| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
Medical Research Council Human Nutrition Research (A.P., F.G., S.J.S., S.C.J., M.A.L.), Elsie Widdowson Laboratory, Cambridge CB1 9NL, United Kingdom; and Centre for Paediatric Epidemiology and Biostatistics (T.J.C.), Institute of Child Health, London WC1N 1EH, United Kingdom
Address all correspondence and requests for reprints to: Dr. Ann Prentice, MRC Human Nutrition Research, Elsie Widdowson Laboratory, Fulbourn Road, Cambridge CB1 9NL, United Kingdom. E-mail: ann.prentice{at}mrc-hnr.cam.ac.uk.
 |
Abstract |
|---|
 Top Abstract IntroductionSubjects and MethodsResultsDiscussionReferences |
|---|
|
Introduction |
|---|
|
TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
|---|
Calcium is a primary bone-forming mineral that has to be supplied by the diet. Approximately 200 mg/d calcium is accreted into the skeleton during childhood (6). Calcium accretion is substantially higher during the period of rapid growth in adolescence. This peaks at around 400 mg/d for boys and 320 mg/d for girls, but can be greater than 500 mg/d in some individuals (7). Significant accretion continues in young adults after the cessation of linear growth during the period of skeletal consolidation. Hence, it is plausible that calcium intake during adolescence and young adulthood affects skeletal growth and bone mineralization and influences peak bone mineral mass.
Several epidemiological studies have suggested an association between the intake of calcium-rich foods, such as milk, during teenage years and young adulthood with higher bone mineral mass in old age (8, 9). Studies in pre- and peripubertal children have demonstrated increases in bone mineral density after calcium supplementation (10, 11, 12, 13, 14, 15, 16). However, less is known about the influence of calcium intake on bone mineral accretion in the late teenage years toward the end of linear growth. This stage of life is of interest because it is a time when young people become responsible for their own diet and lifestyle choices and become aware of health issues. Studies of the influence of calcium intake on bone mineral accretion in late adolescence are needed to provide evidence for the setting of dietary recommendations for young people.
We recently published a study of 16- to 18-yr-old girls that demonstrated significant effects of calcium supplementation on bone mineral status (17) with little effect on bone size. We present here the findings of a complementary study of 16- to 18-yr-old boys, which demonstrated effects of calcium supplementation on bone mineral mass and skeletal growth.
|
Subjects and Methods |
|---|
|
TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
|---|
Male sixth-form students, aged between 16 and 18 yr, were eligible for the study. The volunteers were recruited from six colleges and schools in Cambridge, UK. The aim was to recruit 150 male students to achieve a target of at least 120 completing the final measurements. Initial approaches were made through promotional flyers and introductory presentations. Those boys who expressed an interest were provided with information sheets and given time to discuss the study with their parents/guardians. Exclusions included any medical problem, history of eating disorders, and medications known to interfere with bone metabolism. Written informed consent was obtained from both the participants and a parent or guardian. Approval was obtained from the Ethical Committee of the Medical Research Council (MRC) Dunn Nutrition Unit, of which the Nutrition and Bone Health Group at MRC Human Nutrition Research was formerly a part.
Initial assessments (baseline) were conducted during the autumn term of the first academic year of sixth-form studies. Outcome measurements (final) at the end of the intervention period were conducted in the spring term of the second academic year. The study was phased over 2 yr, and recruitment was conducted in the autumn terms of 1997 and 1998. Of the 150 boys recruited, 143 (95%) completed the study and were included in the final dataset. Of the seven subjects recruited but not included, six had dropped out (one because of a car accident, the others through lack of time) and one developed anorexia nervosa. The mean ± SD time interval between baseline and final measurements was 14.9 ± 1.0 months. Of the participants who completed the study, 140 (98%) also attended for a set of measurements midway through the intervention period (interim). The time interval between baseline and interim measurements was 7.4 ± 1.1 months.
