analyses were based on 521 children with complete data on diet, IGF-I, and all confounding variables.

The nutrients investigated were energy, total fat, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA), total protein, animal protein and vegetable protein, total carbohydrate, starch and sugar, nonstarch polysaccharides (NSP), zinc, phosphorus, magnesium, calcium, potassium, selenium, folate, vitamin C, retinol, carotene, vitamin D, and vitamin E. Where necessary, nutrient intakes were transformed to the natural logarithm or square root in order to reduce skewness in the distribution. Intakes of nutrients (except energy) were adjusted for energy intake using the residuals method (36). In the first stage of the analysis, energy-adjusted nutrient intakes were correlated with measures of the IGF system adjusting only for sex. In the second stage, the correlation was adjusted additionally for maternal education and housing tenure (considered as categorical variables) and BMI and birthweight-for-gestational-age (considered as continuous variables).

The food groups investigated were red meat, processed meat, poultry, fish, vegetables, fruit, tomatoes, tomato products, bread, breakfast cereals, and cheese. The relationship between the IGF axis and the intake of cows' milk and dairy products has already been described.⁵ Food group intakes were classified as group 0 (those children who ate none of the food over the dietary recording period), and groups 1 to 3 or 1 to 2 (tertiles or halves of the remaining children, respectively). The exception was bread—only 2.4% of the children ate no bread, and so they were grouped according to quartile of bread intake (groups 1-4). The food group variables were categorized in this way rather than treated as continuous variables because of the large number of zeros in the distribution. In the first stage of the analysis, the food group variables were entered into a regression model adjusting only for energy and sex. In the second stage of the analysis, we assessed the effect of adjusting additionally for maternal education, housing tenure, BMI, and birthweight-for-gestational-age.

All analyses were initially done for both sexes together. We investigated whether there was any evidence that diet-IGF associations were different in boys and girls by fitting appropriate interaction terms to the models. Where evidence of a significant interaction was found (P < 0.10), analyses were repeated for each sex separately.

To assess the possible effects of under-reporting of dietary intake, all analyses were repeated excluding those children reporting an energy intake of < 1.39 x estimated basal metabolic rate for boys or 1.30 x basal metabolic rate for girls (37).

	Results

Top Abstract Introduction Materials and Methods Results Discussion Appendix 1. Correlation matrix... References

 
Response Rate and Representativeness of Sample
Complete data on diet, IGF, and all confounding variables were available for 541 children, i.e., 38% of the 1,432 children who ever attended a Children in Focus clinic. Excluding multiple births and children from non-White ethnic groups reduced this number to 521 (287 boys). The characteristics of those children for whom complete data on diet, IGF, and all confounding variables were available are shown in Table 1, along with mean nutrient and food intakes. Mean food and nutrient intakes were similar to those in a national survey of British children (38). The children were slightly taller and heavier than national growth standards.

Nutrients
The correlations between energy and energy-adjusted nutrient intakes and IGF-I and IGFBP-3 are shown in Table 2. In analyses adjusting only for age and sex, IGF-I was positively associated with the intakes of total and animal protein (but not vegetable protein), zinc, phosphorus, magnesium, calcium, potassium, and folate, and inversely associated with the intakes of total fat, MUFA, and PUFA. On further adjustment for potential confounders, most of these relationships were of similar strength or only slightly attenuated, and positive relationships between the intakes of NSP and carotene and IGF-I emerged (data not shown). There was evidence that the association between nutrient intakes and IGF-I differed in boys and girls for total fat (Pinteraction = 0.090), MUFA (P interaction = 0.023) and phosphorus (P interaction = 0.067), the correlations between total fat, MUFA intakes, and IGF-I being stronger in girls than in boys, and that with phosphorus being weaker in girls than boys.

In analyses adjusting only for sex, IGF-I/IGFBP-3 was positively associated with the intakes of total and animal protein, zinc, and phosphorus, and negatively associated with the intake of PUFA and vitamin E. These associations remained fairly similar on further adjustment for potential confounders. There was no evidence of any statistical interactions between sex and nutrient intakes in determining IGF-I/IGFBP-3.

Table 3 shows geometric mean levels of IGF-I, IGFBP-3, and IGF-I/IGFBP-3 by quartiles of nutrient intake for all nutrients with strong (P < 0.01) associations with a measure of the IGF system.

Most of the variation in total protein intake was explained by animal protein, and vegetable protein intake was negatively correlated with animal protein intake (see Appendix 1). In a regression analysis including sex and both energy-adjusted animal and vegetable protein intake, protein from both sources was significantly positively associated with IGF-I levels—the P values for animal and vegetable protein were P < 0.001 and P = 0.034, respectively.

