Overnutrition and Scholastic Achievement: Is There a Relationship An 8-Year Follow-Up Study Open Access

In Chile, overweight and obesity in schoolchildren and in the population over the age of 15 years has increased in recent decades, despite efforts at prevention [1,2,3]. As a result of the high prevalence of childhood overnutrition, there is an increase in the incidence of diseases such as type 2 diabetes, cardiovascular disease, hypertension, hyperlipidemia, obstructive sleep apnea, asthma, orthopedic complications, and psychosocial problems which reduce the quality of life beginning at an earlier age [4,5].

Most of the research has focused on studying the effects of overnutrition on health, and fewer studies have examined its associations with scholastic achievement (SA). The causes of the relationship between overnutrition and SA are uncertain; however, several psychosocial (self-esteem, depression), family (parental interaction with the child, number of hours per day that children watch television), nutritional and educational factors (amount of hours of physical education, school characteristics) have been shown to correlate with educational aspects and BMI [6,7,8,9,10,11,12,13]. Considering that students spend most of their awake time in educational institutions, it is possible that the effects of overnutrition will affect their SA, learning skills, and attendance [14].

Socioeconomic status and parental schooling have also been proposed as variables that contribute to explain the relationship between overnutrition and SA, since these social factors may independently affect this relation [15]. The aim of this study was i) to determine the association between overnutrition and SA in school age children of the 5th elementary school grade in the Metropolitan Region of Chile at the onset of the school year in 2010 using the Education Quality Measurement System (SIMCE) tests of language (LSA), mathematics (MSA) and understanding of the natural environment (UNESA), at the end of 2009 when they graduated from the 4th grade of elementary school; ii) to analyze the association between overnutrition and SA 4 and 8 years later, when they graduated from elementary (8th grade of elementary school) and high school (4th grade of high school) using the 2013 SIMCE and the 2017 University Selection Test (PSU), for university admission, respectively. The purpose was to confirm the hypothesis that overnourished school age children achieve significantly lower SA compared with their normal-weight peers.

This is a cross-sectional, correlational and follow-up study.

The target population (N = 91,663; 39% of Chilean school population) included all 5th grade elementary school age children in the Metropolitan Region of Chile in 2010, who took the SIMCE tests at the end of November 2009 and 2013, when they were graduating from the 4th and 8th grades of elementary school, respectively, and the 2017 PSU at the end of November of this year, when they graduated from high school, for university admission. As regards to type of school, they were enrolled in public, private subsidized and private non-subsidized schools from urban areas.

The sampling frame of this study corresponds to the educational establishments from the urban area of the Metropolitan Region of Chile . The sampling was performed as follows: firstly, 33 educational establishments, which represented 2.61% of the total population of urban schools (N = 1,262) were randomly selected by proportional allocation according to stratification by type of school, level of SA of the educational establishments in the 2009 SIMCE tests established by the Ministry of Education (high, medium, and low), and gender. Secondly, all students enrolled in the 5th elementary school grade during March 2010 and who took the 2009 SIMCE tests in each one of the 33 schools were invited to participate in the study. A total of 682 school age children (males n = 379) (95% of the original sample) as well as their parents agreed to participate and signed the informed consent form. In the present study, 477 school age children (mean age 10.7 ± 0.5 years; range 9.9-14.4 years, with 263 (55.1%) males) and their parents were considered since they attended all evaluations. 2009 SIMCE outcomes in the LSA, MSA and UNESA tests were requested from the Ministry of Education in January 2010, when we received the approval of Grant FONDECYT 1100431. At the onset of March 2010, the Ministry of Education gave us the results of the 2009 SIMCE tests for each of the school age children according to type of school, level of SA of the educational establishments in the 2009 SIMCE tests and gender, and then the sample was selected.

This study was approved by the Committee of Ethics in Studies on Humans of the Dr. Fernando Monckeberg Barros, Institute of Nutrition and Food Technology (INTA), University of Chile and ratified by the Committee of Bioethics of the National Fund for Scientific and Technologic Development (FONDECYT), Chile. The subjects' consent was obtained according to the norms for Human Experimentation, Code of Ethics of the World Medical Association (Declaration of Helsinki) [16].

