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Determinants of endogenous calcium entry into the gut^1,2,3

¹ From the Osteoporosis Research Center, Creighton University, Omaha, NE

² Supported by the Health Future Foundation and NIH (AR07912).

³ Reprints not available. Address correspondence to RP Heaney, Creighton University, 601 North 30th Street, Suite 4841, Omaha, NE 68131. E-mail: rheaney{at}creighton.edu.

	ABSTRACT

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Background: In addition to food sources, calcium enters the gut by way of digestive secretions and shed mucosa. In health, such entry is as large as or larger than urinary calcium excretion. Because calcium absorption is inefficient, most of this endogenous intestinal calcium is excreted.

Objective: Our aim was to determine the dietary, anthropometric, and physiologic determinants of calcium entering the digestive stream from endogenous sources.

Design: Multiple regression modeling of intake and excretion data was used with 553 metabolic balance and kinetics studies performed in 190 midlife, white women.

Results: Endogenous intestinal calcium averaged 3.29 ± 0.83 mmol/d. Multiple regression models explaining variation in this endogenous intestinal calcium were developed with use of dietary intake, anthropometric, and serum mineral variables. All 3 groups of predictor variables individually explained up to 22% of the variation in measured values for endogenous intestinal calcium. A composite model, incorporating all 3 groups explained 29% of the variation, with phosphorus and meat protein intakes, height, weight, and serum calcium and phosphorus concentrations all independently entering the model. Phosphorus intake dominated over all the other predictors, explaining 20% of the variance all by itself, with endogenous intestinal calcium rising by 0.037 mmol for every 1 mmol of phosphorus ingested. Meat protein (but not nonmeat protein) was the only other significant dietary contributor, exhibiting a negative coefficient.

Conclusion: As a first approximation, the amount of endogenous calcium entering the digestive stream rises with body size and with the amount of phosphorus-rich food consumed.

Key Words: Endogenous fecal calcium • dietary phosphorus • protein • meat protein • body size

	INTRODUCTION

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Negative calcium balance is both a marker for and a cause of bone loss. Given the generally low calcium intakes of most adults in North America, delineating the factors that control calcium excretion can be helpful in understanding the calcium economy and possibly in altering factors that contribute to negative balance. Although urinary calcium loss has been extensively studied (1-3), endogenous loss through the gut has received comparatively little attention. This lack of attention is mainly because its measurement requires extended fecal collections and isotopic tracer methods. However, in many animal species, such as dogs, endogenous fecal calcium loss (EFCa) is quantitatively much larger than is urinary calcium loss. Even in adult humans the 2 routes are of approximately equal magnitude (4). Thus, delineation of factors that influence EFCa would seem to be as important as studies of factors that control urine calcium loss.

Given the generally poor absorptive efficiency for calcium in adults, it follows that the quantity of endogenous calcium excreted in the feces is necessarily determined mainly by the quantity entering the gut. An improved method for estimating total entry from endogenous sources was recently published (5). Here, we apply this method to an expanded data set from our long-running prospective study of calcium metabolism in midlife women (4) and specifically test associations between endogenous calcium entry and dietary, anthropometric, and physiologic variables.

	SUBJECTS AND METHODS

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Subjects
Data accumulated in our prospective study of midlife women were evaluated to search for plausible associations with total endogenous calcium entry into the gut (see "Analytical methods"). The participants themselves were characterized previously (4). 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 economy or using medications that would be predicted to alter calcium, phosphorus, or potassium handling were excluded from this analysis.

Protocol
As previously reported (4), 191 women participated in 8-d, inpatient balance studies approximately every 5 y over a 25-y period. Each woman contributed from 1 to 5 data sets for this analysis. Of the resulting 707 data sets (treated as independent because multiple visits were 5 y apart), 553 data sets both met the medical and drug inclusion criteria and provided all the data needed for calculation of total intestinal calcium (TIC). Because of medical exclusions, all the data sets from 1 women were dropped. All physiologic 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 recorded as 7-d food records before each admission.

Analytic methods
Diet calcium, phosphorus, and nitrogen were chemically analyzed by methods previously described (4). The variable labeled "diet calcium" includes both food and medication calcium. Medication calcium consists mainly of excipient calcium and was chemically analyzed in each instance, as previously described (6). Studies that involved nonfood calcium intakes >300 mg/d were excluded because of uncertain (and often poor) calcium bioavailability of such products over the years during which these data were accumulated (7). Diet protein was calculated as analyzed diet nitrogen x 6.25. Total protein was fractionated by source into meat and nonmeat protein fractions (with dairy protein included with nonmeat), with use of food table values (ESHA Food Processor Plus, version 7.4, Salem, OR) applied to the precise quantities of each food item in the ingested diet. Diet potassium was calculated similarly. Calcium absorption fraction was measured by the double-tracer method, as described previously (8). Renal net acid excretion (RNAE) was calculated from the diet variables by the method of Frassetto et al (9). Body surface area was calculated with use of the formula of DuBois and DuBois (10):

(1)

where Ht is height in m and Wt is weight in kg.

