QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Tanasescu, M. Articles by Pérez-Escamilla, R. Search for Related Content PubMed PubMed Citation Articles by Tanasescu, M. Articles by Pérez-Escamilla, R.

---

Article

Biobehavioral Factors Are Associated with Obesity in Puerto Rican Children^{1 ,2}

Mihaela Tanasescu, Ann M. Ferris, David A. Himmelgreen^*,, Nancy Rodriguez and Rafael Pérez-Escamilla³

Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269; ^* Hispanic Health Council, Hartford, CT 06106; and Department of Anthropology, University of South Florida, Tampa, FL 33620

³To whom correspondence should be addressed.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
The purpose of this case-control study was to identify predictors of obesity among Puerto Rican children from Hartford, CT. The study included 53 prepubertal children, 31 girls and 22 boys, between 7 and 10 y of age. Children were classified as obese [n = 29, body mass index (BMI) 85th percentile] or controls (n = 24, BMI < 85th percentile). Multivariate logistic regression analyses indicated that frequency of fruit juice consumption [odds ratio (OR), 95% confidence interval (CI); 4.02, 1.48–10.95], hours of daily TV viewing (1.86, 1.02–3.42), maternal BMI (1.39, 1.10–1.77) and lower dairy product intake (0.41, 0.19–0.93) were associated with obesity. Television viewing was correlated (P < 0.05) with lower physical activity in girls, and with higher snacking frequency and sweets consumption in boys. Obese children were more likely than controls to have higher systolic and diastolic blood pressures and to have experienced more ear infections and diarrhea during the previous year. Results provide evidence of the multifactorial nature of childhood obesity in this community.

KEY WORDS: • children • fruit juice • Hispanics • obesity • television

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Obesity is currently an escalating epidemic that affects many countries in the world including the United States (Mokdad et al. 1999 ) where this condition is responsible for 300,000 deaths annually (Allison et al. 1999 ). The treatment of obesity in adults gives disappointing results because rebound weight gain within months is present in the vast majority of adults who lose weight (Thomas 1995 ). Behavioral interventions in pediatric populations may yield greater weight loss and better maintenance than in adults, but relapse is still present in about two thirds of treated cases (Epstein et al. 1998 ).

Overall, the prevention of obesity has not been any more successful than the treatment. Despite efforts to promote more physical activity and the proliferation of low fat foods, the prevalence of childhood obesity increased in the last decades and continues to rise in the U.S. (Freedman et al. 1997 , Mei et al. 1998 , Troiano and Flegal 1998 ). A plausible cause for the failure to prevent obesity in this country may be the use of blanket approaches that do not meet the needs of high risk populations such as minority groups. Hispanics are one of the youngest U.S. ethnic groups highly affected by obesity (Molina and Aguirre Molina 1994 ). Thus, it is essential to identify modifiable risk factors that can guide the design and implementation of obesity prevention programs for this ethnic group.

Researchers have identified potential modifiable risk factors that can lead to obesity in childhood. Prominent among these are television viewing (Andersen et al. 1998 , Robinson 1999 ), physical inactivity (Moussa et al. 1994 , Ward et al. 1997 ), and fruit juice consumption (Dennison et al. 1997 ). The last-mentioned has become controversial because studies have been retrospective and the cause-effect directionality remains unknown. The role of energy or fat intake in the development of childhood and adult obesity remains inconclusive (Maffeis et al. 1996 , Schonfeld-Warden and Warden 1997 , Tucker et al. 1997 , Willett 1998 ). This may be related to the large measurement errors associated with the assessment of energy intake in general and underreporting among obese children and adults (Heitmann and Lissner 1995 , Johnson-Down et al. 1997 , Lichtman et al. 1992 ). Several studies have shown, however, that the diets of obese children have a higher proportion of fat than those of normal weight children (Gazzaniga and Burns 1993 , Maffeis et al. 1996 , Moussa et al. 1994 , Nguyen et al. 1996 ). It has been hypothesized recently that dairy products may protect humans against the development of obesity perhaps as a result of the action of calcium in lipid metabolism (Zemmel 1998 ).

Parental body mass index (BMI)⁴ is correlated with the child’s BMI (Esposito-Del Puente et al. 1994 , Lake et al.1997 , Maffeis et al. 1994 ). Children with two obese parents are more likely to be obese compared with children with only one obese parent, who in turn are more likely to be obese than children without obese parents. The tracking of obesity into adulthood is strongest in children when both parents are obese (Lake et al. 1997 ). Birthweight may be an important predictor of childhood obesity. The higher risk of obesity in children with high birthweight is present in early childhood (Binkin et al. 1988 ) and may be carried on into young adulthood (Sorensen et al. 1997 ). Studies relating infant feeding practices to childhood obesity have yielded inconclusive results. Some studies report a negative correlation between breast-feeding duration and late introduction of solid foods with childhood obesity (Kumanyika 1993 , von Kries et al. 1999 ); however, others found positive correlation of breast-feeding with obesity (Agras et al. 1990 ) or found no correlation between these two variables (Zive et al. 1992 ).

Single parenthood has been related to childhood obesity in a number of studies (Garman et al. 1982 , Gerald et al. 1994 , Wilkinson et al. 1977 ). This finding may be related to psychosocial factors that have been related to childhood obesity such as family dysfunction (Christoffel and Forsyth 1989 ), maternal psychiatric disorders (Favaro and Santonastaso 1995 ) and parental neglect (Lissau and Sorensen 1994 ).

