Milk increases both fasting insulin and insulin-like growth factor 1 (IGF-1), and thereby growth, in healthy prepubertal boys. It is, however, unknown which components in milk are responsible for milk's growth-stimulating effect.
To get closer to the identification of which components in milk that stimulate growth, we have performed an intervention study with 57 eight-year-old boys in which we examined the effects of the two major milk protein fractions, whey and casein, and milk minerals (Ca and P) in a 2 × 2 factorial design on IGFs and glucose–insulin metabolism. The amounts of whey and casein were identical to the content in 1.5 l skim milk. The amounts of Ca and P were similar to 1.5 l skim milk in the high-mineral drinks, whereas the amounts of Ca and P were reduced in the low-mineral drinks.
There were no interactions between milk mineral groups (high, low) and milk protein groups (whey, casein). Serum IGF-1 increased by 15% (P<0.0001), whereas there was no change in fasting insulin (P=0.36) in the casein group. In the whey group, fasting insulin increased by 21% (P=0.006), with no change in IGF-1 (P=0.27). There were no independent effects of a high milk mineral intake on IGF-1 and insulin.
The main milk protein fractions exhibit important but different growth-promoting effects by increasing either fasting insulin (whey) or IGF-1 (casein) levels.
Many studies from developing countries have shown that animal foods have a growth-stimulating effect in populations with a marginal nutritional status (Ruel, 2003). Among these studies, it seems like milk has a stronger effect than meat. However, the effect of milk appears stronger in populations with a marginal or poor nutritional status than in well-nourished populations with adequate intakes of both energy and protein (Hoppe et al., 2006).
The consumption of milk has been linked to a number of noncommunicable diseases, including hormonal cancers of the breast, ovaries and prostate. The results are equivocal because positive, negative and no associations have been reported (Moorman and Terry, 2004). Milk intake has also been suggested to potentially induce (van der Pols et al., 2007) and protect against colorectal cancer (Newmark et al., 1984; Ma et al., 2001) and osteoporosis (Kalkwarf et al., 2003). Also, milk seems to be important in the metabolic syndrome, including insulin resistance, because a high intake of milk is associated with lower prevalence of the metabolic syndrome in adulthood (Pereira et al., 2002; Azadbakht et al., 2005; Choi et al., 2005). However, the underlying mechanisms are not completely elucidated and different milk components might have differential effects.
In an intervention study with healthy 8-year-old boys, we have previously shown that a high intake of milk but not meat resulted in significantly increased concentrations of fasting insulin, which is a marker of insulin resistance (Hoppe et al., 2005) and insulin-like growth factor 1 (IGF-1) in serum (Hoppe et al., 2004a). In accordance with this, we found in a study of 90 two-and-half-year-old children that there was a significant association between milk protein intake and both serum IGF-1 values and height (Hoppe et al., 2004b). In contrast, there was no association between meat intake and IGF-1 or height.
However, it is unknown which components or fractions in milk are responsible for stimulating these growth factors, and one possibility may be that the increase in plasma insulin mediates the increase in IGF-1 or vice versa.
To get closer to the identification of which components in milk that stimulate growth, the aim of the present study was to examine the effects of the two major milk protein fractions, whey and casein, and milk minerals (Ca and P) in a 2 × 2 factorial design on IGFs and glucose–insulin metabolism.
Materials and methods
This study had a double-blinded randomized 2 × 2 factorial design in which 8-year-old boys were randomized to receive 540 ml of one of the following milk-based drinks: (1) whey with low milk mineral content (calcium and phosphate) (WHEY-LOW); (2) whey with high milk mineral content (WHEY-HIGH); (3) casein with low milk mineral content (CASE-LOW) and (4) casein with high milk mineral content (CASE-HIGH) daily for 7 days. In addition, they were asked to eat their normal diet ad libitum. The study was approved by the ethics committee of Copenhagen and Frederiksberg (J. No. KF 01-072/04).
