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Journal of the American College of Nutrition, Vol. 17, No. 2, 136-147 (1998)
Published by the American College of Nutrition

Original Paper

Relation of Nutrition, Body Composition and Physical Activity to Skeletal Development: A Cross-Sectional Study in Preadolescent Females

Jasminka Z. Ilich, PhD, RD1, Mario Skugor, MD1, Thomas Hangartner, PhD2, An Baosh, PhD1 and Velimir Matkovic, MD, PhD1

Bone and Mineral Metabolism Laboratory (J.Z.I., M.S., A.B., V.M.), Dayton, Ohio
Department of Physical Medicine and Rehabilitation, Medicine and Nutrition and Department of Statistics, The Ohio State University, Columbus; and Wright State University (T.H.), Dayton, Ohio

Address reprint requests to: J.Z. Ilich, PhD, RD, Bone and Mineral Metabolism Laboratory, Davis Medical Research Center, The Ohio State University, 480 West 9th Ave., Columbus, OH 43210


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
Objective: To examine the relation of anthropometric and growth parameters (weight, stature, body composition, age, and skeletal age), nutritional factors, and physical activity to the total body and radius bone mineral density and content and radiogrammetry parameters of the second metacarpal.

Study design: The study was a cross-sectional evaluation of 456 healthy, Caucasian girls, ages 8 to 13 years. Multiple regression models were created based on Cp statistics to determine the association between bone parameters and various independent variables.

Results: Mean calcium intake was 956±381 mg/day, about 20% below the RDA of 1200 mg/day and about 36% below the threshold intake of approximately 1500 mg/day. The most significant predictors for total body and radius bone mineral density were corresponding bone areas, lean body mass, body fat, skeletal age, dietary calcium, and stature (only for total body) with corresponding R2(adjusted) of 48% and 36%. The total body and radius bone mineral content was positively associated with corresponding bone areas, lean body mass, body fat, calcium intake, and skeletal age with corresponding R2(adjusted) of 86% and 72%. Energy expenditure (corrected for BMI) was stratified into quartiles and bone mass parameters were distributed accordingly. A statistically significant difference in total body and radius bone mineral density and content was noted between the fourth and lower quartiles (ANOVA, p<0.05 to p<0.0001).

Conclusion: The most significant predictors of bone mass in preadolescent females evaluated in this study are bone area, lean body mass, body fat, skeletal age and dietary calcium.

Key words: bone mass, body composition, activity, nutrition, calcium, preadolescent females

Abbreviations: BMD = bone mineral density • BMC = bone mineral content • BMI =body mass index • CA = cortical area of second metacarpal • L = length of second metacarpal • MA = medullary area of second metacarpal • RDA = recommended dietary allowances • TA = total area of second metacarpal • TEE = total energy expenditure


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
Despite the considerable hereditary influence on bone mass, the environmental factors, namely nutrition and physical exercise, play an important role as well. Among the nutritional factors, calcium (Ca) and its effects on bones receives most attention. Inadequate Ca intake in childhood and adolescence might alter peak bone mass with adverse implications later in life [1]. There is continuous evidence that a substantial proportion of women of all ages in the United States consume lower amounts of Ca than the current Recommended Dietary Allowances (RDA) [2] and that intake declines even more in the adolescent period, the time when it is needed the most [3–6]. This issue receives even more attention when considering the newest Dietary Reference Intakes (DRI) with adequate intake for Ca of 1300 mg/day for teenagers [7] or recommendations of 1500 mg Ca/day for that age group, proposed by the National Institutes of Health Consensus Conference Panel on Optimal Ca Intake [8].

Based on the numerous studies in adults showing the positive effect of exercise on bone mass [9–12], it is reasonable to assume that a higher activity level would have beneficial effects on the growing bones of children and adolescents. Studies evaluating the level of activity and bone mineral density in children and adolescents however, are scarce and not always in agreement [13–16]. This could be due to the fact that children and adolescents are involved in various activities which are hard to assess and quantify and which might be a part of the normal growing processes, and thus making it harder to discriminate their effects on bones [17].