To our knowledge, there are no studies that have recently examined predictors of obesity among Puerto Rican children living in the mainland U.S. The objectives of this study, which is based on an inner-city sample of Puerto Rican children, were to determine the following: 1) Do the diets of obese children diets differ from those of nonobese children in terms of energy, macronutrients and food group intake? 2) Do obese children have different TV viewing and activity and inactivity patterns than nonobese children? 3) Is maternal obesity associated with child’s obesity? 4) Do obese children have different birthweights and infant feeding profiles compared with their nonobese counterparts? 5) Does socioeconomic status influence the likelihood of childhood obesity? 6) Is obesity associated with negative health consequences during childhood?

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Study design and sample size.

The present investigation was designed as a case-control study and was approved by the Human Subject’s Review Committees of the University of Connecticut and the Hispanic Health Council (HHC). Study subjects were 53 Puerto Rican children aged 7.2 to 11 y and their caretakers living in inner-city Hartford. There were 31 girls among whom 19 were obese and 12 were controls, and 22 boys of whom 10 were obese and 12 were controls.

Sample size was calculated from the Student’s t test formula prespecifying and ß errors as 0.05 and 0.20, respectively. The main outcome used for the sample size estimation was percentage of energy from dietary fat. The SD and differences attributed to obesity in energy from dietary fat in girls (SD = 6%, expected difference = 5%) and boys (SD = 6%, expected difference = 8%) were derived from a similar study performed by Gazzaniga and Burns (1993) in Caucasian children aged 9–11 y. Sample size estimations indicated that a total of 44 girls and 18 boys were needed for the study. The original sample size goals were met for boys but, because of the demanding nature of this study, only 70% of the sampling goal for girls was achieved.

Recruitment and obesity classification criteria.

To be included in the study, children had to meet the following criteria: 1) be of Puerto Rican ethnicity; 2) be between the ages of 7 and 11 y; and 3) be free from diagnosed endocrine conditions that could lead to obesity. Wasted children (i.e., weight for height < 5th percentile) and girls that had already reached menarche were also excluded from the study. Children caretakers were recruited from the participant’s list of a previous nutrition knowledge survey (Perez-Escamilla et al. 1998 ) (n = 17), street outreach through a recruitment flier (n = 12), one school clinic (n = 17) and referrals by HHC employees (n = 7). A $10 incentive was offered to all study participants.

Children with an age- and sex-specific BMI 85th percentile were classified as obese. The BMI cut-points used were derived from National Health and Nutrition Examination Survey (NHANES) data (Muecke et al. 1992 , Must et al. 1991 , Sherman et al. 1995 ).

Dietary intake.

Studies comparing parental report of child’s food intake with direct home observations have concluded that parents do provide a reasonably valid assessment of their children’s food intake (Basch et al. 1990 , Eck et al. 1989 , Klesges et al. 1987 ). Studies have also shown that school-aged children are able to report valid 24-h recalls and food-frequency questionnaires (FFQ) (Rocket and Colditz 1997 , Thompson et al. 1997 ). In addition, a study of 6- to 12-y-old children comparing parental vs. child report concluded that parents may not be very aware of the child’s between-meal snack intake (Emmons and Hayes 1973 ). It was decided, therefore, in our study to conduct a 24-h recall by asking the child directly with the active participation of a parent or primary caretaker and using a FFQ by asking the caretaker directly with the active participation of the child. These approaches have been applied successfully by others (Garceau et al. 1999 , Johnson-Down et al. 1997 ).

One 24-h recall was conducted using a standardized four-stage protocol (Gibson 1993 ). In the first stage, a complete list of all foods and beverages consumed during the preceding day was obtained. In the second stage, detailed description of all of the beverages consumed, including cooking methods and brand names were recorded, together with the time and place of consumption. In the third stage, estimates of the amounts of all foods and beverages consumed were obtained. Paper models were used (National Dairy Council, Teacher’s guide) to assist respondents with the portion size estimations. Finally, in the fourth stage, the food recall was reviewed to ensure that all items had been recorded correctly. Caretakers helped children remember what they had eaten at home for breakfast, dinner and snacks. The lunch was generally eaten at school so it was the child’s entire responsibility to report the intake for this meal. Most of the 24-h recalls were conducted for weekdays. Five obese (17.2%) and five nonobese (20.8%) children reported intake for Sundays. No recall was performed for a Saturday. The study could have been strengthened if we had captured the children’s dietary intake for both weekdays and weekends. The reason for not including weekend and weekdays dietary intake was the length of the survey. The 24-h recalls were entered into the Minnesota Data Base [Nutrition Data System (NDS), Version 2.91] for estimates of energy and nutrient intakes. Puerto Rican recipes were obtained from professional cookbooks to break down ethnic dishes into foods that then were entered into NDS. The child’s diet was also assessed with a 71-item FFQ with items classified into 11 "food groups." The fruit juice category does not exclude the possibility that artificially flavored drinks or drink juices with very low fruit juice content were reported as "fruit juices" by the respondents. Caretakers were also asked about their infant feeding practices with the index child.