Randomization to supplement group
The participants were randomized, double-blind, to receive either a calcium supplement (group S) or placebo (group P) for 12 months. Randomization was performed in permuted blocks of four in recruitment sequence, generated from published randomization tables. Randomization was stratified by customary physical activity level and by college. Stratification by physical activity level was achieved by dividing potential recruits into a high (H) and low (L) activity group according to whether they reported participating regularly in greater or less than 9 h sporting activity per week. Of the 143 participants who completed the supplementation intervention, 51 were in group H (S = 26; P = 25) and 92 were in group L (S = 47; P = 45).
In addition to the supplementation intervention involving all subjects, participants in group L were randomly allocated, in the approximate ratio 3:2 (group E = 59; group N = 33), to one of two exercise groups, stratified by supplement group. Group E (S = 30; P = 29) subjects were invited to attend three 45-min exercise classes a week during term time. Group N (S = 17; P = 16) subjects were not invited. The exercise sessions, which were based on high-low impact exercises and circuit training, commenced after baseline measurements had been completed on all subjects and finished after the supplementation period. Despite considerable efforts to motivate and encourage participants in Group E, the exercise classes were poorly attended and unlikely to have resulted in a sustained increase in physical activity. Formal statistical testing revealed no significant effects of participation in the exercise intervention on bone or anthropometric variables at either interim or final, nor were any significant interactions with calcium supplementation noted. Consequently, Groups E and N were considered together (Group L) for all statistical analyses presented in this paper.
Supplementation and compliance monitoring
The calcium supplement provided 1000 mg elemental calcium per day. The tablets were orange-flavored, chewable calcium carbonate (two Calcichew-500 tablets; Shire Pharmaceutical Development, Andover, UK). The placebo contained the same ingredients as the supplement but with the 2.5 g calcium carbonate in each tablet replaced by microcrystalline cellulose and lactose (Nycomed Pharma, Asker, Norway). The placebo tablets were indistinguishable from the supplement in appearance, taste, and texture. The participants were asked to consume one tablet mid-morning and one in the evening to optimize calcium absorption (18). They were also asked to consume the tablets away from meals, to minimize potential nutrient interactions such as a possible adverse effect of calcium on iron absorption (19). Jars of tablets were issued in four batches during the intervention period, each containing enough tablets to last 14 wk. Compliance, defined as the number of tablets consumed relative to the number allocated, was monitored using a check diary and by counting of tablets remaining in the jars at the end of each 14-wk period. The supplementation program began in the second week of January once all the baseline measurements were complete. The mean ± SD time interval between the start of supplementation and the final measurements was 12.7 ± 0.5 months and between baseline and the interim measurements was 5.2 ± 0.7 months. There were no reports of side effects, but one individual in the placebo group stopped taking tablets early in the study because of a self-reported preexisting allergy to citric acid, an ingredient of both the supplement and placebo tablets.
Sample size
A sample size of 60 per supplementation group gave the study the statistical power to detect, at 
= 5% and 1 – ß = 80%, a difference of 0.5 SD in the change in bone mineral over time. Based on results for the placebo group from the companion study of girls aged 16–18 yr (17), coefficients of variation for change in size-adjusted bone mineral content (BMC) over time in late adolescence, after adjusting for baseline value are 1.5% for whole body, 2.3% for lumbar spine, and 3.3% for total hip. The study, therefore, had the power to detect differences between S and P groups at each of these sites of 0.75, 1.15, and 1.65%, respectively, or greater.
Bone mineral measurements and anthropometry
The BMC and bone area (BA) of the whole body, lumbar spine (L1–L4), left hip (total, femoral neck, greater trochanter, and intertrochanter), and nondominant forearm (total, ultradistal, and one-third sites of the radius), were measured by dual-energy x-ray absorptiometry (DXA) using the Hologic QDR 1000-W scanner and software (Hologic, Bedford, MA). The whole-body scan also provided data on lean and fat mass. The Wards region of the hip was not included because, as is common in young people, the instrument failed to locate an area of lowest bone density for many individuals.
