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Original Studies |
Osteoporosis Research Center, Creighton University, Omaha, Nebraska 68131
Address correspondence and requests for reprints to: Robert P. Heaney, M.D., Creighton University, Osteoporosis Research Center, 601 North 30th Street, Omaha, Nebraska 68131.
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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3% of the variance in body weight. |
Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Accordingly, we examined the data accumulated in several studies conducted out of our Osteoporosis Research Center over the past 12 yr. Four of these, for their primary skeletal end points, have been published elsewhere (9, 10, 11, 12). One is an ongoing randomized trial in which the blind has not been broken, but the entry data were available for cross-sectional analysis.
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Materials and Methods |
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The studies from which our data come are: "YWS"
denotes a cohort of 184 healthy women in their early 20s followed for 4
yr (9); "TCD" denotes a similar cohort of young women
participants in a randomized controlled trial of calcium
supplementation; "Nuns" denotes a prospective study of calcium
metabolism and bone health at 5-yr intervals in a cohort of 191 nuns as
they passed from premenopause to postmenopause (12);
"MBx" denotes a study of bone dynamics and biochemical markers in a
cohort of 75 healthy perimenopausal women observed at 6-month intervals
over 5 yr (11); and "Van" denotes a randomized
controlled trial of calcium supplementation in 216 elderly women
(10). The subjects have all been described in greater
detail in the respective publications. Table 1
presents the several studies involved,
providing relevant information with respect to type of analysis, age
group of the subjects concerned, duration of observation, pertinent
intake variables, and method of assessing dietary intake. Table 1 also
contains the numbers of subjects in each study on whom suitable data
were available for this analysis. (For the longitudinal studies we
included only women in whom we had at least three observations over
time, and we excluded women who, while under study, developed illnesses
that might influence weight.) All these projects had been reviewed and
approved by Creighton University’s Institutional Review Board, and all
subjects gave written consent.
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For the nonintervention studies, 7-day food diaries were assessed by registered dietitians using a succession of methods over time. For the Nuns study, beginning in 1967, intakes were assessed using hand calculation, referring to USDA Handbook 8 and later Bowes and Church (13); computer software was used exclusively in the other four studies and in the Nuns study as it became available. The YWS and MBx studies used NutriPractor (Practorcare, San Diego, CA). Finally, TCD began in 1995 and has used Food Processor (ESHA Research, Salem, OR). For YWS and MBx, both of which had 6-month visit intervals, only the initial diet analysis was used. But for the Nuns study, which had 5-yr visit intervals, the average intake values over the period of observation was used.
Calcium intake was expressed as the calcium to protein ratio, both because this stratagem explicitly factors in the countervailing effects of the two nutrients (13) and because the ratio eliminates most of the portion size estimation error. As we have shown previously (9, 14, 15), the ratio better correlates with an outcome variable known to be associated with calcium intake (i.e. bone gain) than does either nutrient alone, probably for the reasons just cited.
Weight and body mass index (BMI)
Weight and height were measured on entry (as well as at each visit) in virtually all of the studies, using a Harpendon stadiometer for height and either a beam balance or an electronic platform balance for weight. The subjects wore light indoor clothing without shoes. In those subjects without osteoporosis, weight was adjusted for height by using the BMI, expressed as kg/m2. In older subjects with osteoporosis, in whom height may be spuriously depressed by the disease, weight change during observation or treatment was the outcome variable.
Data analysis
For the cross-sectional data, BMI on entry was regressed against calcium intake on entry, using standard statistical methods, and the slope of the relationship was taken as the outcome variable. For the longitudinal data, weight change was regressed against calcium intake. In the controlled trial, the difference in amounts of weight gained or lost during observation between calcium-supplemented and placebo-treated groups was tested against a null hypothesis of zero difference, using Student’s t test. Multiple linear regression models were tested using Crunch 4.04 (Crunch Software Corp., Oakland, CA).
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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(top) presents
the slope values and intercepts for the regression of baseline BMI on
dietary calcium to protein ratio (mg/g) for the YWS and TCD studies,
together with their 95% confidence limits. The slopes are for BMI
(kg/m2) regressed against dietary calcium to
protein ratio (mg/g). The slope was significantly negative for each
study. Because the ages, BMI values, and calcium intakes for the two
subject groups (TCD and YWS) were similar, we pooled the two datasets
to improve the precision of the estimate and present the pertinent
statistics for the combination, also in Table 2. The pooled slope was
-0.186 kg/m2/mg/g (P = 0.001).
The intercepts in Table 2 represent the predicted mean BMI values for
zero calcium intake. These regression relationships were also examined
in multivariate models, using combinations of calcium intake, protein
intake, energy intake, and finally the computed calcium to protein
ratio. Reported energy intake was not correlated with entry weight or
BMI, and none of the multivariate models was superior to the simpler
bivariate regression of BMI on calcium to protein ratio.
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plots the data for the combined
set (YWS and TCD). The horizontal dashed line in Fig. 1
represents the boundary between normal and overweight, and the
vertical line represents the median calcium to protein ratio
for these 348 subjects. As can be seen, there are substantially fewer
subjects in the overweight zone for calcium intakes above than below
the median. The odds ratio for being overweight for calcium intakes
below the median was 2.25 (P < 0.02). The difference
was even greater for BMI values greater than 30 (i.e.
obesity): there were seven obese subjects in the lower half of the
calcium intakes and only one in the upper half.
