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Medical Department, University Hospital of North Norway, Tromsø, Norway
2To whom correspondence should be addressed. E-mail: elena.kamycheva{at}unn.no.
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ABSTRACT |
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 TOP ABSTRACT SUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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KEY WORDS: • vitamin D • obesity • calcium • life-style factors
Obesity is a predisposing factor for poor health and all-cause mortality (1
), and an increase in body weight has occurred worldwide (2
). In our region, Northern Norway, there has been an increase in mean body mass index (BMI)3 from 1974 to 1994/1995 of 
1 kg/m2 in both sexes (3
).
The cause of obesity is both genetic and environmental (4
). However, the increase that has taken place recently is the result of life-style changes with a decrease in physical activity and an increase in energy intake observed in most societies (2
). Not only the amount, but also the type of food ingested is important. Thus, there have recently been a few reports that a high intake of calcium is associated with a low body weight (5
,6
), which, if causal, may affect the composition of weight-reducing diets.
There have been several large health surveys in Tromsø; the last one in 1994–1995 included >27,000 subjects. Questions on life-style factors, as well as a food-frequency questionnaire that included questions on calcium and vitamin D intake were part of the study, thus providing a large database for testing the hypothesis on a relation between intake of calcium and vitamin D and overweight.
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SUBJECTS AND METHODS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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). Men and women >24 y old were given a questionnaire concerning medical history, smoking habits, coffee and alcohol intake, and physical activity. The number of cigarettes smoked daily was recorded. An alcohol intake score was calculated by totaling the number of glasses of beer, wine, and spirits per 14 d (assuming an equal amount of alcohol in one glass of each type). A physical activity score was calculated by totaling the hours of moderate and hard physical activity (giving the hours with hard activity double the value). The food-frequency questionnaire included questions on intake of milk, bread, butter on bread, yogurt, cheese and eggs, how often they consumed fatty fish for dinner (e.g., salmon), and use of fatty fish (e.g., mackerel or sardines) in sandwiches. Furthermore, the subjects were asked about their daily use of calcium and vitamin D tablets and cod liver oil supplementation. The food-frequency questionnaire was not designed to allow calculation of energy intake and dietary fat. Height and weight were measured while the subjects wore light clothing and no shoes. Blood samples were drawn from nonfasting subjects and serum 
-glutamyl transferase (-GT) was measured with a spectrophotometric method using the multianalyzer Hitachi 917 (Roche Diagnostics, Basel Switzerland) with reagents from Boehringer Mannheim (Mannheim, Germany). The Regional Ethical Committee approved the study, and all participants gave informed consent.
To check the linearity, we recoded age, vitamin D intake, calcium intake and physical activity into quartiles. In the linear regression model, with BMI as the dependent variable, these covariates were each entered as three dummy variables with the first group as the reference category for the initially continuous variables. Age and physical activity sustained linearity to BMI in both genders, whereas vitamin D intake had linear relation to BMI only in women and calcium intake only in men. Therefore, vitamin D intake in men and calcium intake in women were entered into the final linear model as dummy variables (8
).
We analyzed men and women separately with linear regression using BMI as the dependent variable, and physical activity, alcohol and coffee consumption, smoking, and calcium and vitamin D intake as independent variables. The dependent variable (BMI) appeared to be normally distributed on visual inspection and also had a skewness of 1.0 ± 0.018. Differences between groups were evaluated with one-way ANOVA and independent samples t test. Where appropriate, Bonferroni correction was applied. Linear correlations were evaluated with the Pearson correlation coefficient. Vitamin D intake in both genders was divided into 4 quartiles (< 2.8, 2.8–5.8, 5.9–10.0, >10.0 µg/d), and calcium intake in women into 4 quartiles (<275, 275–381, 382–595, >595 mg/d), to create dummy variables and enter them into the linear model with the lowest quartile as the reference category. Obesity was defined as a BMI >30 kg/m2. We applied a logistic regression model with obesity as the dependent variable and vitamin D intake quartiles as predictors (entered as dummy variables, thus not assuming linearity or exponentiality), adjusted for age, calcium intake, physical activity, coffee and alcohol consumption, and smoking. From this model, the odds ratio was calculated for those in the lowest vitamin D intake quartile compared with those in the highest vitamin D quartile as a reference category.
To evaluate the potential public health role of vitamin D, we calculated the predicted BMI in our population if everyone consumed the recommended daily allowance (RDA) dose of vitamin D (5 µg/d for those <50 y old and 10 µg/d for those 
50 y) (9
). The adjustment of BMI for those who consumed less than the RDA dose of vitamin D was made according to the following formula:
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For those who already consumed the RDA dose of vitamin D or higher:
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where BMIP is the predicted BMI, BMIO is the observed BMI from the Tromsø study, b is the unstandardized regression coefficient from the linear model (vitamin D intake coded as a continuous variable in women, coded as the lowest quartile or not in men), vitamin DRDA is the recommended daily allowance dose of vitamin D and vitamin DO is the observed intake from the Tromsø study.