The performance mode was used for spine and hip scans (software V4.47P). The enhanced whole-body analysis (software V5.61) was used for whole-body and regional measurements of BMC and body composition. The body composition software provided data on total mass, fat mass, and lean mass (excluding BMC). Whole-body fat fraction and lean fraction were calculated by dividing fat mass and lean mass by total mass. Forearm scans were performed with software VS.61Q. Final scans at all skeletal sites were analyzed with reference to the baseline scan using the compare-mode facility, to minimize errors in positioning the region of interest. Quality assurance and long-term stability was assessed using a Hologic spine phantom, which was measured twice before all DXA measurements. Over the 30 months of the study, the coefficient of variation for both BMC and BA was no more than 0.4% with no significant drift over time.
Height (cm) and weight (kg) were measured on the same day as the bone mineral measurements. The measurements were performed predominantly by one individual (S.C.J.). Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer. Weight was measured to the nearest 0.1 kg using an electronic digital scale (Sauter weighing scales, Todd Scales Ltd., Norwich, UK). The time of day when height and bone measurements were made was recorded to account for possible circadian variations in stature caused by changes in intervertebral disc compression (20).
Questionnaire and dietary information
Questionnaires were used to obtain self-reported information on physical activity patterns [EPAQ2 Physical Activity Questionnaire (21)], place of birth, ethnic group, occupation of parents, medical history, smoking and drinking habits, and consumption of dietary supplements and antacids. Social class of the head of household was determined using the Registrar-General’s classification for the United Kingdom (22). A unit of alcohol was defined according to the U.K. Department of Health as 8 g or 10 ml of pure alcohol, or the amount contained in a half-pint of beer, lager, or cider, a small glass of wine, or 25 ml of spirit.
Information on the frequency of consumption of calcium-rich foods was obtained using the structured questionnaire Calquest (23). Calquest is a validated dietary instrument for the assessment of calcium intake commonly used in the United Kingdom. Detailed dietary information was collected the week after the baseline scans using a prospective 7-d food diary using household measures to record intake. Intakes of food and drinks, including water, were recorded in terms of standard household measures with visual aids to assist the volunteer (24). Coding of diet records and computation of nutrient intakes was undertaken using the in-house software programs based on British food table data (25). No account was taken of the potential contribution from dietary supplements and antacids in the assessments of dietary calcium intake because reported ingestion was sporadic and included a wide range of products, many of which contained little or no calcium (see Results).
Plasma testosterone concentration
Nonfasting blood was collected into lithium heparin tubes and stored at –80 C for the measurement of plasma testosterone concentration by ELISA (Testosteron ELISA; DRG Diagnostics, Marburg, Germany) with a detection limit of 0.069 ng/ml. Samples from 126 subjects were available for analysis. No other assessment of pubertal status was performed.
Data analysis
All statistical analyses were performed using a statistical software package (Linear Model Software, DataDesk 6.1.1; Data Description Inc,, Ithaca, NY) and included multiple linear regression analysis, ANOVA, and analysis of covariance (ANCOVA). Summary statistics are presented as mean ± SD and difference ± SE. All bone and anthropometric variables were transformed to natural logarithms. This facilitated the exploration of power relationships between continuous variables and of proportional effects of discrete variables (26). In all cases the log-transformed variables approximated a normal distribution. The regression coefficient for a discrete variable, when the dependent variable is in natural logarithms, once multiplied by 100, corresponds closely to the percentage effect as defined by (difference/mean) x l00 (27). All percentage differences quoted in this paper were derived in this manner.
The percentage changes in anthropometric and bone variables between baseline and final were analyzed by hierarchical ANOVA and ANCOVA models followed by Scheffé post hoc tests with the following independent variables: time point, subject number, and intervention group, nested by subject number. Interactions between time and intervention effects were tested for.
