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Drexel University, Department of Psychology, Philadelphia, PA 19102-1192
* To whom correspondence should be addressed. E-mail: co2193{at}columbia.edu.
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ABSTRACT |
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 TOP ABSTRACT IntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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Introduction |
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TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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In contrast, some literature suggests that calcium may not affect changes in body weight or fat mass (10–13). Jensen et al. (10) randomized obese women on a weight loss diet to calcium-supplemented (1 g/d) or no intervention groups for 3 mo but found no differences in body weight or fat loss. Macdonald et al. (11) examined calcium intake in an observational study and found no relationship with changes in body weight over 5–7 y. Wosje and Kalkwarf (12) found calcium supplementation (1 g/d) did not promote weight or fat loss over 6 mo in postpartum women. In addition, Bowen et al. (13) compared effects of high dairy (2400 mg calcium/d) and moderate dairy (500 mg calcium/d) isoenergetic diets and found no associations between calcium and body weight or composition.
Calcium review articles are similarly split in their conclusions. Parikh and Yanovski (14) stated "...most of the available cross-sectional, longitudinal, observational, and small controlled trials in humans and the available animal studies support the conclusion that dietary calcium may play a role in body weight regulation and lend credence to the hypothesis that increasing dietary calcium or dairy intake may diminish future weight gain" (p. 286). Conversely, Barr (15) states: "...the data available from randomized trials of dairy product or calcium supplementation provide little support for an effect in reducing body weight or fat mass" (p. 245S). Barr (15) also states: "The interpretation of these [null] findings is complicated by the inability to accurately determine the extent of dietary compensation for the increment in energy intake provided by the added dairy products" (p. 245S). This statement acknowledges the potential import of controlling for energy intake when examining the relationship between calcium and weight change. As such, this study used dietary calcium intake to predict weight loss maintenance both with and without controlling for energy intake.
Despite the attention received, any effect of calcium on weight loss itself may hold limited utility in combating the obesity epidemic. Up to 50% of lost weight is typically regained within 1 y and 
90% is typically regained within 5 y (16). To our knowledge, this study is the first to report weight loss maintenance data in relation to calcium. In addition, the fundamental tenet of energy balance posits that, with relatively constant energy expenditure, increased energy intake should predict increased body weight (17). Many studies, however, fail to show this relationship (17–19), suggesting that additional variables need to be controlled for when predicting changes in body weight from energy intake. Changes in dietary calcium have been positively related to changes in energy intake (20,21), suggesting that dietary calcium and energy intake increase concurrently, but may have opposing effects on body weight. This study also examined whether changes in dietary calcium and energy intake were positively associated yet correlated with weight maintenance (regain) in opposing directions.
The primary aims of this study were to examine the predictive value of changes in dietary calcium and energy intake on changes in body weight for 18 mo following a weight loss diet. Specific hypotheses were as follows: 1) Changes in dietary calcium and energy intake would be positively related; 2) without controlling for changes in energy intake, changes in dietary calcium intake would not predict weight regain; 3) controlling for changes in energy intake, changes in dietary calcium intake would inversely predict weight regain; 4) without controlling for changes in dietary calcium intake, changes in energy intake would not predict weight regain; and 5) controlling for changes in dietary calcium intake, changes in energy intake would positively predict weight regain.
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Materials and Methods |
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TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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1047 kJ, 25 g protein, 25 g carbohydrate, and 7 g fat. Participants were given no instructions regarding vitamin or mineral supplements but were asked not to make changes to established vitamin or supplement regimens during the study unless directed by a physician. For the following 14 wk, participants returned to conventional foods and were randomly assigned to the following 3 different weight loss maintenance conditions: 1) Cognitive behavioral therapy for weight loss maintenance; 2) cognitive behavioral therapy plus enhanced food monitoring accuracy training; and 3) cognitive behavioral therapy and enhanced food monitoring plus reduced energy density eating training. However, groups did not differ in weight change across any assessment points (Table 2). Therefore, analyses in this study included data for all participants who had completed the necessary measures at each assessment point. Few participants completed all measures at all assessment points, resulting in a different n for most analyses. The n for individual assessments are reported. All intervention ended at 22 wk. Dietary calcium and energy intake were assessed using the Block 98 FFQ (Block) and 5-d food records (FR) at intake, intervention end, 6-mo follow-up, and 18-mo follow-up.
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Dietary intake. The 110-item Block (22) was designed to assess nutrient intake levels, specific food groups, and vitamin and supplement intake. The Block has been validated for nutrient intake (23) and individual differences in intake (24). In FR, participants recorded time, description (brand and ingredients), portion size/amount, and preparation information for all eating and drinking each day for 5 consecutive days. Participants were given detailed instructions on measuring portion sizes. Data obtained from FR were entered into Nutritionist V software (First Databank) by graduate students supervised by a doctoral-level nutritionist. Recipe and manufacturer's nutrient information were manually entered for food items not available in the software database.
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Results |
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TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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Changes in dietary calcium intake were positively correlated with changes in energy intake across all measurement points (Table 4).