Between March to May 2010 the following data were collected:

2009 SIMCE Tests

SA was assessed through the 2009 SIMCE tests, which has national coverage and is administered by the Agency for Education Quality from the Ministry of Education; this was considered the dependent variable. As we said previously, school age children took the SIMCE tests in November 2009, that is at the end of the school year, when they were graduating from the 4th grade of elementary school at the end of the first cycle of elementary school. As we mentioned before, at the onset of March 2010, the Ministry of Education gave us the results of the 2009 SIMCE tests. The aim of this testing is to improve the quality and equity of the educational process in the different areas covered by the national curriculum. Scores range between 0 and 400, and the results were expressed as scores (mean ± SD) in LSA, MSA and UNESA tests. [17].

Current Nutritional Status

Anthropometric measurements were performed through standardized procedures [18]. Weight (W) was measured in a platform beam balance SECA model L 700 with an accuracy of 50 g. Height (H) was determined with a vertical rod with a measuring scale of up to 2 m of height, with an accuracy of 0.5 cm. When measuring height, the subject stands straight looking ahead with Frankfurt plane horizontal, with shoulders blades, buttocks, and heels almost together touching measurement board, arms at sides, legs straight, knees together and feet flat [18]. The current nutritional status was assessed through the BMI (evaluated as W/H²) which was calculated using biological age derived from Tanner stages and expressed as BMI Z-score (2010 Z-BMI) [19]. 2010 Z-BMI was compared with NCHS-CDC tables, and children were distributed into three categories: normal (N), Z-BMI score between -1 and +1; overweight (OW), Z-BMI score between >+1 and +2 as well as obesity (OB), Z-BMI score >+2 [20,21]. This was considered the independent variable.

Nutritional Quality of Diet

Nutritional quality of diet was determined by means of a self-administered questionnaire previously validated for children and adolescents in Chile [22]. The instrument evaluated five categories: 1. The number of daily meals and the quality of food served at 2. Breakfast, 3. Lunch, 4. Dinner and, 5. Snack at home and at school. A total of 110 foods and beverages were assessed. For each meal, the frequency of consumption of the food was expressed as daily, 3-5 times a week, 1 or 2 times a week, or never. In each meal, the foods were grouped and scored as follows: i) Foods high in fat and sugars were scored with 0; ii) normo-fat foods with score 1 and, iii) foods rich in fiber and low in saturated fat or sugar with score 2. Each of the five categories was evaluated with a score ranging from 0 to 2, so the total score of the questionnaire ranged between 0 and 10. Higher scores are related with better nutritional quality of diet. The cut-offs for the Chilean children were used for classifying the nutritional quality of diet of the sample into three groups: unhealthy (≤p 25, score ≤4.3), fair (>p 25 and <p 75, score 4.4-5.9) and healthy (≥p 75, score ≥6.0) [22].

Scheduled Exercise

Scheduled exercise was measured through a self-administered questionnaire previously validated using activity monitors based on accelerometry [22,23]. The questionnaire considers five categories: 1. daily hours lying down, 2. daily hours of sitting activities, 3. number of blocks walked daily, 4. daily hours of outdoor recreational games and, 5. weekly hours of exercises or scheduled sports. The activities of the weekend are not considered to be very irregular, except sports activities. Each category has a score between 0 and 2, by which the total score ranged from 0 to 10 with 0 indicating null physical activity and 10 representing the compliance with the standards of the World Health Organization (WHO): children and young people from 5 to 17 years of age should perform at least 60 min/day in moderate to vigorous physical activities [24]. The cut-offs for the Chilean children were used to classify children into three groups: physically inactive (≤p 25, score ≤3), moderately active (>p 25 and <p 75, score 3.1-5.9), and physically active (≥p 75, score ≥6) [22,23].

Socioeconomic, Sociocultural, Family, and Educational Variables

School age children and their parents were separately interviewed at school in order to know their socioeconomic status and some sociocultural, family and educational conditions. Socioeconomic status was measured using Graffar's modified scale which includes schooling and occupation of the household head and characteristics of the housing (building materials, ownership, water supply, and ownership of durable goods), which has been validated for Chilean urban and rural populations [25]. This scale classified the sample into five socioeconomic strata: 1 = high; 2 = medium-high; 3 = average; 4 = medium-low; 5 = low and 6 = extreme poverty. In the present study children were grouped as follows: high (1 + 2), medium (3), and low (4 + 5 + 6).

Family recreation activities, the family support for studies at home, and paternal and maternal schooling were also registered. Family recreation activities were considered as an ordinal qualitative variable: ‘Do you participate in recreational activities with your family?' 1 = always, 2 = frequently, 3 = sometimes, 4 = never. Family support for studies at home was also considered as an ordinal qualitative variable: ‘At home, does your family help you in your studies?' 1 = always, 2 = sometimes, 3 = never. Paternal and maternal schooling were discrete quantitative variables expressed as completed years of study.

