To characterize the influence of diet-, physical activity–, and self-esteem-related factors on insulin resistance in 8–10-year-old African-American (AA) children with BMI greater than the 85th percentile who were screened to participate in a community-based type 2 diabetes mellitus (T2DM) prevention trial. In 165 subjects, fasting glucose- and insulin-derived values for homeostasis model assessment of insulin resistance (HOMA-IR) assessed insulin resistance. Body fatness was calculated following bioelectrical impedance analysis, and fitness was measured using laps from a 20-m shuttle run. Child questionnaires assessed physical activity, dietary habits, and self-esteem. Pubertal staging was assessed using serum levels of sex hormones. Parent questionnaires assessed family demographics, family health, and family food and physical activity habits. Girls had significantly higher percent body fat but similar anthropometric measures compared with boys, whereas boys spent more time in high-intensity activities than girls. Scores for self-perceived behavior were higher for girls than for boys; and girls desired a more slender body. Girls had significantly higher insulin resistance (HOMA-IR), compared with boys (P < 0.01). Adjusting for age, sex, pubertal stage, socioeconomic index (SE index), and family history of diabetes, multivariate regression analysis showed that children with higher waist circumference (WC) (P < 0.001) and lower Harter's scholastic competence (SC) scale (P = 0.044) had higher insulin resistance. WC and selected self-esteem parameters predicted insulin resistance in high-BMI AA children. The risk of T2DM may be reduced in these children by targeting these factors.
The increased incidence of type 2 diabetes mellitus (T2DM) in children and adolescents (1,2) has been associated with risk factors such as obesity and family history of T2DM (3,4,5). Race is an important factor also, and African Americans (AAs) have been shown to be at higher risk for T2DM compared with white populations (6). The prevalence of obesity and T2DM in AA children is on the rise (1). Studies in AA adults, as well as in children and adolescents (1), have shown that insulin resistance is a strong predictor of T2DM (7). A number of studies (7,8,9) suggest that obesity in AA children predisposes them to insulin resistance early in life. In addition, AA overweight children with high levels of insulin secretion may “burn-out” their pancreatic β-cells at a faster rate (10,11). Thus, there is a need for risk factors of T2DM to be identified in childhood.
The aim of this study was to identify the factors that related to insulin resistance in a cohort of “at risk for overweight” (BMIs > 85th percentile) and “overweight” (BMI > 95th percentile) AA children. These children and their families are part of an ongoing community intervention trial, in conjunction with the YMCA of the East Bay (Oakland, CA) to reduce the risk for T2DM by improving their diet and increasing physical activity and self-esteem.
Methods and Procedures
This study was approved by the committees for protection of human subjects at both University of California at Berkeley and University of California at San Francisco. Written informed consent was obtained from a parent or guardian of the participants.
AA children aged 9–10 years were recruited from schools and other venues (e.g., brochures and pamphlet distributed in the YMCA, local grocery stores, etc.) in two geographically discrete regions of Oakland, California, containing AA populations with similar socioeconomic characteristics. Children within each region are grouped to compare the long-term effectiveness of two interventions for reducing risk for T2DM by modifying the self-esteem, physical activity, and diet of these children. Data included in this report were attained at baseline—before intervention.
Eligible children met the following criteria: (i) had at least one AA parent, (ii) were 9 or 10 years old at the time of recruitment, (iii) had BMI greater than the 85th percentile when matched for gender and age, (iv) had fasting glucose <120 mg/dl (nondiabetic state), (v) were free of any systemic or metabolic disorders, and free of medication known to affect energy metabolism or body weight, and (vi) were free of physical or emotional factors that would interfere with consistent participation in the program. A total of 175 subjects were initially recruited into the study of which three subjects dropped out of the study before any measurements were taken.
