Energy Balance-related Behavio rs Are Related to Card iom etabo lic Param eters and Pred ic t Ad iposity in 8-14-year-o ld Overw eigh t Chinese Child ren One Year Later＊

Letter to the Editor

Energy Balance-related Behavio rs Are Related to Card iom etabo lic Param eters and Pred ic t Ad iposity in 8-14-year-o ld Overw eigh t Chinese Child ren One Year Later＊

LI Liu Bai, WANG Nan, WU Xu Long, WANG Ling, LI Jing Jing, YANG M iao, and MA Jun#

To identify target energy balance-related behaviors (ERBs), baseline data from 141 overweight or obese schoolchildren (aged 8-14 years old) was used to predict adiposity [body mass index (BM I) and fat percentage] one year later. The ERBs included a modified Dietary Approach to Stop Hypertension diet score (DASH score), leisure-time physical activity (PA, days/week), and leisure screen time (m inutes/day). Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure (SBP), diastolic blood pressure (DBP), blood glucose (GLU), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL-C), and high-density lipoprotein (HDL-C). BM I and fat percentage were measured using a BIA body composition analyzer (MC-980MA, TANITA, Tanita Co., Guangzhou, China). Partial correlation coefficients (partial r) and multip le linear regression models were used to predict BM I and fat percentage one year later. Our samp le consisted of 114 boys and 83 girls w ith a mean BM I of 24.7±3.7 kg/m2and fat percentage of 34.2%±8.3% at baseline. BM I, fat percentage, and certain cardiometabolic variables were negatively associated w ith DASH score and leisure-time PA (all P＜0.05), but positively associated w ith leisure screen time (all P＜0.05) at baseline. Statistically significant predictors of BM I and fat percentage one year later were baseline BM I (partial r=0.85), fat percentage (partial r=0.69), eating out (times/week, partial r=0.18), and DASH Score (partial r=-0.18). Overall, childhood obesity prevention interventions should target reductions in ERBs.

Childhood obesity is a major contributor to many serious health conditions that increase morbidity and mortality, and it has increased at least three-fold in China over the past several decades[1-2]. The obesity epidem ic among children is the result of excess energy intake and inadequate energy expenditure[3]. A meta-analysis showed that interventionstargetingenergy-balance-related behaviors (ERBs) result in clinically significant decreases in body mass index (BMI)[4].

Excess energy intake, physical inactivity, and sedentary lifestyles are prevalent in Chinese schoolchildren[5], but associations of ERBs with childhood obesity or related cardiometabolic outcomes have seldom been studied in China. To identify targets for modifiable behaviors, such as diet, physical activity (PA), and sedentary behaviors, and to establish working priorities for controlling childhood obesity in China, we analyzed data from a cohort of 197 overweight or obese Chinese schoolchildren in 2013[6]. We used the Group of China Obesity Task Force criteria for overweight (85thpercentile ≤BMI＜95thpercentile) and obesity (BMI ≥95thpercentile) based on age and gender[6]. This study was approved by the Medical Research Ethics Comm ittee of Peking UniversityHealthScienceCenterIRB (0000105212062). We exam ined the association of eating behaviors, leisure-time PA, and screen time w ith obesity and the following cardiometabolic variables at baseline: blood pressure (BP), systolic blood pressure (SBP), diastolic blood pressure (DBP), waist circum ference (WC), hip circum ference (HC), waist-to-hip ratio (WHR), blood glucose (GLU), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL-C), and high-density lipoprotein (HDL-C) (Table 1 and Table 2). We used baseline ERB data to predict BMI and body fat percentage one year later (Table 3). A modified Dietary Approach to Stop Hypertension Diet score (DASH score) was created based on a previous publication and was used as a composite eating behavior index to reflect the overall quality of eating behaviors[7].

Our sample consisted of 114 boys and 83 girls

Among all eating behaviors that were measured, only meat consumption was positively associated w ith TC (partial r=0.21, P＜0.05) and LDL-C (partial r=0.22, P＜0.05) at baseline. Stronger associations were found between energy expenditure (e.g., leisure time PA and screen time) and obesity. In fact, BM I, fat percentage, and HC were negatively correlated w ith PA (partial r: -0.162 to -0.316, P＜0.05 for aerobic and anaerobic activities) and positively correlated with sedentary behaviors (partial r=0.170, P＜0.05 for total screen time). Fat percentage and WHR were moderately and negatively correlated w ith participation in team sports in children younger than 4thgrade (partial r: -0.280 to -0.316, P＜0.05). These findings suggest that reducing sedentary behaviors and increasing PA may prevent excessive weight gain in Chinese schoolchildren, and thus should be given high priority.

