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Self-Reported Calcium Intake and Bone Mineral Content in Children and Adolescents

College of Pharmacy and Nutrition (L.M.C., S.J.W., G.A.Z.), University of Saskatchewan, Saskatoon, CANADA
College of Kinesiology (D.T.D., R.A.F., D.A.B.), University of Saskatchewan, Saskatoon, CANADA
Department of Human Movement Studies, University of Queensland, Brisbane, AUSTRALIA (D.A.B.)

Address correspondence to: S.J. Whiting, Ph.D., College of Pharmacy and Nutrition, University of Saskatchewan, 110 Science Place, Saskatoon, SK, S7N 5C9, CANADA. E-mail: Susan.whiting{at}usask.ca.

	ABSTRACT

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Objective: We examined the relationship between self-reported calcium (Ca) intake and bone mineral content (BMC) in children and adolescents. We hypothesized that an expression of Ca adjusted for energy intake (EI), i.e., Ca density, would be a better predictor of BMC than unadjusted Ca because of underreporting of EI.

Methods: Data were obtained on dietary intakes (repeated 24-hour recalls) and BMC (by DEXA) in a cross-section of 227 children aged 8 to 17 years. Bivariate and multivariate analyses were used to examine the relationship between Ca, Ca density, and the dependent variables total body BMC and lumbar spine BMC. Covariates included were height, weight, bone area, maturity age, activity score and EI.

Results: Reported EI compared to estimated basal metabolic rate suggested underreporting of EI. Total body and lumbar spine BMC were significantly associated with EI, but not Ca or Ca density, in bivariate analyses. After controlling for size and maturity, multiple linear regression analysis revealed unadjusted Ca to be a predictor of BMC in males in the total body (p = 0.08) and lumbar spine (p = 0.01). Unadjusted Ca was not a predictor of BMC at either site in females. Ca density was not a better predictor of BMC at either site in males or females.

Conclusions: The relationship observed in male adolescents in this study between Ca intake and BMC is similar to that seen in clinical trials. Ca density did not enable us to see a relationship between Ca intake and BMC in females, which may reflect systematic reporting errors or that diet is not a limiting factor in this group of healthy adolescents.

Key words: dietary assessment, underreporting, calcium, bone mineral, dual-energy X-ray absorptiometry (DEXA), children, adolescents

Abbreviations: BA = bone area • BMC = bone mineral content • BMD = bone mineral density • BMR = basal metabolic rate • DEXA = dual energy X-ray absorptiometry • EI = energy intake • EI:BMR_est = ratio of energy intake to estimated BMR • PAQ-C = physical activity questionnaire for children • PHV = peak height velocity • PI = ponderal index (kg/m³)

	INTRODUCTION

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Osteoporosis is a significant cause of morbidity in older adults [1]. Failure to gain optimal bone density during adolescence and young adulthood contributes to low bone density, a risk factor for osteoporosis [2]. Efforts to prevent osteoporosis should therefore focus on young people in their growing years. Calcium (Ca) intake may be an important modifiable factor related to attainment of peak bone mass [3].

Overall, randomized controlled trials have shown a modest but positive effect of Ca supplementation on bone mineral accretion in children, with Ca supplementation resulting in one to five percent greater gains in bone mineral density (BMD) over controls [4–9]. Despite the relationship between Ca and BMD shown in clinical trials, results from observational studies have often been negative [10,11]. One reason for this discrepancy is the reliance of observational studies on self-reported dietary intake, which has many levels of measurement error, while in clinical trials the Ca intake of the experimental group differs from the controls by a known amount [12].

One major source of error in self-reported dietary intake is underreporting of energy intake (EI), the tendency to underestimate the amount of food actually consumed. Underreporting has been documented in children and adolescents [13–15], as well as in adults [16–18]; the extent of under-reporting appears to vary considerably among individuals [13,15,18]. Therefore it has been suggested that all studies using self-reported dietary intake should attempt to assess the validity of the intake [19]. When no measurement of energy expenditure has been performed, heights and weights of subjects can be used to estimate basal metabolic rate (BMR_est), which can then be compared to reported EI in a ratio of EI:BMR_est [20]. Since a low EI is accompanied by lower intakes of most or all nutrients, several studies have attempted to use different types of energy-adjusted nutrient intake values to adjust for the confounding effect of underreporting [21–22]. Unfortunately, these studies did not reach a clear conclusion regarding the best method of energy adjustment to use. One type of energy-adjusted expression of intake, nutrient density, is defined as the amount consumed per unit of energy. Although often used as a measure of diet quality [23], nutrient density has also been used to correct for differences in reported EI, e.g. [21,24].

