The Metabolic Syndrome and Health Behaviors in School Children Aged 13–16 Years in Ubon Ratchathani: UMeSIA Project

Abstract

Background:

Although metabolic syndrome (MetS) has become a significant public health problem worldwide, little evidence exists to describe the prevalence of MetS in school children using MetS different classifications, and its association with health-related behaviors.

Methods:

Using data on 393 children and adolescents aged 13–16 years participating in the Ubon Ratchathani Metabolic Syndrome in Adolescent (UMeSIA) Project, the prevalence of MetS was determined using the International Diabetes Federation (IDF) 2007 definition and other three classifications reported in the previous literature. The prevalence of the MetS was compared across sex, a family history of diabetes, type of schools, and health behaviors using chi-square test. The prevalence of MetS and proportion of abnormalities in MetS components were compared across different MetS classifications using Cochran Q test.

Results:

The prevalence of MetS in Thai school children was 3.1%, 5.8%, 6.9%, and 11.2% when using IDF, Cook's, Ford's, and De Ferranti's classifications respectively (Difference in MetS prevalence across the four MetS classifications, P < 0.001). Using IDF classification, prevalence was higher in male than female students (5.9% and 1.2%, respectively, P < 0.001) and higher in those with a family history of diabetes than those without (8.5% and 2.3%, respectively, P < 0.001). Students from a sports school had considerably lower MetS prevalence than those from conventional schools (1.9% and 3.5% respectively, P < 0.001). Those with MetS spent significantly longer time watching TV than those without (median (interquartile range) time to watch TV 180.0 (120.0, 240.0) and 120.0 (60.0, 180.0) min per day respectively, P = 0.002).

Conclusions:

The prevalence of MetS in Thai school children was modest and varied greatly when different MetS definitions were applied. Interventions to optimize time spent watching TV and increase physical activity may be beneficial in reducing the risk of the MetS in children and adolescents.

Introduction

The metabolic syndrome (MetS) has become a significant public health problem worldwide. Since first described in the 1970 s, MetS has been used as a risk factor for diabetes and cardiovascular disease in adult populations in many countries.¹ Individuals with MetS have a three-fold higher risk of developing heart attack or stroke than those without, and the syndrome doubles the risk of death from such diseases.²

It is estimated that approximately 20%–25% of adult population worldwide have MetS, with varying prevalence across countries and ethnicity.³ Previous studies have reported that the prevalence of MetS in children and adolescents ranged 17%–50%; however, these studies were mainly done in obese and overweight children.^4

–8 A number of studies have examined the prevalence of MetS in children and adolescents within normal weight ranges, and they reported a prevalence of 1%–11%.^6,9

–13

A systematic review of 85 studies¹⁰ revealed that the overall prevalence of the MetS in children varied considerably, ranging from 0% to 19.2%, and the prevalence in overweight and obese children was 11.9% and 29.2% respectively. However, the review does not address the impact of using different MetS classification on the burden of MetS. Different classifications to define MetS may result in significant discrepancies in the burden of MetS. To our knowledge, no studies so far have compared the prevalence of MetS in Asian children when different classifications were applied.

Therefore, this study was aimed to describe the prevalence of MetS in school children in Ubon Ratchathani, Thailand, using four different classifications. We also compared the prevalence of MetS across sex, a family history of diabetes and type of schools. Lastly, we compared the levels of health-related behaviors between those with and without the to identify target behaviors for prevention.

Methods

This study was based on data from the Ubon Ratchathani Metabolic Syndrome in Adolescent Project (UMeSIA), which is a school-based survey aimed at investigating the burden and risk factors of the MetS in school children. The UMeSIA project was done in five different secondary schools in Ubon Ratchathani, Thailand. Four secondary schools were randomly selected from a total of 25 schools in district and provincial areas of Ubon Ratchathani and the only sports school in the province was included. The sports school is a school for children aged between 13 and 16 years old who have athletic talents. In addition to general education, great emphasis is given to physical education and physical fitness to nurture its students to reach their potentials in sports excellence. In each school, one classroom was randomly selected for each year of study (Grades 7, 8, and 9). All students in each class were invited to participate in this survey. Between October 2013 and September 2014, a total of 393 students were included in the study. Study size was determined based on previous data suggesting MetS prevalence of 4.0% in Thai children in Ongkhaluck District, Nakhonnayok, Thailand.⁶ The UMeSIA project was approved by Ubon Ratchathani University Ethics Committee (Project Number UBU-EC-4/2557). All children and their parents gave written informed consents.

