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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2005-0636
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The Journal of Clinical Endocrinology & Metabolism Vol. 90, No. 9 5118-5120
Copyright © 2005 by The Endocrine Society

Calcium Absorptive Efficiency Is Positively Related to Body Size

M. Janet Barger-Lux and Robert P. Heaney

Creighton University, Omaha, Nebraska 68131

Address all correspondence and requests for reprints to: Robert P. Heaney, M.D., Creighton University, 601 North 30th Street, Suite 4841, Omaha, Nebraska 68131. E-mail: rheaney{at}creighton.edu.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Background: Calcium absorption efficiency is a more important determinant of calcium balance than calcium intake itself. The sources of variability in absorptive performance are only partly elucidated.

Purpose: The aim of the study was to explore the relationship between body size and calcium absorption efficiency.

Design and Setting: Metabolic studies were performed on an inpatient metabolic unit in an academic health sciences center.

Subjects: One hundred seventy-eight women, with an average age of 50.2 yr, were studied from one to five times and yielded an aggregate data set containing 633 individual studies.

Methods: Calcium absorption fraction was measured by the dual-tracer method. Observed values were expressed as residuals from predicted values for each woman’s actual calcium intake, using the previously published relationship between intake and absorption.

Results: Absorption residuals were significantly positively correlated with height, weight, and surface area, and after adjusting for estrogen status, these body size variables accounted for approximately 4% of the total variability.

Conclusion: The magnitude of the effect is such that a woman 1.8 m in height would absorb 30+% more calcium from a given intake than a woman 1.4 m tall.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
CALCIUM ABSORPTIVE EFFICIENCY, when assessed, accounts for more of the variability in calcium balance than does actual calcium intake itself (1). We have previously shown that usual calcium intake, serum 25 hydroxyvitamin D, age, estrogen status, and intestinal transit time independently account for portions of the variability in adult absorptive performance (2, 3). Recently we noted in a cohort of 53 individuals being studied for vitamin D status a positive correlation between absorptive efficiency and percent body fat (4). In this paper we explore this relationship in a larger data set in which we had accumulated measurements of calcium absorption efficiency together with the usual body size variables.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Subjects

Data from our long-running prospective study of midlife women were evaluated specifically to examine the relationship of body size variables and calcium absorption fraction (AbsFx). The participants themselves have been characterized previously (5). The study was approved by the Creighton University Institutional Review Board and each participant gave written consent. Studies in women with medical conditions affecting the calcium (Ca) economy or using medications that would be predicted to alter intestinal absorption were excluded.

Protocol

As previously reported (2, 5), 191 women participated in 8-d, inpatient balance studies approximately every 5 yr over a 20-yr period. Each woman contributed from one to five data sets for this analysis. Of the resulting 707 data sets, treated as quasiindependent because multiple visits were 5–15 yr apart, 633 met the medical inclusion criteria and had the requisite data for this analysis. Using the same criteria, 178 of the first studies in these women were also analyzable; all physiological measurements were made while subjects were inpatients, ingesting a constant diet, with full collection of excreta. Diets were calculated and prepared by the unit dietitian to be similar in nutrient composition to usual intakes analyzed from 7-d food records obtained before each admission. Relevant descriptive statistics for all 633 studies are shown in Table 1Go.


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  		TABLE 1. Descriptive statistics for the full set of studies1

 
Analytical methods

Ca was chemically analyzed by methods previously described (5). The variable, Ca intake, includes both food and medication Ca. [Medication Ca comes mainly from tableting excipients and was chemically analyzed in each instance, as previously described (6).] Studies involving nonfood Ca intakes greater than 300 mg/d (7.5 mmol/d) were excluded from this analysis because of uncertain (and often poor) bioavailability of Ca supplement products over the years during which these data were accumulated (7). Twenty-three studies were excluded for this reason. None of the 178 analyzable first studies had to be excluded on these grounds. Ca absorption fraction was measured by the double-tracer method, as described previously (2), using either a Ca load of 300 mg (7.5 mmol) or one third of total daily calcium intake and adjusting the measured value to a 300-mg (7.5 mmol) load size. Body surface area was calculated using the formula of DuBois and DuBois (8), i.e. surface area = 0.20247 * (height0.725) * (weight0.425), with height in meters and weight in kilograms.