Serum calcium and phosphorus measurements were made on fasting blood specimens and constituted the mean of several such measurements obtained over an 8-d inpatient stay in the metabolic research unit.

Physiologic model
EFCa is, by definition, what is left over after absorptive reclamation of a portion of the calcium entering the gut from endogenous sources. These sources include gastrointestinal secretions that range from saliva through bile and pancreatic juice to colon mucus. Additional calcium from endogenous sources is contributed by the tissue calcium of sloughed mucosa. The relevant calcium transfers into and out of the gut lumen is presented schematically in Figure 1. As depicted, calcium entry into the gut is visualized as consisting of 2 components: proximal intestinal calcium (PIC) and distal intestinal calcium (DIC). Their sum, TIC, is the primary variable of interest in this communication. Proximal and distal in this usage relates to the anatomical relation between points of entry and the principal region of calcium absorptive activity. Thus, gastric juice would contribute to the proximal component, whereas colon mucosal cells and mucus would contribute to the distal component. The justification for this dichotomization of what is undoubtedly a decreasing continuous function is presented elsewhere (11, 12).

View larger version (21K): [in this window] [in a new window]   FIGURE 1. Diagrammatic representation of the principal fluxes of calcium into the gut through ingestion and secretion and out of the gut through absorption and excretion. PIC, proximal intestinal calcium, ie, the calcium content of digestive secretions and sloughed mucosa entering the gut proximal to the major region for calcium absorption; DIC, distal intestinal calcium, ie, the same calcium moieties as for PIC, but entering the gut downstream of the principal region for calcium absorption; TIC (total intestinal calcium, not depicted) is the sum of PIC and DIC; EFCa, endogenous fecal calcium, ie, that portion of TIC not absorbed. (Copyright RP Heaney, 1999. Used with permission.)

(2)

where EFCa is the unabsorbed, externally measurable component of TIC, and AbsFx is the absorption fraction as measured by the double-isotope method (8). The derivation of the equation and its parameters is described elsewhere (5).

Statistics
All statistical analyses were performed with use of SPSS for WINDOWS, Version 11.5 (SPSS, Chicago). Estrogen status was coded as 1 for studies in premenopausal women or in postmenopausal women receiving hormone replacement therapy and as 0 in postmenopausal women not receiving hormone replacement therapy. SPSS routine "Frequencies" was used to obtain counts for estrogen status and the medians and percentiles for the other variables, and the routine "Descriptives" was used for parametric descriptors of the continuous variables. Stepwise linear regression was used to model the dependencies of the individual components of the calcium economy, with P for entry set to 0.05.

	RESULTS

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Mean values (±SEM) for the variables of interest are presented in Table 1. For the most part the values in our subjects are typical of values reported for other women of this age, eg, in Third National Health and Nutrition Examination Survey (13). As would be expected, there were strong intercorrelations between the intake variables (not shown).

Table 2

Table 3

Meat protein intake, as noted, had a negative coefficient. Before entry, both meat protein and total protein exhibited highly significant correlations with TIC. However, once meat protein was entered, both the dietary-only and full models eliminated total protein. The effect of protein intake was thus captured fully by its meat component. However, if meat protein were excluded from the candidate variables, total protein entered, but with a coefficient, also negative, that was about 25% smaller, and an R² for the total model of 0.281. Nonmeat protein, by itself, did not enter any of the models.

Finally, both body size and serum calcium and phosphorus were significantly associated with TIC. Age and estrogen status were not significant predictors of TIC in the full model. Height, weight, and surface area were each strongly associated with the residuals from the regression of TIC on phosphorus intake. Surface area is, of course, a nonlinear multiplicative combination of height and weight. The fact that both height and surface area were independent contributors to the final model suggested that the surface area formula did not adequately capture the contributions of body size. We tried 3 other surface area formulations (14); however, the DuBois formula (10) proved superior to the others in this modeling process.

	DISCUSSION

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With a mucosal turnover rate of 20%/d, it is energetically expensive to maintain a larger mucosal mass than is needed to process current food loads. Diamond (15) showed, for example, that gut mass varies linearly with the amount of food an animal must process. Thus, it is to be expected that TIC would vary with ingested intake (as well as with body size). Our observations are to some extent consistent with these expectations, at least insofar as stature and consumption of phosphorus-containing foods are concerned. The statistically weaker negative correlation with meat protein is opposite in sign to a predicted positive relation between food intake and TIC and might reflect the usually more efficient digestion of high protein, animal-source foods than of plant-source foods. This distinction is, in fact, recognized as contributing to relative gut size and proportions in primates (16).

It is not immediately clear how a nutrient such as phosphorus operates in this context. We had earlier reported this same association of phosphorus intake and TIC in a smaller set of studies (12), and, in this now substantially expanded data set, the relation is, if anything, even stronger. Hence, it is unlikely to be a chance association. Following Diamond (15), we had initially presumed that, because of the ubiquity of phosphorus in foods generally, phosphorus intake was a proxy for total food intake. However, in various models we deliberately excluded phosphorus, entering instead all the plausible remaining intake variables: energy, protein (both as meat and nonmeat), calcium, potassium, and RNAE. Adjusted R² for the resulting model was only 0.167 (compared with 0.217 for the intake model that included phosphorus). The predictor variables included calcium, protein, and potassium intakes, all of which covary strongly with phosphorus. Hence, from our data, it appears that phosphorus itself is more than just a proxy for total food.