Students were questioned by researchers about their personal and medical history along with health behaviors, including diet, physical activity, and leisure-time activity. Data on participant's age, sex, time spent watching TV, tutorial time at weekend, a history of breast feeding, birth weight, a family history of diabetes mellitus were collected using interviewer-administered questionnaire. We also developed a new simple dietary scores questionnaire to assess dietary behavior. The questionnaire was consisted of 10 simple questions about the frequency of dietary behaviors, for example, “How often do you drink sugary beverages? () never () occasionally () regularly.” The content validity of the questionnaire was determined by obtaining the item-objective congruence value for each questionnaire item, which ranged from 0.7 to 1.0. Its reliability was tested, with the Cronbach Alpha coefficient of 0.571. Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ) and reported as MET-minutes per week.^14,15

Physical examination was taken and fasting blood samples were also obtained. Blood pressure was taken after 15-min rest in a sitting position using a standard mercury sphygmomanometer. Blood pressure was measured twice 1 min apart and the average of the two readings was used as an individual's blood pressure.¹⁶ Waist circumference was measured using a nonstretch tape measure at the midpoint between the lowest rib and iliac crest at the end of exhalation.¹⁷ Body mass index (BMI) was calculated by dividing an individual's weight in kilograms by the square of height in meters. Overweight was defined as a BMI at or above the 85th percentile and below the 95th percentile for children and teens of the same age and sex. Obesity was defined as a BMI at or above the 95th percentile for children and teens of the same age and sex.¹⁸ Blood samples were drawn after 12-hr overnight fasting. Approximately 30–45 min after the samples were obtained, they were centrifuged at 2500–3000 rpm for 10 min, and the serum was stored in a refrigerator at the temperature of below 8°C before being sent to the laboratory. Fasting blood sugar (FBS), triglycerides (TG), total cholesterol and high-density lipoprotein (HDL) were measured using Beckman Coulter AU680 automate.

Classifications of the MetS

Four different classifications were used to define MetS in this study (Table 1). Subjects who had central obesity as defined by each classification and the presence of two or more abnormalities of other clinical features (increased plasma glucose, elevated TG, low HDL-cholesterol, and high blood pressure) were categorized as having MetS. High blood pressure was defined individually after ranking blood pressure of students with the same age and sex.

Table 1.

Classifications Used to Define Metabolic Syndrome in Children

Definition	WC	TG (mg/dL)	HDL (mg/dL)	FBS (mg/dL)	BP (mmHg)
IDF 2007 (3)	≥90th percentile	≥150	<40	≥100	SBP/DBP ≥130/85
Cook et al.2003 (22)	≥90th percentile	≥110	≥40	≥110	≥90th percentile
De Ferranti et al.2004 (23)	≥75th percentile	≥100	<50	≥110	≥90th percentile
Ford et al.2005 (21)	≥90th percentile	≥110	≥40	≥100	≥90th percentile

TG, triglycerides; FBS, fasting blood sugar; WC, waist circumference (age and sex-specific); BP, blood pressure; SBP/DBP, systolic/diastolic blood pressure.

Statistical analyses

Participant characteristics were presented as mean (standard deviation: SD) and number (%) for continuous and categorical variables, respectively. Chi-square test was used to compare categorical variables between individuals with and without MetS. Student t-test and Mann–Whitney-U test were used to compare normally and non-normally distributed continuous variables between the two groups, respectively. Cochran Q test was used to compare the prevalence of MetS and abnormalities in each MetS component across four different classifications. We also carried out a concordance test to assess the agreement between two different MetS classifications and reported Cohen's kappa. We also examined the prevalence of having high cardiometabolic risk, according to the concept of metabolic health,¹⁹ in all students and those with normal weight, overweight, and obesity and also across tertiles of BMI and waist circumference. Metabolically unhealthy was defined as individuals who had one or more abnormalities of the following metabolic parameters: blood glucose, triglyceride, HDL-cholesterol, and blood pressure.¹⁶ A P-value of <0.05 was considered as statistically significant.