The impact of the calcium intervention on bone mineral status at each site was examined in four ways: 1) effect on BMC to determine whether bone mineral mass had been altered, 2) effect on bone size (BA) and on statural height to determine whether skeletal size had been affected, 3) effect on bone mineral density (BMD = BMC/BA), a marker of bone status that is commonly used as an index of fracture risk but that can be influenced by bone size and is prone to size-related artifacts (26), and 4) effect on BMC, and hence BMD, after full correction for BA, weight, and height (size-adjusted BMC (SA-BMC)) to determine the impact on the skeleton independently of bone and body size (26). Multiple linear regression models were constructed with the final value as the dependent variable, the baseline value, and intervention group (S/P = 1/0) as independent variables. Baseline value was included in all models to adjust for regression toward the mean. The coefficient for the S/P variable can be interpreted as the effect of the intervention on the dependent variable, after adjusting for baseline value, or on the change in the variable since baseline, after adjusting for baseline value. The two interpretations are equivalent. Adjustment for variables that changed over time, such as BA, weight, and height, was performed by including the mean and difference of the logged variable at final and baseline as independent variables in the models (28).
A consistent approach to the statistical analysis was adopted. All variables of interest were included in each initial model, followed by backwards elimination of nonsignificant factors (P > 0.05), the least significant being removed first, to produce a final, parsimonious model. All analyses were conducted first on an intention-to-treat basis, by comparing all participants according to their original supplement allocations, and second by restricting the analyses to those participants in both S and P groups with compliance of at least 50% (n = 89). Potential confounding by age, phase of recruitment, customary calcium intake, customary physical activity level (H/L), and days since baseline were considered by including these variables in the initial model. Interaction terms were added as appropriate to examine the possible influence of customary physical activity level, plasma testosterone concentration, and customary calcium intake on the effect of the supplement. Differences in the time of day when measurements were made were also considered for those variables that might be influenced by diurnal variations in spinal compression, viz. statural height and whole body BA and lumbar spine BA.
|
Results |
|---|
|
TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
|---|
The average weight, height, and BMI of the subjects were 101, 103, and 100%, respectively, of nationally representative data for 16-yr-old boys in the UK (29). Ninety percent were white; the others were from various ethnic groups. Ninety-four percent were from families where the head of the household was social category I+II, representing the two highest grades of professional and managerial occupations. There were no significant differences between S and P in age, anthropometry, bone measurements, or plasma testosterone concentration at baseline (Tables 1
and 2). There were also no significant differences in calcium intake (Table 1) or, in the subgroup with diary information, in the intakes of energy, protein, fat, or phosphorus (data not shown). Sixty-two subjects (43%) reported periodic or regular use of dietary supplements before or during the intervention period (27S and 42P), most of which contained little or no calcium. Only four subjects (3%) reported taking calcium supplements at some time before the study (2S and 2P) and none during the intervention period. Twenty-eight subjects (20%) reported occasional ingestion of antacids before or during the intervention period, some of which contained calcium (12S and 16P).
|
|
The consumption of tablets relative to the number allocated averaged 58.9 ± 30.5% in all subjects, with 40% consuming at least 75%, and 62% consuming at least 50%. There was a significant difference in tablet compliance between the intervention groups (Table 1
); no explanation was found for this difference. Supplement consumption increased total calcium intake in S by 652 ± 279 mg/d.
Changes over time
Subjects in both S and P groups experienced increases in height, weight, lean and fat masses, and most bone indices over the 15 months between baseline and final measurements (Tables 3 and 4). The exceptions were bone variables at the hip and radius. There were no significant changes over time noted in body composition, expressed either as lean and fat fraction (Table 3) or as lean and fat mass corrected for change in weight and height. Significant time x intervention group interactions were noted in these unadjusted models for height, lean mass, and bone indices at the whole body (BMC and BMD), lumbar spine (BMC and BA), total hip (BMC, BMD, and SA-BMC), femoral neck (BMC and BMD), and intertrochanter (BMC, BMD, and SA-BMC). These interactions were in the direction of greater increases over time in S compared with P (Tables 3 and 4).
