Joint association of physical activity/screen time and diet on CVD risk factors in 10-year-old children

Abstract

The increasing prevalence of childhood overweight and obesity has been associated with an increased risk for cardiovascular disease (CVD). While several studies examined the effect of single behaviors such as physical activity (PA), sedentary behavior or diet on CVD risk, there is a lack of research on combined associations, specifically in children. Therefore, the purpose of this study was to examine the joint association of PA or screen time (ST) and diet on CVD risk factors in children. PA, ST and diet were assessed via questionnaire in 210 fifth grade students (age: 10.6±0.4 years). The healthy eating index (HEI) was subsequently calculated as indicator for diet quality. Height, weight, % body fat, and resting blood pressure were measured according to standard procedures and blood samples obtained via fingerprick were assayed for blood lipids. Total cholesterol HDL ratio (TC:HDL), mean arterial pressure (MAP), and % body fat were used as indicators of CVD risk. 55% of children did not meet current PA recommendations on at least 5 days/week and 70% exceeded current recommendations for ST. Further, only 2.5% possessed a “good” diet (HEI>80). There was no significant association of PA or ST and diet on CVD risk score. Neither TC:HDL, MAP, and % body fat nor the total CVD risk score was significantly correlated with diet, PA, or ST. Children in the high PA group, however, had significantly better diet scores. Despite the fact that self-reported PA, ST, or dietary intake were not directly related to CVD risk in this sample, higher activity levels were associated with a healthier diet and lower ST indicating an overall healthier lifestyle of this subgroup.

Keywords

exercise / sedentary behavior / metabolic syndrome / health behavior / adolescents / TV time / healthy eating index

Introduction

The increasing prevalence of overweight and obesity in children contributes to the onset of type 2 diabetes, hypertension, and dyslipidemia [1,2]. The co-occurrence of cardiovascular disease (CVD) risk factors, such as obesity, impaired glucose tolerance, dyslipidemia, and hypertension is well established and has been termed the metabolic syndrome [3]. Approximately 4%–7% of US children and adolescents possess the metabolic syndrome phenotype [4,5] with the proportion reaching nearly 50% among obese youth [6]. This is concerning since several studies have shown that these CVD risk factors track from adolescence into adulthood [7–11] and lead to the development of CVD and type 2 diabetes in later life [12,13]. Modifiable behaviors such as low physical activity (PA) and poor diet are commonly associated with obesity and CVD risk. Studies examining the association of diet and CVD risk factors in children [14,15] have generally focused on a single component (e.g., fat intake or fruits and vegetables), while only a few studies have utilized a composite dietary index which allows for an assessment of the overall quality of diet rather than isolated components [16]. The healthy eating index (HEI) [17], which was developed by the United States Department of Agriculture to assess concordance with dietary guidelines in the US population [18], is a commonly used dietary index and it has been shown that higher HEI scores are associated with a lower prevalence of metabolic syndrome in children [2].

In addition to diet and PA, sedentary behavior, such as time spent watching TV or sitting at the computer, may also be an important determinant of obesity and CVD risk factors in youth [19–21]. Increased sedentary time, for example, has been associated with insulin resistance in children [22]. It should also be considered that PA and sedentary behavior represent different paradigms [23,24], which is reflected by low correlations between screen time (ST) and PA [25,26]. Further, several studies have shown an increased prevalence of CVD risk with higher ST independent of PA [27–29]. The increased risk for CVD with higher ST may be due to increased caloric intake while watching TV rather than displacement of PA [30]. Despite some concordance between these lifestyle factors, not all children who achieve recommendations for PA or ST eat a prudent diet and vice versa. Therefore, interaction effects of sedentary behavior, PA, and diet should be considered when examining the role of these behaviors with regards to CVD risk. To our knowledge, no study has previously examined the joint association of diet and PA or ST on CVD risk factors in children, which is the purpose of this study.

Materials and methods

Subjects

Baseline data from 354 (207 females, 147 males) 5th grade students who participated in a school-based intervention in central Michigan were examined. The sample was drawn from schools in which more than 50% of students qualify for free lunch in order to recruit children with a low socio-economic status (SES), as these children are at particular risk for overweight and accompanying co-morbidities, including CVD [31]. After potential schools were identified, children were recruited through the school. Of the initial sample 59.3% (112 females, 88 males) provided complete data and were included in the analysis. The study was approved by the institutional review board and by the participating school’s educational boards. Parental consent as well as child assent was obtained prior to data collection.