Body weight was measured to the nearest 0.1 kg using a digital electronic scale (BWB 800; Tanita, Tokyo, Japan), and height was measured to the nearest 0.1 cm using a portable stadiometer and with subjects lightly dressed and without shoes or jewelry. Measurements were repeated till two consecutive values differed by only 0.1 kg weight or 0.5 cm in height. BMI was calculated as weight (kg) divided by height squared (m2). BMI percentiles and BMI z-scores were generated using an age- and gender-specific Centers for Disease Control and Prevention calculator program (http:www.cdc.govnccdphpdnpagrowthchartssas.htm). Using a plastic nonelastic measuring tape, waist circumference (WC) was measured just above the iliac crest, and hip circumference was measured at the widest point above the great trochanters, and waist-to-hip ratio was calculated. Both circumferences were measured in the standing position and all measurements were taken two times. If agreement between repeats was >0.4 cm, a third measurement was taken and the mean calculated using the closest two values. Total body resistance was measured using the bioelectrical impedance analysis (Model BIA-101Q; RJL systems, Detroit, MI) according to manufacturer's instructions, and percent body fat was computed using the Horlick's equation (12). This method and equation have been validated in AA children by Lewy et al. (13).
All subjects were asked to report to Children's Hospital and Research Center Oakland, (Oakland, CA) after an overnight fast to obtain blood samples for assessment of plasma glucose, serum insulin, estradiol (girls only), and luteinizing hormone (LH). Blood tubes obtained for serum and plasma (EDTA and NaF coated) were kept refrigerated until centrifugation within 4 h of collection.
Plasma glucose. Plasma glucose was determined using the hexokinase-peroxidase method (Glucose HK-60 assay; Diagnostic Chemicals, Oxford, CT). Reference standards of normal and elevated glucose concentration (DC-Trol; Diagnostic Chemicals, Oxford, CT) were analyzed with each assay for quality-control purposes.
Serum insulin. Fasting insulin concentrations were determined using enzyme-immunoassay (Linco, St. Charles, MO). The insulin assay has <1% cross-reactivity with proinsulin. The intra-assay variation was 4.5% and inter-assay variation was 10%.
HOMA-IR. Fasting glucose and insulin values were also used to calculate the homeostatic model parameter, homeostasis model assessment of insulin resistance (HOMA-IR), defined as fasting glucose (mmol/l) × insulin (μU/ml)/22.5 (ref. 14) and used as an index of insulin resistance (15).
Serum levels of LH in boys and estradiol and LH in girls were assayed using Enzyme immunoassays (Biocheck, Foster City, CA), and the concentrations of these hormones were used to assign children to one of three pubertal stages: prepubertal (stage I), early pubertal (stage II), or late pubertal (stage III). On the basis of data available for nonobese children, girls were classified as prepubertal, early pubertal, or late pubertal using Estradiol concentration cutoffs of 0–38, 38.01–68, and >68 pg/ml, respectively (16,17,18), and LH cutoffs of 0–3, 3.01–6, and >6 IU/l, respectively (16,17,18,19). Boys were classified as prepubertal, early pubertal, or late pubertal using LH cutoffs of 0–2, 2.01–3, and >3 IU/l, respectively (17,18,20). In girls, if Estradiol and LH concentrations suggested different pubertal stages, the higher stage was assigned to that girl.
Body image. Body image, which is a mental picture of oneself, is known to represent one aspect of an individual's self-esteem (21,22). The questionnaire used to assess body image consisted of two identical sets of female or male silhouettes. The first set of silhouettes was marked “body now,” and participants were asked to mark the silhouette that best represented their current body shapes. The second set of silhouettes was marked “want body” and participants were asked to mark the silhouette that best represented how they would like their bodies to look. Silhouettes developed by Stunkard et al. (23) and modified to be suitable for children (24) were used. These modified silhouettes have been validated in children to produce reliable results (25).
Body satisfaction index. Body satisfaction index (BSI) scores were calculated using the difference in the numbers assigned to the two silhouettes on the body image questionnaire, where BSI equals value for “body now” minus value for “want body” (26). Values for “body now,” “want body,” and BSI were used in the multivariate regression model of insulin resistance.
