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4 Medstar Research Institute, Hyattsville, MD 20783; 5 Center for American Indian Health Research, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73190; 6 National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892; 7 Department of Epidemiology and Preventive Medicine, Sackler Medical Faculty, Tel Aviv University, Ramat Aviv 69978, Tel Aviv, Israel; and 8 Henry N. Neufeld Cardiac Research Institute, Sheba Medical Center, Tel-Hashomer 52621, Israel
* To whom correspondence should be addressed. E-mail: eilatsi{at}017.net.il.
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ABSTRACT |
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 TOP ABSTRACT IntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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873 mg/d in the 5th quintile) vs. lower calcium intake (<313 mg/d in the 1st quintile). No relation between calcium intake and BMI or PBF was observed in NHANES III participants. Our data may be used to develop nutritional interventions aimed at weight control in culturally appropriate clinical trials.
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Introduction |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Despite evidence of a relationship between calcium and adiposity, findings on this association have been inconsistent (7). Further, ethnic minorities, such as American Indians, have not been adequately represented in any of the national health and dietary intake surveys (8). Dietary studies in American Indians have been limited to only a few tribes or have small sample sizes (9,10). These limitations are notable because of the high prevalence of obesity in American Indians (11). Studies of American Indian diets have focused on specific groups, such as pregnant women, children, or adolescents (12–18). The current study was designed to address these limitations by examining the role of dietary calcium in the development of obesity in a broad sample of American Indians.
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Subjects and Methods |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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2510 kJ/d (n = 131) or 33472 kJ/d and/or BMI 50 kg/m2 (n = 38); had conditions that might affect energy intake, such as dialysis, kidney transplant, and cirrhosis (n = 153); and those missing data for key variables (n = 7). These exclusions yielded an analysis sample of 2975, representing 82% of individuals participating in the 2nd examination. The Indian Health Service, institutional review boards, participating tribes, and the MedStar Research Institute approved the study. Written informed consent was obtained from each participant.
Measurements. As described elsewhere (19), the 2nd SHS examination included measures of body circumferences and fat as well as blood chemistry. Height was obtained with the participant standing erect in a Frankfort plane, using a stadiometer fixed to the wall. Weight was measured using a Detecto model 683-p scale, which was calibrated and adjusted daily (20). BMI was calculated as weight (kg)/height (m2). Percentage of body fat (PBF) was estimated by bioelectrical impedance with an RJL impedance meter (model B1410) using an equation based on total body water (21). The examination also included a fasting blood draw from which determinations, including fasting glucose, were measured (19).
All participants at the 2nd examination had dietary data collected via a single 24-h dietary recall. Interviewers were centrally trained and certified in data collection and form completion according to standardized methods (22). Use of dietary supplements was assessed as part of the medication inventory. Dietary intake was analyzed using the Minnesota Nutrition Data System (NDS, version 2.1) (23,24).
U.S. population data.
To compare the relationship between calcium intake and obesity in American Indians in the SHS vs. the general U.S. population, we used data for U.S. adults, 47–79 y of age, from NHANES III (1988–1994) (25,26), which occurred at about the same time as the 2nd SHS examination. Exclusion criteria in NHANES III were similar to those for the SHS sample: reported energy intake 2510 kJ /d or 33472 kJ /d; BMI 50 kg/m2; dietary calcium intake >5000 mg/d (no one had this extreme calcium intake in the SHS); those missing data on key variables; and those without a fasting blood draw, including all participants assigned to an afternoon or evening examination. These criteria yielded an NHANES III analysis sample of 2755 individuals. Both NHANES III and SHS excluded dietary supplements.
As in the SHS, total energy intake in NHANES III was estimated using a single 24-h dietary recall. However, NHANES III nutrient estimates were obtained using the USDA Survey Nutrient Data Base (26).
Statistical analysis. SHS data were analyzed with the Statistical Analysis System (SAS), version 9.00 (27). NHANES III data were analyzed with SAS-Callable SUDAAN software, version 9.0 (28), which permits appropriate weighting of NHANES III data per published guidelines (29).
For both SHS and NHANES III, continuous variables were presented as means and standard errors of means (mean ± SEM). In the current study, the t test and 
2 test compared the means and proportions within genders between the SHS and NHANES III.
Both SHS and NHANES III excluded supplemental calcium intake. Consistent with previous methodology (30), dietary calcium and energy intake were categorized into quintiles, with the first quintile serving as the reference group for the multivariate analyses.