Anthropometry and CVD risk factors

Height and weight was measured according to standard procedures [32] and body mass index (BMI) was calculated (kg/m²). Body fatness was estimated via foot-to-foot bioelectrical impedance (BC-534, Tanita Corporation, Tokyo, Japan). Resting blood pressure (BP) was assessed using manual blood pressure devices with appropriate sized cuffs after having subjects sit for 5 min, and mean arterial pressure (MAP) was calculated (MAP= 2/3×diast. BP+ 1/3×syst. BP) [33]. Blood samples to measure cholesterol levels were obtained by fingerprick and collected in 35 μl heparinized capillary tubes. Blood samples were analyzed within 5 min by a portable cholesterol/glucose analyzer according to the protocol of the manufacturer (Cholestech LDX System, Hayward, CA). Z-scores for total cholesterol:HDL ratio (TC:HDL), MAP, and % body fat were calculated and summed to create a composite CVD risk score.

Dietary intake

Dietary intake was assessed using the Block Food Frequency Questionnaire (FFQ) for Kids (Block Dietary Data Systems, Berkeley, CA; http://www.nutritionquest.com). FFQs are commonly used to assess nutritional intake and have been shown to be as accurate as multiple 24-h recalls or food records, while reducing the subject burden [34,35]. The Block FFQ for Kids includes 77 food and beverage options that are coded based on frequency of consumption during one week. Serving size is estimated with the help of visual cues. Overall, the Block FFQ for Kids has been shown to provide reasonably accurate information on dietary intake in children [36,37]. The questionnaire was administered by trained staff at the school on the same day when anthropometric measurements were taken. After questionnaires were checked for completeness, they were mailed to Nutritionquest (Berkeley, CA) for analysis. Reported data included daily estimates of macro-nutrients, major micro-nutrients, and servings per day of key food groups. Based on these results HEI scores were calculated for each subject. The HEI consists of 10 components (grains, vegetables, fruit, dairy, meat, total fat, saturated fat, cholesterol, sodium, food variety) that are scored from 0 to 10 points each and then summed for an overall score [17,38]. HEI scores, therefore, range from 0 to 100. A higher score reflects a better quality of dietary intake. An HEI score above 80 is considered “good,” while a score under 50 is classified as “poor” [39].

Habitual physical activity and screen time

Questionnaires were administered by trained staff. Questions on PA and ST were based on the Youth Risk Behavior Surveillance System (YRBSS). Specifically, children were asked to specify the number of days on which they achieve current PA recommendations of 60 min/day as well as the amount of time (h/day) spent watching TV (including video and DVD) and playing computer. Participants were considered to have high PA if they reported participating in 60 minutes of moderate-to-vigorous PA (MVPA) on 5 or more days per week. Equal or more than 2 h/day of ST was considered high ST vs.<2 h/day of ST was considered low ST.

Statistical analysis

Initially, Pearson correlations between PA, ST, HEI scores, and individual CVD risk factors as well as combined CVD risk score were calculated. To examine the joint association of PA and diet, 4 groups were created based on cross-tabulation (e.g., high PA-high HEI, high PA-low HEI, low PA-high HEI, low PA-low HEI). Similarly 4 groups for ST and HEI were established (high ST-low HEI, high ST-high HEI, low ST-low HEI, low ST-high HEI). The joint association of PA/ST and diet on individual CVD risk factors as well as the combined CVD risk score was examined via 2×2 ANCOVA, controlling for sex and total caloric intake. All statistical analyses were carried out with SPSS Version 16.0 with a significance level set at α≤0.05 and Bonferroni adjustment for multiple analyses.

Results

Descriptive characteristics of the sample are shown in Table 1. While girls had significantly higher % body fat, BMI percentiles did not differ between sexes. There was a higher prevalence of overweight in girls, while boys had a higher prevalence of obesity. In the total sample, 19.1% were obese and 17.2% were overweight. There were no significant sex differences in MAP and cholesterol but boys displayed a lower total CVD risk score compared to girls due to their lower body fat content. Dietary intake, total daily caloric intake, and the HEI scores did not differ between girls and boys either. The median HEI score was 62 (range, 36–84). Only 5 subjects (2.5%) possessed a “good” diet (HEI>80) and 18 subjects (8.6%) displayed a “poor” diet (HEI<50). 55.2% (54.5% of the boys and 55.7% of the girls) of the children reported participating in 60 min of MVPA on 5 or more days per week, and 70% of the sample (80.7% of the boys and 62.3% of the girls) reported≥2 h/day of ST. Mean ST was significantly higher in boys (P = 0.05), while no sex differences occurred in PA.