Among the 141 children that remained at follow-up, BM I increased by 0.88±1.79 kg/m2and fat percentage increased by 0.59±5.58 percent points. In a multiple linear regression model, individual and composite indicators of eating behaviors (e.g. DASH score),PA(days/week),andscreentime (m inutes/day) were entered or removed from the equation as explanatory variables using the Enter method, while simultaneously adjusting for the following covariates that were significant at baseline: (for one-year later BM I: baseline age, baseline BMI, DASH score, and other screen time; for one-year later fat percentage: baseline fat percentage, eating out, DASH score, and PA). We found that baseline adiposity (measured by either BM I or fat percentage) was the strongest predictor of adiposity one year later (BM I or fat percentage), followed by age (only for BMI) and eating out (only for fat percentage). DASH score was less strongly associated w ith BM I. Our regression equations explained 78.5% and 51.6% of the variance of BMI and fat percentage at follow-up, respectively (Table 3).

Table 2. Baseline/Follow-up Anthropometric and Cardiometabolic Variables (mean±SD)

Table 3. Use of Baseline Energy-balance-related Behaviors to Predict Change in BM I and Fat Percentage Over One Year (n=141)

In this study, DASH score was used as an index of healthy eating to capture the overall effects of eating behaviors in Chinese children. Our results showed that lower DASH scores could be used to predict excess body fat gain and demonstrated their usefulness as a composite index of overall eating patterns in children. In addition, we showed that non-TV screen time plays a role in promoting adiposity.

Although many previous studies have reported the prevalence of individual ERBs in healthy children, few studies concurrently considered the three components of energy balance, which may be important in predicting excess body fat gain and thus setting intervention targets for obesity control. In the present study, we exam ined the relative contributions of energy intake and expenditure to the development of childhood obesity, both cross-sectionally and prospectively. Overall, our results suggest that ERBs are associated w ith changes in adiposity in Chinese schoolchildren over time, and that behaviors relating to energy expenditure should be intervention priorities.

Our study has several lim itations and thus our findings should be interpreted with caution. First, our study was conducted in overweight Chinese schoolchildren and cannot be generalized to other populations. Second, the small variability in some variables and possible systematic under-reporting of some foods may have affected the associations between exposures and outcomes. Third, we did not adjust for total energy intake, which may be an important confounder. Fourth, the use of a self-adm inisteredquestionnairemayhave introduced inaccuracies in our assessment of eating and PA behaviors. Finally, our analyses were based on cross-sectional and short-term longitudinal data.

ACKNOWLEDGMENTS

We sincerely appreciate the great support of the parents, the children, and the teachers in the participating schools, and all participating education adm inistration and medical professionals of the local CDC.

CONFLICTS

The authors declare that they have no conflict of interest.

AUTHORS' CONTRIBUTIONS

LI Liu Bai, WANG Ling, LI jing Jing, and YANG M iao developed the measuring tools and participated in the collection and analysis of data. LI Liu Bai, MA Jun, WANG Nan, and WU Xu Long w rote the first draft of this paper. MA Jun provided advice on recruitment strategies, as well as school and parentalengagementactivities.Allauthors contributed to the final version of the paper. All authors read and approved the final manuscript.

#Correspondence should be addressed to MA Jun, E-mail: majunt@bjmu.edu.cn

Accepted: October 1, 2016

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10.3967/bes2016.100

*Research special fund of the Ministry of Health public service sectors funded projects (201202010); The 12th Five-year Key Project of Beijing Education Sciences Research Institute (AAA12011).

Institute of Child & Adolescent Health, School of Public Health, Peking University Health Science Center, Beijing 100191, China

Biographical note of the LI Liu Bai, born in 1966, MD, PhD, Associate Professor, specialized in human nutrition and nutrition-related diseases.

April 14, 2016;