|
|
The intention-to-treat analyses of the effect of supplementation on anthropometric and bone variables are given in Tables 3 and 4. The calcium intervention resulted in significantly greater statural height (equivalent to approximately 7 mm), lean mass, and BMC of the whole body, lumbar spine, and hip. The increases in BMC were partly matched by increases in BA and statural height such that they were attenuated by expressing the data as BMD and SA-BMC. After full size adjustment, a significant effect was evident only at the total hip, with trends indicating effects of a similar magnitude at the femoral neck (P = 0.07) and intertrochanter (P = 0.08). Similarly, the increase in lean body mass was associated with an increase in body size, and there was no significant difference in lean fraction between S and P (P = 0.7). There were no significant effects of the intervention on body weight (P = 0.1), fat mass (P = 0.6), or fat fraction (P = 0.4).
Significant effects of the calcium supplement on height and bone mineral status were also observed midway through the intervention period. In general, the pattern of response was similar to that at final, but the magnitude and the statistical significance were less (Fig. 1).
|
).
|
At interim, but not final, differences in time of day (
T) when the measurements were made was a negative predictor of change in height and lumbar spine BA (both P = 0.005), indicating that subjects measured later in the day than at baseline had smaller gains in these variables. However, the effects of calcium supplementation on height and lumbar spine BA remained significant after correcting for T and no significant S/P x T interactions were noted.
Influence of customary calcium intake, physical activity, and plasma testosterone
Except at the radius, calcium intake was not a significant predictor of baseline value for any anthropometric or bone variable except at the forearm. At the ultradistal radius, baseline BMC and BMD were positively related to calcium intake (P = 0.03 and 0.003, respectively), but neither relationship was independent of energy intake and weight. At the distal third radius, BMD and SA-BMC were both positively related to calcium intake (P < 0.001), and these associations remained after adjustment for energy intake. Calcium intake was not a significant predictor of change between baseline and final, and no significant interactions were observed with the effect of supplementation. This was the case using either customary or total (i.e. diet plus supplement) calcium intake and using intake data expressed either as a continuous variable or dichotomized into intakes above and below the median. These results were not affected by adjustment for energy intake and were similar for intake assessments using the diet record or the food frequency questionnaire.
Significant differences were noted at baseline between participants with high and low customary physical activity level, as defined by hours of sports activities at baseline (H/L). H subjects were significantly heavier (+5.3 ± 2.5%, P = 0.04), with greater lean mass (+5.6 ± 2.3%, P = 0.01) but no significant difference in height, fat mass, age, or plasma testosterone concentration. H subjects tended to have greater BMC at all skeletal sites. This was significant at the whole body (+5.9 ± 2.7%, P = 0.03), hip (total +8.5 ± 3.0%, P = 0.006; neck +9.5 ± 2.8%, P = 0.001; trochanter +9.6 ± 3.6%, P = 0.009; intertrochanter +8.0 ± 3.2%, P = 0.01) and ultradistal radius (+7.8 ± 3.0%, P = 0.01). At most sites, this was partly matched by greater BA, but significant differences in BMD were observed at the whole body (+2.9 ± 1.4%, P = 0.04) and hip (total +5.4 ± 2.0%, P = 0.007; neck +6.7 ± 2.2%, P = 0.003; trochanter +7.4 ± 2.2%, P 0.001; intertrochanter +4.2 ± 2.0%, P = 0.04). Size adjustment diminished the distinction further, and significant differences in SA-BMC were apparent only at the femoral neck (+5.2 ± 2.0%, P = 0.01) and trochanter (+6.7 ± 2.1%, P = 0.002).