The post-hoc approach used to validate our dietary intake methodology consisted of correlating the food groups derived from the FFQ with the nutrient intakes derived from the 24-h recall. The 24-h recall covered the day before the interview, whereas the FFQ examined the usual intake of the child during the previous year. However, there should be consistency between both estimates (i.e., children who consume a particular food group more frequently should be more likely to have had a higher consumption of that group, and the nutrients predominantly provided by it, during the day before the interview). Our analyses indeed showed consistency between the dietary intake methods. For example, the consumption of dairy as assessed with the FFQ was correlated with calcium (Spearman’s r = 0.293, P = 0.028) and vitamin D (r = 0.359, P = 0.007) as determined by the 24-h recall; fruit juice was correlated with vitamin C (r = 0.470, P < 0.001); fruit was correlated with fiber (r = 0.319, P = 0.017) and ß-carotene (r = 0.368, P = 0.005); eggs were correlated with protein (r = 0.292, P = 0.029), the percentage of monounsaturated fatty acids (r = 0.355, P = 0.007) and cholesterol (r = 0.282, P = 0.036); and breads and cereals were correlated with protein (r = 0.285, P = 0.033), carbohydrate (r = 0.296, P = 0.027), calcium (r = 0.338, P = 0.011), iron (r = 0.243, P = 0.071), zinc (r = 0.281, P = 0.036) and fiber (r = 0.290, P = 0.030).

Physical activity.

Children’s levels of physical activity and inactivity were assessed through a 13-item physical activity questionnaire. The questionnaire was developed from the Harvard Medical School’s survey "Growing Up in the 90’s" (Colditz 1996 ). This questionnaire was modified, however, during the testing of the instrument by removing questions that were considered irrelevant for our study group (e.g., gardening). Weekly frequency of engaging in different types of activities was recorded for the cold and the warm seasons in a single interview, as reported by children. The physical activity scores were broken down by season into a "moderate-to-vigorous" activity score summarizing weekly frequency of engaging in sports, dancing and playing outside [i.e., activities involving an effort 4 MET (metabolic rate at rest)] and a "low-to-moderate" activity score (<4 MET) summarizing weekly frequency of walking, playing inside and other house activities (Ainsworth et al. 1993 , Pereira et al. 1997 , Ward et al. 1997 ).

Caretakers were asked to classify the usual level of child’s physical activity as "sedentary," "low," "average," "active" or "very active." Levels of inactivity were assessed through the caretaker’s report of the time the children spent watching TV, reading, doing homework and playing computer games. Daily time spent in these sedentary activities was recorded separately for weekdays and weekends as reported by parents.

Anthropometry.

Weight, height, triceps skinfold, waist and hip circumferences were measured in children and their mothers by a single researcher following standard procedures (Gibson 1993 ). Measurements were made and recorded twice, and the average of the two measurements was used in the analysis. If the measurements did not agree within 5 mm for height, 0.2 kg for weight, 1 mm for triceps skinfold and 0.5 cm for waist and hip circumference, a third measurement was taken. Triceps skinfold thickness was measured using a Lange skinfold thickness caliper (Cambridge Scientific Instruments, Cambridge, MD).

Food insecurity.

The 10-item Radimer/Cornell hunger scale was used to assess food insecurity patterns. Questions were grouped into four variables reflecting food security in general, food insecurity of the household and of the individual, and child hunger (Kendall et al. 1996 )

Blood pressure and health assessment.

Blood pressures were measured in duplicate to the nearest 2 mm Hg in children and mothers. The blood pressure cuffs used were specific for adults (Mabis Match Mates, Lake Forest, IL) and children (Omron, Tokyo, Japan). Mercury sphygmomanometers were used for all recordings. The first and fifth Korotkoff sounds were recorded as the systolic and diastolic blood pressure, respectively.

The child’s health was assessed on the basis of the mother’s perception of the overall health of the child and her recall of the incidence of common childhood illnesses (diarrhea, colds, fever, ear infections, nausea/vomiting) during the year preceding the interview. Caretakers also reported if the child had been hospitalized since birth or whether chronic or allergic conditions (e.g., diabetes, liver disease, heart disease, allergies or asthma) had occurred.

Statistical analyses.

All data except for the 24-h recalls were originally entered into SPSS for Windows (Release 7.0, Chicago, IL). The 24-h recalls were entered in the Nutrition Data System (NDS Version 2.91). The project summary file from NDS containing nutrient amounts in each food and daily nutrient totals per individual subject was exported into Microsoft Excel. Then the nutrient totals per individual subject were imported into SPSS for Windows. SPSS for Windows was used to conduct all statistical analyses. The analyses were interpreted using P 0.05 (two-tailed) as the level of significance.

    Definition of variables. As indicated previously, obesity was defined on the basis of a BMI 85th percentile (Muecke et al.1992 , Must et al. 1991 , Sherman et al. 1995 ). A BMI 95th percentile could have been chosen as the cut-off point to define obesity. However, there were only seven subjects with a BMI between the 85th and the 95th percentiles. Similar results were obtained with and without these subjects in the analyses.

The following independent variables were included in the analysis: birthweight (based on maternal recall), infant feeding practices (ever breast-fed, age when juice and solid foods were introduced), maternal socioeconomic and demographic characteristics (age, education, parity, marital status), maternal anthropometry (weight, height, BMI, triceps skinfold), paternal characteristics (father living in the house, father overweight reported by mothers, and father’s BMI based on graphic representations), socioeconomic characteristics (receiving food stamps and household appliances score summarizing availability of microwave, telephone, radio, refrigerator, stove, toaster, TV, washing machine, car), food insecurity and hunger (Radimer/Cornell hunger scale), food group and nutrient intakes, physical activity patterns (weekly frequency of engaging in physical activity, maternal assessment, daily hours of TV viewing, use of computer and video games), and health patterns (allergies, asthma, cold or fever, nausea or vomiting, ear infections, diarrhea and child’s blood pressure).