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Postintervention to 18-mo follow-up. Neither changes in dietary calcium nor energy intake alone predicted weight regain according to Block and FR data. Controlling for changes in energy intake, changes in dietary calcium intake inversely predicted weight regain according to Block (P = 0.033) but not FR data. Controlling for changes in dietary calcium intake, changes in energy intake positively predicted weight regain according to Block (P = 0.047) but not FR data (Table 5). Using Block data, the unstandardized coefficient (not shown) for (1 mg) change in dietary calcium intake was –0.016. Therefore, holding energy intake constant, a 100-mg increase in dietary calcium intake was associated with 1.6 kg less weight regain over this period.
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6-Mo follow-up to 18-mo follow-up. Neither changes in dietary calcium nor energy intake alone predicted weight regain according to Block and FR data. Controlling for changes in energy intake, changes in dietary calcium intake inversely predicted weight regain according to Block (P = 0.048) and FR (P = 0.025) data. Controlling for changes in dietary calcium intake, changes in energy intake positively predicted weight regain according to Block (P = 0.009) and FR (P = 0.049) data (Table 6). According to Block and FR data, the unstandardized coefficient (not shown) for (1 mg) change in dietary calcium intake was –0.012. Therefore, holding energy intake constant, a 100-mg increase in dietary calcium intake was associated with 1.2 kg less weight regain over this period.
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Changes in dietary fat, protein, carbohydrate, fiber, and supplemental calcium intake all failed to predict weight regain across all measurement points. Results were consistent both with and without controlling for changes in energy intake according to Block and FR data (not shown). In addition, while controlling for each individual predictor variable (dietary fat, protein, carbohydrate, fiber, and supplemental calcium), changes in energy intake failed to predict weight regain across all measurement points according to Block and FR data (not shown).
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Discussion |
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TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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Controlling for changes in energy intake, dietary calcium was the only measured nutrient that predicted weight regain. Consistent with previous literature (26,27), dietary calcium in our sample was derived mainly from dairy sources. The failure of changes in supplemental calcium to predict changes in body weight is also consistent with literature reporting that dairy sources of calcium may be more strongly related to changes in body weight than supplemental calcium carbonate (7,28). Further prospective research is needed to elucidate this issue as well.
The failure of changes in energy intake to predict weight regain was also consistent with predictions and indicates that related variables may be suppressing the effect of changes in energy intake on changes in body weight. This study tested the contention that dietary calcium intake would account for enough variance in weight regain for energy intake to predict weight regain when dietary calcium intake is held constant. Analyses revealed a positive relationship between changes in energy and dietary calcium intake, suggesting that when more kilojoules are ingested, more dietary calcium is as well. Further analyses revealed a positive relationship between greater energy intake and body weight from 6- to 18-mo follow-up controlling for changes in dietary calcium. Combined with results suggesting an inverse relationship between changes in dietary calcium and body weight, these findings suggest that dietary calcium and energy intake may increase concurrently but have opposing effects on body weight.
Results also demonstrated that changes in energy intake failed to predict weight regain when individually controlling for changes in dietary fat, protein, carbohydrate, fiber, and supplemental calcium intake. Thus, changes in energy intake predicted weight regain only when controlling for changes in dietary calcium intake. The inability to find a significant relationship between changes in energy intake and body weight has often been attributed to food intake underreporting and other difficulties in the assessment of energy intake (19,29). However, there is little data supporting this contention. Although only speculations can be made based on correlational data, results in this study suggest that controlling for changes in dietary calcium intake may increase the ability of energy intake to predict weight change. Energy intake assessment (17,30) and weight loss maintenance (16) continue to be areas of particular difficulty in obesity research. We hope this study will inspire further work aimed toward improving the assessment of energy intake and preventing weight regain following weight loss diets in overweight and obese individuals.
Limitations. The central limitation of this study is its retrospective design. As Davies et al. (2) point out, it cannot be determined whether the effect noted in this study was due to calcium per se or to other nutrients with which calcium is a correlate. In addition, energy expenditure was not measured. Differences in Block and FR data attested to difficulties associated with assessing energy intake and it is not uncommon for these 2 measures to yield different results (31,32). It remains unclear why significance was not found in postintervention to 6-mo follow-up analyses. Weight regain from postintervention to 6-mo follow-up (1.9 ± 3.4 kg) ranged from –12.75 to 7.98 kg vs. –11.52 to 13.24 kg from 6- to 18-mo follow-up (3.3 ± 3.8 kg). It is possible that the smaller range in, and amount of, weight regain from postintervention to 6-mo follow-up provided insufficient power to detect an effect. However, power analyses revealed suboptimal power coefficients across all measurement points, ranging from 0.58 to 0.76 calculated for a medium effect size. With previous literature reporting relatively small- to medium-effect sizes (2,4,25), higher correlation coefficients may have been found with a larger sample.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Author disclosures: C. N. Ochner and M. R. Lowe, no conflicts of interest.
Manuscript received 11 April 2007. Initial review completed 19 May 2007. Revision accepted 24 July 2007.
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LITERATURE CITED |
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TOPABSTRACTIntroductionMaterials and MethodsResultsDiscussionLITERATURE CITED |
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