The objective of this study was to assess the skeletal, and anthropometric status, dietary intake, and total energy expenditure, in a large population of preadolescent females and identify the biological parameters and potential lifestyle factors which might be related to bone mass. Specifically, we evaluated bone mineral density and content of the whole body and radius, as well as the radiogrametric measurements of the second metacarpal with regards to: 1) growth parameters such as weight, stature, chronological age and skeletal age; 2) lean body mass and body fat; 3) total dietary intake with special emphasis on calcium; and 4) daily energy expenditure and activity patterns. The number of measured variables and extent to which they were evaluated in this large population of preadolescent females makes this study one of the most comprehensive and complete cross-sectional studies in this area.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
Subjects
The subjects are participants of the 7-year longitudinal project designed to assess the effect of Ca supplementation on bone mass acquisition throughout the puberty and skeletal formation. The data presented are from the 456 participants, recorded at the time of enrollment. All subjects were healthy, Caucasian females ages 8 to 13 years, premenarcheal, in pubertal stage 2, and free of any systemic disease or treatment known to affect bone mineralization process. The group comprised of the girls of various economic status, recruited from about 20 school districts in five counties of Central Ohio.

In the recruitment process, we worked closely with school districts’ officials and obtained their approval and letters of support for the study. Initially we contacted about 15,000 families of potentially qualifying girls (Caucasians, in grades 3 through 7 in elementary/middle schools) with letters explaining the purpose of the planned study and received about 2,000 (approximately 13%) responses with the agreement for participation in the study. Out of these, we selected 456 girls who satisfied the inclusion criteria—general health, pubertal developmental stage 2 for either breast or pubic hair development, premenarcheal, and either low or high calcium intake (below the RDA of 1200 mg/day or above the threshold of approximately 1500 mg/day) assessed by the Food Frequency Questionnaire [18]. The reason for selecting subjects with either low or high Ca intake was due to the overall design of the planned study (longitudinal, double-blind, placebo controlled clinical trial). The girls (n=354) with lower Ca intake were supplemented with Ca/placebo tablets. From the rest of the girls who qualified for the study we selected 102 whose Ca intake was above 1500 mg/day (Ca intake threshold) [19]. These girls with habitual high-dairy intake could not be supplemented with Ca/placebo tablets, but were kept as a separate, non-interventional group and a natural counterpart to the supplemented group.

The study protocol was approved by the Human Subjects Review Committee at The Ohio State University, and the Subject Informed Consent was signed by parents or guardians.

Bone Densitometry, Body Composition, and Radiogrammetry
The total body and forearm (nondominant arm) bone measurements were performed using the dual X-ray absorptiometry technique (DXA), on Lunar DPX-L densitometer (Lunar Corp., Madison, WI), as described earlier [20,21]. The total body measurement yields the analysis of body fat and lean body mass, with the coefficients of variation in our laboratory of 2.62% and 1.11%, respectively. The forearm measurements presented refer to the radius at 33% distance from styloid process. The measurements were expressed as bone mineral content (BMC) in g, areal bone mineral density (BMD) in g/cm2, and bone area in cm2. The coefficients of variation for different skeletal sites regarding the long and short-term stability of the instrument were described previously [20,21]. The radiogrammetric assessment was performed on the second metacarpal bone from the hand-wrist radiographs, as described previously [21].

Growth Parameters
Stature, recorded on a wall mounted stadiometer and weight, recorded on a balance scale, were measured without shoes, in light clothing. Body mass index (BMI) was calculated by dividing weight (kg) with squared stature (m2), and was used to correct for adiposity or overweight when dealing with the energy expenditure. None of the participants were excluded due to a high (above 95 percentile) or low (below 5 percentile) BMI. The pubertal stage was self determined by the subjects using the drawings and written descriptions of pubertal stages of breast and pubic hair development on the scale from 1 to 5 for breast and from 1 to 6 for pubic hair development (1 presenting no development, 5 and 6 presenting full maturation) [22]. The subjects selected corresponding figures which most accurately reflected their own appearance. This method was found to correlate well with the physician’s exam and assessment (r=0.81 and 0.91 for breast and pubic hair, respectively) [23]. Skeletal age (SA) in years was assessed by the FELS method using the hand X-ray of the non-dominant arm [21,24].