Differences with P < 0.05 were considered significant. All tests were two-sided. The data were analyzed with SPSS for Windows, version 10.0 (SPSS, Chicago, IL).
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RESULTS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The demographics of the study population are given in Table 1
; the table also gives the correlation coefficients and standardized regression coefficients for BMI vs. the other variables. In the multiple linear regression model, age was the most significant predictor of BMI (P < 0.001). In both sexes, BMI and coffee intake were positively associated (P < 0.05) and in women, BMI and alcohol intake were negatively associated (P < 0.001). The alcohol intake score and -GT levels were positively correlated (r = 0.143; P < 0.001, men and women together).
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Vitamin D intake had a negative correlation with BMI in men, and after correction for age and other variables in the multiple regression analysis, a significant negative association was found in both sexes (P < 0.001). Associations between vitamin D intake quartiles and BMI after adjusting for age were found for both genders (Fig. 1
).
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and 3
). The predicted BMI after ingestion of the RDA or higher dose of vitamin D was almost 0.20 kg/m2 lower in men and 0.25 kg/m2 lower in women in each age group.
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DISCUSSION |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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), and as expected, we found a negative correlation with BMI (11
).
In both sexes, smoking had a significant negative association with BMI, which is consistent with other studies (12
,13
). Only in women did we find an inverse relationship between alcohol intake and BMI. Other studies have shown a negative association between alcohol intake and BMI in both sexes, but in heavy drinkers only (12
). Our questionnaire is likely not an accurate measure of alcohol intake. The correlation between alcohol intake and -GT activity was positive and significant, but the correlation coefficient was rather low, indicating doubtful validity of the self-reported data.
We found a weak but significant positive relation with BMI for coffee intake in both sexes. Theoretically, coffee intake should reduce BMI because coffee consumption decreases the amount of fat tissue by elevating thermogenesis (14
) and stimulating the sympathoadrenal system (15
). However, previous reports on associations between coffee and BMI differ in their conclusions (16
,17
).
Recently, Davies et al. (5
) reported a negative association between calcium intake and BMI in women. In their study, a meta-analysis of four separate studies on calcium supplementation for osteoporosis in which weight was also recorded, an increment in the calcium intake of 100 mg/d led to a 0.82 kg weight decrement (5
). They also estimated that the calcium intake could explain 3% of body weight variability (5
). Furthermore, in a trial in young women carried out by Teegarden et al. (18
), subjects with a high calcium intake had less fat tissue and failed to gain weight, and in a study in young children, Skinner et al. (19
) found that dietary calcium had a negative relation to body fat mass.
As noted by Davies et al. (5
), the lack of focus on calcium intake and weight can be explained by the lack of a plausible explanation for the association. However, there are some recent theories that link calcium metabolism to body weight. Thus, Zemel et al. (6
) suggested that intracellular calcium enhances lipid deposition in adipocytes. The calciotrophic hormones, parathyroid hormone (PTH) and 1,25-dihydroxyvitamin D, promote calcium influx into the lipid cell, thus mediating lipogenesis. A low calcium intake increases these hormones and hence lipid storage as well (6
,20
). In support of this, there are repeated observations of a high BMI in subjects with elevated PTH (21
,22
).
To our surprise, we could not confirm these observation about calcium intake and body weight. In our study, there was no association between BMI and calcium intake in women, whereas a positive and significant association was found in men. This is hard to explain.
For both sexes, the calcium intake, which was calculated mainly from dairy sources, was remarkably low. This may indicate a substantial underreporting of calcium intake. The questionnaire was obviously less accurate than an interview with a dietician (23
), although the reproducibility of dietary data from this self-administered questionnaire was evaluated previously and considered acceptable (24
). In addition, our questionnaire cannot account for most of the variability in individuals who do not consume much calcium from dairy sources. Furthermore, a linear relationship between BMI and calcium intake did not exist in women, causing us to use dummy variables in the multiple regression model. These shortcomings could mask an association between calcium intake and BMI in women; in this respect, our findings must be viewed with great caution. On the other hand, this cannot explain the significant and positive association found in men. One possible explanation could be an association between calcium intake from dairy products and total energy intake, a factor for which we made no adjustments.
Our food-frequency questionnaire was concerned mainly with calcium intake from dairy products, which in a Norwegian diet represent 75% of the total calcium intake (7
); this is rather high compared with other populations (25
). The relationship in men may therefore not be calcium specific, but could reflect a number of other components in dairy products. There might be a weight-increasing factor in dairy products that overrides a weight-reducing effect of calcium. Thus, in populations in which calcium intake is not based on dairy products, its effect on body weight may become apparent.
We found significant inverse associations between BMI and vitamin D intake in men and women, in both the correlation and regression analyses. The difference was observed in all age groups. When stratifying the data for age, the difference in BMI between those in the upper and lower quartiles of vitamin D intake was 1 kg/m2 in both subgroups.