Harter scale for assessment of self-esteem. The Harter self-perception profile for children is a self-administered questionnaire, which has been used for school children from third through sixth grade (26,27). This questionnaire was designed to evaluate six domains or subscales of perceived competence and self-adequacy: global self-worth, scholastic competence (SC), athletic competence, physical appearance (PA), behavioral conduct, and social acceptance. Each subscale contains six items, scored from 1 to 4, which represent the least and most adequate self-judgments, respectively. Scale scores are the average of item scores. In this analysis, we assessed the internal consistency of the items within each subscale using the Cronbach's criterion (28). To accommodate reading difficulties, questions were read to individual children when needed. The Harter subscale scores were used in the multivariate regression model of insulin resistance.
The East Bay YMCA's “feeling good” physical activity questionnaire was used to assess physical activity habits, participants' version of his/her physical capabilities compared to his peers, his/her weight perception, academic competence, and personal values. (For items included in specific indices, refer to Supplementary Table S1 online).
Physical activity assessment
Physical fitness measure. Physical endurance and fitness were assessed using the 20-m shuttle-run test which, when originally tested in men and women, was shown to be a valid and reliable test for predicting fitness (29). Briefly, participants were asked to run 20 m at a starting speed of 8 km/h, with speed increasing by 0.5 km/h every 2 min. The maximum number of laps run without stopping was used as a measure of fitness. We did not compute the maximal oxygen consumption capacity using laps in this study because this computation has not been validated in overweight children. This method has been validated in 6–23-year olds (30), and has been widely adopted by schools in California as part of the California Fitness-gram measure of aerobic fitness in elementary school age children (31). Its reliability and ease of use made it an appropriate instrument for measuring aerobic fitness in children participating in this study. The number of laps completed by individual child was used as a measure of physical fitness in this study.
Three-day activity diary. Physical activity was assessed using a 3-day activity diary (2 weekdays and 1 weekend day), which was modified from Kimm et al. (32) to include one additional activity category specifically for television viewing. This activity diary is a self-administered questionnaire, which has a pictorial representation of some of the common activities performed by children of similar ages. Three-day physical activity dairies were administered to the children before their participation in the study. The dairies consisted of 11 activity pictures ranging from low-intensity activities such as sitting, reading, and watching television, to high-intensity activities such as skateboarding, running, and playing basketball. Alongside each activity were duration choices (1–15, 16–30, and 30 min or more) used to quantify time spent on each type of activity. Additional lines were provided to include any activity not listed in the 11 items. Bedtime and wake time were also recorded. PA dairies were reviewed by trained staff for “completeness” of record keeping and 83% of the children in the study (137 of 165 children) had “complete” 3-day dairies. All reported activity minutes were summed to compute the “minutes spent in accounted physical activity” minutes.
Published METs (metabolic equivalents) values from Ainsworth et al. compendium of PA MET intensities were used (33) to assign MET values to each activity group on the questionnaire. Activities were then classified into low-intensity (MET value <3), moderate-intensity (MET value between 3 and 6) and high-intensity activity (MET value >6). METs per activity were obtained by multiplying the MET value by the mean time spent on that activity (7.5 min for 1–15 min interval; 22.5 min for 16–30 min interval; 45 min for 30 min or more) per individual (32). For each participant, values were calculated per day, and then averaged over the 3 days. Time (minutes) spent on all recorded activities and on low-, moderate-, and high-intensity activities, METs per min during recorded activities, and METs per min during the average 24-h day were used in the multivariate regression model of insulin resistance.
Three-day food diaries. Three-day food diaries were used to assess intakes of nutrients, and servings of selected foods. Three-day diaries have been shown to be a more accurate tool for diet assessment than 24-h diet recall or food frequency questionnaire methods (34). Before completing a 3-day food diary (2 weekdays and 1 weekend day), participants were trained to record portion sizes, brand names, place consumed, and time of day food was consumed. Trained staff reviewed the questionnaires with the child, expanding on descriptions of foods and portion sizes when needed.