Dietary Intake Assessment
The initial screening tool for the assessment of Ca intake was a food frequency questionnaire developed in our laboratory and validated against 3-day dietary records [18]. The questionnaire included 52 food items defined as major contributors of Ca due to the frequency of consumption or the amount of Ca contained and was geared toward children’s selection of food items (Fig 1). Upon entering the study, subjects completed a 3-day dietary record including 2 weekdays and 1 weekend day and all the dietary analyses presented here are based on this assessment. The nutrient intake from the record was analyzed with a nutritional software package, Nutritionist III®, version 8.5 for Macintosh (N-Squared Computing, The Hearst Corp., San Bruno, CA), and the mean daily intake for each nutrient was calculated. Dietary analyses included the assessment of average caloric intake (kJ/day), protein, carbohydrate, dietary fiber, total fat, cholesterol, sodium, caffeine, calcium, phosphorus, magnesium, zinc, iron, fluoride, and vitamins A, D and C. None of the girls was taking vitamin or mineral supplements at the time of recruitment. The Ca/phosphorus and Ca/protein ratios were calculated, as well as the Ca density with respect to caloric intake (mg Ca/kJ), and used to correct for the different amounts of food consumed between bigger and smaller children.





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  		Fig. 1. Questionnaire to evaluate the nutritional dietary intake of students.

 
Energy Expenditure Assessment
We assessed the total energy expenditure (TEE) throughout two 24-hour periods, including resting and sleeping, as well as any other habitual activity and sport. The energy expenditure was assessed using the method given by Bouchard et al [25], by recording the activity in 15-minute intervals within 2 days (1 week and 1 weekend day). Each 15-minute interval was quantified in terms of metabolic equivalents (METS) on a scale from 1 to 9, sleeping being categorized as 1 and very strenuous exercise as 9. The total energy expenditure in kJ per day per kg body weight was then calculated for each subject using the appropriate METS depending on the categorical value of the recorded activity. Since weight was included in calculation, heavier subjects ended up with the higher energy expenditure, which was not necessarily a result of higher activity. Thus, the derived TEE was corrected for BMI for each subject to account for overweight and adiposity.

Each participating girl (and her parents) was instructed on an individual basis by the registered dietitian on how to complete the dietary and activity records. Measuring cups and food models were used in the instructions, and each subject was given food and activity diaries with written instructions and a measuring cup for use at home. Any uncertainties were clarified through telephone calls.

Data Analysis and Calculation
All data are presented as mean ± standard deviation, and were analyzed using statistical packages Data Desk Professional, version 4.1 (Odesta Corp. Northbrook, IL), Statistica/Mac (Stat Soft Inc. Tulsa, OK), and Statistical Analysis System (SAS) (SAS Institute Inc., Cary, NC). Subjects were divided into quartiles based on several parameters (Ca intake, energy intake, energy expenditure) and bone measurements were then compared between the lowest and highest quartiles, using two sample t-test and analyses of variance (ANOVA), with levels of significance set at p<=0.05.

Multiple regression models were created to evaluate the influence of growth and dietary/activity parameters on bone mineral density and content and coefficients of determination (R2) were calculated. Diagnostics and residual plots for these models were analyzed and heteroskedascisity and non-normality were not detected. The Cp statistics was used to screen all possible regression models and find the one with the least bias and best prediction for each bone variable. The Cp statistics accounts for the biases introduced in the estimates of the dependent variable, by leaving the less predictive parameters out of the regression model. Based on the Cp statistics selection, it is possible to develop a regression model with the highest predictive ability and the least bias. Usually, the model with the lowest Cp value, the value also being close to the number of independent variables chosen, plus the intercept of the regression, is the one with the least bias and the best predictability. The bone mineral density and bone mineral content measurements of the total body and forearm and radiogrammetry parameters were treated as dependent variables. The appropriate bone area was forced into each model to correct for the bone size [26]. To examine the functional relationship between bone parameters and each independent variable controlling for other variables in the model, the "added variable plots" were created [27]. For example, the residuals from regression of total body BMD on all the predictors in this model except one (e.g., skeletal age), were plotted against residuals from regression of that one variable on these same predictors. This allowed us to inspect the form of the relationship between dependent and each of the independent variables in the models, and select the best functional form.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 CONCLUSION
 REFERENCES
 