Type of school was considered as a nominal qualitative variable and categorized as follows: 1 = public, 2 = private subsidized, 3 = private non-subsidized. The number of repeated school years, a discrete quantitative variable, was classified from 0 (no year repeated) to a maximum of 3 years. Student relationships with their teachers and classmates were considered as ordinal qualitative variables, grouped according to the following categories: 1 = very adequate, 2 = adequate, 3 = fair, 4 = inadequate, 5 = very inadequate.

Four years later, when school- age children graduated from elementary school (8th grade of elementary school), they took the 2013 SIMCE tests in November 2013 which have national coverage and administered by the Agency for Education Quality from the Ministry of Education. Results were expressed as scores (mean ± SD) in LSA, MSA and UNESA tests [17].

Eight years later, at the end of November 2017, school age children graduated from high school (4th grade), and they took the 2017 PSU. LSA and MSA scores in the 2017 PSU were provided by the Department of Evaluation, Measurement and Educational Registry (DEMRE) of the University of Chile and by the Studies Center of the Ministry of Education at the onset of June 2018 as part of the development of Grant FONDECYT 1150524. The PSU, the baccalaureate examination with national coverage for admission to university, has minimum and maximum scores of 150 and 850, respectively. Each test, language and mathematics, considers 80 items each [26]. Results were expressed as mean ± SD.

In both follow-up periods, the current nutritional status was assessed through the BMI which was also calculated using biological age derived from Tanner stages and expressed as Z-BMI [19,20,21]. 2013 SIMCE and 2017 PSU outcomes were considered dependent variables in each follow-up period.

Comparisons between current nutritional status expressed as 2010 Z-BMI groups, N and overnutrition (OW + OB) were performed using chi-square or Fisher's exact tests for categorical variables and Student's t test for comparisons of means. Variables were correlated using Pearson's and Spearman's correlation coefficients for continuous and categorical variables, respectively. A multivariate statistical modelling was performed using multiple linear regressions [27]. The model was defined in the statistical model based on statistical parameters [28]. In the linear regression analysis, the stepwise procedure was used to establish the most important variables that could affect LSA, MSA, and UNESA in the 2009 SIMCE tests (dependent variable). 2010 Z-BMI was considered as independent variable and nutritional quality of diet, scheduled exercise and socioeconomic, sociocultural, family and educational variables as control variables. A saturated model (independent variable + dependent variable) and an adjusted model including the control variables that correlated significantly with SA or relevant variables for an adequate fit of the model were considered. The level of statistical significance of the study was set at p < 0.05. Four and 8 years later, 2013 SIMCE and 2017 PSU outcomes were considered dependent variables and 2010 Z-BMI the independent variable. Data were analyzed using the Stata Statistical Software, version 14 (College Station, TX, USA).

According to the 2010 Z-BMI, 45.5% of school age children were N and 32.1% and 22.4% presented with OW and OB, respectively. 2010 Z-BMI was not significantly associated with gender; 43.4% and 33.4% of males presented with OW and OB, respectively, among females, the respective rates were 48.1% and 30.4% (Fisher's exact test p = 0.310). Table 1 shows that demographic, socioeconomic, sociocultural, family and educational characteristics of the sample did not differ significantly by gender and 2010 Z-BMI groups. 2010 Z-BMI values did not differ significantly through the follow-up study periods (r = 0.90 p = 0.001); this is the reason why all comparisons in the follow-up study were made with the 2010 Z-BMI.

The proportion of children with unhealthy diet was slightly but significantly higher in overnourished males compared with their normal-weight peers (fig. 1); however, in this group, also a high percentage of children with healthy diets was observed (p = 0.037). In females, a similar situation was observed (p = 0.050).

No significant differences were observed between schedule exercise by gender and Z-BMI groups (fig. 2).

Figure 3 shows scatterplots of 2009 SIMCE scores, for LSA, MSA and UNESA scores and 2010 Z-BMI by gender. Pearson's correlation coefficients were not significant both in males and females.

