چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | علوم پزشکی کرمانشاه |
| کد مقاله | 11804 |
| عنوان فارسی مقاله | |
| عنوان لاتین مقاله | Does dietary calcium intake is effective in regulating of body weight in children? |
| نوع ارائه | سخنرانی |
| عنوان کنگره / همایش | 11th international Conference on Childhood Obesity and Nutrition |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | اسپانیا |
| شهر محل برگزاری کنگره/ همایش | بارسلون |
| سال انتشار/ ارائه شمسی | 1396 |
| سال انتشار/ارائه میلادی | 2018 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1396/12/24 الی 1396/12/25 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | https://childhoodobesity.conferenceseries.com/2018 |
| آدرس علمی (Affiliation) نویسنده متقاضی | 2.0000 |
| نویسنده | نفر چندم مقاله |
|---|---|
| امیر باقری | دوم |
| مهنوش صمدی | اول |
| عنوان | متن |
|---|---|
| کلمات کلیدی | dietary calcium, fat mass index, obesity |
| چکیده | Fat Mass Index (FMI) relates body fat mass to height and allows comparing body fat mass of individuals at different heights. In this study, dietary calcium intake and some nutritional factors in obese children aged 8-10 were investigated. In this study, 580 children with FMI at or above the 90th percentile of age-specific data (FMI 6.9 kg/m2) are known as obese in case group and 600 children with FMI less than the 90th percentile are known as normal weight in control group was classified. In this study, body fat mass was measured by Bio Impedance analyzer and for calcium intake, we used a validated food frequency questionnaire. In the case group calcium intake was significantly lower than the control group (574±112 and 836±161 mg/d) (P< 0.01), respectively. In this study, we use 3 model of logistic regression. In model 1 after Adjustment for total energy intake and the percentage of energy from macronutrients inverse association between calcium intake and obesity was significant. In model 2 by further adjustment for the effect of fruits and vegetable intake inverse association between calcium intake and obesity was significant and in model 3 by further adjustment for the effect of physical activity level inverse association between calcium intake and obesity became weaker but yet was significant. Even after controlling for confounding factors the inverse relationship between calcium intake and FMI remained significant. In this study, we showed that FMI may be a more accurate index that shows the association between calcium intake and obesity. |
| متن مقاله | Introduction Obesity has become an epidemic issue all around the world among children (Wells, 2001). According to the National Health Examination Survey 18.8% of children 6-11 years old in the United States were overweight in year 2003-2004 (Ogden, Yanovski, Carroll, & Flegal, 2007). According to Center for Disease Control (CDC) growth chart for children, overweight was defined as being at or above the 95th percentile and at risk for overweight was defined as being between the 85th and the 95th percentile of sex-specific BMI-for-age in a specified reference population (Himes, & Dietz, 1994). Unlike CDC, the terminology used in this study referrers to overweigh as obese and being at risk of overweight as overweigh, relative to body mass index (BMI). Prevalence of obesity and overweight in school age children in Iran has been estimated to be 4.4% and 9.8%, respectively (Haeri, Dorosti, & Eshraghian, 2010). Several factors are associated with the increase in incidence of obesity in children. Among these factors low physical activity, high intake of foods, presence of obesity in parents, socio-economic and nutritional factors are important (Izuno, Yoshida, Miyaleausa, & Sugimon, 2001; Maddah, & Nikooyeh, 2010; Kelishadi et al., 2007; Mardones et al., 2008). Several studies on nutritional behaviors in children have demonstrated that high intake of milk and dairy products (Lorenzen, Molgaard, Michaelsen, & Astrup, 2006), high intake of fiber and vegetables (Zemel, SHI, greer, & dierenzo, 2000), and low intake of sweet beverages(McCarron, 1983) are in an inverse association with BMI and body fat mass. However, studies exploring the association between calcium intake, specifically from dairy sources and BMI, body weight, or body fat are not conclusive. To determine the long-term effect