On the premise that the balance between animal-source and plant-source food is determinative of gut mass, phosphorus intake could reflect the high phosphorus content of seed-based foods (because of their high phytate content). However, cereal grain products were not a prominent part of the diets of our subjects, and much of their cereal intake was in the form of leavened foods (in which yeast phytase hydrolyzes phytic acid). Hence, this seems at best an incomplete explanation for the prominence of phosphorus intake in these explanatory models.

The negative association of TIC with meat protein probably does not signify anything unique about the protein itself. Rather, we suspect that meat protein in this study was a marker for meat foods. The weakening of the association (and the reduction of the regression coefficient when total protein is substituted for meat protein suggests that it is meat per se, rather than high protein foods generally, that are responsible for the association). This conclusion is supported by the observation that nonmeat protein intake was not significantly correlated with TIC, and, when forced into a model, it had a positive rather than a negative coefficient. Thus, as far as TIC is concerned, the 2 protein sources behaved quite differently.

If one examines the principal changeable factors in the model (ie, the intake variables), one sees that, because intakes of phosphorus and meat protein covary and because the coefficients for phosphorus and meat protein intakes are opposite in sign, the 2 nutrients to some extent neutralize one another when certain foods are consumed. For example, a single additional food intake of 100 g beef tenderloin contains 27 g protein and 7.3 mmol (227 mg) phosphorus. With the use of the coefficients for protein and phosphorus from the full model (Table 3), the extra protein intake is predicted to lower TIC by 0.85 mmol, and the extra phosphorus is predicted to raise it by 1.0 mmol, for a net increase in TIC of only 0.15 mmol (6 mg calcium). Nonmeat phosphorus sources, by contrast, exhibit only the TIC-enhancing effect of their phosphorus, with no protein mitigation. Because nonmeat phosphorus sources accounted for more than two-thirds of total phosphorus intake in these studies, the net effect of phosphorus intake across the diet is to enhance TIC.

The full model explains less than one-third the variance in TIC. One obvious source of the remaining variation is measurement imprecision. TIC is a calculated variable, based on a combination of absorption fraction and EFCa, and measurement of the latter, in turn, is dependent on the timing of fecal collections (17). The decreasing exponential character of clearance of an intravenous tracer through the gut reduces the effect of the error inherent in fecal timing (17), but it does not eliminate it entirely. Additionally, the application of the double-isotope absorption fraction (measured at a single meal) to total food intake over an 8-d period is also a potential source of error. Thus, it is likely that a substantial fraction of the variability remaining after the full model is applied is related to measurement uncertainties. However, that cannot be a satisfactory total explanation. We have shown elsewhere that patients with celiac disease have high values for TIC (18), and it is plausible that milder, asymptomatic aberrations of gut function could also be associated with increased weeping of body fluids into the gut lumen.

It might be useful to note that there was no hint of an association between TIC and either potassium intake or RNAE, food factors that were suggested as important influences on the calcium economy, particularly urinary loss. [In a separate study we deal in detail with potassium effects on the calcium economy (19).]

The small effects of serum calcium and phosphorus concentrations must also be noted, but they are of uncertain biological significance. We had not observed significant associations between TIC and serum calcium and phosphorus in a smaller data set (11, 12), presumably because of insufficient power. Taking the point estimates for the coefficients for these 2 serum variables, a rise in fasting serum calcium of 0.1 mg/dL (1.0%) would be predicted to elevate TIC by 0.03 mmol/d, or 1.2 mg/d. Hence, given the usually narrow range of values for serum calcium and phosphorus, their effect on TIC is biologically quite small. As these serum measures were obtained on fasting blood specimens, they are probably not markers for food intake. Thus, these serum variables should be examined further, particularly in conditions in which one of the other analyte might vary outside the relatively constricted range found in this study.

In summary, losses of endogenous calcium into the feces in humans are a quantitatively important drain on the calcium economy of adults. Their magnitude is influenced, in our data, mainly by body size and by phosphorus intake. Body size, other things being equal, probably serves as a proxy for gut mass (and hence for the volume of gut secretions). Phosphorus intake, originally considered a surrogate for the quantity of food consumed, is more strongly correlated with total endogenous calcium entry into the gut than are either energy or macronutrient intake. Hence, the phosphorus association likely reflects, to some degree, a nutrient-specific effect.

	ACKNOWLEDGMENTS

 
KMD was responsible for hypothesis framing; statistical modeling and interpretation and helped with exposition. KR was responsible for diet analyses, literature review and evaluation and statistical modeling and helped with exposition. RPH was responsible for problem conceptualization, hypothesis framing, statistical modeling and interpretation and was lead writer. None of the authors had a conflict of interest.

	REFERENCES

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 

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