Participants were recruited by random extractions from the National Danish Civil Registry. Caucasian boys with a habitual milk intake of ⩽500 ml per day were eligible for the study. Children with chronic illnesses and children who suffered from any condition likely to affect their protein metabolism or growth were excluded from the study. From a total of 831 invited subjects, 89 agreed to participate, and all were eligible for the study. However, only 87 attended the introduction visit and 19 declined further participation in the study, primarily because they did not like the milk-based drink. Of the 68 remaining subjects, 11 did not complete the study because they did not come to the visit (n=3), did not find the milk drinks acceptable (n=5), suffered from acute illness, including influenza and common cold (n=2) or had a death in the family (n=1). Only 57 subjects completed the intervention study.
All interviews and examinations were performed at the Department of Human Nutrition. Written consent was obtained from the custody holders of the child. Randomization was performed, and the child was allowed to taste the milk-based drink. Children could at all times withdraw from the study. The children were examined before the start of the intervention and at the end of the intervention 1 week later.
The amounts of milk components in the milk-based drinks were aimed to be identical with the contents in 1.5 l of skim milk. The contents of protein, calcium, phosphate and lactose in the four milk-based drinks are given in Table 1 as well the contents of 1.5 l of skim milk. In all milk drinks, the amounts of whey and casein were identical to the content in 1.5 l skim milk. The daily amount of each milk-based drink was 540 ml, which was divided into three cartons of each 180 ml that the subjects could drink throughout the day.
Height was measured on barefooted children with an accuracy of 0.1 cm using a stadiometer. Body weight was measured with an accuracy of 0.1 kg on a digital scale (Lindeltronic 8000; Samhall Lavi AB, Kristianstad, Sweden). Subjects wore only underpants when weighed. Triceps skinfold (TSF) and subscapularis skinfold (SSF) were measured with a standard skinfold caliper (Harpenden; Chasmors Ltd, London, UK) according to standard procedures (Tanner and Whitehouse, 1975). Body fat percentage was calculated from the sum of TSF and SSF (Slaughter et al., 1988). Circumferences of waist and hip were measured thrice with a regular tape measure according to standard procedure.
Most of the measurements were made by one observer and the remaining by one of two well-trained stand-ins. All anthropometrical measures were performed in triplicates, and results are given as means.
A venous blood sample was taken between 0800 and 0900 hours from the forearm after an overnight fast. Local anaesthesia of the skin was given by use of EMLA patch (AstraZeneca AB, Södertälje, Sweden) if desired. Serum was stored at −20 °C until analyzed.
Concentrations of serum urea nitrogen (SUN), which is regarded as a valuable biomarker of recent protein intake (Axelsson et al., 1987; Fomon, 1993) and glucose, were analyzed with a kinetic UV method in accordance with routine methods with Cobas Mira (Hoffmann-La Roche & Co. AG, Basel, Switzerland). Concentrations of insulin and C-peptide in serum and concentrations of IGF-1 and insulin-like growth factor binding protein 3 (IGFBP-3) in plasma were analyzed using automated chemiluminescent immunoassay (IMMULITE 1000; DCP Biermann GmbH, Bad Nauheim, Germany). We used the following equivalents for conversion: 1 ng/ml IGF-1=0.133 nM IGF-1 and 1 ng/ml IGFBP-3=0.033 nM IGFBP-3. Intra- and inter-assay coefficients of variation (CVs) for insulin were 2.5 and 7.4%, respectively, and for C-peptide 5.4 and 8.0%, respectively. For IGF-1 and IGFBP-3, intra-assay CV% were 2.8 and 1.9%, respectively, and inter-assay CV% were 7.8 and 5.2%, respectively.
An index of insulin resistance was obtained by using the homeostasis model assessment (HOMA) for calculating relative insulin resistance and beta cell function (Matthews et al., 1985): relative insulin resistance=glucose (mmol/l) × insulin (pmol/l)/135. Beta cell function=(3 1/3 × insulin (pmol/l))/(glucose (mmol/l)−3.5). A disposition index was calculated as beta cell function/relative insulin resistance.