of a dairy-rich diet in weight control of young children, Kelishadi et al. through a randomized controlled trial showed that a dairy rich diet could be a practical strategy for weight control in overweight children (Kelishadi et al., 2009). Dioxn et al. concluded that calcium intake was inversely associated with BMI, sum of skinfolds, and trunk skinfolds in children (Dixon, Pellizzon, Jawad, & Tershakovec, 2005). However, Gunther et al., by conducting an interventional study on young women, showed that there were not any differences in weight change, BMI, fat mass and fat free mass (which was measured with dual energy X-ray absorptiometry) among subjects with high calcium and normal calcium intake over one year (Gunthe, Legowski, Lyle, mccabe, & Eagan, 2005). Another interventional study showed that longer intake of calcium was not associated with lower body fat (Yanovski, Parikh, Yanoff, Denkinger, & Karim, 2009). Body fat was assessed by dual energy X-ray absorptiometry (DXA). BMI is a crude but readily obtainable measure of excess body fat. Therefore, it is not an accurate measure of changes in body fat and body compounds (Kyle, Kossovsky, Genton, & Pichard, 2007). It measures excess weight relative to height, and does not discriminate body fat from fat-free mass (Khongsdier, 2005). Moreover, Fat Mass Index (FMI), which is defined as body fat (kg) divided by the square of height (m2), is an indicator of nutritional status (VanItallie, Yang, Heymsfield, Funk, & Boileau, 1990) and takes into account fat mass content in addition to height and weight. That may explain the inconsistency in the results of the studies. In the present study, we used FMI as an indicator of obesity in children. By using FMI, we were able to compare body fat mass of individuals with different heights, and to compare the intake of daily calcium in obese and non obese children in a case and control study. Material and Methods Subjects In this case-control study, the study population was 8-10 years old girls from primary schools in the city of Isfahan. Multistage cluster random sampling was conducted for sample selection. Ten schools were randomly selected from five education and training districts of the city and 11 children were selected from each school as cases. For each case, 3 controls were randomly selected from the same class and the same school. As the reference standards for FMI based on a large subject population have not been clearly defined, on account of larger sample size of Nakao's study which led creating a reference norm for Japanese boys and girls, we applied the latter cutoff for our study (Nakao, & Komiya, 2003). We considered 90th percentile of fat mass index value for 9-11 years girls as the cut off point for obesity. Subsequently, FMI at or above 7.2 kg/m2was considered as a criterion for selecting cases and FMI less than 7.2 kg/m2 was considered as a criterion for setting controls. Further more, we computed the 90th percentile value of FMI for the 420 participants in our study. The value was 7.4, which was 0.2 more than Nakao et al. value for 90th percentile (7.2). In total, 110 girls in the case group and 307 girls in the control group were recruited. Anthropometric measurements Body weigh was measured without shoes and with minimum clothing using a digital scale (Tanita, Japan) with 0.1 kg accuracy. The scale was calibrated before each measurement session. Height was measured with 0.1 cm accuracy with non-stretchable tape. BMI was calculated as weight in kilogram divided by height in meters squared. To measure fat mass, we used Bio Impedance Analyzer BIA (Tanita 418MA, Tokyo, Japan). Compared to DXA, BIA has an acceptable accuracy and provides on average 2–6% lower values for fat mass (Volgyi et al., 2008). The device uses a multi frequency electrical level to estimate both extracellular and intracellular fluids. It has eight polar electrodes and it incorporates a digital scale for measuring body mass. The device was used under constant conditions, before exercise, in fasting situation. For all the subjects the same device was used. Fat mass (kg) were divided by height squared (m2) to calculate FMI. FMI at or above 7.2 according to 90th percentile of reference norm of Nakao study in Japan (Nakao, & Komiya, 2003) was considered as a criterion for