    Bivariate analyses. Bivariate analyses for testing between group variable differences were done using the independent sample t test and the chi-square test of independence for continuous and discrete variables, respectively. Nonparametric Spearman’s correlation analysis was used for correlations between continuous variables.

    Multivariate analyses. The independent association of predictors with childhood obesity was examined using logistic regression. Group membership (obese vs. nonobese) was the dependent variable. Twelve models including four independent variables at a time were tested before a final full model was generated. These variables were selected for variables associating at a P-value < 0.20 in the bivariate analyses. As a result, a multivariate logistic regression analysis was performed on group membership (obese vs. nonobese) as outcome and five predictors, i.e., maternal BMI, TV viewing, fruit juice consumption, dairy consumption, and marital status.

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Bivariate analyses

    Socioeconomic and demographic. The socioeconomic characteristics of obese and controls were similar as shown by the additive score derived from the household appliances. Also, a similar proportion of the obese and control children’s caretakers had access to a car. A high proportion of respondents were receiving food stamps (Table 1 ).

Obese children were heavier and taller, and had larger triceps skinfolds than nonobese children (Table 2 ). Children’s BMI correlated strongly with triceps skinfold (Spearman’s r = 0.858; P < 0.001). Mothers of obese children were heavier, and had significantly higher BMI and triceps skinfolds than mothers of controls, but maternal height did not differ between groups (Table 2) .

    Household food insecurity and hunger. The prevalences of food security/insecurity were as follows: household food secure (28.6 vs. 17.4% for obese and control groups, respectively, P = 0.35); household food insecure (46.4 vs. 39.1%, P = 0.60); caretaker insecure (25.0 vs. 34.8%, P = 0.45); and child hunger (0 vs. 8.7%, P = 0.11). Significantly more caretakers in the control group gave positive answers to the question "I can’t afford to eat properly" (10.7 vs. 39.1%, P = 0.02). Food insecurity at the adult level was significantly lower in obese boys compared with nonobese boys (0 vs. 36.4%, P = 0.01).

    Dietary intake. Food groups frequency of consumption. Obese children had a higher frequency of consumption of "fruit juice" than controls (Table 3 ). Frequency of fruit juice intake was also positively correlated with child BMI expressed as a continuous variable (r = 0.383, P = 0.005) and with triceps skinfold (r = 0.364, P = 0.009). A positive correlation was also found between snack consumption (i.e., sum of frequency of chips, peanuts, peanut butter and popcorn consumption) and BMI (r = 0.27, P = 0.049).

Energy and nutrient intakes. Obese children had similar total energy intakes but lower energy intakes per kilogram of body weight compared with nonobese children (Table 3) . The difference in the percentage of energy from saturated fat was marginally higher in boys (15.5 vs. 13.0% P = 0.08) but was absent in girls (13.2 vs. 12.8% P = 0.74). In agreement with the dairy intake data, there was a significantly lower intake of calcium and vitamin D in obese girls compared with controls (Table 3) .

    Physical activity patterns. Obese children performed lower levels of physical activity in the cold season compared with controls (P = 0.01) and marginally lower levels in the warm season (P = 0.07). When data were analyzed by gender, differences in physical activity were significant in the warm season for girls and in the cold season for boys (Table 4 ). Obese children were significantly less likely than controls to be described by their mothers as being "active" or "very active" (P = 0.007). Although obese girls were less likely to be considered "active" or "very active" by their mothers, this finding was not significant among boys (P = 0.22). The time obese children spent watching TV on weekdays was significantly greater than in controls. The time children spent using the computer and/or playing computer games, was higher in controls than in obese children. Among girls, obese subjects engaged less frequently in moderate-to-vigorous activities but were not different from controls with respect to activities of low-to-moderate intensity. Unlike girls, obese boys reported lower levels of "low-to-moderate" activities, but no difference was observed in the "moderate-to-vigorous" activities (Table 4) .

Multivariate logistic regression analyses indicated that frequency of fruit juice consumption, hours per day of weekday TV viewing, maternal BMI and lower dairy product intake were positively associated with child obesity (Table 6 ).

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Consistent with previous studies (Dennison et al. 1997 ), the frequency of consumption of fruit juice was higher among obese children compared with controls. This result should be interpreted with caution because it is possible that some of the beverages reported as "fruit juice" were in fact artificially flavored beverages or those containing little or no fruit juice. These beverages may contribute to the development of obesity in childhood through the dietary energy that they provide. However, in the study of Dennison et al. (1997) , as well as in this study, obese children did not have a greater energy intake than controls. It is possible that the relatively high glycemic index of fruit beverages may explain this association at least in part (Morris and Zemel 1999 ). From this study, however, it is not possible to fully understand precisely how fruit juice consumption and child obesity are associated. Although it is possible that a greater fruit juice consumption leads to obesity, Puerto Rican obese children may have tried to improve their diet by drinking more fruit juice because it is more affordable than fruits and more palatable for them than vegetables. It has been reported previously that among low income children, fruit juice tends to replace a more than desirable number of daily servings of fruits and vegetables (Basch et al. 1994 ). It is also possible that obese children may overreport fruit juice consumption because of its "healthy" connotation.

In the multivariate analyses, lower intakes of dairy products were found in obese children. Zemel (1998) has proposed a mechanism by which dairy products may protect children from the development of obesity. In particular, he postulates that intracellular calcium is a potent regulator of fatty acid synthase in adipocytes. Another possibility is that children may be substituting fruit juices and other high glycemic index beverages for milk.