The descriptive characteristics of subjects are presented in Table 1. The mean skeletal age was about half a year higher than the mean chronological age, implying that the actual skeletal maturity of our subjects was somewhat higher than their chronological age, as described in detail earlier [21]. Mean values for stature, weight and BMI were in the normal range for the girls of that age group and slightly above the 50th percentile of nationally reported values [28,29]. Several high values of BMI (over 30 kg/m2) indicated the presence of obesity in few girls.


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  		Table 1. Descriptive Characteristics and Bone and Soft Tissue Measurements of Subjects (Mean±SD)

 
The values for total body and radial bone mineral measurements were reasonably comparable to the measurements from other research centers with similar populations and methods [30,31] and were normally distributed.

The energy and nutrient intakes with corresponding Recommended Dietary Allowance (RDA) [2] for this age group are presented in Table 2. The average caloric intake of about 8240 kJ/day was slightly below (2 to 10%) the RDA of 8370 kJ/day. The mean total energy expenditure of about 7560 kJ/day, assessed from the 2-day activity records, was below the energy intake, as characteristic for the growing period and positive energy balance. Mean protein intake was about 15% of total energy intake but almost double in regard to the RDA (1 g of protein/kg body weight). Fat and carbohydrate intakes of 34% and 53%, respectively, of the total energy intake, and cholesterol of 200 mg/day, were in accordance with the dietary guidelines, while the dietary fiber intake was lower than the dietary goal (for adults) of 22 g/day or more. Mean dietary zinc and vitamin D intakes were below the RDA by 24% and 45%, respectively, while the assessed dietary fluoride was below the estimated safe and adequate intake. With the exception of Ca, all other nutrients were at or above the RDA level (Table 2).


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  		Table 2. Total Daily Energy Intake, Expenditure and Nutrients (Mean±SD)

 
Since our subjects ranged in age from 8 to 13 years, we examined Ca intake with regard to RDA in younger group (228 girls from 8.34 to 10.99 years; RDA 800 mg/day) and in older group (164 girls from 11.00 to 13.18 years; RDA 1200 mg/day). The mean Ca intake of the whole population was 956±381 mg/day, while the mean intakes of the younger and older groups were 943±339 and 974±433 mg/day, respectively. There was no statistical difference between the groups in energy and nutrient intake (two-sample t-test). The groups differed however, in anthropometric parameters, bone measurements, as well as energy expenditure (p<0.001). The comparison of the mean intakes with corresponding RDA’s and Ca threshold intake (the level of Ca below which skeletal Ca accumulation is a function of intake and above which skeletal saturation occurs. It is estimated to be 1480 mg/day for the young females) [19] is given in Fig 2. The younger girls were about 18% above the RDA of 800 mg/day, but about 36% below the threshold intake of 1480 mg/day. The older girls did not meet the RDA of 1200 mg/day, for their age group. They were about 19% below the RDA and about 34% below the threshold intake. The ratios, Ca/protein (mg/g), Ca/phosphorus (mg/mg), and Ca/caloric intake (Ca/kJ) were 13.1, 0.8, and 0.1, respectively. The Ca/phosphorus ratio of 0.8 was somewhat lower than the recommended ratio of one-to-one. It is possible that this ratio was even lower due to the actual higher consumption of phosphorus with processed foods. This phosphorus is not accounted for in data bases for nutritional calculation which could lead to the underestimation of phosphorus intake by about 25% [32]. There was a strong correlation between Ca and protein (r=0.574), Ca and phosphorus (r=0.875), as well as protein and phosphorus (r=0.742).



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  		Fig. 2. Calcium (Ca) intake (mg/day) of the population divided in groups according to age, in comparison to corresponding Recommended Dietary Allowances (RDA) and Ca threshold level.