Results

Characteristics of 393 UMeSIA participants are presented in Table 2. The mean (SD) age of study participants was 14.9 (0.98) years, with 38.7% being male. Individuals with MetS had higher BMI, waist circumference, systolic blood pressure, and triglyceride than individuals without MetS (P < 0.05). Those with MetS were more likely to have a family history of diabetes and spent more hours watching TV than those without MetS (P = 0.043 and P = 0.002 respectively). Individuals with and without MetS were similar regarding FBS levels, history of breastfeeding, birth weight, simple dietary score, and tutorial time at weekend. Table 3 shows the prevalence of MetS by sex, family history of diabetes, type of school, and the abnormalities of MetS components, using different MetS classifications. The prevalence of MetS in these school children was 3.1%, 5.8%, 6.9%, and 11.2% when using the International Diabetes Federation (IDF), Cook's, Ford's and De Ferranti's classifications respectively (P-for-trend <0.001). The highest concordance was found between the definitions by Cook et al. and Ford et al. (kappa = 0.842), while the lowest concordance was between the de Ferranti et al. and IDF definitions (kappa = 0.029). The prevalence of MetS defined by the IDF classification was considerably higher in boys than girls (5.9% vs. 1.2% respectively, P < 0.001). Those with a family history of diabetes had a higher MetS prevalence than those without (8.5% vs. 2.3%, P < 0.001). Regarding types of schools, students from a sports school had lower MetS prevalence than those from conventional schools (1.9% vs. 3.5% respectively, P < 0.001). MetS prevalence was 45.0%, 5.3%, and 0.3% in students with obesity, overweight, and normal weight, respectively (P < 0.001). Those within the 3rd tertile of BMI had higher MetS prevalence than those in the 2nd and 1st tertiles of BMI (8.4%, 0%, and 0.8% respectively, P < 0.001). Similar results were observed regardless of the MetS classification used.

Table 2.

Characteristics of 393 School Children Participating in the UMeSIA Project

Characteristics	All participants (n = 393)	With MetS^* (*n = 12)	Without MetS^* (*n = 381)	P-value
Male sex^*	152 (38.7)	9 (5.9)	143 (94.1)	0.020
Age^†, years	14.9 (0.9)	15.1 (0.9)	14.9 (0.9)	0.488
BMI^†, kg/m²	20.5 (3.6)	29.8 (5.5)	20.2 (3.1)	<0.001
BMI category^*				<0.001
Obese	20 (5.1)	9 (75.0)	11 (2.9)
Overweight	38 (9.6)	2 (16.6)	36 (9.5)
Normal weight	335 (85.3)	1 (8.4)	334 (87.6)
Waist circumference^†, cm	70.6 (9.4)	97.0 (13.3)	69.8 (8.0)	<0.001
HDL cholesterol^†, mg/dL	48.6 (12.0)	35.0 (11.0)	48.8 (11.7)	<0.001
Triglyceride^†, mg/dL	71.5 (36.0)	117.5 (47.7)	70.0 (30.0)	0.002
Systolic blood pressure^†, mmHg	114.6 (13.5)	131.4 (13.0)	114.1 (13.3)	<0.001
Diastolic blood pressure^†, mmHg	68.0 (9.7)	73.9 (7.5)	67.7 (9.6)	0.030
Fasting blood sugar^†, mg/dL	84.1 (6.1)	86.0 (6.5)	83.8 (5.7)	0.212
Breast feeding for 6 months^*	221 (57.3)	7 (3.2)	214 (96.8)	0.826
Birth weight^†, grams	2935.4 (512.4)	3233.3 (924.6)	2924.7 (493.7)	0.348
Family history of diabetes mellitus^*	47 (12.0)	4 (8.5)	43 (91.5)	0.043
metabolic equivalent^†, MET minutes/week	4857.6 (4542.8)	3724.8 (2808.0)	4925.1 (4562.2)	0.339
Dietary Score^†	22.1 (2.4)	24.6 (4.0)	22.0 (2.3)	0.081
Time spent watching TV^‡, minutes/day	120.0 (60.0, 180.0)	180.0 (120.0, 240.0)	120.0 (60.0, 180.0)	0.002
Tutorial time^†, minutes/weekend	240.0 (225.0)	380.0 (91.6)	315.0 (159.3)	0.286