The primary objective of this study was to investigate the relationship between Ca intake and bone mineral content (BMC) in a group of children and adolescents, following the method proposed by Prentice et al. [25]. We hypothesized that an energy-adjusted expression of Ca intake such as Ca density would be a better predictor of bone mass than unadjusted Ca intake because of the likelihood that variable underreporting occurs in self reports of dietary intake.

	METHODS

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Subjects
Subjects were participants in the University of Saskatchewan Pediatric Bone Mineral Accrual Study, a mixed longitudinal study which recruited children in grades three to eight from two elementary schools in Saskatoon in 1991. Of the eligible 375 students, 234 students (110 boys and 124 girls) and their parents provided written consent for their children to participate in the study. A number of additional children joined the study in subsequent years. The majority of subjects were Caucasian and from middle socioeconomic neighborhoods. The study protocol, which involved collection of anthropometric and physical activity data every six months, dietary data two to four times per year and annual DEXA scans, has been outlined previously [26]. The sample used in this analysis comprised all subjects (n = 227) who had bone measurements in the fall of 1993.

Bone Mineral Assessment
Bone scans were performed during the period of October to December 1993 in the Department of Nuclear Medicine, Royal University Hospital, Saskatoon. Subjects wore t-shirts and loose-fitting shorts during measurement, with shoes and metal objects such as jewelry removed. Dual energy X-ray absorptiometry (DEXA) using the Hologic 2000 QDR (Hologic, Waltham MA) in the array mode was used to measure BMC of the total body and anterior-posterior lumbar spine between L1–L4, as well as bone area at these sites. Total body scans were analyzed using software version 5.67A while software version 4.66A was used to analyze lumbar spine scans. All scans were analyzed by the same qualified individual. In our laboratory, short-term in vivo precision (expressed as coefficient of variation) was found to be 0.6%.

Dietary Assessment
Dietary assessment was performed using two to four 24-hour recalls; one administered at the time (October to December) of bone scans and the others in a school setting within one year of the bone scan. All days of the week except Friday and Saturday were recalled, and the recalls covered more than one season. Initially, subjects received a 20-minute training session on food portion sizes which was reviewed prior to each session. Boards containing life-size pictures (National Dairy Council, 1990, Rosemont II) of a wide assortment of food items were displayed at each recall. All subjects completed their own forms, except for those in grades three and four who recited their answers to study personnel. Undergraduate and graduate nutrition students were present at the classroom visits and trained study personnel were present during the hospital visits to review the procedure with subjects and offer assistance. The recall form included a question asking if the subject took a "vitamin pill" during the recall period and, if so, what type or brand. If a subject was deemed to be a consistent user of supplements [27], the amount of Ca contained in the supplement was added to their values of Ca intake.

Food intake was analyzed using the Nutritional Assessment Systems (NUTS) program, version 3.7 (Quilchena Consulting Limited, Victoria BC) which used the 1988 Canadian Nutrient File and provided imputed values for nutrients that were missing from the Canadian Nutrient File. Recalls were coded by nutrition students with the same individual checking all forms. Average daily intake values for each subject were determined for the same time period as the bone scans.

To adjust for differences in reported EI, Ca from food was divided by EI to give Ca density, expressed as mg of Ca per MJ.

Anthropometric, Physical Activity and Maturity Assessments
Anthropometric measurements including height and weight were made every six months by trained study personnel. Subjects wore t-shirts and loose-fitting shorts during measurement, with shoes and jewelry removed. Height was measured twice as stretch stature using a wall stadiometer and recorded to the nearest 0.1 centimetre. Weight was measured twice on a calibrated electronic scale and recorded to the nearest 0.1 kilogram. Height and weight measurements used here were obtained on the same day as the DEXA scan.

Ponderal index (PI, kg/m³) was chosen as a measure of adiposity in our sample because it was uncorrelated with height (r = -0.02, p = 0.73) and has been used for children by other researchers [28]. Schofield equations [29] using height and weight were used to estimate each subject’s BMR.

The Physical Activity Questionnaire for Older Children (PAQ-C) was used to assess general levels of physical activity of subjects in the study [26]. Subjects were asked to rate their physical activity level during their spare time in the previous seven days, resulting in a rating from one to five, with higher scores suggesting higher levels of activity. For high school students, the PAQ-C was modified by omitting one item regarding activity at recess. The PAQ-C was found to have significant correlations with other measures of physical activity in children from grades four to eight [30], as well as in high school students [31]. For the current analysis, the average PAQ-C score for tests administered during 1993 (n = 2) was used.