Statistics

Most of the statistical analyses were performed using SPSS for Windows (version 11.5; SPSS Inc., Chicago, IL). Estrogen status was coded as 1 for studies in premenopausal women or postmenopausal women receiving hormone replacement therapy and as 0 in postmenopausal women not receiving hormone replacement therapy. Stepwise linear regression was used to model the dependencies of the individual components of the Ca economy, with P for entry set to 0.05. Additionally, we developed models based only on first studies for each participant (n = 178), thereby avoiding the bias possibly introduced by multiple measurements in some subjects.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Because of the well-documented inverse relationship between absorption efficiency and calcium intake, we first computed the residuals between the measured values for fractional absorption and the predicted value for a given Ca intake, using the previously published, curvilinear relationship between absorption fraction and dietary Ca intake (2). These residuals, which represent the spread of absorption fraction around the values predicted for a given Ca intake, constituted the dependent variable in our analyses and were found to be normally distributed. Table 2Go presents the regression coefficients and the r2 values for bivariate correlations between absorption fraction and the four principal body size variables for the aggregate of all studies and for first studies only. The pattern is essentially the same for both data sets. Height and weight and their nonlinear combination (surface area) were each significantly positively associated with absorption fraction, whereas body mass index (BMI) was not. Height and surface area produced r2 values, indicating that body size accounted for about 3.5–4.0% of the total variability in absorption.


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  		TABLE 2. Bivariate correlations between body size variables and absorption fraction residuals

 
Various multiple regression models, in which the body size variables were entered in a stepwise fashion, revealed that weight, height, and surface area (but not BMI) all contributed significantly (but not independently) to the prediction. Figure 1Go plots the residual absorption fractions against body height for first studies only and gives a visual indication of both the amount of variability concerned and the relationship of the absorption residuals to body height. As Table 2Go (and the various multiple regression models) indicates, BMI, i.e. fatness, was not the factor here. Height, as the indicator of body size, was positively correlated with absorption fraction, just as were weight and surface area. Placing height in the denominator of the predictor variable, as occurs when BMI is used, posits an effectively inverse relationship, which is contrary to what the data show.



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  		FIG. 1. Plot of AbsFx residuals on body height in 178 first studies of adult women. The solid line represents the best fit, least-squares regression line through the data and the dashed lines the 95% confidence interval for the regression line. [Copyright, Robert P. Heaney, 2005. Used with permission.]

 
The regressions for the whole data set (Table 2Go), although highly significant, produced lower values for r2 than did the set of first studies only. We had previously reported that absorption efficiency drops across menopause (2), a fact that would introduce heterogeneity into the relationship when studies in estrogen-replete and estrogen-deprived women are pooled. Accordingly we specifically analyzed for the effect of estrogen in the aggregate data set. Mean residuals differed significantly by estrogen status (estrogen replete: +0.0198 ± 0.004 vs. estrogen deprived: –0.0051 ± 0.005; P < 0.05). When adjusted for presence or absence of estrogen, r2 values for body size improved significantly. For example, r2 for surface area in the entire data set was 0.016 but rose to 0.031 when the analysis was confined to estrogen-replete women. A multiple regression model incorporating both estrogen status and surface area produced an R2 value higher still (0.043).