Foods
Table 4 shows mean concentrations of IGF-I, IGFBP-3, and IGF-I/IGFBP-3 by levels of food consumption. There were no strong associations between any of the foods investigated and IGF-I, IGFBP-3 or IGF-I/IGFBP-3, although there was a trend towards lower IGF-I with increasing bread intakes (fully adjusted P = 0.065). There was evidence that associations between fruit intake and IGF-I and IGF-I/IGFBP-3 differed in boys and girls (P interaction = 0.036 and P interaction = 0.044, respectively). On repeating the analyses in boys and girls separately, associations were stronger in boys (fully adjusted P for linear trend in boys = 0.014 and 0.010 for IGF-I and IGF-I/IGFBP-3, respectively). In increasing quantiles of fruit intake, geometric mean IGF-I concentrations in boys were 132.8, 121.5, 146.4, and 143.0 ng/mL, and IGF-I/IGFBP-3 levels were 0.151, 0.142, 0.169, and 0.166.

	Discussion

Top Abstract Introduction Materials and Methods Results Discussion Appendix 1. Correlation matrix... References

 
In this group of 7- to 8-year-old children, we have found that circulating levels of IGF-I and IGFBP-3 are associated with several aspects of their diet. Strong positive associations between IGF-I concentrations and protein and mineral intakes were found, and inverse associations with total fat and MUFA intake. IGFBP-3 was positively associated with energy and inversely associated with monounsaturated fat. There seemed to be some differences between boys and girls in the dietary influences on the IGF system—some of these may be chance findings arising as a result of multiple testing. When possible under-reporters were excluded, a positive association between IGF-I and energy intake emerged.

The dietary assessment method chosen in our study was a 3-day unweighed food record. The gold standard of dietary assessment is generally considered to be the 7-day weighed intake, however, in the context of the cohort study upon which this research is based, it was felt that this approach would be unmanageable due to the large sample size and would also place an unacceptable burden on the study participants. Unweighed food records have been shown to compare well with the results from weighed intakes (39). However, restricting the dietary recording period to 3 days will have limited the accuracy with which individuals were ranked for nutrient intakes. This is particularly true for school children, as it has been shown that more days of dietary recording are required to rank their nutrient intakes accurately than for adults or toddlers (40). This is also likely to be a problem with ranking food intakes, especially for foods that are not dietary staples, however, there is a lack of information on how many days of dietary recording are required to rank food intakes with a given degree of accuracy. This imprecision in ranking nutrient and food intakes means we are likely to have underestimated the strength of relationships between foods/nutrients and IGF. The relative lack of children from lower socioeconomic groups may also have reduced the range of food and nutrient intakes observed, and limited our ability to detect important IGF-diet associations. However, unless the relationships between the IGF system and nutrient intakes differ according to social class, it is unlikely to have significantly affected the generalizability of our results.

In our multivariate analysis, we chose to control for maternal educational level and housing tenure, two measures of possible socioeconomic confounding. As it is possible that these factors may only influence the IGF system via their association with diet, and possibly physical activity, it could be argued that they are not true confounders and should therefore not be controlled for. However, by controlling for them in multivariable models, we have assessed the possibility that other unmeasured or unknown socially patterned confounders underlie the associations of diet with the IGF system. As none of the observed associations were much changed on controlling for socioeconomic confounders, this does not seem to be the case.

We have examined associations of 24 nutrients and 11foods with three measures of the IGF system. As we have looked at such a large number of associations, it is possible that some of the significant relationships observed will be due to type I error. However, many of the associations observed are not only highly significant but also consistent with the literature on adults, in particular, the associations of protein and minerals with IGF-I. This was our strongest finding, and was observed in both sexes. A number of small studies (n = 35-153) in elderly subjects have found no association between protein intakes and IGF-I (4, 15, 18, 41), but may have lacked the statistical power to detect associations of the size observed in some of the larger studies. As mentioned above, it is also possible that some or all of the sex differences in the strengths of associations we report here are chance findings arising as a result of multiple testing. Unfortunately, there is a lack of other studies comparing diet-IGF associations between the sexes with which to compare our own.

