11. obesity in children from sonora

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Obesity in Mexican children

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11. obesity in children from sonora

  1. 1. Original Paper Ann Nutr Metab 2008;52:227–232 Received: January 23, 2007 Accepted: October 4, 2007 DOI: 10.1159/000140514 Published online: June 19, 2008Common Predictors of ExcessiveAdiposity in Children from a Regionwith High Prevalence of OverweightNancy Basaldúa Erwin ChiqueteHospital Civil de Guadalajara ‘Fray Antonio Alcalde’, Centro Universitario de Ciencias de la Salud,Universidad de Guadalajara, Guadalajara, MéxicoKey Words having first-degree relatives with obesity (OR 2.59, 95% CIAdiposity Body mass index Overweight children 1.41–4.74), female gender (OR 5.60, 95% CI 3.22–9.77) and be-Obesity, Mexico Overweight, risk factors ing the third child or younger in offspring (OR 2.07, 95% CI 1.22–3.51). These effects could not be explained by social class, ethnicity, maternal age and duration of breastfeeding.Abstract Conclusions: Risk factors easily identified by history-takingAim: To identify risk factors other than energy intake or can predict childhood adiposity and the high risk of obesityexpenditure that can predict adiposity and overweight in in adulthood. Having a first-degree relative with obesity un-children from a region with high prevalence of obesity. derscores the impact of genes and the family lifestyle on ex-Methods: We studied 551 children aged 6–12 years (50.5% cessive adiposity. Being the third child or younger may de-girls) from a city in the North of Mexico. Tetrapolar bioimped- note different nurture practices in offspring; however, thisance was used to assess body fat content. Overweight was factor deserves more exploration.estimated by analysis of age- and gender-standardized body Copyright © 2008 S. Karger AG, Baselmass index (BMI) relative to reference data of the Interna-tional Obesity Task Force (BMIs that predict obesity in adult-hood). Multivariate analyses were modeled to find indepen- Introductiondent predictors of adiposity. Results: The frequency ofoverweight/obesity was 37.6%. There were no differences The prevalence of childhood overweight has increasedbetween genders with respect to weight, height and BMI; in most populations [1]. This picture is also observed inhowever, age-standardized percentage of body fat and a low-income countries, where undernutrition is still a ma-sedentary lifestyle were higher in girls than in boys (p ! jor issue [2–5]. These problems integrate the paradox of0.001). Independent predictors of overweight/obesity were the low-income populations living the transition towardshaving first-degree relatives with obesity [adjusted odds ra- a developed or ‘occidental’ lifestyle, in which both obe-tio (OR) 2.26, 95% confidence interval (CI) 1.40–3.64], seden- sity and undernutrition concur affecting the less privi-tary lifestyle (OR 1.58, 95% CI 1.05–2.37) and being the third leged classes [2, 6].child or younger in offspring (OR 1.59, 95% CI 1.02–2.47). Pre- It is commonly thought that the medical illnesses as-dictors of body fat in the highest quartile of the sample were sociated with obesity preferentially affect to adults; how- © 2008 S. Karger AG, Basel Dr. Erwin Chiquete 0250–6807/08/0523–0227$24.50/0 Subdirección General de Enseñanza e InvestigaciónFax +41 61 306 12 34 Hospital Civil de Guadalajara ‘Fray Antonio Alcalde’E-Mail karger@karger.ch Accessible online at: Hospital 278, Guadalajara, Jalisco C.P. 44280 (México)www.karger.com www.karger.com/anm Tel./Fax +52 33 3613 3951, E-Mail erwinchiquete@runbox.com
  2. 2. ever, in recent years it has been demonstrated that most ational physical activities), for at least 3 days per week. For an-of the biological conditions that mediate the harmful thropometrics, children were measured in light sportwear after they had emptied their bladders. Height was measured withoutconsequences of the excessive amount of adipose tissue shoes to the nearest 0.5 cm using a wall-mounted metric rule.also occur early in life [7–11]. Childhood overweight pre- Waist and hip circumferences (cm) were measured with an an-dicts obesity in the following decades [12, 13] and implies thropometric tape. Waist was measured at the minimum cir-a high risk of cardiovascular disease and early mortality cumference between the iliac crest and the rib cage, below thein adulthood [11]. Therefore, prevention and effective sternum. The hip measurement was taken at the maximum pos- terior protrusion of the buttocks, around the greater trochanter.treatment of overweight in children is essential to pro- Weight and body fat content were assessed by bioimpedancemote a longer and healthier lifespan. analysis (BIA) with a four-pole impedance meter at 800 mAmp There are several reports that have described deter- and 50 kHz (BIA 310 Bioimpedance Analyzer, Biodynamics, Se-minants of childhood overweight, mainly lifestyle hab- attle, Wash., USA), at least 2 h after food ingestion. This instru-its [14–16]. Nevertheless, little is known on whether ment has a maximum