2009 SIMCE outcomes in the different tests were not significantly correlated with 2010 Z-BMI, nutritional quality of diet, schedule exercise, and student relationships with their teachers and with their classmates (table 2). Positive and significant correlations between 2009 SIMCE outcomes in LSA, MSA and UNESA were observed with socioeconomic status and parental schooling in both genders. However, a negative and significant correlation was observed with the number of repeated school years with the exception of LSA in females. In females, type of school (private non-subsidized schools) positively and significantly correlated with all tests. Males achieved significantly higher scores in MSA and UNESA and females in LSA, but these differences were not significant.

Table 3 shows the multiple regression analysis between 2009 SIMCE outcomes in LSA, MSA or UNESA (dependent variables) andh 2010 Z-BMI (independent variables) and most relevant control variables. 2010 Z-BMI did not contribute to explain 2009 SIMCE outcomes in these tests, but variables such as socioeconomic status, the number of repeated school years, and maternal schooling contributed significantly to explain the LSA (Model R² = 0.178; p < 0.00001), besides of gender in MSA (Model R² = 0.205; p< 0.00001) and UNESA (Model R² = 0.272; p < 0.00001) scores in the adjusted models..

No significant differences with regard to 2010 Z-BMI groups and gender were found between 2009 and 2013 SIMCE and 2017 PSU outcomes in the different tests (table 4). 84% and 62% of the original sample took the 2013 SIMCE 4 years later and the 2017 PSU 8 years later, respectively.

The findings of this study reveal that in male and female school age children of the 5th elementary school grade 2010-Z-BMI and 2009 SIMCE outcomes were not significantly associated. As a consequence, there were no significant differences in LSA, MSA and UNESA scores between school age children with overnutrition compared with their N peers. Variables such as socioeconomic status, the number of repeated school years, maternal schooling, and gender were significantly associated with 2009 SIMCE scores and are strong predictors of SA.

These results are not in agreement with other studies and systematic reviews which reported a negative and significant association between overnutrition of children or teenagers and SA [29,30,31,32,33,34,35]. It is unclear why overnutrition did not show a significant correlation with SIMCE 2009 outcomes in our study where most part of the sample presented with overnutrition (56.7% and 51.8% of men and women, respectively). The findings from other authors revealed that obese children had the highest prevalence of school absences and repetition rates and, thus, low school engagement [36]; however, this is not the case for the sample of the present study.

Our previous findings of studies carried out in Chile and those from other countries did not find any associations between the current nutritional status (according to Z-BMI) and SA; these results confirmed that Z-BMI is not suitable to explain SA [37,38,39,40,41,42,43,44,45,46].

In the present study, nutritional quality of diet did not contribute to explain 2009 SIMCE outcomes; this is in agreement with other findings [47]. Other studies did not find any conclusive association between the impact of dietary intake and SA [48,49]. Schedule exercise did not have any effect on 2009 SIMCE outcomes. These findings are in agreement with our previous findings and with those from other authors; however, results are controversial since some authors have found a positive and significant association between the level of physical activity and SA but others did not [47,50,51].

SA is more properly explained by child intelligence, parental intelligence, schooling level (especially maternal schooling). the occurrence of undernutrition in the first year of life, nutritional background parameters, head circumference (the anthropometric indicator of both nutritional background and brain development), brain volume, and some demographic and educational system variables [37,38,39,40,41].

In the present study, the regression models showed that variables such as gender, socioeconomic status, the number of repeated school years, and maternal schooling are stronger risk factors for 2009 SIMCE outcomes in LSA, MSA and UNESA, although with a low explanatory power in the variance. Males significantly outperformed females in MSA and UNESA while females outperformed males in LSA, although differences were not significant. These findings are consistent with those from other investigators who explored the effect of sex differences in data obtained during one decade by the PISA assessments including the mathematics and reading performances of nearly 1.5 million 15-year-olds in 75 countries. Across nations, males scored higher than females in mathematics, but lower than females in reading [52]. Similarly, the regression models showed a positive effect on 2009 SIMCE outcomes when socioeconomic status and maternal schooling increased and a negative effect when the number of repeated school years increased. This is in agreement with the findings from several studies whose authors concluded that SA does not depend solely on the current nutritional status of school age children; the possible significant association between Z-BMI and SA were not significant after including socioeconomic strata and maternal education variables in the statistical regression model [8,9,53]. These variables could be stronger predictors of SA, indicating that overnutrition was a marker but not a causal factor for SA; This is also consistent with findings from other authors who found that school age children with OW showed lower SA compared with their N peers; however, these associations were not significant after adjusting for variables such as parental schooling, sociodemographic factors, breakfast consumption ,and screen time [42,43].