Both the activity scores and the maternal rating of child’s physical activity indicated a lower level of physical activity in obese children compared with controls. This finding is consistent with previously published data (Moussa et al. 1994 , Ward et al. 1997 ). Energy intake per unit body weight was greater among control children, which may reflect their higher levels of physical activity.

In general, school-age boys and girls have similar levels of "vigorous" and "low-to-moderate" physical activity (Simmons-Morton et al. 1990 ). Once adolescence is reached, girls perform less "vigorous" activity than boys (Sallis et al. 1996 ). We found that although obese girls performed less "moderate-to-vigorous" physical activity than controls, levels of "low-to-moderate" activity did not differ. By contrast, obese boys had lower levels of "low-to-moderate" physical activity than controls but there was no difference in "moderate-to-vigorous" activity. This evidence points to the public health relevance of promoting physical activity among children for the prevention or treatment of obesity. These interventions must pay particular attention to the development of self-confidence in children in their ability to increase their levels of physical activity (Trost et al. 1997 ) and the promotion of vigorous activity among ethnic minority girls.

Increasing physical activity, with an emphasis on vigorous activity in girls, may prevent obesity among inner-city Puerto Rican children. In inner-city neighborhoods, safety is a critical aspect of parents’ decision to let their children play outside (Sallis et al. 1996 , Sallis et al. 1997 ). The trend for schools to eliminate physical education programs (Kohl and Hobbs 1998 ) is worrisome. Moreover, in existing physical education classes, children spend little time doing moderate or vigorous physical activity, with most of the class time filled with sedentary and minimal activity (Simmons-Morton et al. 1993 ).

In agreement with previous studies (Andersen et al. 1998 , Robinson 1999 ), obese children viewed significantly more hours of television than controls. Television viewing may influence obesity through less physical activity, increased opportunity for snacking and marketing of high energy products. Our study suggests that the mechanisms underlying the relationship between television viewing and obesity may be dependent on gender.

In this study, hours playing computer and video games were not associated with obesity. Watching television and playing computer and video games are quite different behaviors; computer and video games require good fine motor skills and a "risk taking" personality that may not be present in obese children. Thus, reverse causality may be confounding this result. As in previous studies (Binkin et al. 1988 , Sorensen et al. 1997 ), birth weight was positively associated with childhood obesity in the bivariate analyses. This association, however, was not detected in the multivariate analyses. Similarly, we were unable to detect a significant association between ever breast-feeding and the likelihood of obesity. In this study, the data did not support the hypothesis that obese children live in more food-insecure households and for this reason may consume a diet high in energy and low in nutrient-dense foods such as fresh fruits and vegetables.

Consistent with previous studies (Lake et al. 1997 , Maffeis et al. 1994 ), the mother’s BMI was a significant predictor of the obesity status of her child. This is likely to be the outcome of both genetic influences as well as the common environment. Finally, obese children were more likely to have experienced infectious diseases during the previous year and to have higher blood pressure. Thus, this study confirms that obesity has a negative influence on health not only in adulthood but also in childhood. The blood pressure findings were fully expected and confirm the validity of the obesity classification used in this study.

Study limitations.

This is an observational case control study that precludes making definitive causal inferences about the associations between behavioral and biological factors and childhood obesity. In addition, because of a relatively small sample size and the self-selection of subjects into the study, these findings cannot be generalized blindly to this or other populations. It is important to underscore, however, that a number of obesity correlates identified in this study such as television viewing, fruit juice consumption and maternal BMI have also been identified with children of other ethnic groups, suggesting that key determinants of childhood obesity are common to different cultures.

There are sound theoretical reasons for expecting differences in the biological and behavioral mechanisms underlying obesity in boys and. girls. However, gender-specific findings from this study should be interpreted with caution because they may partly reflect between-gender analyses differences in statistical power. These results, however are thought-provoking and deserve to be considered when designing future studies of childhood obesity.

In conclusion, this study indicates that the problem of pediatric obesity is multifactorial and can be further understood only through multidisciplinary approaches.

View larger version (24K): [in this window] [in a new window]   Figure 1. Systolic and diastolic blood pressures by obesity status and gender of low income children living in inner-city Hartford, CT. Values are means ± SD, n = 48 due to 5 cases with missing data. **Indicates significant differences (P < 0.01) between obese and controls within each gender and blood pressure type. *Indicates significant differences (P < 0.05) between obese and controls within each gender and blood pressure type.

	ACKNOWLEDGMENTS

	FOOTNOTES

 
¹ Presented at Experimental Biology 99, April 1999, Washington, DC [Tanasescu, M., Ferris, A. M., Himmelgreen, D. A., Rodriguez, N and Pérez-Escamilla, R. (1999) Predictors of obesity among inner-city Puerto Rican children. FASEB J. 13: A596 (abs.)].

² Funded by the Storrs Agricultural Experiment Station (SAES) and the U.S. Department of Agriculture Food Stamp Program. SAES Contribution # 1942.

⁴ Abbreviations used: BMI, body mass index; FFQ, food-frequency questionnaire; MET, metabolic rate at rest; NDS, Nutrition Data System.

Manuscript received November 15, 1999. Initial review completed January 6, 2000. Revision accepted March 8, 2000.