Data in the table are presented as number (%), mean (SD) and median (IQR), and comparison across groups was performed using Chi-square test, Student t-test, and Mann–Whitney-U test for categorical (^*), normally (^†), and non-normally (^‡) distributed continuous variables respectively. ^** MetS = metabolic syndrome defined using the IDF definition. Obesity, overweight, and normal weight are defined as BMI of ≥95th, ≥85th–<95th and <85th percentiles respectively.

Table 3.

Prevalence of the Metabolic Syndrome According to Different Classifications in Adolescents Aged 13–16 Years by Sex, Family History of Diabetes, School Type, Categories of BMI and Waist Circumference, Obesity, and Abnormalities in MetS Components (n = 393)

	IDF 2007 (3)	Cook et al. 2003 (22)	De Ferranti et al. 2004 (23)	Ford et al. 2005 (21)	P-value ^a
Number of students having MetS (percentage)	12 (3.1)	23 (5.8)	44 (11.2)	27 (6.9)	<0.001
Sex					<0.001
Male	9 (5.9)	16 (10.5)	24 (15.8)	18 (11.8)
Female	3 (1.2)	7 (2.9)	20 (8.3)	9 (3.7)
Family history of diabetes					<0.001
Yes	4 (8.5)	5 (10.6)	10 (21.3)	7 (14.6)
No	8 (2.3)	18 (5.2)	34 (9.8)	20 (5.8)
Type of school					<0.001
Sports school	2 (1.9)	3 (2.8)	4 (3.8)	3 (2.8)
Conventional school	10 (3.5)	20 (7.0)	40 (14.0)	24 (8.4)
BMI tertiles					<0.001
3rd tertile	11 (8.4)	22 (16.7)	40 (30.5)	24 (18.2)
2nd tertile	0	0	3 (2.2)	2 (1.5)
1st tertile	1 (0.8)	1 (0.8)	1 (0.8)	1 (0.8)
BMI category^b					<0.001
Obesity	9 (45.0)	12 (60.0)	17 (85.0)	12 (60.0)
Overweight	2 (5.3)	7 (17.9)	15 (39.5)	9 (23.1)
Normal weight	1 (0.3)	4 (1.2)	12 (3.6)	6 (1.8)
WC					<0.001
3rd tertile	12 (8.1)	23 (15.5)	43 (29.1)	27 (18.2)
2nd tertile	0	0	1 (0.7)	0
1st tertile	0	0	0	0
Abnormalities in MetS components
TG	13 (3.3)	56 (13.7)	73 (17.8)	56 (13.7)	<0.001
HDL-c	102 (25.6)	113 (28.4)	204 (51.3)	113 (28.4)	<0.001
SBP	50 (12.2)	50 (12.2)	50 (12.2)	50 (12.2)	0.999
DBP	19 (4.6)	48 (11.7)	48 (11.7)	48 (11.7)	<0.001
FBS	3 (0.8)	1 (0.2)	1 (0.2)	3 (0.8)	0.446
WC	64 (15.6)	64 (15.6)	140 (34.6)	64 (15.6)	<0.001

Data in the table are presented as number (percentage).

P-value for comparison across different MetS definitions using Cochran Q test.

Obesity, overweight, and normal weight are defined as BMI of ≥95th, ≥85th–<95th and <85th percentiles respectively.

Mets, metabolic syndrome; HDL-c, high density lipoprotein cholesterol; BMI, body mass index.