possible difference between any two mea- surements for the same subject of 0.68 kg [95% confidence inter-common early factors, other than caloric intake and en- val (CI) 0.58–0.84 kg] for estimation of body fat content inergy expenditure can predict adiposity in children per- children [21]. Children were asked to stand barefoot and withouttaining to a population with a high prevalence of over- metals on an insulating sole, and electrodes were placed in theweight. Therefore, we sought to identify independent four limbs, as corresponded. Gender and height details were en-predictors of adiposity in childhood and of the high risk tered manually into the electronic system via a keyboard. Body weight and total as well as percentage body fat (PBF) were esti-of obesity in adulthood, in a cohort of schoolchildren mated using the standard built-in prediction algorithms forfrom a region in the North of Mexico with one of the children. The printed report provided readings of fat mass, leanhighest prevalences of overweight in children and adults mass, bone mineral content, total body mass (sum of fat mass,[17–19]. lean mass and bone mineral content), body mass index (BMI) and PBF. The whole body scan time was 2–3 min. Overweight was estimated by comparing BMI standardized for age and sex, relative to reference data of the International Obesity Task Force Materials and Methods (IOTF, standardized BMI cut-off points that predict overweight and obesity at age 18 years) [13]. Study Population This cross-sectional study was performed between February Statistical Analysisand July 2004, in the urban population of Magdalena de Kino, The main dependent variables were PBF and the compositeState of Sonora. This city in the North of Mexico has a total pop- overweight/obesity, defined as the current BMI that predicts a fu-ulation of about 24,447 inhabitants and is located at 80 km (49.6 ture BMI 625 when adult, according to IOTF reference tablesmiles) from the frontier with the USA. The city of Magdalena de [13]. BMI and PBF were divided in percentiles, taking the highestKino has 33.5% of people aged !15 years [20], and pertain to a quartiles (percentile 75th or more) as dependent variables. Pear-state of the Mexican Republic with an estimated prevalence of son 2 and Fisher exact tests were used to assess nominal variablesoverweight and obesity combined of near 35%, in persons aged in bivariate and homogeneity analyses. To compare quantitative3–17 years [18]. The internal Committee of Ethics of our institu- variables between two groups, Student’s t test and Mann-Whitneytion approved the present study. Informed consent was obtained U test were performed in distributions of parametric and non-from the children’s parents or legal proxy. parametric variables, respectively. Pearson correlation was used in continuous variables (e.g. BMI, body fat content, height, weight, Design and other somatometric variables). To find independent predic- Parents and teachers of children from 2 public institutions of tors of adiposity, BMI in the highest quartile of the sample and ofthe 16 elementary schools (either public or private) of the city were overweight/obesity, multivariate models were constructed byasked for their alumni to participate in the analysis (669 children stepwise logistic regression. Input variables were those that re-aged 6–13 years). Parents of 551 (82.3%) children responded to our sulted significantly associated with adiposity in bivariate analy-request. A standardized, structured questionnaire was used to ses, but demographic variables and known risk factors for over-collect data directly from the parents regarding demography, rel- weight (other than caloric intake) were also included in logisticevant antecedents and current alimentary and exercise habits. regression analyses for adjustment, as potential confounders. Ad-The questionnaire was administered as an interview by trained justed odds ratios with the respective 95% CIs are provided. Thepersonnel. Informed consent was obtained from the parents or fitness of the models was evaluated by using the Hosmer-Leme-legal proxies. show goodness-of-fit test, which was considered as reliable if p 1 0.2. All p values are two-sided and considered significant when Anthropometry and Assessment of Body Fat Content p ! 0.05. SPSS Version 13.0 for Windows (SPSS Inc., Chicago, Ill., A sedentary lifestyle was defined as 13 h per day spent sitting USA) was used for all statistical calculations.down during leisure time (i.e., television watching, computeruse, and similar activities) in a child who is not engaged in a sys-tematic exercise practice (i.e., sports, dance, and other recre-228 Ann Nutr Metab 2008;52:227–232 Basaldúa/Chiquete