	REFERENCES

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 

1. Agras W. S., Kraemer H. C., Berkowitz R. I., Hammer L. D. Influence of early feeding style on adiposity at 6 years of age. J. Pediatr. 1990;116:805-809[Medline]

2. Ainsworth B. E., Haskell W. L., Leon A. S., Jacobs D. R., Jr, Montoye H. J., Sallis J. F., Paffenbarger R. S., Jr Compendium of physical activities: classification of energy costs of human physical activities. Med. Sci. Sports Exerc. 1993;25:71-80[Medline]

3. Allison D. B., Fontaine K. R., Manson J. E., Stevens J., VanItallie T. B. Annual deaths attributable to obesity in the United States. J. Am. Med. Assoc. 1999;282:1530-1538[Abstract/Free Full Text]

4. Andersen R. E., Crespo C. J., Bartlett S. J., Cheskin L. J., Pratt M. Relationship of physical activity and television watching with body weight and level of fatness among children. J. Am. Med. Assoc. 1998;279:938-942[Abstract/Free Full Text]

5. Basch C. E., Shea S., Arliss R., Contento I. R., Rips J., Gutin B., Irigoyen M., Zybert P. Validation of mothers’ reports of dietary intake by four to seven year-old children. Am. J. Public Health 1990;80:1314-1317[Abstract/Free Full Text]

6. Basch C. E., Zybert P., Shea S. 5-A-DAY: dietary behavior and the fruit and vegetable intake of Latino children. Am. J. Public Health 1994;84:814-818[Abstract/Free Full Text]

7. Binkin N. J., Yip R., Fleshood L., Trowbridge F. L. Birth weight and childhood growth. Pediatrics 1988;82:828-834[Abstract/Free Full Text]

8. Christoffel K. K., Forsyth B. W. Mirror image of environmental deprivation: severe childhood obesity of psychosocial origin. Child Abuse Neglect 1989;13:249-256[Medline]

9. Colditz G. Growing Up in the 90’s Survey 1996 Harvard Medical School Cambridge, MA

10. Dennison A. B., Rockwell H. L., Baker S. L. Excess fruit juice consumption by preschool-aged children is associated with short stature and obesity. Pediatrics 1997;99:15-22[Abstract/Free Full Text]

11. Eck L. H., Klesges R. C., Hanson C. L. Recall of a child’s intake from one meal: are parents accurate?. J. Am. Diet. Assoc 1989;89:784-789[Medline]

12. Emmons L., Hayes M. Accuracy of 24-hr. recalls of young children. J. Am. Diet. Assoc. 1973;62:409-415[Medline]

13. Epstein L. H., Myers M. D., Raynor H. A., Saelens B. E. Treatment of pediatric obesity. Pediatrics 1998;101:554-570[Abstract/Free Full Text]

14. Esposito-Del Puente A., Scalfi L., De Filippo E., Peri M. R., Caldara A., Caso G., Contaldo F., Valerio G., Franzese A., Di Maio S. Familial and environmental influences on body composition and body fat distribution in childhood in southern Italy. Int. J. Obes. Relat. Metab. Disord. 1994;18:596-601[Medline]

15. Favaro A., Santonastaso P. Effects of parents’ psychological characteristics and eating behaviour on childhood obesity and dietary compliance. J. Psychosom. Res. 1995;39:145-151[Medline]

16. Freedman D. S., Srinivasan S. R., Valdez R. A., Williamson D. F., Berenson G. S. Secular increases in relative weight and adiposity among children over two decades: the Bogalusa Heart Study. Pediatrics 1997;99:420-426[Abstract/Free Full Text]

17. Garceau A. O., Crepinsek M. K., Smith K. W., Hoelscher D., Zive M. M., Barosso G. M., Clesi A. L. Incorporating parent information with the self-reported intakes of seventh graders has a statistically significant, but small, effect on mean nutrient intakes. J. Am. Diet. Assoc. 1999;99:1566-1569[Medline]

18. Garman A. R., Chinn S., Rona R. J. Comparative growth of primary schoolchildren from one and two parent families. Arch. Dis. Child. 1982;57:453-458[Abstract]

19. Gazzaniga J. M., Burns T. L. Relationship between diet composition and body fatness with adjustment for resting energy expenditure and physical activity, in preadolescent children Am. J. Clin. Nutr. 1993;58:21-28

20. Gerald L. B., Anderson A., Johnson G. D., Hoff C., Trimm R. F. Social class, social support and obesity risk in children. Child Care Health Dev 1994;20:145-163[Medline]

21. Gibson R. Nutritional Assessment 1993 Oxford University Press New York, NY.

22. Heitmann B. L., Lissner L. Dietary underreporting by obese individuals—is it specific or non-specific?. Br. Med. J. 1995;311:986-989[Abstract/Free Full Text]

23. Johnson-Down L., O’Loughlin J., Koski K. G., Gray-Donald K. High prevalence of obesity in low income and multiethnic schoolchildren: a diet and physical activity assessment. J. Nutr. 1997;127:2310-2315[Abstract/Free Full Text]

24. Kendall A., Olson C. M., Frongillo E. A., Jr Relationship of hunger and food insecurity to food availability and consumption. J. Am. Diet. Assoc. 1996;96:1019-1024[Medline]

25. Klesges R. C., Klesges L. M., Brown G., Frank G. C. Validation of the 24-hour dietary recall in preschool children. J. Am. Diet. Assoc. 1987;87:1383-1385[Medline]

26. Kohl H. W., Hobbs K. E. Determinants of physical activity behaviors among children and adolescents. Pediatrics 1998;101(suppl.):549-554[Abstract/Free Full Text]

27. Kumanyika S. Ethnicity and obesity development in children. Ann. N.Y. Acad. Sci. 1993;29:81-92

28. Lake J. K., Power C., Cole T. J. Child to adult body mass index in the 1958 British birth cohort: associations with parental obesity. Arch. Dis. Child. 1997;77:376-381[Abstract/Free Full Text]