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(bottom) also presents the regression parameters
from the two longitudinal observational studies, with, in these
studies, change in weight (kg/yr) regressed against dietary calcium to
protein ratio (mg/g). In both studies the slope was negative and
individually of borderline significance (0.15 > P
> 0.05). Because the ages, intakes, and weights of the two groups of
subjects were similar, we pooled the two datasets (Nuns and MBx), as we
had done with the younger women, and recomputed the regression. Table 2
also contains the regression parameters for the combined set, and Fig. 2 presents the data visually. The
slope (-0.0383) was highly significantly different from zero
(P = 0.008). The intercepts represent the predicted
value for weight change at mid-life at zero calcium intake. As with the
data of the younger women, multivariate models incorporating energy,
calcium, and protein intake were not superior to a bivariate model
using either the calcium to protein ratio or simply calcium intake
alone.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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As Table 2 notes, calcium intake in our studies explains 3% of the
variability in weight. There are several likely reasons why this
association may be small. Most importantly, body weight is a highly
multifactorial variable, and it is unlikely that a very large fraction
of its variability could be attributed to any single factor. Also the
imprecision of the methods for estimating calcium and protein intakes
renders estimates of the independent variable inherently uncertain.
With the methods we used (7-day diet diaries and calcium to protein
ratios) these errors are less than would be produced by the usual food
frequency questionnaire, but they are still not negligible, as we have
previously reported (16). Moreover, the intake estimates
for the young women were obtained at one point in time, and, for the
older women, over a relatively brief portion of their lives, and may
well not have been consistent across the years leading up to their
contact with us.
The size of the presumed effect can be estimated best by taking apart the calcium to protein ratio and BMI. In the two studies in young women, each 1.0-mg increment in this ratio was associated with a 0.186-kg/m2 decrement in BMI. For the mean protein intake in these two studies (62.4 g/day), and the mean height (1.66 m), these numbers translate to a predicted 0.82-kg weight decrement for each 100-mg calcium intake increment. And in the middle-aged women, the best estimate of weight change is -0.038 kg/yr/100 mg calcium intake. At a 55% compliance level in the calcium-supplemented group in the Van study (10), the observed difference in weight change translates to -0.052 kg/yr/100 mg calcium intake. This rate of change is of approximately the same magnitude as in the middle-aged women and the difference between them is probably not biologically meaningful.
It may be of interest to note that the predicted weight change in the
Nuns and MBx combined cohort (Fig. 2) crosses zero at a calcium to
protein ratio of almost exactly 20 mg/g, a figure very close to that
derived from current dietary recommendations for both nutrients. Very
few women in this age range achieve calcium to protein ratios even
close to 20 (see Table 1), and what our data suggest is that the
general tendency to gain weight observed in mid life may be due to
effectively very low calcium intakes.
Perhaps the largest barrier to prior recognition of a role for calcium intake in body weight has been the lack of a conceptual framework in which to situate the effect or explain its operation, even when it might have been observed. M. B. Zemel (personal communication) has commented that, in his 1990 study of hypertensive blacks (17), he observed substantial weight loss with calcium supplementation but did not report it because it did not seem to fit with what was known either about calcium metabolism or about obesity. However, the same investigator has recently shown that high blood PTH and 1,25(OH)2 vitamin D levels, as would be evoked by a low calcium diet, increase cytosolic [Ca2+] in human adipocytes in culture, switching their metabolism from lipolysis to lipogenesis (2, 6, 7, 8). Furthermore, in mice expressing the agouti gene, high calcium diets raised core body temperature and reduced the body fat accumulation that accompanies a baryogenic diet (2, 6). Conversely, low calcium diets resulted in lowered core body temperature and increased fat accumulation.
A plausible background to these phenomena may be found in reflection on the fact that the primitive human diet would have been calcium rich, with calcium to energy ratios two to four times what modern humans ingest (18). High circulating PTH [and correspondingly elevated levels of 1,25(OH)2 vitamin D] would have been experienced only intermittently (i.e. at times of food shortage). Because a low calcium intake would have been tantamount to a low food intake, it may be that human physiology used the PTH and 1,25(OH)2 vitamin D response evoked by low calcium intake to regulate its energy metabolism and thereby adapt to imminent food shortage. Today, with calcium intake disconnected from energy intake, the primitive energy-conserving response predisposes to weight gain.
Zemel’s mouse model also presents a useful way of thinking about the calcium effect. Briefly, full expression of obesity in the mouse requires a combination of the obesity gene, a baryogenic diet, and low calcium intake. It is likely that some analogous combination is involved in the weight effects observed in humans (i.e. ready access to excess energy intake, low calcium intake, a genetic predisposition that impairs adipocyte regulation of cytosolic [Ca2+], and perhaps other factors as well).
It should be noted that, with the exception of the controlled trial, in which calcium carbonate was the calcium source, it cannot be unequivocally determined whether the effect noted in our studies was due to calcium per se or to other nutrients for which calcium was a fortuitous marker. The bulk of the calcium in the diets of those with higher intakes was from dairy sources, as would have been expected, and other coingested nutrients may well have been partly responsible for the observed association, as in the DASH study (19). However, calcium itself, presumably through its effect on circulating PTH and 1,25(OH)2 vitamin D, would clearly seem to be involved, as both our controlled human trial and the animal data show. What cannot be excluded at this point is some additional effect produced by other unrecognized dietary elements.
Finally, it may be worth noting the importance of maintaining a high calcium intake during attempts to lose or control weight. The tendency to eliminate milk from many reducing diets may be a partial reason for their frequent failure.
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Footnotes |
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Received May 8, 2000.
Revised August 1, 2000.
Accepted September 1, 2000.
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References |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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