  3. 3. Table 1. Main characteristics of the 551 children analyzedVariable Total Girls (n = 278) Boys (n = 273) p valueaAge, median (range), years 9 (6–12) 9 (6–12) 9 (6–12) 0.58Age, years 8.981.8 8.981.8 8.981.8 0.59Child number 3 or more in offspring, n (%) 124 (28.9) 59 (26.6) 65 (31.4) 0.27First-degree relative with obesity, n (%) 307 (71.7) 157 (69.8) 150 (73.9) 0.34First-degree relative with diabetes, n (%) 225 (52.6) 120 (53.3) 105 (51.7) 0.74Sedentary lifestyle, n (%)b 187 (43.7) 122 (54.2) 65 (32) <0.001Height, m 1.3580.12 1.3580.13 1.3480.12 0.14Weight, kg 35.1812.1 35.4812.2 34.9812.1 0.65Waist circumference, cm 66.7810.5 66.2810.4 66.4810.6 0.51BMI, kg/m2 18.784.0 18.683.9 18.984.1 0.51BMI in the highest quartile, n (%) 138 (25) 69 (24.8) 69 (25.3) 0.90PBF 17.487.3 20.486.4 14.487.0 <0.001PBF in the highest quartile, n (%) 124 (24.7) 95 (37.5) 29 (11.6) <0.001Overweight/obesity, n (%)c 207 (37.6) 106 (38.1) 101 (37) 0.78Obesity, n (%)c 73 (13) 31 (11.2) 42 (15.4) 0.14 b BMI = Body mass index; PBF = percentage of body fat. >3 h per day spent sitting down during leisure time (i.e., oth- a p value for differences between boys and girls; Pearson 2 , er than school hours) and absence of systematic exercise practiceStudent t test (for means) or Mann-Whitney U test (for medians), (i.e., sports, dance, and similar activities).as corresponded. c Current BMI that predicts overweight or obesity in adult- hood, according to the International Obesity Task Force reference tables. Results 60 A total of 551 children aged 6–12 years were included Homogeneity, p = 0.02 *in the final analysis (table 1). All the children were of Lat- 50 * Frequency (%) 40in-American ethnicity and their family had an annual * *income of USD !15,000. 30 * Age, ethnicity, social class, relevant antecedents, 20height, weight, BMI, waist circumferences, the relative 10frequency of obesity and the composite overweight/obe- 0sity did not differ according to gender. However, seden- 6 7 8 9 10 11 12 Age (years)tary lifestyle and a high body fat content were more fre-quent in girls than in boys (table 1). Although the naturalincrement of BMI with every year of age was identified, Fig. 1. Distribution of the percentage of body fat in the highestthe relative frequency of overweight/obesity was homo- quartile across the age groups in girls (g), boys (k) and bothgeneous across the age groups. PBF standardized as in- genders combined (i). * p ! 0.05.crements of 1 year remained higher in girls than in boysin age 6, and from 8 to 11 years (in all, p ! 0.01), but notin age 7 and 12 years (fig. 1). As expected, PBF positively correlated with height,weight, BMI and waist circumference (fig. 2). Age also relative with obesity, sedentary lifestyle, and being themoderately correlated with PBF (r = 0.174, r2 = 0.030, p ! third child or more in offspring. Independent predictors0.001) and total body fat content (r = 0.426, r2 = 0.181, of PBF in the highest quartile were female gender, havingp ! 0.001). a first-degree relative with obesity and being the third After multivariate analyses (table 2), independent pre- child or more in offspring. BMI in the highest quartiledictors of overweight/obesity were having a first-degree was predicted only by the antecedent of a first-degree rel-Predicting Excessive Adiposity in Ann Nutr Metab 2008;52:227–232 229Children
  4. 4. 50 50 r = 0.224, r2 = 0.059, p < 0.001 r = 0.639, r2 = 0.408, p < 0.001 40 40 Body fat (%) Body fat (%) 30 30 20 20 10 10 0 0 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 10 20 30 40 50 60 70 80 90 a Height (m) b Weight (kg) 50 50 r = 0.791, r2 = 0.625, p < 0.001 r = 0.637, r2 = 0.406, p < 0.001 40 40 Body fat (%) Body fat (%) 30 30 20 20 10 10 0 0Fig. 2. Correlations between percentage 10 15 20 25 30 35 40 20 40 60 80 100 120of body fat with height (a), weight (b), BMI c BMI (kg/m2) d Waist circumference (cm)(c) and waist circumference (d). S = Girls,U = boys.Table 2. Determinants of excessive adiposity and overweight: three binary logistic regression modelsVariables Multivariate odds ratios (95% CI) overweight/obesitya PBF in the highest quartileb BMI in the highest quartilecFemale gender NS 5.60 (3.22–9.77) NSFirst-degree relative with obesity 2.26 (1.40–3.64) 2.59 (1.41–4.74) 2.50 (1.40–4.44)Sedentary lifestyle 1.58 (1.05–2.37) NS 1.95 (1.24–3.05)Third child or more in offspring 1.59 (1.02–2.47) 2.07 (1.22–3.51) NS b BMI = Body mass index; PBF = percentage of body fat; NS = Hosmer-Lemeshow test for goodness-of-fit in the final stepnon-significant variable, hence, not appearing as predictor after of the regression model: 2 = 5.67, 6 d.f., p = 0.46. Only significantmultivariate analysis, but included in prediction models as a con- predictors are shown. Adjusted for age, birth weight, duration offounder. breastfeeding, maternal age, sedentary lifestyle and family his- a Hosmer-Lemeshow test for goodness-of-fit in the final step tory of diabetes mellitus.of the regression model: 2 = 1.14, 6 d.f., p = 0.98. Only significant c Hosmer-Lemeshow test for goodness-of-fit in the final steppredictors are shown. Adjusted for age, gender, birth weight, du- of the regression model: 2 = 0.03, 2 d.f., p = 0.99. Only significantration of breastfeeding, maternal age and family history of diabe- predictors are shown. Adjusted for age, gender, birth weight, du-tes mellitus. ration of breastfeeding, maternal age, being the third child or more in offspring and family history of diabetes mellitus.230 Ann Nutr Metab 2008;52:227–232 Basaldúa/Chiquete