The participants (with their parents) kept a 3-day (2 week days and 1 weekend day) weighed food record before the intervention and during the last 3 days of the intervention. The importance of maintaining usual dietary intake during the first 3-day period was emphasized to the children and parents. Average daily intakes of energy and nutrients were calculated for each subject by using a national food composition database (DANKOST 3000; Dansk Catering Center, Herlev, Denmark).
Normality was checked with histograms and the Shapiro–Wilk test of normality. All data were analyzed with the SPSS (version 13.0; SPSS Inc., Chicago, IL, USA) and SAS (version 9.1; SAS Institute, Cary, NC, USA). A significance level of P<0.05 was used. Results are given as means±s.d. Anthropometrical, dietary and biochemical variables were compared between the four intervention groups with one-way analysis of variance (ANOVA), and if there was a significant main effect, Bonferroni test was used to detect pair-wise differences. Bivariate correlations were performed with Pearson's product moment correlation.
Biochemical variables were entered as outcome variables in two-way ANOVA, the general linear model, with milk protein intervention and milk mineral intervention as fixed factors. If no significant interaction or effect of the milk mineral intervention was seen, the milk protein intervention groups were compared in one-way ANOVA. The milk mineral intervention had a significant effect only on beta cell function, that is why this effect was adjusted for in the analysis of the milk protein intervention.
Paired comparison of baseline and after intervention values within each group were carried out using Student's t-test and nominal variables were tested with one-way χ2-test.
Because all baseline dietary and biochemical variables were normally distributed (with the exception of C-peptide), parametric statistics were used. Most baseline anthropometrical variables were skewed (except for height and hip circumference).
Table 2 shows anthropometrical, dietary and biochemical characteristics of the children at baseline. Coincidentally, there were significant differences between the intervention groups regarding several of the anthropometrical variables. Also, both energy intake and milk intake were unintentionally lowest in the WHEY-HIGH group and highest in the WHEY-LOW group.
According to international cutoff values (Cole et al., 2000), overweight (adult body mass index (BMI) ⩾25) and obesity (adult BMI ⩾30) correspond to a BMI of 18.44 and 21.60, respectively, in 8-year-old boys. In this group of children, two boys (3.5%) were overweight and two (3.5%) were obese. Bivariate relations of selected baseline characteristics are presented as Pearson's correlation coefficients in Table 3. Fasting insulin and relative insulin resistance were positively correlated with several markers of obesity in this group of healthy, prepubertal boys.
The average daily protein intake (including protein from the milk drinks) was increased by 17%, from 58 g per day (2.23 g/kg per day, 12.98 PE%) to 68 g per day (2.56 g/kg per day, 15.42 PE%) (P<0.001) by the whey drink, and the casein drink increased the average daily protein intake by 51%, from 68 g per day (2.30 g/kg per day, 14.30 PE%) to 103 g per day (3.44 g/kg per day, 23.40 PE%) (P<0.001).
Apart from a significant effect of the mineral intervention on beta cell function (B=36.6, P=0.011), there was no significant effect of the milk mineral intervention, and no significant interaction between the milk mineral groups and the milk protein groups. Therefore, the milk protein intervention groups were combined. In Table 4, a description of the biochemical variables at baseline and after 7 days of intervention is given for the combined high and low milk mineral whey group and combined high and low milk mineral casein group. The increase in SUN, IGF-1 and the molar ratio of IGF-1/IGFBP-3 was significantly higher in the casein group than in the whey group. Conversely, whey increased fasting insulin more than did casein. Also in the whey group, but not in the casein group, significant increases in the HOMA insulin resistance (P=0.010) and the HOMA beta cell function (P=0.022) were found. However, the responses were not statistically significantly different between the two groups. Conversely, the intervention did not change the disposition index significantly in either group.