 
When SA, BMI, and total fat tissue were stratified by the quartiles of Ca intake, there was no statistically significant difference between lower and higher quartiles implying that dietary Ca did not contribute to any of these variables. However, stature was significantly higher in the fourth quartile than in each of the first three (two-sample t-test: p=0.0025 for 1 and 4, p=0.0430 for 2 and 4, p=0.0470 for 3 and 4), lean body mass in the fourth than in each of the first two (2-sample t-test: p=0.0043 for 1 and 4, p=0.0760 for 2 and 4, p=0.0574 for 3 and 4), and weight was higher in the fourth than in the second quartile (p=0.0393). To account for Ca density of food and its potential effect on stature, weight, and/or body composition (lean body mass and body fat), Ca/kJ was used as a dependent variable and regressed on each of these variables in single regression models. The resulting coefficients of determinations (R2 adjusted) for stature, weight, lean body mass, and body fat were low and statistically significant only for stature: 1.2% (p=0.0168).

The caloric intake and energy expenditure corrected for BMI were also stratified into quartiles. When dietary Ca intake was distributed among the quartiles of caloric intake, there was a statistically significant difference between the fourth and each of the lower quartiles (p<0.05). However, when the bone parameters as well as lean body mass and body fat were distributed by the quartiles of caloric intake, the values did not show statistical difference (data not presented). Measurements for lean body mass were significantly higher in the fourth quartile than in each of the first three (p<=0.0001), and measurements of body fat did not show statistical difference when distributed by quartiles of energy expenditure.

The mean±SD values of total body and radial bone parameters stratified by quartiles of the BMI-corrected total energy expenditure are presented in Table 3. Total body and radius BMC measurements of the subjects in the fourth quartile were significantly higher than in each of the first three quartiles, while the total body and radius BMD measurements were significantly higher in the fourth quartile than in each of the two lower ones. These results suggest that higher energy expenditure, indicating higher activity level, could lead to the higher bone mass values. Measurements for lean body mass were also significantly higher in the fourth quartile than in each of the first three (ANOVA, p<=0.0001), while the measurements of fat mass did not show statistically significant difference when distributed by quartiles of energy expenditure (data not presented).


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  		Table 3. Total Body Bone Mineral Content and Density (BMC, BMD) and Radial Bone Mineral Content and Density Stratified by Quartiles of Body Mass Index (BMI)-Corrected Total Energy Expenditure (TEE) (n=387)

 
The multiple regression models were created with each bone variable: total body BMD and BMC, radius BMD and BMC, total area of second metacarpal (TA), cortical area of second metacarpal (CA—representing bone mass), ratio CA/TA (representing bone density), and length of second metacarpal (L) as dependent ones. The models were constructed based on the Cp criterion with all potential confounding variables. These included: growth parameters (chronological age, skeletal age, weight, stature), body composition parameters (lean body mass and body fat), total energy expenditure or TEE corrected for BMI, and the nutritional factors (energy intake, protein, Ca, phosphorus, magnesium, zinc, iron, iodine, fluoride, sodium and vitamins A, D and C, and Ca/protein, Ca/phosphorus, and Ca/kJ ratios). The appropriate bone area, either for total body or radius, was forced into each model to correct for the bone size. Pubertal stage was not entered as an independent variable, since all girls were in the same stage of sexual development (inclusion criterion). The "added variable plots" [27] were examined to find the best form of the functional relationship between dependent and independent variables in each of the models. Table 4 presents the resulting regression models selected by the Cp criterion for each bone variable.


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  		Table 4. Best Regression Models for Different Bone Variables as Dependent Ones, with Cp Values and Coefficients of Determination (R2 adjusted), and Independent Variables in Each Model with Corresponding Coefficients, t-Ratios, and p-Values