In a study of 119 postmenopausal women in Australia (20), zinc was the dietary factor most strongly associated with IGF concentrations, and had an effect that was independent of protein intake. Zinc supplementation has recently been associated with an increased risk of developing prostate cancer in middle-aged men (42), possibly reflecting zinc-induced increases in IGF-I levels. Zinc, protein, and three of the other minerals that were positively associated with IGF-I concentrations (phosphorus, magnesium, and potassium) have all been classified as "type II nutrients" (43, 44), i.e., a deficiency of these nutrients is characterized by a primary cessation in growth without a reduction in tissue concentration. This is consistent with the associations we have observed between these nutrients and IGF-I, as IGF-I is a key hormonal regulator of growth (45, 46).

Among the girls, IGF-I was strongly inversely associated with total fat and MUFA intake. This inverse association is at odds with the fact that both high fat intakes (47) and high IGF-I concentrations (467) have been observed to be positively associated with the risk of developing certain cancers. However, they are consistent with the positive association between both high fat intakes (48) and low IGF-I concentrations (3) and the risk of coronary heart disease. The risk of cancer and coronary heart disease is reduced by similar dietary factors (fruit, vegetables, etc.) but influenced in different directions by IGF-I levels. This implies that if dietary alterations of the IGF system are an important factor in the etiology of either disease, then different mechanisms must be at work.

Other studies of the relationship between the IGF system and fat intake have produced inconsistent results. In the largest study in men, the Health Professionals' Follow-up Study (13), fat intake was not associated with IGF variables, several other studies have also found no association between fat and measures of the IGF system (14, 15, 17, 18, 20). Among 344 middle-aged men in England, IGF-I was positively associated with PUFA, and IGFBP-3 was weakly positively associated with PUFA, and weakly inversely associated with SFA intake (12), whereas in Greek men and women, IGF-I was positively associated with fat intake (41). However, in the Nurses' Health Study, the largest study in women (16), vegetable fat was negatively associated with IGF-I, and total fat was weakly negatively associated, although total fat, SFA, and MUFA were all negatively associated with IGFBP-3.

We have found very little association between particular foods and IGF concentrations, the only significant association being a positive association between fruit and IGF-I in boys. We are aware of only one other study that has investigated the association with fruit intake, i.e., the Nurses' Health Study, where there was no association. The positive association we have found between IGF-I and fruit in boys was to some extent unexpected, as fruit consumption is negatively associated with the incidence of many cancers (47), although in a recent review of the relationship between diet and prostate cancer, the authors concluded that if anything fruit eating was associated with a slightly elevated risk of prostate cancer (49). The association may be attributable to the fact that among the boys in this study increasing fruit consumption was associated with a more mineral-rich diet—fruit intake was significantly positively associated with energy-adjusted intakes of magnesium, potassium, and zinc (r = 0.330 P < 0.001, r = 0.321 P < 0.001, and r = 0.138 P = 0.001, respectively), and energy-adjusted protein intake was also weakly positively associated with increasing fruit consumption (r = 0.076, P = 0.083). The positive association between fruit and IGF-I may reflect the fact that fruit is one of the foods with the most socially patterned intake (50, 51), however, the association was little affected following adjustment for maternal education and housing tenure (see Table 4). The positive association between fruit and IGF-I is consistent with the protective effect of both fruit and IGF-I against coronary heart disease (3, 52).

In our study, there was no association between the intake of tomatoes or tomato products and IGF concentrations. Two studies in middle-aged and elderly men have found inverse associations between tomato or tomato product consumption and IGF-I or IGF-I/IGFBP-3 (12, 14), and a positive association between lycopene and IGFBP-3 was observed in the Nurses' Health Study (16), although one small study in elderly men found no association (15). The lack of association observed in the current study may be related to the low overall level of consumption of tomatoes and tomato products—the mean daily intake of raw and cooked tomatoes was 5 g with a median intake in both sexes of 0 g, and the mean intake of all tomato products in total was 25 g, or as mentioned above to the limitations of the dietary assessment method in ranking individuals for food intakes.

Despite positive associations between animal protein and IGF-I, we found no association with intakes of red meat or processed meat. Positive associations between red meat consumption and IGF-I have been reported in Greek adults (41) and a negative association with IGFBP-3 was found in the Nurses' Health Study (16). However, two studies in middle-aged men (12, 13) found no association between red meat and IGF-I. We also found no association with fish, processed meat, and poultry, other major sources of animal protein, although in a previous analysis, we did find a positive association between IGF-I and intake of dairy products.⁵ Dairy products were the major source of protein in this population, accounting on average for 24% of total protein intake (in comparison with 11%, 8%, and 7% from chicken, processed meat and red meat, respectively). In a previous analysis, we found that the association of dairy products with IGF-I in boys was greatly attenuated on controlling for protein intake. The regression coefficients of dairy and nondairy protein on IGF-I in boys were very similar [ß (SE) = 0.006005 (0.003), P = 0.052 and ß (SE) = 0.006846 (0.003), P = 0.009], implying that protein per se rather than protein from any particular food source may be the important factor.