categorizing obesity and FMI less than 7.2 was considered as criterion for not being obese. Assessment of dietary intakes A 56-item food frequency questionnaire (FFQ) was applied to evaluate the calcium intake of children. The questionnaire was filled out by interviewing mothers or caregivers. Validity and reliability of this questionnaire in children in Tehran had been examined previously (Neyestani et al., 2012). Validity of FFQ was determined by comparison with five 24-hour dietary recalls. After controlling for total energy intake, correlation coefficients between the two methods were 0.35 and 0.65 for validity and reliability, respectively. The amount and frequency of food consumption during the previous month on a daily, weekly or monthly basis was assessed by this FFQ. This questionnaire categorized food items into the following subcategories: group 1: cereals including bread, rice, cookie, biscuit and pancakes; group 2: grains; group 3: milk, milk shake, yoghurt, cheese, ice-cream, yoghurt drink; group 4: egg, red meat, fish and poultry; group 5: tomato, cucumber, lettuce, cabbage, spinach and green leaf vegetables; group 6: orange, apple, date, dried fruits, raisin, sloe, blueberry; group 7: nuts, cocoa chocolate, soy bean, seeds, and sesame paste. Quantities were estimated from photographs of portion sizes and from household measures (Ghaffarpour, Houshiar Rad, & Kianfar, 1999). In addition to FFQ, subjects completed three-day food records including one weekend and two weekdays to access their total intake of energy and macronutrients. These records were filled out by mothers or caregiver and children. Explicit written instructions in addition to an explanatory oral session on how to complete records on beverages, food, supplements, and estimating portion sizes were provided to mothers. Expert nutritionists controlled all records for accuracy (USDA. Food Guide Pyramid, 2004). Each food and beverage was coded according to the prescribed protocol and then analyzed for content of energy and the other nutrients and serving of daily dairy products, fruits and vegetables intake, based on USDA database and Iranian food composition table. In order to evaluate physical activity of the children, we utilized Beacke questionnaire (Baek, Burema, & Frijters, 1982). Information was collected on the level of habitual physical activity. The questionnaire consisted of components of physical activity, sports during leisure time and physical activity during leisure time excluding sports (Baek et al., 1982).To compare physical activity scores between the two groups, Beacke scores were computed across quartiles. Statistical analysis Quantitative variables were expressed as mean ± SD and qualitative variables, as median and interquartile range. The Kolmogorov–Smirnov test was used to test the normality of the distribution of variables. Independent samples comparisons in terms of quantitative variables were done using two independent samples t-test and Mann-Whitney U test as appropriate. Multinominal logistic regression model was used for modeling the relationship between calcium intake with FMI adjusting for confounder variables including: energy intake (in kcal/d), percentage of energy from fat, carbohydrate and protein (percent/d), physical activity level (light, moderate, or severe), and consumption of fruit and vegetable (serving/d). We categorized physical activity scores based on quartiles. Quartiles cut points for physical activity were based on the distribution of scores among the control group. Cochran – Armitage test for trend also was conducted. All statistical analysis was done using SPSS software (version 16; SPSS Inc, Chicago IL). P-value, 0.05 was considered significant. Results Mean and standard deviation of FMI and BMI of the 417 children were 6 ± 2.1 kg/m2 and 19.4 ±3 kg/m2, respectively. The means and SDs of age (y), anthropometric measurements and physical activity status for case and control groups are shown in Table 1. The mean of, BMI (P<0.05), fat mass (P<0.01), percent of fat mass (P<0.01), waist circumference (p<0.01) and fat free mass (P<0.01) were significantly higher in cases. Most of the subjects in the control group were in 4th quartiles of physical activity score and the majority of the subjects in the case group were in 3rd quartiles. Cases