When using the IDF classification, the prevalence of abnormality in waist circumference, triglyceride, HDL-cholesterol, systolic and diastolic blood pressure, and FBS was 15.6%, 3.3%, 25.6%, 12.2%, 4.6%, and 0.8% respectively (Table 3). De Ferranti's classification almost doubled the prevalence of abnormality in TG, HDL-cholesterol, and waist circumference compared with using other classifications. IDF classification resulted in a significantly lower prevalence of high diastolic blood pressure and triglyceride than other classifications. There was no difference in the prevalence of abnormalities in FBS and systolic blood pressure when different MetS classifications were applied.

In an additional analysis, students from the sports school had lower BMI and waist circumference than those from conventional schools (Appendix Table A1). Sports school students had substantially higher levels of physical activity than the students from conventional schools [median (IQR) 7800 (4920–12,160) and 2400 (1060–4860) MET-min/week respectively, P < 0.001]. Those from the sports school spent lesser time watching TV than those from conventional schools [median (IQR) 60.0 (60.0–112.5) and 120.0 (60.0–180.0) min/day respectively, P < 0.001]. They had lower FBS and more favorable blood lipids than those from conventional schools. Students from both types of schools were similar regarding systolic blood pressure, breast feeding for 6 months, and birth weight.

Regarding metabolic health, 35% of the school students were at cardiometabolic risk (Table 4). Male students had a higher prevalence of being metabolically unhealthy than their female counterparts (prevalence of 41% and 30% respectively, P = 0.034). The prevalence of being metabolically unhealthy was 29%, 62%, and 85% in students with normal weight, overweight, and obesity respectively (P-for-trend <0.001). The prevalence of this metabolically unhealthy condition was 22%, 31%, and 51% in those in the lowest, middle, and highest tertiles of BMI (P-for-trend <0.001).

Table 4.

Prevalence of Being Metabolically Unhealthy in Adolescents Aged 13–16 Years by Sex, Family History of Diabetes, School Type, and Categories of BMI and Waist Circumference (n = 393)

	Metabolically unhealthy	P-value
Overall	137 (34.9)
Sex		0.034
Male	63 (41.4)
Female	74 (30.7)
Family history of diabetes		0.003
Yes	26 (55.3)
No	110 (32.0)
Type of school		<0.001
Sports school	16 (15.1)
Conventional school	121 (42.2)
BMI		<0.001
3rd tertiles	67 (50.8)
2nd tertiles	42 (31.1)
1st tertiles	28 (22.0)
BMI category^a		<0.001
Obesity	17 (85.0)
Overweight	24 (61.5)
Normal weight	96 (28.7)
Waist circumference		<0.001
3rd tertile	72 (48.6)
2nd tertile	44 (32.6)
1st tertile	21 (19.2)

Data in the table are presented as number (percentage), and comparison across groups was performed using Chi-square test.

Obesity, overweight and normal weight are defined as BMI of ≥95th, ≥85th–<95th and <85th percentiles respectively.

Figure 1A–D show levels of health behaviors in students with and without the MetS. Students with the MetS had a similar level of physical activity to those without (P = 0.339) (Fig. 1A). Those with MetS spent significantly longer time watching TV than those without [median (IQR) time watching TV 180.0 (120.0, 240.0) and 120.0 (60.0, 120.0) min per day respectively, P = 0.002] (Fig. 1B). Those with and without MetS were similar regarding dietary score and tutorial time at a weekend (P = 0.081 and P = 0.286 respectively) (Fig. 1C, D).

FIG. 1.

Boxplots comparing levels of health behaviors (A) physical activity, (B) time spent watching TV, (C) dietary score, and (D) tutorial time at weekend between school children with and without metabolic syndrome in the UMeSIA project, P-values from Student t-test or Mann–Whitney-U test.