Macronutrient intake and numbers of servings of food groups were determined using “the nutrient database” (35). To do this, the foods listed on the 3-day food diaries were labeled according to the 8-digit food codes in the United States Department of Agriculture (USDA) nutrients database (“what's in the foods you eat?”), portion sizes or weights were entered into this software, and computer programs were used to calculate the 3-day average intakes of energy and specific macronutrients. In addition, the above information was used to compute the servings of various major food groups and subgroups using the Pyramid servings USDA database (36). Analyses were carried out on the data set as a whole and, following the protocol for the National Health and Nutrition Examination Survey (NHANES), no quantification or exclusion for underreporting or overreporting was made. Three-day average macronutrient intakes were determined and percent calories from each macronutrient were established and used in the multivariate linear regression model.
Nutrition and physical activity knowledge assessment
To assess the nutrition and physical activity knowledge of the participants, a 35-item questionnaire was developed specifically for this study with the help of experts in the fields of nutrition and epidemiology. One goal was to assess nutrition knowledge with respect to the nutrition goals of the main study—namely to increase fruit and vegetable intake, increase whole grain and low-fat dairy consumption, and to decrease sweetened beverage consumption. The second goal in the nutrition and physical activity questionnaire was to assess the PA knowledge (e.g., knowledge regarding the activities that improve physical activity; knowledge regarding warm up and cool down before and after exercise; etc) in accordance with the PA goals of the main study—namely to increase the physical activity duration by 30–60 min per day including 30 min of moderate to vigorous activity per day. (For items included in specific indices, refer to Supplementary Table S1 online).
Demographics and food-purchasing behavior. One adult parent or guardian of each participant completed a 26-item family demographic and food-purchasing behavior questionnaire, which was modified from Shankar and Klassen (37). This questionnaire consisted of questions pertinent to the sociodemographics of the family (age of respondent, education, employment status, number of family members employed, and type of residence); family structure (number of family members, siblings—older and younger); family meal patterns (e.g., home/restaurants; frozen/fresh foods; fast-food frequency); and food shopping patterns of the family (supermarket/corner store, who purchases, frequency of shopping, etc).
For the purposes of this analysis, socioeconomic status of participant families was determined using two approaches—education of adult respondent alone; and via a socioeconomic index (SE index) that included the respondent's education, “per capita employment” (number of family members employed by family size with every two children taken as one adult), and type of residence.
Family health. This 23-item questionnaire was formulated to assess the overall health of the participant as perceived by the guardian (e.g., How would you rate the overall health of the child?), and family history or risk for diabetes (e.g., physician-diagnosed diabetes in biological parents and/or siblings) and intrauterine history of type 2 diabetes (maternal gestational diabetes; participant weighing >9 or <5 lb at birth; prematurity). These questions were taken or modified from the “risk test” advocated by the American Diabetes Association to assess an individual's risk of T2DM (http:www.diabetes.orgrisk-testtext-version.jsp). In addition, four questions were administered to assess the “stages of change” in exercise (38), fruit and vegetable intake (39), and high-fat foods (40) of the parent/guardian accompanying the child.
Baseline data collected from children and adult family members were entered into Epi. data Version 6 (41) and then transferred into SPSS (SPSS for UNIX, Release 6.14; Chicago, IL) for statistical analysis. Means across genders were compared with two-sample t-tests and bivariate correlations among measures were computed using Pearson's correlation.
Multiple linear regression analyses were used to evaluate diet, physical activity, and self-esteem factors related to insulin resistance (HOMA-IR). Parameters were selected based on scientific literature-defined associations with the measures of insulin resistance. The relationships among these parameters were evaluated after adjusting for potential confounders—namely, the participant's age, gender, pubertal stage, family history of diabetes, and family socioeconomic status. The study was insufficiently powered to allow for gender-specific modeling, but gender differences were adjusted for in all models. The final regression model was selected based on the goodness-of-fit and theoretical importance. Statistical significance was defined at P < 0.05.
Baseline anthropometric and biochemical parameters
A complete set of anthropometric and biochemical data were available for 165 (96% of total) participants (Table 1). Percent body fat was significantly higher in girls than in boys. Girls also had significantly lower concentrations for fasting glucose but significantly higher fasting insulin concentrations and higher values for HOMA-IR compared with boys. Anthropometric measures of body weight, BMI, BMI-z score, and % body fat were significantly correlated with HOMA-IR using bivariate analyses (data not shown).