Time spent in sports activities remained higher in H than L at the end of the intervention (P < 0.001), although this had decreased significantly from baseline in H (median change = –3.9 h/wk, P < 0.001) but not in L (0.5 h/wk, P = 0.9). Over time, H experienced greater gains in BMC at the whole body (+1.2 ± 0.6%, P = 0.05), lumbar spine (+2.2 ± 0.9%, P = 0.02), and distal third radius (+1.4 ± 0.5%, P = 0.009) than L, but there were no significant differences in gain in height, weight, or lean mass. Differences in gain in BMD were observed at the whole body (+0.8 ± 0.4%, P = 0.03) and spine (+1.3 ± 0.6%, P = 0.03), but these were not significant after full correction for bone and body size. Conversely, at the femoral neck, although no significant difference in gain in BMC was observed, H had significantly greater gains in BMD and SA-BMC than L (BMD = +1.6 ± 0.7%, P = 0.03; SA-BMC = +1.4 ± 0.4%, P = 0.05).
There was no indication of any significant interaction between supplementation and physical activity level on gain in height, weight, or bone variables at any skeletal site, except the intertrochanter, where a weak positive interaction on gain in BMC was observed (S/P x H/L = +4.4 ± 2.2%, P = 0.05, representing the difference between subjects in both S and H against the rest). This interaction was reflected at the total hip (S/P x H/L = +3.9 ± 1.9%, P = 0.04).
Plasma testosterone concentration increased during the study (+0.29 ± 0.10 ng/ml, P = 0.006), but the change was not significantly different between S and P (P = 0.6). Individuals with the lower mean testosterone concentration showed the greater increase (P < 0.05) in height, weight, lean mass, lumbar spine BA, and distal third radius BMC and BA. In addition, change in testosterone concentration was a significant predictor of change in height. No other relationships with plasma testosterone were observed. Inclusion of plasma testosterone in the statistical models did not materially alter the effect of the supplement on height or any bone variable. In addition, no significant interactions between supplementation and mean testosterone concentration were noted. However, significant positive interactions (P < 0.05) were observed between change in testosterone and intervention group at the whole body (BMC, BMD, and SA-BMC) and spine (BMC), such that the supplement effect was larger in those with the greater increase in testosterone. No other significant interactions between supplement effect and change in testosterone were observed.
|
Discussion |
|---|
|
TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
|---|
The definition of a skeletal site in DXA depends on the scanning region of interest (ROI) specified. For the whole body and spine, these are determined by anatomical features such that alterations in bone size can be readily accommodated. However, the definitions of ROI at the hip and forearm sites are more arbitrary. By convention, a fixed box size is used at the femoral neck, distal third radius, and ultradistal radius. In addition, the ROI for total hip, intertrochanter, and total radius have a cutoff between the scanning site and the distal femur or proximal radius and therefore incorporate only a part of the anatomical region. Accepted practice in longitudinal studies is to keep the ROI at the hip and forearm as similar as possible for all scans of the same individual. Consequently, in growing individuals, there may be systematic shifts in the ROI relative to anatomical markers. Moreover, increases in size close to ROI boundaries may mean that a portion of bone that fell within the ROI on one occasion may fall outside it in subsequent scans, thus diminishing the detection of growth effects. It is probable, therefore, that the effects of the calcium supplement on bone mineral mass and skeletal size may have been underestimated at these sites and that changes in ROI may account for the observed decreases in forearm BA over time.
This is one of the few calcium supplementation studies of boys aimed at investigating bone mineral accretion and, to our knowledge, the first to consider adolescents. The results, where increases in BMC were largely accompanied by increases in size, contrast with those of our companion study of girls (17). In the adolescent girls, increases in BMC were not associated with increases in skeletal or somatic growth, a result that parallels most published studies in younger girls supplemented with calcium salts (15, 16, 30, 31). The results also contrast with published studies of younger boys. In these, the effects of calcium supplementation were studied in boys and girls together, and no significant interactions with gender were reported (10, 11, 14, 32). However, these studies involved predominantly prepubertal boys and were either of insufficient sample size to test for gender differences (10, 32) or were in populations where growth may have been limited by shortages of other nutrients (11, 14). Taken together, our results suggest that there may be gender differences in the adolescent skeletal response to calcium supplementation, but this needs further investigation with boys and girls matched for pubertal status and skeletal maturity.