  5. 5. ative with obesity and a sedentary lifestyle. Therefore, the tions in weight [26]. However, with the methodology usedantecedent of obesity in family members was the con- in this study we could not account for other non-system-stant predictor for the three measures of overweight. atic physical activities like playing outside or bicycle rid- ing, as these activities were very inconstant in nature and duration in the sample studied, but that can indeed affect Discussion energy expenditure [26]. The constant predictor of the three measures of exces- We found a high relative frequency of overweight/obe- sive adiposity was having a first-degree relative with obe-sity. Over the past years, the prevalence of pediatric over- sity. This risk factor has been identified iteratively [11, 12,weight has risen dramatically, so that 115% of the chil- 27] and underscores the impact of genes and a shared life-dren are now considered overweight [22]. Our findings style on the accumulation of adipose tissue. The exactare in agreement with previous reports about the preva- meaning of being the third child or younger in offspringlence of overweight in Mexican children [17, 18]. Compa- and its relationship with overweight could not be deter-rable dietary patterns are shared between persons living mined with the original methodology of our study. Ain the North of Mexico with those of southern USA [15, plausible explanation to this finding may be that with a17], but, as is shown in the present report, with a higher large offspring the breastfeeding practice becomes morefrequency of overweight in the Mexican people, as com- difficult, leaving without this protective factor for child-pared with US inhabitants [22]. It has been demonstrated hood overweight [28] to the younger offspring. This find-that similar dietary habits in persons living in the same ing, however, deserves more exploration, since we did notregion, but with distinct backgrounds (i.e., race), are as- assess breastfeeding practices on patient’s siblings. An-sociated with a higher frequency of overweight [23]. In a other deficiency that can be accounted on our study is theprevious study on 1,350 children from the North of Mex- method used to assess body fat content (i.e., bioimped-ico, a 39% prevalence of overweight was found and a risk ance), which, although simple, inexpensive and accept-factor related to this condition was the regular ‘crossing’ ably reliable when analyzing large cohorts [21], it is notfrom Mexico to USA [17]. Different genes concurring in the most accurate method to estimate adiposity.a similar environment imply different interactions and In summary, common variables included in a regularconsequences on health [24]. history-taking can predict childhood adiposity and the In the present report, except for a more sedentary life- high risk of obesity in adulthood. In a population withstyle and a higher body fat content in the female gender, high prevalence of obesity, the constant predictor of over-there were no relevant differences between genders. Sed- weight in childhood is the family history of this condi-entarism is a risk factor that is more common in girls than tion, which might underscore the importance of heritagein boys [25] and together with the hormonal changes that or more likely the shared dietary and exercise habits.characterize puberty, it may contribute to the higher body Identification of children at risk before they develop ex-fat content observed in girls. We found that independent cessive adiposity is necessary to prevent unhealthy prac-predictors of overweight/obesity were having a first-de- tices that led to a positive energy balance.gree relative with obesity, a sedentary lifestyle, and beingthe third child or younger in offspring. Factors associatedwith PBF differed in that sedentarism was replaced by the Acknowledgmentsfemale gender, as a predictor. On the other hand, BMI in The authors are indebted to Dr. Sandra M. De la Herrán andthe highest quartile was associated with a sedentary life- Dr. Martín A. Grijalva for their invaluable efforts and assistancestyle and the family history of obesity. A concern may in anthropometry, interviews and important suggestions to thisarise on whether a sedentary lifestyle may be the conse- work. Also, the authors gratefully acknowledge the interest forquence of excess weight rather than a cause. Indeed, with this study of the school authorities, directors and personnel of thethe cross-sectional design of this study we can only con- local health system in Magdalena de Kino, Sonora; as well as the children’s parents.clude that a lower physical activity is present in over-weight children, but cannot assess whether the sedentarylifestyle has preceded the weight gain. Recently it hasbeen demonstrated that variations in posture and simplemovements that are associated with the routines of dailylife could be biologically determined and precede varia-Predicting Excessive Adiposity in Ann Nutr Metab 2008;52:227–232 231Children
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