had significantly higher scores of physical activity in 3th and 4th quartile (P<0.05), and lower score in 1th quartile. Mean intake of dietary variables for the two groups are presented in Table 2. Compared with the subjects in the control group, cases had more intake of total energy (p<0.01) and more intake of fat (percent of total energy from fat) (p<0.01). Children in both groups showed a significant difference in calcium intake and dairy products, fruits and vegetables consumption. Compare to cases, controls consumed significantly more from mentioned food groups(p<0.01). Multinominal-adjusted odds ratios and 95% CIs for obesity and its features for calcium intake in quartiles of physical activity are shown in Table 3. In low quartiles of physical activity the association between calcium intake and obesity is stronger. In model 1, we adjusted obesity for the effect of total energy intake, the percentage of energy from fat, carbohydrate and protein (OR and 95% CI in quartiles of physical activity respectively 0.69(0.59-0.71), 0.74(0.61-0.86), 0.83 (0.81-0.85), 0.87(0.95-0.96). After adjustment, the inverse association between calcium intake and obesity was significant and in the 1st quartile of physical activity the association was significantly stronger than 4th quartile. In model 2, we further made adjustments for the effect of fruit and vegetable intake, resulting that the OR became weaker (OR 95% CI in quartiles of physical activity respectively 0.85(0.63-0.96), 0.86(0.79-0.99), 0.89(0.74-0.88), 0.99(0.97-0.98). The inverse association between calcium intake and obesity became weaker but significant. Discussion The result of this study indicates that FMI is inversely associated with daily dietary calcium intake and the inverse relationship between FMI and calcium intake remained significant after adjusting for confounding factors. Results from other studies are not conclusive in this regard. Skinner et al. reported an inverse association between calcium intake and body fat in 8-year- old children by conducting a 5-year longitudinal study (Skinner, Bounds, Carruth, & Ziegler, 2003). In their study, body fat was measured with DXA and calcium intake was assessed by food record. Gianvincenzo et al. reported an inverse association between BMI and body weight in children (Gianvincenzo, Ersilia, Paola, Antonella, & Alfonso, 2005). Dioxn et al. reported inverse association between BMI, sum of skinfolds and trunk skinfolds and calcium consumption in children (Dixon et al., 2005). Other studies showed beneficial effects of dairy products in regulating body weight (Barba, & Russo, 2006; Teegarden, 2005; Barr, 2003). Zemel et al. reported that isocaloric substitution of dairy products in obese adults reduced total body fat and decreased trunk fat (Zemel, 2002). Contrary to our findings, some other studies found no relationship between calcium intake and BMI, body weight and fat mass in children. Lappe et al. by conducting an interventional study showed that children with rich calcium diet had an increase in BMI, weight and fat mass similar to the group with normal calcium diet (Lappe, Rafferty, Davies, & Lypaczewski, 2004). Venti et al. assessed calcium intake in children through 24h recall and measured body fat with DXA. They did not find any association between calcium intake and BMI, body weight or body fat (Venti, Tataranni, & Salbe, 2005). Some interventional studies, as well, did not find any association between calcium intake and weight or fat mass change. Barr et al. reviewed 9 randomized dairy product supplementation trials and reported that 7 of the studies did not find any association between calcium intake and weight change. Two studies found greater weight gain in the group with dairy product intake (Barr, 2003). Gunther et al. did not find any association between higher intake of dairy product and change in body weight or fat mass over one year in young women (Gunther et al., 2005). Another study reported inverse association between calcium supplements and weight gain (Gonzalez, White, Kristal, & Littman, 2006). In contrast, Lorenzen et al. found no association between calcium supplementation and body fat, but reported an inverse association between body fat and dietary calcium intake (Lorenzen, et al., 2006) Our study was in agreement