The effect of calcium supplementation on growth was reflected in a significant increase in statural height. This was unexpected, given that no height effects have been observed in most calcium supplementation studies of younger children designed to test the effects on bone mineral acquisition (1, 31). An exception was a weakly significant effect on height of a milk-derived calcium phosphate supplement in 7-yr-old girls with a dietary calcium intake of less than 880 mg/d (12). In addition, studies in developing countries investigating linear growth retardation mostly found no significant effects of supplementation with calcium salts on stature (6, 11). Small changes in height are difficult to measure. However, the use of randomization in permuted blocks of four in our study is likely to have minimized bias caused by observer variability or technical artifact. Moreover, the observed increase in lumbar spine BA provides independent evidence of an effect on height. The credibility of the data is further supported by the detection of time-of-day effects on height (33). The lack of an interaction between time of day and intervention group suggests that the supplement effect on height was unlikely to be a result of changes in the patency of the intervertebral discs or alterations in the diurnal periodicity of spinal disc compression (20).
The study demonstrated greater bone mass, skeletal size, lean mass, and bone accretion among boys participating in high levels of physical activity. This has been further investigated in this study sample by a detailed examination of fitness and weight-bearing activities in relation to bone mineral status (34). Although these observations are only cross-sectional, they support other studies that suggest an association between physical activity during childhood and adolescence and BMC and bone size (35, 36). In addition, the observed interaction between calcium supplementation and customary physical activity level on the increase in BMC at the intertrochanter supports similar findings at the femur and tibia in prepubertal children (31, 37). This strengthens the arguments for recommending that attention be paid to both physical activity and calcium intake for the optimization of bone health during childhood and adolescence. However, our experience in this and its companion study, where attendance at specially arranged exercise classes was poor despite the subjects being sufficiently motivated to complete the other aspects of these demanding studies, adds to the cautionary note sounded by others concerning the extent to which promoting exercise can be an effective public health measure for the reduction of fracture risk (36).
This study raises several questions. First, what is the mechanism of the supplement effect? Evidence from calcium supplementation studies of younger children, where both BMC and BMD were increased with no increase in bone or body size, suggests that effects may be mediated through a reduction in bone turnover, similar to that seen in adults (38, 39, 40). If so, it would be expected that the effect, known as the bone-remodeling transient, would disappear once supplementation was withdrawn. This has been borne out in some follow-up studies in children (41, 42), but not all (12, 43). This is a plausible mechanism for the effect in adolescent girls but, for boys, the increase in height, lean mass, and bone size suggests anabolic effects, perhaps through the GH/IGF or sex hormone systems, or an interaction between the two. Suppression of bone resorption with a lesser effect on bone formation, as observed in one study of 11- to 14-yr-old girls (44), might also increase bone size. The increase in lean mass in the calcium-supplemented boys, of itself, may have promoted bone growth through increases in mechanical loading and muscle forces (45). Alternatively, the apparent supplement effect may have arisen by chance (type I error) as a result of small differences in lean mass, activity levels, or pubertal status, although this possibility will have been minimized by the randomized study design.
Effects on growth in calcium intervention studies have been reported previously only in response to supplementation with milk or foods fortified with milk mineral extracts (12, 13). These have been ascribed to the increased protein intake associated with milk consumption or to the presence of growth-promoting factors in milk and milk extracts (13, 38). The anabolic effect of calcium carbonate in the present study suggests either that there are effects of calcium on skeletal growth or that anabolism has been driven by ingredients of the supplement other than calcium. The most plausible alternative would be the decrease in urinary acid output that results from the ingestion of carbonate (46, 47). It is also possible that the supplement increased the tempo of growth and skeletal maturity rather than representing an effect on skeletal size that will persist into adulthood. Follow-up studies and analysis of blood and urine samples are in progress to provide insights into the mechanisms of the anabolic effect of calcium carbonate observed in this study.