Maturity age is defined as the age offset to age of peak height velocity (PHV) and was used as a measure of developmental age to control for maturational differences among subjects. Age of PHV was determined by entering twice-annual height measurements into GraphPad PRISM Version 2.0 (GraphPad Software, Inc., San Diego, CA) and fitting them with a cubic spline curve. Only subjects for whom a height measurement was obtained from both before and after PHV were entered into analyses which included maturity age (n = 144). Thus subjects who achieved PHV prior to the beginning of the study in 1991 or who had still not achieved PHV by 1997 were excluded from analyses including maturity age. Age of PHV was subtracted from the age of the subject at the time of bone scans to obtain an age offset to age of PHV. Because the maturity age distribution had negative values, a constant was added to make all values positive for inclusion in a multiple linear regression model.

Data Analysis
A cross-section of data from the Pediatric Bone Mineral Accrual Study was used in this analysis. Values were expressed as mean ± standard deviation (SD). Student’s t test (two-tailed) was used to compare characteristics between males and females. The association between underreporting and characteristics including age, gender, ponderal index and activity score was determined using chi-square analysis. Precision estimates for the number of days of recalls for an 80% accuracy in dietary intakes were determined on a subset of subjects having completed six recalls in the first two years of the longitudinal study, using the method of Miller et al. [32]. Statistical tests were considered significant if the p-value was 0.05 or less, but all p-values below 0.10 were reported. Pearson’s r was used to determine the crude association between BMC (total body and lumbar spine) and the variables BA (total body or lumbar spine, respectively), height, weight, activity score, maturity age, EI, Ca intake and Ca density.

The relationship between Ca and the dependent variables total body BMC and lumbar spine BMC was investigated using multiple linear regression. Total body BA, weight and height were included in all multiple linear regression models of total body BMC to correct for body size, following the recommendation of Prentice et al. [25]. The other dependent variables examined were maturity age, activity score, EI, Ca intake and Ca density. Likewise, all models of lumbar spine BMC originally included lumbar spine BA, height and weight to control for size. The other covariates were the same as for total body BMC. A backwards elimination procedure was used to determine the final model for each dependent variable, whereby all independent variables were included in the model at first and then eliminated one by one. At each step, the independent variable with the highest p-value was eliminated until all remaining independent variables were significant. Variables were transformed logarithmically for inclusion in the models of BMC. Statistical analysis was performed using SPSS (Statistical Package for the Social Sciences) version 7.5 for Windows 95 (SPSS Inc., Chicago IL).

	RESULTS

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Characteristics of the 227 subjects who had DEXA measurements for the specified period are presented in Table 1. The mean age was approximately 13 years. Compared to females, the males tended to be taller, have lower ponderal index and have higher EI, EI:BMR_est, Ca intake, activity score, total body BMC, total body BA and lumbar spine BA. EI and Ca intake were positively correlated in both males (r = 0.67, p < 0.01) and females (r = 0.55, p < 0.01) (data not shown). We determined crude associations of subject characteristics with underreporting status (data not shown), in which subjects were considered underreporters if they were in the lowest overall quartile (males and females combined) of EI:BMR_est (0.99). Gender, age and PI were all significantly related to underreporting by chi-square analysis. Females, older children and children with higher PI were all more likely to underreport their EI compared to males, younger children and children with low PI, respectively. An interaction between gender and age was noted after stratifying the subjects by gender, whereby among females, increased age led to increased odds of underreporting while no age relationship was observed for males (Table 2). Precision estimates of the number of days of recall for an 80% accuracy in dietary intakes revealed that girls required fewer days than boys at every age tested.

	DISCUSSION

Follow-up studies from several clinical trials of Ca and bone have reported that bone mineral differences between subjects and controls disappeared 18 to 36 months after Ca supplementation ceased [5,6,33]. However, measurements performed 12 months after the end of a Ca supplementation trial in girls still showed a significant difference in mean BMD [8]. More research is needed to determine whether a persistently high intake of Ca results in higher peak bone mass.

This study is one of few to show a relationship between Ca and bone mineral in boys. Of five randomized controlled trials which have been conducted, three involved female subjects only [7–9] while the remaining two [4–5] included both males and females. As well, few observational studies have shown this association. A recent cross-sectional study which examined total body bone mineral in Swedish adolescents found that BMC was correlated with Ca intake in bivariate analyses in boys only; however, Ca failed to be a significant predictor of BMC in multivariate analyses in either gender after adjustment for size [34]. Another study examined the association between height, weight, pubertal stage, Ca intake, physical activity and BMD in 500 males and females aged 4 to 20 years. Again, in males there was a significant positive correlation between Ca intake and total body BMD, but Ca was not a significant predictor of BMD in either gender in their linear regression models [11].