 
Total bone area, body fat, lean body mass, stature, skeletal age and dietary Ca were selected by Cp statistics as the most significant independent variables determining total body BMD with a contribution of 47.8%. The same variables, except stature, were selected as best determinants for radius BMD (radial bone area included) with a contribution of 35.9%. When phosphorus or protein was entered instead of Ca, the coefficients of determination and Cp value changed only slightly. The t-levels decreased while the p-values increased but still remained statistically significant (e.g., for total body BMD, p value of 0.0007 for Ca in the model changed to 0.0108 for phosphorus and to 0.0157 for protein, with all other variables the same). However, when Ca, phosphorus and/or protein where kept in the model at the same time, only Ca remained statistically significant. Total body and radius BMC were best determined by corresponding bone areas, body fat, lean body mass, Ca intake, and skeletal age with contribution of 85.9% and 71.8%, respectively. Again, replacing Ca with phosphorus or protein resulted in the slight change in coefficients of determination and increased p-value for each of the variables when compared with Ca, but when entered all together, only Ca remained statistically significant.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
CONCLUSION
 REFERENCES
 
Bone mineral status is influenced by exogenous (nutrition, lifestyle) and endogenous (heredity, hormones) factors. Although exogenous factors do not have such a strong influence as endogenous ones, they give the opportunity for modification and eventual improvement. The objective of this study was to determine exogenous factors acting on skeletal status of preadolescent females as well as to identify the biological parameters that are best associated with bone mass. Stature, weight, BMI, age, and pubertal stage have often been strongly associated and showed a high correlation with bone mineral density and/or bone mineral content of a whole body or a particular skeletal region of interest [15,16,33–39]. The results of this study, using simple regression models, are consistent with the above findings, showing strong association of all bone parameters with weight, stature, and/or BMI (data not presented). The age and pubertal stage of our population were of a narrow range, rendering their lower influence on bone variables than it was commonly found in literature with wider age range of subjects.

The strong correlation between bone and anthropometric parameters is probably due to the high collinearity of those parameters, since weight and stature intrinsically include a bone component, especially when whole body measurements are of concern. Thus, those parameters are used in multiple regression models to correct for the effect of body size on either BMC or BMD. However, this does not provide a full correction and might lead to collinearity with other variables related to size, such as dietary intake of nutrients and energy expenditure. Therefore, inclusion of bone area along with the anthropometric parameters as the predictor variables into the multiple regression models while assessing BMC and/or BMD of the participants in the cross-sectional studies is recommended as the better approach [26].

In our multiple regression models, body fat, lean body mass, skeletal age, and Ca intake (each in the appropriate functional form) repeatedly appeared as the most significant independent variables for predicting bone mineral content and density when controlled for bone area. Radiogrammetry parameters of second metacarpal also showed significant association with lean body mass and/or body fat. Weight, stature, BMI, and age, which initially were included in the models and showed high influence (all except age), lost their significance after the inclusion of lean and/or fat tissue and skeletal age. These results are in partial agreement with previously reported data [31,41,42]. These studies, as well as ours, are among the few to relate soft tissue measurements to the bone mineral status in children and young adults. Faulkner et al measured 234 boys and girls ages 8 to 16 years in order to provide normative data for total bone mineral content and density. They found that the best predictor for bone density of a whole body, in both boys and girls, was lean body mass. Body weight and fat mass did not contribute to any of the additional variance in their regression models [31]. Young et al found that both lean body mass and body fat were independent predictors of total body BMC, and only lean body mass for spinal and femoral BMD in over 200 female twins [42].

The average dietary intake of our group closely matched RDAs [2] for most of the nutrients (Table 2). As a group, participants had higher dietary protein and lower dietary fiber intake with regard to recommendations and dietary goals. However, high protein intake did not appear to increase urinary Ca excretion and by that indirectly compromise bone mass, as already reported for this population [43]. The total energy expenditure was slightly below the caloric intake, reflecting a positive energy balance characteristic of the growth period. The average caffeine intake of 18 mg/day (range 0 to 152 mg) was probably too low to notice any calciuric effect, as also reported previously [43]. The lower dietary zinc in our population was in agreement with other nationwide surveys of zinc intake for this age group [44,45]. Generally, dietary estimates of vitamin D and fluoride are not very reliable. The input from sunlight is not taken into consideration for the former. Fluoride content varies in different water supplies and its content from some beverages is not even taken into account in nutritional data bases. Nevertheless, since our participants were well instructed how to complete dietary records, we believe, that by eliminating subjective errors, we were able to obtain relatively good estimates for most of the nutrients, as well as the trends in intake for vitamin D and fluoride. In some cases the values for some nutrients appeared to be very low and we examined these records individually. For example in the case of vitamin C (minimum, 3 mg/day, Table 2), the diet contained mostly sweets, turkey sandwich, and pizza. Since this girl participated in the longitudinal study, we were able to examine and compare her records in the subsequent visits, and they revealed similar analyses.