Mean energy, PBF, and BMI were presented according to quintiles of calcium intake. To test linear trend according to ascending quintiles of calcium intake, the statement CONTRAST in the general linear models procedure (PROC GLM) in SAS was used with the SHS data (27); PROC REGRESS in SUDAAN was used with the NHANES III data (28).
The adjusted mean PBF and BMI within quintiles of calcium intake were calculated by using the statement LSMEANS in PROC GLM in SAS (27) for the SHS data and by using PROC REGRESS in SUDAAN for the NHANES III data (28). Variables entered into the model for all participants included gender, age, study center, years of education, income, alcohol consumption, smoking, diabetes status (diabetes, impaired fasting glucose [IFG], or normal fasting glucose), diabetes duration in participants with diabetes, and energy intake. Diabetes was defined according to American Diabetes Association criteria (31). The data were examined for interactions between gender or diabetes and quintiles of calcium. No interactions were observed. We also evaluated the effect of adding fat, protein, potassium, and magnesium intakes on the association between calcium and PBF and BMI by adding these factors to the model as potential confounders.
To examine odds ratios of being overweight (defined as 25 BMI <30 kg/m2) and obese (BMI 30 kg/m2) compared with normal weight (BMI 25) across calcium quintiles after controlling for the confounders listed above, the generalized logit model was used (SAS PROC CATMOD or SUDAAN PROC MULTILOG), because the parallel lines assumption was violated using the logistic regression model. Tests for trend were conducted by modeling the median of each quintile of calcium intake as a continuous variable in the generalized logit model.
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Results |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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In NHANES III, mean calcium intake was higher overall and in each quintile among both men and women compared with SHS participants (Table 2). However, in women, energy intake was lower in NHANES III compared with SHS. As expected, energy intake was higher with increasing calcium intake in each study (Fig. 1A). In both SHS and NHANES III, a significant trend of decreasing PBF was observed with increasing quintile of calcium intake in a univariate analysis (Fig. 1B), but this association was not observed for BMI in either study (Fig. 1C).
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In sex-specific analyses of SHS data, PBF decreased as calcium intake increased in women, but not in men after controlling other confounders. In women, the adjusted mean PBF difference and 95% CI in ascending calcium intake quintiles compared with the 1st quintile (41.7%) were –0.44% (95% CI: –1.41 to 0.53), –0.55% (95% CI: –1.54 to 0.45), –0.45% (95% CI: –1.48 to 0.57), –1.28% (95% CI: –2.36 to –0.20); P for trend = 0.026, whereas in men the adjusted mean PBF difference and 95% CI in ascending calcium intake quintiles compared with the 1st quintile (29.0%) were –0.42% (95% CI: –1.48 to 0.65), –0.75% (95% CI: –1.83 to 0.33), –0.61% (95% CI: –1.74 to 0.51), and –0.80% (95% CI: –2.03 to 0.43); P for trend = 0.30. There were similar trends with increasing BMI, but they were not significant (P for trend = 0.17 in women and 0.12 in men).
Calcium intake tended not to be correlated with BMI (P = 0.33 in women, P = 0.94 in men) or PBF (P = 0.92 in women and P = 0.18 in men) for either gender in NHANES III participants.
In the SHS, physical activity data were collected only at the 1st examination, and these levels were low (32). Adjustment for baseline physical activity did not change the results.
In the SHS, the odds of being obese vs. normal weight decreased over increasing calcium intake quintiles, applying a generalized logit mode after controlling for cofounders (Table 4). This association was not observed in NHANES III.
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Discussion |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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A previous report from NHANES III that focused on data for younger adults (mean age 28.7 y in women and 43.5 y in men) showed that after controlling for energy intake, the odds of being in the highest quartile of percent body fat vs. other quartiles were 1.00, 0.75, 0.40, and 0.16 for the lowest to highest quartiles of calcium intake (255 ± 20, 484 ± 13, 773 ± 28, and 1346 ± 113 mg/d), respectively, in women (n = 380, P < 0.0009). A similar inverse association was noted in men (n = 7114, P < 0.0006), although a comparable dose-response relation was not evident from the model (5). When recalculating PBF applying the equation used by Zemel et al. (5,34) in our analysis, the odds of being in the highest quartile of PBF vs. other quartiles for participants in the lowest to highest quartiles of calcium intake also were not evident (data not shown). The differing results across the 2 analyses may result from differences in the age and gender distributions, as well as fewer adjustments for confounders in the NHANES III analysis.