   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
In this study of midlife adult women, as in the study of peripubertal girls by Abrams et al. (9), we show that body size is a significant determinant of absorptive efficiency for Ca. Both data sets show that, after adjusting for Ca intake, body size accounts for similar fractions of the variability in absorption. This interstudy consistency strongly supports the conclusion that the effect is not a chance occurrence. Whereas 1–4% might seem a small fraction of the total variability, it is not a great deal less than the previously reported effect of intestinal transit time and serum 25 hydroxyvitamin D (3). Furthermore, even dietary Ca intake, the principal known determinant of absorption efficiency, accounts for less than one third of total variability. Thus, as in all multifactorial physiological performance issues, individual factors typically account for only small fractions of the total variability.

What is there about body size that seemingly up-regulates absorption efficiency? We suspect that the effect is purely physical, rather than physiological. Whereas it is not possible in intact humans to measure mucosal surface, it is likely that larger individuals have more intestinal surface and longer intestinal transit time than do smaller individuals. They would therefore absorb a larger fraction of any given Ca load. Certainly that is true across a broader range of animal body sizes. Diamond has shown that, even within species, mucosal mass can vary by a factor of two times or more and that mucosal mass is a direct determinant of absorptive transport capacity (10). In our models the major body size variables were all correlated with absorption efficiency.

The slope of the relationship between height and absorption (Table 2Go) gives an indication of the biological import of variations in body size. The difference in absorption fraction predicted for a 1.4- and 1.8-m woman would be 0.0884. In other words, for a calcium intake of 800 mg/d (20.0 mmol/d), the larger woman would absorb 30+% more Ca than the smaller. This better absorptive performance of larger women may constitute a part of the physiological mechanism for their tendency to have higher values for bone mass. Also, it may constitute a physiological offset of the tendency of larger individuals to have more urinary Ca loss (11).


   Footnotes
 
This work was supported by National Institutes of Health Grant AR07912 and the Health Future Foundation.

First Published Online July 5, 2005

Abbreviations: AbsFx, Absorption fraction; BMI, body mass index; Ca, calcium.

Received March 23, 2005.

Accepted June 23, 2005.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

  1. Heaney RP 2001 Osteoporosis. In: Coulston AM, Rock CL, Monsen ER, eds. Nutrition in the prevention and treatment of disease. San Diego: Academic Press; 653–684
  2. Heaney RP, Recker RR, Stegman MR, Moy AJ 1989 Calcium absorption in women: relationships to calcium intake, estrogen status, and age. J Bone Miner Res 4:469–475[Medline]
  3. Barger-Lux MJ, Heaney RP, Lanspa SJ, Healy JC, DeLuca HF 1995 An investigation of sources of variation in calcium absorption efficiency. J Clin Endocrinol Metab 80:406–411[Abstract]
  4. Barger-Lux MJ, Dowell MS, Heaney RP 2004 A relationship between body composition and calcium absorption efficiency. J Bone Miner Res. 19(Suppl 1):S302
  5. Heaney RP, Recker RR, Saville PD 1977 Calcium balance and calcium requirements in middle-aged women. Am J Clin Nutr 30:1603–1611[Abstract/Free Full Text]
  6. Heaney RP, Davies KM, Recker RR, Packard PT 1990 Long-term consistency of nutrient intakes. J Nutr 120:869–875
  7. Carr CJ, Shangraw RF 1987 Nutritional and pharmaceutical aspects of calcium supplementation. Am Pharm. NS27:49, 50:54–57
  8. DuBois D, DuBois EF 1916 Clinical calorimetry X. A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med 17:863–871
  9. Abrams SA, Griffin IJ, Hawthorne KM, Liang L 2005 Height and height Z-score are related to calcium absorption in five to fifteen-year-old girls. J Clin Endocrinol Metab 90:5077–5081[Abstract/Free Full Text]
  10. Diamond J 2002 Quantitative evolutionary design. J Physiol. 542(Part 2):337–345
  11. Heaney RP, Recker RR, Ryan RA 1999 Urinary calcium in perimenopausal women: normative values. Osteoporos Int 9:13–18[CrossRef][Medline]



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