Table 1. Anthropometric and fasting biochemical values
A full set of qualitative data was not available for all 172 participants (number of participants at the beginning of the study) in this study. To determine whether these missing data introduced a bias into the remaining analyses, HOMA-IR values of the 72 participants who were missing one or more data for the variables used in our analysis were compared to the means from the (n = 100) participants who had complete data. Differences between these two groups of participants were not statistically significant for HOMA-IR% (P = 0.389). Thus, for these variables, the participants who had a full set of quantitative data were considered to adequately represent the original 172 participants, and comparisons were made using all participants who had available data for each specific analysis.
Body satisfaction and self-esteem scales
Girls and boys viewed their bodies to have similar overall shapes but, in comparison to boys, girls wanted their bodies to be significantly more slender (Table 2). BSI, however, was not significantly different for boys and girls. Using the combined data for girls and boys, HOMA-IR was significantly correlated with “body now” (r = 0.33, P < 0.001) and “BSI” (r = 0.19, P = 0.017).
Table 2. “Body image” and “Harter's self-esteem” scales in overweight African-American children
Analysis of the data for our cohort showed that internal consistency was acceptable for five of the Harter subscales (Cronbach's α ranged from 0.62 to 0.72; Table 2). Internal consistency was not acceptable, however for the “social acceptance” scale (α = 0.48) and, hence, data for this subscale were not used in our regression analysis. Behavior subscale was significantly different between boys and girls in this cohort with girls perceiving themselves to behave better than boys (P = 0.018). None of the other scales were different between boys and girls. In addition, none of the Harter's subscales were significantly correlated with insulin resistance (HOMA-IR) in this study population.
Approximately 83% (137 of 165 children) of participants had complete 3-day PA dairies. Using 3-day physical activity records, participants in this study reported activities (“accounted physical activity” minutes), on average, for 4.4 ± 0.23 h out of 24 h, with no significant difference between boys and girls (P = 0.157) (Table 3). The amount of time spent in low- or moderate-intensity activities was not significantly different between boys and girls. Boys spent significantly more time on high-intensity activities than girls (P = 0.019). Also, fitness levels (laps from the 20-m shuttle-run test) for girls and boys were not significantly different. Compared with boys, girls had significantly higher scores for PA knowledge (P = 0.003) but significantly lower scores for PA capabilities (P < 0.001). Physical activity habits of the child and family were significantly higher in boys (P = 0.040) and their families (P = 0.044) having higher PA habit scores than girls and their families. HOMA-IR (r = 0.30, P < 0.001) was significantly correlated with fitness laps as was child PA capabilities (r = −0.22, P = 0.005).
Table 3. PA and physical fitness in overweight African-American children and their families
The children in this study consumed an average of 1,923 ± 58 kcal/d, with ∼50% of calories coming from carbohydrates, 15% from protein, and 35% from fat, and with no significant differences between girls and boys (Table 4). Indices of the child's nutrition knowledge, the child's food habits, and the family's food habits did not differ between boys and girls, but girls had healthier food preferences than boys (P = 0.011) (Table 4). Using the combined data for girls and boys, HOMA-IR was not significantly correlated with any of these dietary indices.
Table 4. Dietary intake
Factors related to insulin resistance
Insulin resistance, assessed using HOMA-IR, served as the dependent variable when evaluating the influence of self-esteem, physical activity, and dietary intake on risk for T2DM. Independent factors were selected based on scientific literature showing a relationship between various aspects of the main study and insulin resistance or sensitivity. In all our models, covariates included the child's age, gender, and pubertal stage, family history of diabetes (family and intrauterine history), and family SE index (results were the same when socioeconomic status was adjusted using education alone). Forward stepwise regression models were used to analyze the effect of various parameters on HOMA-IR. Parameters quantifying the anthropometrics of the child (BMI-z score, % body fat, WC), physical activity (fitness laps, minutes spent in various intensity activities, average METs per minute, child PA knowledge, and family PA habits), self-esteem (body now, BSI, five of the Harter subscales), and diet (% calories from protein, grams of fiber in the diet, child food habits, child food preferences, and family food habits) of the child and family were evaluated as predictors of insulin resistance (HOMA-IR). After adjusting for SE index, family history of diabetes, age, pubertal stage, and gender (these variables were forced into the model), HOMA-IR was significantly correlated with WC (β = 0.065; P < 0.001) and with Harter's SC scale (β = −0.406; P = 0.044) (Table 5). No other factors were significantly related to this dependent variable. Each 1 unit (cm) increase in WC corresponded with a 0.06 unit increase in HOMA-IR and each 1 unit increase in Harter's SC scale corresponded with a 0.41 unit decrease in HOMA-IR.