Analyses in 65 Pima Indian adults failed to find an association between calcium intake and body size or adiposity (35). Differences between this study and our findings may be the result of the much larger sample size in the SHS.
To the best of our knowledge, this is the first examination of diet- and obesity-related factors that compares American Indians to the general U.S. population. The higher PBF and BMI and the lower calcium intake in American Indian men and women, compared with the general U.S. population in NHANES III, is notable and supported by a suggested biological mechanism, as well as by some longitudinal and small intervention trials (36). Other trials of calcium intake (in both diet and supplements) in other populations have generally shown little effect on weight loss (37,38).
One possibility is that calcium intake is reflective of a diet lower in energy density. Stang et al. (39) showed that intakes of several vitamins were low in this population, possibly reflecting a diet low in vegetables and fruits. Our analyses show that the SHS diet is lower in potassium and magnesium, as well as calcium (data not shown). This supports a possible association between lower calcium intake and a less energy-dense diet. Another possible explanation for our findings of lower calcium intake among American Indians may be related to the high prevalence of lactose intolerance among American Indians (40). The range between the lowest and the highest calcium intake quintiles in the SHS is potentially large enough to produce an association, whereas this range in the NHANES III was not as large.
Other epidemiologic studies have reported associations between dietary intake of dairy products and calcium and obesity. The CARDIA study reported that overweight individuals consumed fewer dairy products than their normal-weight counterparts (6). Dairy consumption was also reported to be inversely associated with prevalence of insulin resistance among individuals with a baseline BMI of 25 kg/m2. Consistent with this finding is a report from the Women's Health Study that dietary intakes of calcium and dairy products may be associated with a lower prevalence of metabolic syndrome, as well as incidence of type 2 diabetes (41). A meta-analysis of 13 randomized controlled trials failed to show an association between increased consumption of either calcium supplements or dairy products and weight loss (37). However, it is possible that higher calcium intake is associated with lower weight in an obese population. This hypothesis is supported by data from at least 1 study (42). In an additional study, calcium intake of 
600 mg/d was associated with a predicted mean gain of 0.5 kg/y. By contrast, at a range of intake of 1000–1500 mg/d, mean weight gain was zero kg/y (43).
The inverse relationship between adiposity and calcium intake observed in American Indians and the lack of any relation among the general U.S. population may reflect other nutritional or lifestyle differences between these groups. Nonetheless, mechanisms underlying the association between calcium intake and adiposity merit further investigation. Because we do not have data on food sources for the relevant nutrients from these dietary recalls that were performed in the mid-1990s (the NDS database at that time did not allow extraction of food data), we cannot present the results by food groups or food patterns.
This study was limited by several factors. Because this was a cross-sectional analysis, cause and effect cannot be established. Dietary intake alone is unlikely to be the sole factor underlying lower BMI and PBF. The use of a single 24-h recall is limited because of day-to-day individual variability (44). In addition, diet was only measured once during the SHS and may have changed during the follow-up period. The 24-h recall can provide detailed information on specific foods (45) and, therefore, is considered ideal for intercultural comparisons of mean dietary intake levels, because it is an open-ended method that allows detailed reporting of heterogeneous types of food (43). However, it tends to underestimate energy intake. Our aim was to use the SHS cohort to compare quintiles of calcium intake with PBF and BMI. For this more limited objective, we believe it is sufficient to assume that constant scaling bias is of a relatively constant magnitude, as in NHANES III (44). Finally, although SHS is a population-based sample of 13 communities, the data may not be reflective of all American Indian groups (46).
Our data support an association between calcium intake and obesity in American Indians and may be useful in developing approaches for nutritional interventions aimed at weight control. However, well-designed, culturally appropriate clinical trials are needed to determine whether interventions to increase calcium intake are effective in weight control in American Indians.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Author disclosures: S. Eilat-Adar, J. Xu, C. Loria, C. Mattil, U. Goldbourt, and H. E. Resnick, no conflicts of interest; B. V. Howard has served on the advisory boards of Merck, Shering Plough, the Egg Nutrition Council, and General Mills, and has received research support from Merck and Pfizer.
3 The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the Indian Health Service.
9 Abbreviations used: IFG, impaired fasting glucose; PBF, percentage of body fat; SAS, Statistical Analysis System; SHS, Strong Heart Study.
Manuscript received 14 December 2006. Initial review completed 24 January 2007. Revision accepted 19 May 2007.
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LITERATURE CITED |
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