with studies that showed inverse association between dietary calcium and dairy products consumption, on the one hand, and body fat, BMI, and the incidence of obesity, on the other (Yannakoulia, Ntalla, Papoutsakis, Farmaki, & Dedoussis, 2010; O'Connor, Yang, & Nicklas, 2006; Jaqumain, Doucet, Despres, Bouchard, & Tremblay, 2003; Albertson, Good, Holschuh, & Eldridge, 2003; Pereira et al., 2002; Loos et al, 2003; Rosell, Hakansson, & Wolk, 2006; Snijder et al., 2008). The results on association between calcium intake and obesity remain controversial. One reason could be that previous studies focused mostly on BMI or fat mass as an indicator of obesity. BMI is not a good yardstick for assessing obesity, because BMI does not discriminate body fat from fat-free mass (Kyle, 2007; Khongsdier, 2005). In this study, by applying FMI we were, therefore, able to compare body fat mass of individuals with different heights, and further assess the association between calcium intake and obesity. One explanation for effect of calcium on body fat is that by high calcium intake calciotropic hormones (calcitriol and parathyroid hormones) are suppressed and this ultimates a decrease in intracellular calcium concentration in adipose tissue and subsequently increases lipolysis and thermogenesis. As a consequence, a decrease in body fat deposition occurs (Zemel, 2004). Another mechanism which can describe calcium's anti-adiposity effect is that with high calcium intake, high fecal fat loss and inhibiting fat absorption occurs (Zemel, 2005). Furthermore, protein content of dairy products and their amino acids composition could play a role in reducing obesity. Compared to other macronutrients, protein is more effective in suppressing appetite. One component of dairy products that could have a role in modulating adiposity is whey protein. The rich concentration of branched-chain amino acids in whey protein (26% leucine, isoleucine, and valine) (Bos, Gaudichon, &Tome, 2000; Layman, 2003) could play a specific metabolic role (leucine specifically) in supporting protein synthesis (Ha, & Zemel, 2003) by repartitioning of dietary energy from adipose tissue to skeletal muscle (Zemel, 2004). This suggests that high amounts of calcium in dairy products in combination with whey protein can have synergistic effect to diminish fat mass (Zemel, 2004). Another mechanism that expresses the anti-obesity role of dairy products is related to calcitriol which suppresses the expression of uncoupling protein2 (UCP2) via vitamin D nuclear receptor (Seckl, & Walker, 2001). UCP2 has a role in inducing a mitochondrial proton leak and hence increasing thermogenesis. In addition, it induces apoptosis in adipocytes. High intake of calcium in mice, by suppressing 1,25-(OH)2-D3 levels enhanced adipocytes apoptosis and UCP2 expression and inhibited the decline in thermogenesis (Shi, DiRienzo, & Zemel, 2001). So high calcium diet increased fat oxidation and decreased lipid accumulation. There are some limitations in our study. We designed a case-control study to explore the association between calcium intake and FMI. Further interventional studies will provide stronger evidence on this association. In this study, we utilized BIA (Tanita 418MA) to assess fat mass. Even though the device is one of the most valid instruments after DXA to measure fat mass, measuring fat mass with DXA can provide stronger evidence. Furthermore, we did not have a reference norm in Iran to define a cutoff point for FMI. Even though we computed 95th percentile for percent of fat mass for our population, the sample size (410) was not large enough to conclude a cut off points for FMI. Therefore, we applied reference norms from Nakao study. To our knowledge this was the only study in this age group with large enough sample size that could be used to indicate a cutoff point for fat mass index. It is recommended that future studies assess the components of dairy products and related mechanisms of action which are responsible for this effect. An advantage of this study was using fat mass index as a measure to assess obesity. Whereas BMI may not provide accurate information about changes in body fat mass, fat mass index (FMI) relates body fat mass to height and let us compare the body fat mass of individuals with different heights. In