The importance of maternal schooling as a significant predictor of 2009 SIMCE outcomes in LSA, MSA, and UNESA was also evident in the current study and is in agreement with other findings [37,39,40,41,54]. Other authors found that maternal schooling could have a considerable effect on a child's intelligence, because mothers are the main source of intellectual stimulation and of the enrichment of the psychosocial environment, which also affects SA as this parameter and intelligence are closely related [37,39,40,41,55].

Socioeconomic status was positively and significantly correlated with 2009 SIMCE outcomes in LSA, MSA and UNESA, and was also a significant predictor of SA. School age children with low socioeconomic status obtained the lowest SA scores since they have lowered intelligence levels compared with their peers with or high socioeconomic status [37,39,40,41]. Poverty brings negative consequences for child development and family structure, including the school environment, nutritional status, cognition, SA, dropout rates, and job status [37,39,40,41,56].

Higher repetition rates could adversely affect SA, as observed in our study where a significant negative correlation was observed between the number of repeated school years and 2009 SIMCE outcomes in LSA, MSA and UNESA. Other authors found that students who became OB during the study period had significantly higher repetition rates compared with their N peers [14]; however, this was not observed in the present study

Type of school did not contribute to explain 2009 SIMCE outcomes in the present study. However, other findings confirmed that students attending private non-subsidized schools achieved significantly higher SA scores compared with students enrolled in public and private subsidized schools [37,41,54]. Private non-subsidized schools probably create a more stimulating environment and provide a more adequate infrastructure which favors the learning process, while parents themselves have higher levels of education and income. Students attending these schools develop higher levels of intellectual abilities, have higher head circumference-for-age Z-scores, and are probably trained by teachers with better academic backgrounds who apply more efficient teaching methodologies [37,41,54]. This should be deeply analyzed, since it might be a barrier and an inequality factor for SA, educational situation along the school year, and later job status [56].

Overnourished children did not achieve significantly lower 2013 SIMCE and 2017 PSU outcomes after 4 and 8 years of follow-up study, respectively. In summary, our data reveal that there are no associations between 2010 Z-BMI and SA during an 8-year follow-up study. As a consequence, these findings do not confirm the hypothesis formulated for this study that overnourished schoo age children achieve significantly lower SA compared with their N peers. A limitation of the present study is that these results should be considered as showing a statistical association and do not represent a direct cause and effect relationship, by which further research is needed. Notwithstanding, this study highlights the relevance of these individual and standardized tests with countrywide coverage in Chile as a measuring tool of the quality of education. These findings may be useful for planning public policies in the health and educational sectors in this and other countries.

The authors are grateful to the Agency for Quality Education, the Studies Center of the Ministry of Education of Chile, and to the Department of Evaluation, Measurement and Educational Registry (DEMRE) of the University of Chile for all the facilities given to carry out this research, for reviewing, approving and authorizing this study, and for providing the 2009 SIMCE results. We would like to thank the principals and teachers of the educational establishments and parents and their children, which made possible the realization of this study. We are also grateful to Dr. Oscar Brunser, MD, for helpful comments and suggestions. The data for the present article are part of a thesis submitted by the first author O. Flores, R.D., MSc, in partial fulfilment of the requirements for a Master of Sciences in Nutrition and Food degree from the Dr. Fernando Monckeberg Barros, Institute of Nutrition and Food Technology (INTA), University of Chile. Date were presented in part at the 4th Congress of the European Academy of Paediatric Societies (EAPS), Istanbul, Turkey, October 5-9, 2012.

DIM is guarantor, and participated in all the steps of the research. DIM and OCF conceived and designed the study. OCF, YZO, and BDL analyzed the data. OCF and DIM wrote the first draft of the manuscript. All authors reviewed the manuscript critically and approved the final version of the manuscript.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Committee of Bioethics of the National Fund for Scientific and Technologic Development (FONDECYT) (reference No. 18/FONDECYT/EDUCATION/544 dated on Jun 3 2010), Committee of Ethics in Studies on Humans of the Dr. Fernando Monckeberg Barros, Institute of Nutrition and Food Technology (INTA), University of Chile (reference No. 8907-O/2011-EKU, 285/PI/11) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards [16]. Informed consent was obtained from all individual participants included in the study.

This work was supported by Grants 1100431 and 1150524 from the National Fund for Scientific and Technologic Development (FONDECYT) (PI: Daniza M. Ivanovic).

The authors note no conflicts of interest.