29. Lichtman S. W., Pisarska K., Berman E. R., Pestone M., Dowling H., Offenbacher E., Weisel H., Heshka S., Matthews D. E., Heymsfield S. B. Discrepancy between self-reported and actual caloric intake and exercise in obese subjects, N. Engl. J. Med. 1992;31:1893-1898

30. Lissau I., Sorensen T. I. Parental neglect during childhood and increased risk of obesity in young adulthood. Lancet 1994;343:324-327[Medline]

31. Maffeis C., Pinelli L., Schutz Y. Fat intake and adiposity in 8 to 11-year-old obese children. Int. J. Obes. Relat. Metab. Disord. 1996;20:170-174[Medline]

32. Maffeis C., Micciolo R., Must A., Zaffanello M., Pinelli L. Parental and perinatal factors associated with childhood obesity in north-east Italy. Int. J. Obes. Relat. Metab. Disord. 1994;18:301-305[Medline]

33. Mei Z., Scanlon K. S., Grummer-Strawn L. M., Freedman D. S., Yip R., Trowbridge F. L. Increasing prevalence of overweight among US low-income preschool children: The Centers for Disease Control and Prevention Pediatric Nutrition Surveillance, 1983 to 1995. Pediatrics 1998;101:e1-e6[Abstract/Free Full Text]

34. Mokdad A. H., Serdula M. K., Dietz W. H., Bowman B. A., Marks J. S., Koplan J. P. The spread of the obesity epidemic in the United States, 1991–1998. J. Am. Med. Assoc. 1999;282:1519-1522[Abstract/Free Full Text]

35. Molina C. W., Aguirre Molina M. A. Latino Health in the US: A Growing Challenge 1994:3-21 American Public Health Association Washington DC.

36. Morris K. L., Zemel M. B. Glycemic index, cardiovascular disease, and obesity. Nutr. Rev. 1999;57:273-276[Medline]

37. Moussa M. A., Skaik M. B., Sclwanes S. B., Yaghy O. Y., Bin-Othman S. A. Factors associated with obesity in school children. Int. J. Obes. Relat. Metab. Disord. 1994;18:513-515[Medline]

38. Muecke L., Simons-Morton B., Wei Huang I., Parcel G. Is childhood obesity associated with high-fat foods and low physical activity?. J. Sch. Health 1992;62:19-23[Medline]

39. Must A., Dallal G. E., Dietz W. Reference data for obesity: 85th and 95th percentiles of body mass index (wt/ht²) and triceps skinfold thickness. Am. J. Clin. Nutr. 1991;53:839-846[Abstract/Free Full Text]

40. Nguyen V. T., Larson D. E., Johnson R. K., Goran M. I. Fat intake and adiposity of lean and obese parents. Am. J. Clin. Nutr. 1996;63:507-513[Abstract/Free Full Text]

41. Pereira M. A., Fitzgerald S. J, Gregg E. W., Joswiak M. L., Ryan W. J., Suminski R. R., Utter A. C., Zmuda J. M. A collection of physical activity questionnaires for health-related research. Med. Sci. Sports Exerc. 1997;29:S1-S205[Medline]

42. Perez-Escamilla R., Himmelgreen D. A., Gonzalez A., Segura-Millan S., Mendez I., Haldeman L. Nutrition knowledge, attitudes and practices among Latinos in Hartford, CT: essential information for developing appropriate nutrition education 1998 American Public Health Association 126th Annual Meeting, November15–19 Washington, DC.

43. Robinson T. N. Reducing children’s television viewing to prevent obesity: a randomized controlled trial. J. Am. Med. Assoc. 1999;282:1561-1567[Abstract/Free Full Text]

44. Rockett H. R., Colditz G. A. Assessing diets of children and adolescents. Am. J. Clin. Nutr. 1997;65:1116S-1122S[Abstract/Free Full Text]

45. Sallis J. F., McKenzie T. L., Elder J. P., Broyles S. L., Nader P. R. Factors parents use in selecting play spaces for young children. Arch. Pediatr. Adolesc. Med 1997;151:414-417[Abstract]

46. Sallis J. F., Zakarian J. M., Hovell M. F., Hofstetter C. R. Ethnic, socioeconomic, and sex differences in physical activity among adolescents. J. Clin. Epidemiol. 1996;49:125-134[Medline]

47. Schonfeld-Warden N., Warden C. H. Pediatric obesity, an overview of etiology and treatment. Pediatr. Clin. N. Am. 1997;44:339-361[Medline]

48. Sherman J. B., Alexander M. A., Dean A. H., Kim M. Obesity in Mexican-American and Anglo children. Prog. Cardiovasc. Nurs. 1995;10:27-34

49. Simons-Morton B. G., O’Hara N. M., Parcel G. S., Huang I. W., Baranowski T., Wilson B. Children’s frequency of participation in moderate to vigorous physical activities. Res. Q. Exerc. Sport 1990;61:307-314[Medline]

50. Simons-Morton B. G., Taylor W. C., Snider S. A., Huang I. W. The physical activity of fifth-grade students during physical education classes. Am. J. Public Health 1993;83:262-264[Abstract/Free Full Text]

51. Sorensen H. T., Sabroe S., Rothman K. J., Gillman M., Fischer P., Sorensen T. I. Relation between weight and length at birth and body mass index in young adulthood: cohort study. Br. Med. J. 1997;315:1137[Free Full Text]

52. Thomas P. R. eds. Weighing the Options: Criteria for Evaluating Weight-Management Programs 1995 National Academy Press Washington, DC.