In conclusion, we have found several significant associations between nutrient intakes and the IGF system. These results are consistent with observations in adults and with the relationship between dietary deficiencies in childhood and growth failure. Diet-induced variations in IGF concentrations in childhood may have important implications for long-term risk of several chronic diseases.

	Appendix 1. Correlation matrix of energy-adjusted nutrient intakes at 7 years (n = 521)

Top Abstract Introduction Materials and Methods Results Discussion Appendix 1. Correlation matrix... References

 

Starch Sugar Protein Animal protein Vegetable protein Fat SFA MUFA PUFA NSP Zinc Calcium Potassium Selenium Folate Vitamin C Retinol Carotene Vitamin D Vitamin E Carbohydrate 0.26 0.71 –0.42 –0.46 0.21 –0.90 –0.64 –0.76 –0.26 0.14 –0.37 –0.21 0.11 –0.05 0.24 0.33 –0.35 0.08 –0.07 –0.11 Starch –0.48 –0.11 –0.33 0.55 –0.24 –0.32 –0.18 0.18 0.31 –0.10 –0.29 –0.09 0.34 0.09 –0.20 –0.14 –0.13 0.08 0.22 Sugar –0.31 –0.17 –0.22 –0.63 –0.34 –0.55 –0.37 –0.10 –0.26 0.03 0.18 –0.29 0.16 0.45 –0.21 0.16 –0.12 –0.26 Protein 0.89 –0.03 –0.00 0.04 0.02 –0.06 0.16 0.70 0.50 0.43 0.32 0.23 –0.09 0.13 0.15 0.13 –0.12 Animal protein –0.47 0.09 0.17 0.12 –0.14 –0.10 0.59 0.50 0.35 0.15 0.09 –0.09 0.11 0.13 0.14 –0.18 Vegetable protein –0.21 –0.27 –0.23 0.19 0.55 0.05 –0.14 0.09 0.31 0.22 0.05 0.01 0.01 –0.06 0.20 Fat 0.70 0.83 0.30 –0.22 0.09 0.00 –0.33 –0.09 –0.37 –0.32 0.34 –0.15 0.03 0.16 SFA 0.52 –0.24 –0.26 0.18 0.27 –0.20 –0.17 –0.21 –0.23 0.39 –0.08 –0.18 –0.22 MUFA 0.35 –0.23 0.07 –0.09 –0.25 –0.11 –0.37 –0.30 0.15 –0.18 0.01 0.13 PUFA 0.13 –0.12 –0.26 –0.07 0.15 –0.13 –0.09 0.04 –0.10 0.20 0.75 NSP 0.33 –0.05 0.41 0.39 0.25 0.11 –0.03 0.28 0.06 0.19 Zinc 0.48 0.39 0.28 0.25 –0.08 0.25 0.13 0.15 –0.15 Calcium 0.32 0.03 0.21 –0.07 0.33 0.03 –0.05 –0.22 Potassium 0.06 0.53 0.43 –0.04 0.29 –0.10 –0.03 Selenium 0.00 –0.05 0.15 0.01 0.20 0.15 Folate 0.40 0.08 0.22 0.22 –0.01 Vitamin C –0.03 0.33 –0.06 –0.01 Retinol 0.01 0.27 0.06 Carotene 0.05 0.05 Vitamin D 0.28

	Acknowledgments

 
We thank the mothers who have taken part in this study, and the midwives for heir cooperation and help in recruiting the mothers during pregnancy. We would like to acknowledge the dedicated work of the ALSPAC study team; this includes interviewers, computer technicians, clerical workers, research scientists, volunteers, and managers.

	Footnotes

 
Grant support: Supported by the grant from the World Cancer Research Fund.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: The ALSPAC study is part of the WHO-initiated European Longitudinal Study of Pregnancy and Childhood. The ALSPAC study could not have taken place without the financial support of the University of Bristol, the Medical Research Council, Wellcome Trust, the Department of the Environment, MAFF, and various medical charities and commercial companies.

¹ Rogers, et al. ALSPAC Study Team. Milk as a food for growth? The IGF link, American Journal of Epidemiology (submitted for publication).

Received 3/12/04; revised 8/ 6/04; accepted 8/20/04.

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