Discussion

Based on this school-based survey, the prevalence of MetS in school children of Ubon Ratchathani was modest and varied greatly when different MetS classifications were applied. Male students studying in conventional schools who had a family history of diabetes were at greater risk of the MetS. The most prevalent components of MetS were low levels of HDL cholesterol and increased waist circumference, while the least prevalent component was hyperglycemia. High prevalence of adolescents being at cardiometabolic risk was observed and metabolically unhealthy normal weight was prevalent in these school students. The only health-related behavior that was positively associated with the risk of having MetS in these school children was time spent watching TV.

Using different classifications is likely to result in varying burdens of MetS. In this study, great variation in MetS prevalence was observed when different classifications were used and the lowest prevalence was observed when the IDF classification was used. This is consistent with other previous studies in children,^4,12,13 which showed more than 10-time difference in MetS prevalence when different classifications were applied. Our study suggests generally low concordance between the de Ferranti et al. and IDF definitions, while the highest concordance was found between the definitions by Cook et al. and Ford et al. This makes it difficult to compare MetS prevalence across populations and studies. Similar to our study, the previous studies also found that high fasting glucose was the least prevalent MetS component and increased HDL cholesterol was the most prevalent component regardless of the MetS definitions used. Such findings have also been observed in adult populations.^20
–22

Previous studies largely examined the prevalence of MetS in obese and overweight children,^4

–8 with a few studies examining MetS prevalence in normal weight children. As expected, studies in Thai obese children and adolescents showed considerable burden of MetS, although the prevalence varied greatly from 17%^5,6 to 50%.⁷ A systematic review by Friend et al.¹⁰ similarly showed that the overall prevalence of MetS in children varied considerably, ranging from 0% to 19.2% with a median of 3.3%, while the prevalence in overweight and obese children was 11.9% (range 2.8%–29.3%) and 29.2% (range 10%–66%) respectively. However, the review does not address the impact of using different MetS classifications on the burden of MetS. To our knowledge, no studies have compared the prevalence of MetS in Asian children when different classifications were applied.

Previous studies in Thai children within all weight ranges reported relatively low MetS prevalence compared to our study. Using De Ferranti's classification, Rerksuppaphol and Rerksuppaphol⁶ found that the prevalence of the MetS was 4.0%, which was far lower than our findings. However, the investigators did not use all components of the classification to define MetS. This might have led to underestimation of MetS prevalence in their study and might explain the difference in MetS prevalence between their study and our own.

Using the IDF 2007 classification, the prevalence of MetS in Vietnamese children aged 10–18 years was 4.6%,¹¹ slightly higher than our findings when using the same classification. This might be explained by different age groups of study participants in the two studies. Also, inclusion of sports school students in our study may lead to a low overall MetS prevalence. Of note, the difference in MetS prevalence observed between Thai and Vietnamese children and adolescents seemed to disappear when they grew up. The prevalence of MetS in Thai adults was higher than that of Vietnamese adults (23.2% vs. 18.5% respectively).^23,24

Generally, MetS and abnormalities in its components are more prevalent in obese and overweight than normal weight individuals and this is true for both general and central obesity. Although school children in this study were fairly lean, the trend toward higher MetS prevalence in those within higher tertiles of BMI and waist circumference was clearly observed. This is consistent with previous studies in populations with higher average adiposity.^4,12,13

Increasing interest has focused on normal weight individuals who have high cardiometabolic risk. Although BMI within a normal range has been reported to be associated with a reduced risk of cardiometabolic diseases and all-cause mortality, not all individuals in this BMI range have similarly low risk and metabolically unhealthy phenotypes may be already present in normal weight people.¹⁹ Previous studies suggest that normal weight but metabolically unhealthy individuals were at a three-fold higher risk of all-cause mortality and/or cardiovascular events than normal weight individuals who are metabolically healthy.¹⁹ Metabolically unhealthy normal weight is prevalent. It has been reported that approximately 20% of normal weight adults were metabolic unhealthy. Our study found that normal weight children and adolescents had an even higher prevalence of being metabolically unhealthy (29%).