There were no significant correlations between PA and CVD risk factors or ST and CVD risk factors, and all correlations approached zero (Table 2). Likewise, there were no significant correlations between HEI score and CVD risk factors. HEI scores, however, were significantly correlated with BMI percentile (r = -0.16, P = 0.02), PA (r = 0.25, P<0.01), and total caloric intake (r = 0.36, P<0.01). After controlling for total caloric intake, the correlation between HEI and BMI percentile was no longer significant (r = -0.12, P = 0.08). The correlation between ST and PA was low and non-significant (r = -0.08, P = 0.27).

Table 3 shows the descriptive results for the cross-tabulation of PA and HEI. There were no significant PA by diet interactions on CVD risk, but there was a trend toward a significant main effect for diet on MAP with the high HEI group displaying lower MAP values (mean±SE: 77.2±1.6 vs. 79.5±1.4; P = 0.06). There were no significant main effects for PA. Similarly, there were no significant interactions of ST by HEI concerning CVD risk. There were also no main effects of either ST or HEI on CVD risk. Table 4 displays the descriptive results for the cross-tabulation of ST and HEI.

Even though there was no main effect of PA on CVD risk, subjects in the high PA group had significantly better HEI scores compared to those in the low PA group (mean±SE: 64.1±0.8 vs. 59.2±0.9) and these results remained significant after controlling for sex and total caloric intake (P<0.01). No difference in ST was shown between the high and low HEI groups. Initially, subjects with higher HEI scores (HEI≥62) displayed a lower CVD risk score and % body fat compared to subjects with low HEI scores (P = 0.02 and P = 0.04, respectively), but these results were no longer significant after controlling for total caloric intake, which was higher in the high HEI group, and sex (P = 0.32 and P = 0.39, respectively). However, there was a difference in PA levels between the high and low ST group. Subjects who reported more than 2 h/day of ST reported lower PA levels (4.4±0.2 vs. 5.3±0.3 days of 60 min or more of MVPA/week; P = 0.01), but self-reported ST did not differ significantly between PA groups (P = 0.29). Despite the lack of significant results on joint associations, Fig. 1 indicates that low diet scores and low PA as well as high ST are related to a higher CVD risk score.

Discussion

Although several studies have considered the associations between PA, ST and diet on CVD risk factors separately, to our knowledge this is the first study to examine the joint association of PA or ST and diet on CVD risk factors in youth. Despite the lack of interaction between PA or ST and diet on CVD risk factors, some main effects between diet, PA, ST, and CVD risk factors were shown. The prevalence of unhealthy lifestyle behaviors is also important to note. In this sample of 5th grade students from mid-Michigan schools in low SES neighborhoods, 36% were overweight or obese, which is slightly higher than the US average in 6- to 11-year-old children [40]. The difference is possibly due to the lower SES of the present sample as a higher prevalence of overweight and obesity has been reported in low SES children [41]. There was no difference in the combined prevalence of overweight and obesity between boys and girls and BMI percentiles did not differ between boys and girls either. The lack of sex-related differences in BMI percentiles has been reported previously, despite differences in % body fat [42]. Concerning physical activity, 55% of the children did not achieve 60 min/day of MVPA on at least 5 days/week and 70% exceeded recommendations for ST. Furthermore, only 2.5% had a HEI>80 (“good” diet).

Even though no interactions between PA or ST and diet occurred, there were apparent mean differences at the “extremes” — that is, those with high ST/low HEI showed the highest mean values for BMI percentile, MAP, and CVD risk score. There were significant main effects of diet on % body fat, PA, and CVD risk score. A healthier HEI score was associated with increased PA while there were no significant main effects for PA or ST on CVD risk factors. Casazza et al. [14] did not report a relationship between PA and CVD risk in 7- to 12-year-old children either while a significant relationship between carbohydrate intake and a combined CVD risk score was shown. Pan and Pratt [2] also showed that the prevalence of metabolic syndrome in 12- to 19-year-old US adolescents was higher in those with lower HEI scores. In contrast to the findings of this study, several studies [2,43–45] reported a lower CVD risk with higher levels of PA. The lack of assessment of vigorous PA may explain the null findings in the current study since Sassen et al. [46] emphasize the role of vigorous PA on CVD risk reduction. These authors showed that only vigorous PA was related to lower CVD risk scores, while no significant relationship between moderate PA and CVD risk was observed. Therefore, PA intensity, specifically vigorous intensity, may be the PA variable of interest in relation to CVD risk. The present study, however, showed that higher PA levels were associated with better HEI scores, which suggests an overall healthier lifestyle in the more active cohort. This association could in the long term positively alter CVD risk. Interestingly, no differences in ST occurred between high/low PA groups, but lower ST was related with higher PA, which has been reported previously as well [25].