Our results also confirm the importance of adjusting for size-related variables in multivariate analyses of BMC before examining the effect of independent variables such as dietary intake, as recommended by Prentice et al. [25]. As expected, in our models of total body BMC, the size variables BA, height and weight together explained 97% of the variability in BMC in both genders (Table 4). The low additional variability explained by Ca intake does not imply lack of association, but rather stems from the use of BMC, rather than BMD, as the dependent variable. Despite this apparent disadvantage, the use of BMC as the dependent variable is preferred over BMD because of BMD’s positive correlation with size, which may lead to spurious associations with size-related variables (e.g., Ca intake) in multivariate models [25].

Other covariates considered in the multiple linear regression models of total body BMC and lumbar spine BMC were maturity age, activity score and EI. Maturity age, measured as the age offset to age of peak height velocity, was used because pubertal stage has been shown to be a significant predictor of bone mass [35–37]. Maturity age was not found to be a significant predictor of BMC, likely due to its providing information about both age and maturity status of the subjects and its being highly correlated to other size variables such as height and weight which were retained in the model (data not shown). Weight-bearing physical activity is believed to be an important factor in the attainment of peak bone mass [2,10]. In the current study, activity score was found to be a significant predictor of lumbar spine BMC in females only. EI was also included as a covariate to ensure that if a significant effect of Ca was found on bone, it was not merely reflecting an overall high EI or high intake of other vitamins or minerals. EI was not found to be a significant predictor of BMC.

A relationship between Ca intake and BMC was observed in males despite the use of self-reported dietary recalls, which are subject to underreporting of EI. To assess the validity of self-reported EI in our study, a ratio of EI to estimated basal metabolic rate (EI:BMR_est) was calculated [20] using heights and weights to estimate BMR [29]. This approach revealed a mean EI:BMR_est of 1.33 which indicated underreporting [38]. Females had a lower mean EI:BMR_est than the males (Table 1), suggesting they underreported to a greater extent. As shown in Table 2, older girls underreported more than younger girls, while for boys, no age effect was noted.

To attempt to control for the confounding effect of underreporting on the analysis of Ca and bone, we expressed Ca in two ways: unadjusted Ca intake (mg/day), and Ca adjusted for EI, Ca density (mg/MJ). The aim of energy adjustment is to obtain an expression of intake which is not correlated with EI. We used Ca density, rather than energy-adjusted intake using residuals [39–40], because it is a simpler expression and because it was not significantly correlated with EI in our data set (R = -0.07, p > 0.05). However, Ca density was not a better predictor of BMC than unadjusted Ca in this group. Unadjusted Ca intake was a predictor of total body BMC (p = 0.08) and lumbar spine BMC (p = 0.01) in males, while no relationship between either unadjusted Ca intake or Ca density and BMC was observed in females (Tables 4 and 5). Ca density was not a significant predictor of total body BMC in males, but was significant in the final model of lumbar spine BMC in males when substituted for unadjusted Ca intake (p = 0.03).

The use of a nutrient density value to adjust for underreporting of EI relies on the assumption that the foods not reported have the same composition as the foods reported. This would be true if the errors were random; however, systematic omissions of certain types of food such as snacks or high-fat foods may occur [41]. The lack of relationship between Ca intake and BMC in females, even when Ca intake was expressed as Ca density, may be a result of systematic reporting errors. In males, unadjusted Ca intake may have became somewhat "adjusted" merely by its inclusion in the multivariate model after inclusion of size-related variables, since EI is related to size. Further research is needed on how to control for errors in self-reported dietary intakes.

A lack of significant association between Ca intake and BMC in females may also reflect the difficulty in determining nutrient intake with precision in this group. In agreement with Miller et al. [32], we found that girls required fewer days than boys, suggesting that a difference in precision was not a factor. However, we did not attain the number of recalls necessary for estimating calcium intake with desirable precision at every age. Thus, our study suggests that a lack of significant association between Ca intake and BMC in females may reflect one or a combination of systematic reporting errors by this group, a diet that is not a limiting factor in this group of healthy adolescents and inadequate precision in estimating calcium intake.

	ACKNOWLEDGMENTS

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This work was supported in part by a grant from the National Health Research and Development Program, Health Canada.

Received April 18, 2000. Accepted April 30, 2001.

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