conclusion, we found evidence that indicates an inverse relation between calcium intake and FMI after controlling for confounding factors. Further studies are required to corroborate our findings. Acknowledgements The present study was supported by a grant (no: 91-1-27-17361) from the Vice-chancellor for Research, Tehran University of Medical Sciences, Tehran, Iran. Conflict of Interests Authors have no conflict of interests. Authors' Contributions HSY carried out the design of the study. HSY and MS developed the study. HSY, MS and LA were responsible for data analysis, and interpretation of results. MS and KJ were responsible for data gathering. AF was responsible for data analysis. HSY and MS were responsible scientific writing of the manuscript. 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FASEB Journal, 14, 1132–1138. TABLE 1 Characteristics of the subjects in case and control group Variable Case Control Pvalue¹ (n = 110) (n=307) Age (y) 8.6 ± 0.42 8.8 ± 0.72 <0.07 BMI (kg/m2) 22.2 ± 2.7 18.5 ± 2.6 <0.01 Fat mass (kg) 15.6 ± 2.4 9.4 ± 3.3 <0.01 Fat mass percent (p) 34.2 ± 3.6 27 ± 4.7 <0.01 Fat free mass (kg) 30.1 ± 3.4 24.4 ± 4.5 <0.01 Waist circumference (cm) 73.2±6.8 62±6.8 <0.01 Quartile of Physical activity score3 1 (%) 22.4 7.2 <0.054 2 (%) 23.3 25.9 <0.054 3 (%) 27.8 32.8 <0.054 4 (%) 26.5 34.1 <0.054 1Pvalues based on t-test or Mann-Whitney test. 2 Mean ±SD (all such values), unless indicated. 3Cutoffs were: <2.15; 2.15 to <2.28; 2.28 to <2.56; and ≥2.56 for quartiles 1–4, respectively. 4 The result of Cochran-Armitage test for trend. TABLE 2 Dietary intakes of participants in case and control group Dietary intake Case Control Pvalue¹ (n = 110) (n=307) Nutrients Total energy (kcal/d) 2354 ± 2912 1887± 2983 < 0.01 Carbohydrate (% of total energy) 35 ± 3.1 2 36±3.62 0.08 Protein (% of total energy) 23 ± 3.5 2 25 ± 3.22 0.07 Fat (% of total energy) 40± 6.1 3 36 ± 5.23 < 0.01 Calcium (mg/d) 657± 103 2 981 ± 1522 < 0.01 Food groups (serving/d) Fruits4 1.3± 0.3 2 2.1 ± 0.72 < 0.01 Vegetables5 0.7± 0.2 2 1.1 ± 0.52 < 0.01 Dairy6 1± 0.3 2 2.5 ± 0.42 < 0.01 1P values based on t-test or Mann-Whitney test 2 mean ±SD 3median ± interquartile range. 4Includes apples, oranges, bananas, peaches, grapes, strawberries, pears, watermelon, grapefruit, prunes, pomegranates, kiwi, persimmons, raisins, figs, coconuts, apricots, sweet lemon, and lemon. 5Includes onions, cucumbers, lettuce, carrots, cauliflower, brussels sprouts, kale, cabbage, spinach, mixed vegetables, corn, green beans, green peas, peppers, beets, potatoes, tomatoes, broccoli, and celery. 6Includes milk, yogurt, and cheese. TABLE 3 Multinominal-adjusted odds ratios (OR and 95% CIs) for obesity across calcium intake. Quartile of physical activity 1(n=45) 2(n=104) 3(n=129) 4(n=132) OR (95% CI) 1Model1 0.69 (0.59-0.71) 0.74 (0.61-0.86) 0.83 (0.81-0.85) 0.87 (0.95-0.96) 2Model2 0.85 (0.63-0.96) 0.86 (0.79-0.99) 0.89 (0.74-0.88) 0.99 (0.97-0.98) 1Model 1 was adjusted for total energy, percentage of energy from fat, carbohydrate and protein. 2Model 2 was further adjusted for fruits and vegetables intake. |
| نتیجه مقاله | In this study, body fat mass was measured by Bio Impedance analyzer and for calcium intake, we used a validated food frequency questionnaire. In the case group calcium intake was significantly lower than the control group (574±112 and 836±161 mg/d) (P< 0.01), respectively. In this study, we use 3 model of logistic regression. In model 1 after Adjustment for total energy intake and the percentage of energy from macronutrients inverse association between calcium intake and obesity was significant. In model 2 by further adjustment for the effect of fruits and vegetable intake inverse association between calcium intake and obesity was significant and in model 3 by further adjustment for the effect of physical activity level inverse association between calcium intake and obesity became weaker but yet was significant. Even after controlling for confounding factors the inverse relationship between calcium intake and FMI remained significant. In this study, we showed that FMI may be a more accurate index that shows the association between calcium intake and obesity. |
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| invitation letter spain.pdf | 1397/01/26 | 272023 | دانلود |