There was no combined influence of diet and ST on CVD risk, which has been reported previously in adults [47]. These authors, however, reported a significant effect of fat intake as well as ST on CVD risk. In children or adolescents we are not aware of any previous studies that examined the combined influence of ST and diet on CVD risk. Several studies, however, looked at single components and showed a higher prevalence of CVD risk factors with increased ST independent of PA levels [48,49]. Owen et al. [50] emphasized that too much sitting impairs metabolic health even if people meet PA guidelines. This may be due to the suppression of lipoprotein lipase (LPL) in response to prolonged sitting as LPL is involved in the regulation of fat metabolism as well as HDL production, triglyceride uptake and fat metabolism [51]. Further, increased time spent watching TV has been associated with abnormal glucose metabolism and metabolic syndrome in adults [52,53]. In children, higher ST has been shown to be related to higher blood pressure [54]. From a behavioral perspective higher TV time has been shown to increase the consumption of energy dense foods [30], which would further increase the risk for overweight and related co-morbidities such as CVD. The relationship between overweight and obesity in children and adolescents with increased ST as well as lower PA has been previously reported as well [55]. In the present study, however, no differences in obesity prevalence existed between the high HEI/high PA and low HEI/low PA group as well as high HEI/low ST and low HEI/high ST. The equal distribution of overweight and obesity across groups and the strong contribution of overweight and obesity to CVD risk [55,56] may also have offset the potential benefits of high PA, low ST or high HEI in the present sample and could explain the lack of significant results.

Besides behavioral/lifestyle factors, such as those considered here, CVD risk has been shown to be biologically pre-determined as well. Several studies indicate that the perinatal environment and maternal BMI alter offspring CVD risk [57–59] by altering the regulation of metabolic and hormonal processes in the offspring [60,61]. Further, CVD risk is influenced by genetics. Indeed, offspring of individuals with metabolic syndrome are more likely to develop similar symptoms [62] and a heritability of increased CVD risk over three generations has been shown [63]. Aside from inherited factors, obesity and CVD risk can also be influenced by the shared environment, including diet and opportunities for PA and ST [64,65]. Therefore, the gene-environment interaction between PA, diet, and body composition needs to be considered when examining CVD risk in children and adolescents [66]. The complexity of processes involved in CVD risk may be one reason why results concerning the influence of PA, ST, and diet as well as the combined influence of these components have not been consistent.

Additionally, some limitations in the current study may have contributed to the lack of significant results. The limitations of self-reported ST, PA, and diet are well-acknowledged. There was also no differentiation between moderate and vigorous PA, which has been shown to influence CVD risk factors differently [58,67]. Further, diet quality index scores such as the HEI may be able to quantify the risk of some health outcomes but more research is needed to improve the validity of such indexes [68]. Maturity status, which is known to influence body composition [69], was not considered either. Finally, the cross-sectional study design does not allow for establishing causality even though cross-sectional studies allow for explaining the % variance in a phenotype at a given time and, therefore, can contribute to the understanding of the development of CVD.

In summary, this study provided a unique approach to examining the combined influence of ST, PA and diet on CVD risk factors in children. Even though health behavior and CVD risk are associated, there were no significant interactions between PA or ST and diet on CVD risk factors in this sample of 10-year-old children. Given the increasing prevalence of obesity and metabolic syndrome in children and adolescents, further work is needed to understand the development and progression of these risk factors. Particularly more objective measurements that allow for the differentiation of light, moderate and vigorous PA as well as different types of sedentary behaviors are warranted to explore the complex interactions of different behaviors. In addition, longitudinal data are needed to gain insights in the causal relationship between behavioral as well as environmental aspects with CVD risk in children and adolescents. A better understanding of the interaction of various constraints associated with CVD risk would facilitate the development of appropriate intervention strategies in order to address the increasing occurrence of CVD risk factors in a young population. As CVD risk is a complex phenotype, it is assumed that successful interventions would have to target different components of a healthy lifestyle such as dietary behavior, PA, and ST simultaneously.

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