Vitamin D is important for the intestinal absorption of calcium, and the mechanism whereby a high intake of vitamin D could prevent weight increase should therefore theoretically be identical to that discussed above for calcium. However, this is not the case because we have found opposing effects of calcium and vitamin D intakes, at least in men. It is therefore tempting to speculate that the associations observed do not reflect causal relationships.
Like differences in calcium intake, differences in meal patterns between populations may be of importance. In Norway, the main sources of vitamin D are cod liver oil and fat fish rather than exposure to sunlight, and the effect on weight, if causal, could be from a number of substances other than vitamin D.
Furthermore, dairy products in Norway are not fortified with vitamin D as in the United States and are therefore a negligible source of vitamin D. Concomitantly, vitamin D intake from exposure to sunlight differs in Norway, and it is to be expected that other associations between vitamin D intake and factors such as BMI exist in our population.
The number of subjects included in the study was almost 19,000 and the significant associations detected may not necessarily have relevance in a public health sense. To evaluate this, we calculated the expected BMI if those with a low intake had consumed the RDA dose of vitamin D. This resulted in a decrease in BMI of almost 0.20 kg/m2 for men and 0.25 kg/m2 for women in each age group, which could have considerable effect on diseases such as hypertension and diabetes (26
).
In conclusion, we have found many of the expected relationships between life-style factors and BMI. In addition, our data point out the need for further studies on BMI and calcium and vitamin D intakes. In particular, placebo-controlled intervention studies with calcium and vitamin D, in which the primary end point is weight change, are warranted.
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FOOTNOTES |
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3 Abbreviations used: BMI, body mass index; CI, confidence interval; PTH, parathyroid hormone; RDA, recommended dietary allowance; -GT, -glutamyl transferase.
Manuscript received 2 May 2002. Initial review completed 28 May 2002. Revision accepted 26 September 2002.
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LITERATURE CITED |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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1. Lean, M.E.J. (2002) Pathophysiology of obesity. Proc. Nutr. Soc. 59:331-336.
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11. Thune, I., Njølstad, I., Løchen, M-L. & Førde, O. H. (1998) Physical activity improves the metabolic risk profiles in men and women. Arch. Intern. Med. 158:1633-1640.
12. Tavani, A., Negri, E. & La Vecchia, C. (1994) Determinants of body mass index: a study from northern Italy. Int. J. Obes. Relat. Metab. Disord. 18:497-502.[Medline]
13. Perkins, K. A., Sexton, J. E. & DiMarco, A. (1996) Acute thermogenic effects of nicotine and alcohol in healthy male and female smokers. Physiol. Behav. 60:305-309.[Medline]
14. Tagliabue, A., Terracina, D., Cena, H., Turconi, G., Lanzola, E. & Montomoli, C. (1994) Coffee induced thermogenesis and skin temperature. Int. J. Obes. 18:537-541.[Medline]
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17. Merkus, M. P., Mathus-Vliegen, L. M., Broekhoff, C. & Heijnen, A. M. (1995) Extreme obesity: sociodemographic, familial and behavioural correlates in The Netherlands. J. Epidemiol. Community Health 49:22-27.[Abstract]
18. Teegarden, D., Lin, Y-C., Weaver, C. M., Lyle, R. M. & McCabe, G. P. (1999) Calcium intake relates to change in body weight in young women. (Abstract). FASEB J. 13:A873.
19. Skinner, J., Carruth, B. & Coletta, F. (1999) Does dietary calcium have a role in body fat mass accumulation in young children?. Scand. J. Nutr. 34:45S(abs.).
20. Shi, H., Dirienzo, D. & Zemel, M. B. (2001) Effects of dietary calcium on adipocyte lipid metabolism and body weight regulation in energy-restricted aP2-agouti transgenic mice. FASEB J. 15:291-293.
21. Hey, H., Lund, B., Sørensen, O. H., Lund, B. & Christensen, M. S. (1979) Impairment of vitamin D and bone metabolism in patients with bypass operation for obesity. Acta Med. Scand. 24:73S-78S.
22. Jorde, R., Sundsfjord, J., Haug, E. & Bønaa, K. H. (2000) Relation between low calcium intake, parathyroid hormone, and blood pressure. Hypertension 35:1154-1159.
23. McCarron, D. A., Morris, C. D., Henry, H. J. & Stanton, J. L. (1984) Blood pressure and nutrient intake in the United States. Science (Washington, DC) 224:1392-1398.
24. Jacobsen, B. K. & Bønaa, K. H. (1990) The reproducibility of dietary data from a self-administered questionnaire. The Tromsø study. Int. J. Epidemiol. 19:349-353.
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30 kg/m2) men in different age groups; "obese" differs from "nonobese" (P < 0.01) in all age groups. Values are means ± SEM, n = 9252.