53. Thompson F. E., Moler J. E., Freedman L. S., Clifford C. K., Stables G. J., Willett W. C. Register of dietary assessment calibration-validation studies: a status report. Am. J. Clin. Nutr. 1997;65:1142S-1147S[Abstract/Free Full Text]

54. Troiano R. P., Flegal K. M. Overweight children and adolescents: description, epidemiology, and demographics. Pediatrics 1998;101:497-504[Abstract/Free Full Text]

55. Trost S. G., Pate R. R., Saunders R., Ward D. S., Dowda M., Felton G. A. Prospective study of the determinants of physical activity in rural fifth-grade children. Prev. Med. 1997;26:257-263[Medline]

56. Tucker L. A., Seljaas G. T., Hager R. L. Body fat percentage of children varies according to their diet composition. J. Am. Diet. Assoc. 1997;97:981-986[Medline]

57. von Kries R., Koletzko B., Sauerwald T., von Mutius E., Barnert D., Grunert V., von Voss H. Breast feeding and obesity: cross sectional study. Br. Med. J. 1999;319:147-150[Abstract/Free Full Text]

58. Ward D. S., Trost S. G., Felton G., Saunders R., Parsons M. A., Dowda M., Pate R. R. Physical activity and physical fitness in African-American girls with and without obesity. Obes. Res. 1997;5:572-577[Medline]

59. Wilkinson P. W., Parkin J. M., Pearlson J., Philips P. R., Sykes P. Obesity in childhood: a community study in Newcastle upon Tyne. Lancet 1977;12:350-352

60. Willett W. C. Dietary fat and obesity: an unconvincing relation. Am. J. Clin. Nutr. 1998;68:1149-1150[Medline]

61. Zemel M. B. Nutritional and endocrine modulation of intracellular calcium: implications in obesity, insulin resistance and hypertension. Mol. Cell. Biochem. 1998;188:129-136[Medline]

62. Zive M. M., McKay H., Frank-Spohrer G. C., Broyles S. L., Nelson J. A., Nader P. R. Infant-feeding practices and adiposity in 4-y-old Anglo- and Mexican-Americans. Am. J. Clin. Nutr. 1992;55:1104-1108[Abstract/Free Full Text]

This article has been cited by other articles:

				C. E. O'Neil and T. A. Nicklas A Review of the Relationship Between 100% Fruit Juice Consumption and Weight in Children and Adolescents American Journal of Lifestyle Medicine, July 1, 2008; 2(4): 315 - 354. [Abstract] [PDF]

				T. A. Nicklas, C. E. O'Neil, and R. Kleinman Association Between 100% Juice Consumption and Nutrient Intake and Weight of Children Aged 2 to 11 Years Arch Pediatr Adolesc Med, June 1, 2008; 162(6): 557 - 565. [Abstract] [Full Text] [PDF]

				N. F. Krebs, J. H. Himes, D. Jacobson, T. A. Nicklas, P. Guilday, and D. Styne Assessment of Child and Adolescent Overweight and Obesity Pediatrics, December 1, 2007; 120(Supplement_4): S193 - S228. [Abstract] [Full Text] [PDF]

				M. M. Davis, B. Gance-Cleveland, S. Hassink, R. Johnson, G. Paradis, and K. Resnicow Recommendations for Prevention of Childhood Obesity Pediatrics, December 1, 2007; 120(Supplement_4): S229 - S253. [Abstract] [Full Text] [PDF]

				B. A. Spear, S. E. Barlow, C. Ervin, D. S. Ludwig, B. E. Saelens, K. E. Schetzina, and E. M. Taveras Recommendations for Treatment of Child and Adolescent Overweight and Obesity Pediatrics, December 1, 2007; 120(Supplement_4): S254 - S288. [Abstract] [Full Text] [PDF]

				P. K. Newby Are Dietary Intakes and Eating Behaviors Related to Childhood Obesity? A Comprehensive Review of the Evidence J. Law Med. Ethics, March 1, 2007; 35(1): 35 - 60. [PDF]

				M. S. Faith, B. A. Dennison, L. S. Edmunds, and H. H. Stratton Fruit Juice Intake Predicts Increased Adiposity Gain in Children From Low-Income Families: Weight Status-by-Environment Interaction Pediatrics, November 1, 2006; 118(5): 2066 - 2075. [Abstract] [Full Text] [PDF]

				J. K Lorenzen, C. Molgaard, K. F Michaelsen, and A. Astrup Calcium supplementation for 1 y does not reduce body weight or fat mass in young girls Am. J. Clinical Nutrition, January 1, 2006; 83(1): 18 - 23. [Abstract] [Full Text] [PDF]

				M. B. Zemel The Role of Dairy Foods in Weight Management J. Am. Coll. Nutr., December 1, 2005; 24(suppl_6): 537S - 546S. [Abstract] [Full Text] [PDF]

				N. Stettler, M. R. Elliott, M. J. Kallan, S. B. Auerbach, and S. K. Kumanyika High Prevalence of Overweight Among Pediatric Users of Community Health Centers Pediatrics, September 1, 2005; 116(3): e381 - e388. [Abstract] [Full Text] [PDF]

J Kuepper-Nybelen, A Lamerz, N Bruning, J Hebebrand, B Herpertz-Dahlmann, and H Brenner Major differences in prevalence of overweight according to nationality in preschool children living in Germany: determinants and public health implications Arch. Dis. Child., April 1, 2005; 90(4): 359 - 363. [Abstract] [Full Text] <