Average calcium intake was slightly above the RDA of 800 mg/day for the younger girls (<= 10 years), but below the threshold intake of 1500 mg/day. The older girls (>= 11 years) did not meet the RDA of 1200 mg/day and were about 35% and 25% below the threshold intake [19] and DRI [7], respectively. This was in agreement with previous studies [3–6], showing that consumption of Ca declines among females at the preadolescent-adolescent period. There were several participants with very low Ca intake (in the range from 200 to 400 mg/day). These girls were not "milk drinkers," mostly because they did not like milk (a few might have had a mild lactose intolerance). In their subsequent visits, Ca intake remained low.

Ca was positively associated with stature, lean body mass and weight when those variables were distributed by the quartiles of Ca intake in the entire population. Ca density of food (mg Ca/kJ) had a positive effect on stature in a single regression model, although the biological significance of this association is questionable due to the low coefficient of determination (R2adjusted=1.2%, p=0.0168).

Numerous studies in athletes and sportists have shown the positive effects of training on bone mass of particular skeletal regions. Similar findings resulted from studies involving the effect of moderate weight bearing exercise on bone mass of ordinary people and even children [10–16]. McCulloch et al showed the influence of childhood physical exercise, but not childhood Ca intake on bone mineral density of oscalcis in young females [46]. The study of Welten et al examined a population of females and males from 13 to 28 years of age and found that only weight bearing activity and weight (not Ca intake) contributed to the lumbar spine bone mineral density during puberty and affected the peak bone mass [14]. Slemenda et al assessed the activity level and the involvement in different sports in a group of 59 monozygotic twin pairs aged 5 to 14 years. Their data suggest that different weight bearing activities are positively associated with the development of bone mass in several skeletal regions of interest, including the hip [13]. More recently, Ruiz et al and Boot et al showed positive effect of sports activities on spinal and femoral sites among pubertal girls [15] and group of boys ranging in age from 4 to 20 years [16].

We measured the total energy expenditure during the entire 24-hour period, including sleeping and resting as well as exercise, and associated it with the whole body and/or radius shaft bone mineral measurements. It is hard to accurately estimate the level of physical activity in children; certain level of approximation is probably always present. Since all children are more or less active, it is also hard to distinguish to what extent the increase in bone mass comes from the exercise itself and independently of a growing process. Nevertheless, we found significant positive association between bone mass parameters and energy expenditure when they were distributed among the quartiles of TEE/BMI.

The results of this study showed positive association between dietary Ca and bone mass parameters: BMC and BMD of the whole body and forearm, and total and medullary area of the second metacarpal. The influence of Ca remained significant in the multiple regression models even when bone area and other body composition parameters were included in the model. However, similar association (although with the lower level of significance) was found with protein and phosphorus, when each was entered in the models instead of Ca. This effect was probably due to the strong collinearity between Ca, phosphorus, and protein, all being present in the same food sources, as also reported from other centers [47]. Although protein and phosphorus are crucial for bone structure and development, they are rarely deficient in Western diets and thus not considered as critical. When the multiple regression models were presented with Ca, phosphorus and/or protein at the same time, only Ca was significantly associated with bone parameters.

Other studies examining the influence of dietary Ca on bone mass in young individuals showed different outcomes, depending on the designs and methodologies as well as on the sample sizes and statistical analyses. In many cases when bone parameters were controlled for weight, stature, age, pubertal stage, or soft tissue values, which all are highly related to skeletal mass, the importance of other potential predictors was greatly diminished and very often overlooked (probably due to the problem of collinearity). However, the general agreement leads toward the notion that dietary calcium intake at/or above the threshold level is necessary throughout the entire bone modeling and consolidation phase (from childhood to young adulthood) if genetically predetermined peak bone mass is expected to be reached [48].