Sedentary behaviors and low levels of physical activity have reportedly been associated with an increased risk of the MetS.²⁵ In our study, although no significant difference in physical activity levels between those with and without MetS, the sports school students were at a lower risk of MetS than those in a conventional school. This may be explained by that the sports school's curriculum requires many more hours of physical education than that of conventional schools, or simply the explicit difference in student recruitment. No previous studies have compared MetS prevalence between different types of schools. Moreover, longer time spent watching TV was associated with an increased risk of having MetS. This is similar to previous studies in adult populations^26,27 and children.^28,29

There remains uncertainty about which MetS classification is preferred for determining and monitoring trends in MetS over time. This may not be solved by evidence on MetS prevalence. Choices of classifications should rather be based on the ability of MetS in children using different classifications to predict diabetes and cardiovascular disease in later life.³ As time spent watching TV was associated with MetS in many studies, including our own, public health interventions to optimize TV watching hours should be encouraged. Furthermore, longitudinal follow-up and effective surveillance of the MetS and related health behaviors should also be put in place to better monitor impact of public health interventions given.

This study was among a few studies that compared MetS prevalence in Asian children and adolescents when using different MetS classifications, and it was the first to report the MetS prevalence in students from different types of schools, for example, sports versus conventional secondary schools. All MetS components were assessed using standard equipment and procedures. However, our study had a number of limitations. Health-related behaviors, particularly diet and physical activity, were assessed using self-report questionnaire. This might have led to over- or underestimation of their behaviors; for example, people are likely to report their health behaviors better than they actually are. Consequently, this might have altered their association with MetS. However, GPAQ is widely used and may be suitable for large-scale epidemiologic studies. In addition, a simple diet questionnaire was tested before use in this study and its validity and reliability were acceptable. Lastly, further studies are needed to account for several other factors that may be associated with the presence or development of MetS in children and adolescents, such as early life exposures and psychosocial factors.

The overall prevalence of MetS in Thai school children was modest and varied greatly when different classifications were applied. MetS prevalence was different by type of schools and time spent watching TV. This warrants further research on interventions addressing sedentary behaviors in school children. Longitudinal studies to examine which classification best predicts the future risk of diabetes and cardiovascular disease in midlife are also needed

Footnotes

Acknowledgments

The authors thank Narinukun School, Lukhamhan School, Ubon Ratchathani Sports School, and Avemaria School for their participation in this study. We are grateful to the lab technician team of the College of Medicine and Public Health, Ubon Ratchathani University for their assistance in laboratory analysis.

Author Disclosure Statement

No competing financial interests exist.

Appendix Table 1.

Comparison of Characteristics of Students from the Sports and Conventional Schools in the UMeSIA Project

Characteristics	Sports school (n = 107)	Conventional school (n = 286)	P-value
Male sex^*	79 (73.8)	79 (26.2)	<0.001
Age^†, years	14.1 (0.5)	15.1 (0.9)	<0.001
BMI^†, kg/m²	19.2 (2.3)	20.9 (3.9)	<0.001
Waist circumference^†, cm	69.5 (12.1)	74.3 (15.2)	<0.001
HDL cholesterol^†, mg/dL	54.3 (8.7)	38.7 (8.5)	<0.001
Triglyceride^†, mg/dL	69.6 (32.5)	78.2 (26.8)	<0.001
Systolic blood pressure^†, mmHg	108.3 (14.5)	119.9 (23.5)	0.202
Diastolic blood pressure^†, mmHg	55.7 (14.5)	69.0 (11.7)	0.014
Fasting blood sugar^†, mg/dL	83.6 (2.5)	84.5 (4.6)	0.002
Breast feeding for 6 months^*	62 (58.5)	167 (56.6)	0.098
Birth weight^†, grams	2826.2 (380.7)	2948.8 (525.2)	0.177
Family history of diabetes mellitus^*	8 (7.5)	39 (13.7)	0.097
metabolic equivalent^†, MET minutes/week	7800 (4920–12,160)	2400 (1060–4860)	<0.001
Dietary Score^†	20.5 (2.5)	21.7 (2.4)	<0.001
Time spent watching TV^‡, minutes/day	60.0 (60.0–112.5)	120.0 (60.0–180.0)	<0.001

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