We have previously shown that a daily intake of 1.5 l of skim milk increased both IGF-1 (Hoppe et al., 2004a) and fasting insulin (Hoppe et al., 2005) considerably after 1 week. In this study, we divided the protein contents of 1.5 l skim milk into whey and casein. We found that the same amount of whey as in 1.5 l skim milk (10.5 g per day) increased fasting insulin significantly more than does 42 g per day of casein. Also, insulin resistance and beta cell function were significantly increased in the whey group, and not in the casein group, but the increases did not differ significantly between groups. Conversely, IGF-1 and the molar ratio of IGF-1/IGFBP-3 increased significantly more after 1 week with casein than with whey. Furthermore, habitual milk intake was positively correlated with circulating IGF-1 and IGFBP-3, as also seen in 2½-year-old Danish children (Hoppe et al., 2004b), and in 7- to 8-year-old British children (Rogers et al., 2006).
The observed effect on insulin concentration might be from simple carbohydrates in the milk drinks. However, the lactose content was identical in all milk drinks. Furthermore, from a study based on regular or fermented milk products, where a discrepancy between glycemic and insulinemic index was found, it was concluded that the insulinotrophic effect was not only related to the carbohydrate component of milk, but also to some yet unidentified food component (Ostman et al., 2001).
The finding of hyperinsulinemia and insulin resistance after intake of whey can be attributable to either that whey primarily increases the insulin secretion and secondarily induces insulin resistance as a cause of the hyperinsulinemia, or alternatively that whey primarily induces the insulin resistance and the insulin secretion is increased secondarily to this. Thus, it is not possible from these data to conclude whether the effect of whey on the insulin levels is positive, that is, protective against development of type 2 diabetes mellitus (T2DM), or negative because of insulin resistance and increased risk of development of the metabolic syndrome and T2DM. However, the fact that the disposition index remained unchanged by the intake of whey denotes that the insulin secretion was perfectly balanced in relation to the insulin resistance. Thus, the beta cell did not suffer under these ‘semiacute’ or ‘subacute’ circumstances, and this is the reason that the plasma glucose level remained unaffected.
At baseline, fasting insulin and relative insulin resistance were positively correlated with several markers of obesity in this group of healthy boys, in which only 3.5% were overweight and 3.5% were obese. This might be indicative of the fact that an association between adiposity and insulin resistance exists even among nonobese, healthy children, as seen in other studies (Hoppe et al., 2004a). In addition, the fact that fasting insulin and IGF-1 were closely correlated might be explained by the relative hyperinsulinemia, as some data suggest that insulin stimulates the hepatic IGF-1 production in young subjects with T1DM (Amiel et al., 1984) in diabetic rats (Olchovsky et al., 1990) and in rat hepatocytes (Johnson et al., 1989). Also, in short-statured normal 9-year-old children, IGF-1 and IGFBP-3 were associated with insulin resistance (Bleicher et al., 2002). Importantly, the documented differential effects of whey versus casein on insulin and IGF-1 in this study strongly indicate that the increase in IGF-1 by milk (and casein) is not mediated by an increase in plasma insulin levels.
There was no effect of the milk mineral intervention. This might be due to unintended differences in content of Ca and P especially in the low-mineral milk drinks. However, the effects of minerals on IGF-1 are not completely elucidated. IGF-1 levels might be influenced by potassium, magnesium and zinc (Devine et al., 1998), but these data are mostly from animal experiments and studies of malnourished children (Dorup et al., 1991; Ninh et al., 1996; Estivariz and Ziegler, 1997), and their function in well-nourished children is to our knowledge largely unstudied. In some observational studies of men (Giovannucci et al., 2003) and women (Holmes et al., 2002), circulating IGF-1 and the molar ratio of IGF-1/IGFBP-3 tended to increase with higher intake of several minerals, including zinc and calcium. However, distinguishing definite effects of individual minerals is challenging because these tend to come from the same sources and probably act in shared pathways. The results from our study suggest that there is no significant interaction between milk protein fractions and milk minerals and that the effect on both the IGFs and on insulin–glucose metabolism is stronger of the milk protein fractions than of the milk minerals.