Numerous cross-sectional studies [15,16,49–57] and several interventional clinical trials [58–65] conducted among children and adolescents indicated that higher calcium intakes have a positive effect on bone mass. Few cross-sectional and prospective/retrospective observational studies, which were not designed to look for the effect of calcium on bone and were characterized with small samples during particular developmental stage, did not show the effect of calcium on bone mass in children [15,34,38,42,66,67].

Rajalakshmi et al from India were the first to supplement young children with calcium carbonate in a 6-month clinical trial. Supplemented children in that study had thicker cortical bone of the second metacarpals and of the femurs than the controls [59]. This positive effect of Ca supplementation on bones was later confirmed in other clinical trials [51,60–65]. However, when the supplementation with Ca was terminated, the differences in bone mass between controlled and supplemented groups subsequently diminished or disappeared [68–70]. A 3-year clinical trial in monozygotic prepubertal twins living in the US showed that supplementation of one of the twin-pairs with calcium citrate-malate significantly increased bone mineral density of the forearm (5.1%), spine (2.8%), and proximal femur (3.2%), relative to the control twins [60]. The total calcium intake in this study averaged at about 1612 mg/day (including diet and supplementation adjusted for noncompliance). However, the follow-up study of bone mass measurements without calcium supplementation and on self-selected diets indicated that the difference in bone mass between the twin pairs diminished with time [68]. Similar situation occurred in the 4-year clinical trial with 500 mg/day Ca citrate malate supplementation in 112 premenarcheal females. The initial gain in bone mineral content, density and area of the total body and different regions of interest (spine, pelvis) in the supplemented group was not maintained 1 year after intervention [65,70]. The follow-up of the initial bone gain in lumbar spine and radius caused by 300 mg/day Ca carbonate supplementation for 18 months in 7-year old Chinese children reflected a similar pattern [64,69].

There are different explanations for this phenomenon. The one proposed by Heaney [71] could be due to the bone-remodeling transient with Ca supplement as the suppressor of bone turnover leading to an increase in measurable bone mass which then disappears after intervention is withdrawn. For the difference in bone mass to persist throughout puberty, high calcium intake in the intervention group should be maintained so that bone turnover, within the expanding periosteal envelope, will continue to be suppressed. In the mathematical simulation developed by Heaney [71] and based on the twin study [60,68] it is shown that the intervention could lead to a permanent gain in bone mass. Another reason for diminishing differences between placebo and calcium groups could be due to a possible change in the dietary patterns and increased intake of Ca by placebo group, as well as in a decrease in calcium intake in previously supplemented group. And finally, it is possible that some of the subjects in the above mentioned clinical trials were re-evaluated after already reaching skeletal maturity. In this case, Ca requirements decline rapidly, lowering the threshold from approximately 1,500 mg/day to approximately 1,000 mg/day [19]. This lower Ca requirement might parallel the habitual intake of these individuals which might then be enough to catch-up with the bone consolidation and attainment of peak bone mass. A definite answer to these speculations should come from the long-term clinical trial with Ca supplementation throughout the entire bone growth and skeletal consolidation phase: from prepubertal period up to the age of 18 years when most of the bone mass is accumulated and peak bone mass is reached for most of the skeletal regions of interest.


   CONCLUSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
REFERENCES
 
In conclusion, our study showed the highest influence of lean body mass, body fat, skeletal age, and dietary Ca on bone mineral density and content of the whole body and radius shaft as well as on the radiogrammetry parameters of second metacarpal. The level of physical activity expressed as the total energy expenditure was also positively related to bone mass parameters when they were distributed by the quartiles of BMI-corrected energy expenditure, but did not show a significant effect on bones in the multiple regression models.


   ACKNOWLEDGMENTS
 
This work was supported in part by research grants: NIH RO1 AR40736, NIH-GCRC MOI-RR00034, NRICGP/USDA-37200-7586

The authors want to express their appreciation and thanks to all 456 young girls and their families, the participants of the research, without whose cooperation and loyal participation this study would not be possible. Each one of them was special and each family was unique in the way of expressing the interest for the project and helping it to go smoothly.

Received April 1, 1997. Accepted August 1, 1997.


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 DISCUSSION
 CONCLUSION
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