Table 5. Linear regression model for predicting glucoregulation
Although the risk for T2DM in AA children is higher than in white children (42), data on the factors related to risk for this condition in children are lacking. Using multivariate regression analysis, we have demonstrated that WC and selected self-esteem measures were significantly related to insulin resistance, even after accounting for SE index, family history of diabetes, and the child's age, pubertal stage, and gender (Table 5). Interestingly, insulin resistance was not associated with energy/macronutrient intakes or child's PA behavior in this study.
Fasting indices of insulin resistance have been shown to be well correlated with estimates obtained using the “gold standard” methods of the euglycemic-hyperinsulinemic clamp in 9–10-year-old children (43) and oral glucose-tolerance test in premenarchal girls (44), and these indices have been used to show that overweight children are at greater risk for type 2 diabetes than normal-weight children (45).
Our data confirm that AA children with higher WCs have greater insulin resistance even after adjusting for SE index, family history of diabetes, and the child's age, pubertal stage, and gender. This is consistent with previous studies reporting that overweight status and obesity are important risk factors for insulin resistance and type 2 diabetes in children (8,46,47).
In addition, we assessed the self-esteem in these children using the Harter's self-perception scales and body image to see whether they were related to insulin sensitivity. SC was independently related to insulin resistance. Observational and epidemiological studies have shown that long-term behavioral and lifestyle modification can be accomplished effectively only when the social, cultural, and environmental factors associated with individual populations are taken into consideration. In fact, Burnet and colleagues (48) in their extensive review on behavioral models for reducing the risk for type 2 diabetes in minority (AA) youth have focused on behavior interventions that effectively aid in decreasing risks or “preventing” type 2 diabetes (48). Our study has shown comparable finding in that insulin resistance measures are significantly related with measures of self-esteem. Improving the self-esteem and hence self-perception of one's ability could be an important first step in achieving the dietary and physical activity behavioral modifications needed to decrease the risk for T2DM.
Although, previous studies have shown a high correlation between the surrogate indices of insulin resistance and the “gold standard” measures of insulin resistance, clamp studies are more sensitive measures of insulin sensitivity than the HOMA-IR. Although, normal glucose-tolerant children could not be differentiated from impaired glucose-tolerant children in this study, as can be done using a glucose-tolerance test, none of the children enrolled in this study had fasting glucose concentrations >120 mg/dl (http:www.diabetes.orgrisk-testtext-version.jsp), and the fasting glucose concentration was used as a indicator of glucose intolerance. However, the nature of this community-based study with nonhospitalized participants precluded the more invasive procedures.
Pubertal hormone assays are a validated method to assess puberty. We acknowledge, however, that the ranges encompassing each pubertal stage using sex hormones is so large that the pubertal staging we used could cause over- or under-estimation of the effect of pubertal stage on the dependent variable of interest. Using self-reported tanner (data not shown), however, did not alter our conclusions.
Community interventions to reduce the risk of type 2 diabetes in overweight children should aim at decreasing the WC of the child to reduce insulin resistance.
We gratefully acknowledge the collaboration and excellent assistance provided by the YMCA of the East Bay in Oakland, CA. Essential funding was provided by USDA CSREES grants 2004-35214-14254 and 2005-35215-15046, the Agriculture Experiment Station, and the YMCA. We are indebted to the participating children and their families, to the large number of UC Berkeley student assistants, and to Barbara Green, Rita Mitchell, and Mark D. Fitch. Valued consultants on this project include all members of the Advisory Board of the Taking Action Together Project.