The study has some limitations. First, the subjects were allowed to eat their habitual diet as usual, which means that there might be other factors in the diet contributing to the findings. However, the diet was appropriately recorded, and this has been controlled for in the analysis. Second, because of our previous findings, we chose to formulate the whey and casein drinks with protein contents similar to the protein content in 1.5 l skim milk. As the whey/casein ratio in cow's milk is 20:80, the protein intake was higher in the casein group. Therefore, it might be argued that the finding IGF-1 and IGF-1/IGFBP-3 increased more in the casein group than in the whey group could be caused by a higher protein intake. However, this does not explain the other main finding, namely, that fasting insulin increased more in the whey group than in the casein group. Furthermore, the results were not changed markedly after controlling for energy intake, protein intake, SUN, which is a marker for recent protein intake (Axelsson et al., 1987; Fomon, 1993) or milk intake.
The consumption of milk has been linked to a number of noncommunicable diseases, possibly through the growth factors IGF-1 and insulin. In adults, low concentrations of IGF-1 may be associated with increased rates of cardiovascular disease (Juul et al., 2002), whereas high concentrations of IGF-1 are associated with an increased risk of prostate, breast and colorectal cancer (Chan et al., 1998; Hankinson et al., 1998; Ma et al., 1999; Giovannucci et al., 2000). It is unknown whether a high level of fasting insulin in healthy, normal-weight children with high growth velocities is negative or positive. In adults, a high level of fasting insulin is a marker of insulin resistance and for the early development of T2DM. Because insulin also is a growth factor, its actions are more complex in growing children. Therefore, a high level of fasting insulin might thus be an indicator of high velocity of linear growth. It is similarly unknown whether our finding of a stimulating effect of a high intake of whey for 1 week on fasting insulin is a transitional phenomenon or whether the same is valid over a longer period. Linking our finding of increased fasting plasma insulin levels after milk intake compared with meat intake in 8-year-old children (Hoppe et al., 2005) with the observation that a high level of dairy consumption is associated with a 9% decreased risk of developing T2DM in men (Choi et al., 2005), it may be speculated whether the hyperinsulinemia induced by milk intake in children can provide a way to understand the protective effect of milk on the development of T2DM.
The implications of the findings of this study could be several. As both insulin and IGF-1 have a function in a number of noncommunicable diseases, this might provide a better understanding of the function of milk in preventing and developing noncommunicable diseases. Furthermore, identifying the components in cow's milk responsible for growth stimulation is important for designing milk-based foods for nutritional rehabilitation in developing countries (Hoppe et al., 2008) and in clinical nutrition.
Whey protein stimulates fasting insulin and casein stimulates circulating IGF-1. Both milk protein fractions seem to be important, but different, in the growth-stimulating effect of milk.
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We thank Birgitte Hermansen, Vivian Anker and Marianne Støckel for their valuable help with the study. Also, we thank Arla Food Ingredients for designing, producing and sponsoring the milk drinks. The study was funded by a grant from the Danish Research Agency (FELFO). The authors (CH, CM and KFM) designed the study and were responsible for the collection of data (CD, CM and KFM). CH was responsible for analysis of data and writing of the article. All authors participated in the discussion of the results and revision of the article. CH, CM and KFM have received research grants from the Danish Dairy Research Foundation to perform research on milk and growth. The milk-based drinks were manufactured and donated by ARLA Foods Ingredients a.m.b.a.
Conflict of interest
The authors declare no conflict of interest.
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Hoppe, C., Mølgaard, C., Dalum, C. et al. Differential effects of casein versus whey on fasting plasma levels of insulin, IGF-1 and IGF-1/IGFBP-3: results from a randomized 7-day supplementation study in prepubertal boys. Eur J Clin Nutr 63, 1076–1083 (2009). https://doi.org/10.1038/ejcn.2009.34
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