Second, what are the implications for future fracture risk of an increase in BMC mediated partly through an increase in skeletal size? Studies of the predictive value of DXA measurements for future fracture risk in older people indicate that BMC is predictive, as well as BMD, with a relative risk of about 2 for a decrease of 1 SD at most skeletal sites (48). On this basis, an increase in BMC of 3%, which represents a change of approximately 0.2 SD (the population coefficient of variation for adult BMC is around 15%), if sustained to peak bone mass, would represent a decrease in relative risk of about 15%. However, BMC and BMD, as measured by DXA, are functions of bone size, shape, density, and turnover (40). It is not known to what extent alterations in one of these aspects alters fracture risk compared with another (35, 36, 40). It is possible, therefore, that the increase in BMC observed in boys may alter fracture risk differentially to girls because of the different effect on size. This would accord with recent studies in elderly individuals where differences in hip size and geometry are considered important explanatory factors in the differences in hip fragility between men and women (49).
Third, what is the optimal calcium intake for bone health in adolescent boys? The subjects were of average height, weight, and BMI for boys of their age and were from high socioeconomic backgrounds. Their mean dietary calcium intake was above the UK reference nutrient intake for this age group (1000 mg/d) (1) but below that recommended by the United States/Canada (adequate intake 1300 mg/d) (50) or by a National Institutes of Health Consensus Conference (1200–1500 mg/d) (51). The intervention increased the mean calcium intake above these levels. However, no relationship was detected between the magnitude of the response to the supplement and customary calcium intake, despite a wide range of intakes within the study sample. In addition, there was no evidence that the response increased with the amount of extra calcium consumed because no significant correlation with tablet compliance was observed, although inaccuracies in self-reported tablet consumption may have contributed to this lack of an association. It is, therefore, difficult to interpret the bone response as the correction of an underlying dietary calcium deficiency, and it is not possible to use the results of the study to determine an estimated average requirement for calcium that could be used to formulate dietary recommendations (1, 50) or to gauge what level of calcium intake can be regarded as optimal for this age group.
This study in 16- to 18-yr-old boys, and its companion study in girls (17), has demonstrated that the window of opportunity for the maximization of bone mineral accretion extends well into late adolescence and that the effects of calcium carbonate supplementation on the adolescent skeleton differ between boys and girls of the same age. Future studies are required to determine the mechanisms underlying these effects and their long-term significance for peak bone mass and future fracture risk.
|
Acknowledgments |
|---|
|
Footnotes |
|---|
The views expressed in this publication are those of the authors and not necessarily those of the sponsors.
First Published Online March 8, 2005
Abbreviations: ANCOVA, Analysis of covariance; BA, bone area; BMC, bone mineral content; BMD, bone mineral density; BMI, body mass index; DXA, dual-energy x-ray absorptiometry; H, high-activity group; L, low-activity group; P, placebo group; S, calcium-supplemented group; ROI, region of interest; SA-BMC, size-adjusted BMC; T, differences in time of day.
Received October 27, 2004.
Accepted February 25, 2005.
|
References |
|---|
|
TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
|---|
This article has been cited by other articles:
 |
|
 |
|
  T. Lehtimaki, J. Hemminki, R. Rontu, V. Mikkila, L. Rasanen, M. Laaksonen, N. Hutri-Kahonen, M. Kahonen, J. Viikari, and O. Raitakari The Effects of Adult-Type Hypolactasia on Body Height Growth and Dietary Calcium Intake From Childhood Into Young Adulthood: A 21-Year Follow-up Study--The Cardiovascular Risk in Young Finns Study Pediatrics, October 1, 2006; 118(4): 1553 - 1559. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. J Prynne, G. D Mishra, M. A O'Connell, G. Muniz, M A. Laskey, L. Yan, A. Prentice, and F. Ginty Fruit and vegetable intakes and bone mineral status: a cross sectional study in 5 age and sex cohorts. Am. J. Clinical Nutrition, June 1, 2006; 83(6): 1420 - 1428. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Endocrinology | Endocrine Reviews | J. Clin. End. & Metab. |
| Molecular Endocrinology | Recent Prog. Horm. Res. | All Endocrine Journals |
0.05; **, P