Skip to content

Advertisement

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Is single-child family associated with cardio-metabolic risk factors: the CASPIAN-V study

BMC Cardiovascular Disorders201818:109

https://doi.org/10.1186/s12872-018-0844-y

Received: 7 November 2017

Accepted: 22 May 2018

Published: 4 June 2018

Abstract

Background

In the present study, the association of the cardio-metabolic risk factors and the status of single-child family were studied in a national representative sample of Iranian children and adolescents.

Methods

This cross sectional study was conducted as the fifth round of “Childhood and Adolescence Surveillance and PreventIon of Adult Non- communicable disease” surveys. The students’ questionnaire was derived from the World Health Organization-Global School Student Health Survey. Using survey data analysis methods, data from questionnaires’; anthropometric measures and biochemical information analyzed by logistic regression analysis.

Results

Overall, 14,274 students completed the survey (participation rate: 99%); the participation rate for blood sampling from students was 91.5%. Although in univariate logistic regression model, single child students had an increased risk of abdominal obesity [OR: 1.37; 95% CI: 1.19–1.58)], high SBP [OR: 1.58; 95% CI:1.17–2.14)], high BP [OR: 1.21; 95% CI:1.01–1.45)] and generalized obesity [OR: 1.27; 95% CI:1.06–1.52)], in multiple logistic regression model, only association of single child family with abdominal obesity remained statistically significant [OR: 1.28; 95% CI:1.1–1.50)]. Also in multivariate logistic regression model, for each increase of a child in the family the risk of abdominal obesity [OR: 0.95; 95% CI: 0.91–0.97), high SBP [OR: 0.88; 95% CI: 0.81–0.95)] and generalized obesity [OR: 0.95; 95% CI: 0.91–0.99)] decreased significantly.

Conclusion

The findings of this study serve as confirmatory evidence on the association of cardio-metabolic risk factors with single-child family in children and adolescents. The findings of study could be used for better health planning and more complementary research.

Keywords

Family dimensionSingle-child familyCardio-metabolic risk factorsChildrenAdolescents

Background

Over the past decade, the global pattern of diseases has significantly shifted from communicable diseases to the non-communicable diseases (NCDs). This concern mainly rooted in epidemiological transition and rapid changes in lifestyle [1]. Considering the behavioral and biological related risk factors, the backgrounds of childhood NCDs is well documented [2].

More than three-quarters of Cardio Vascular Disease (CVD) deaths occur in low and middle-income countries [3]. Through past three decades, we were witnessing an epidemic of obesity in the world among the children and adolescents [4] has been reported the significant increase in waist circumference (WC), low density lipoprotein (LDL), triglyceride (TG), blood pressure (BP), metabolic syndrome (MetS) and the reduction in high density lipoprotein (HDL) among the adolescents in some countries [5, 6]. In children and teens of developing countries such as Iran and Turkey, it has been shown that the most common factors of MetS are high TG and low HDL [7].

Most of these adverse health outcomes could be prevented by addressing the environmental risk factors such as using tobacco, unhealthy diet and obesity, physical activity, alcohol consumption and harms of using broad population strategies [3].

As another related important point, following the demographic transitions happened in most countries in the world, there has been observed the fertility reduction and changes in family structures [8]. As a result, the numbers of the households have been decreases and the single-child families have been increased [8]. In Iran, such reduction was observed both in urban area and rural areas [9]. The impact of family structure on cardio- metabolic risk factors discussed in many previous attempts. Results of a study showed that, compared to single child, children who are siblings, have more daily physical activity [10, 11]. The association of some of cardio-metabolic risk factors assessed through some scattered studies [12, 13].

Despite the priority of the problem, yet there is an evident gap in the related evidence. Many studies have investigated the association between the cardio- metabolic risk factors and the family structure [14, 15], but due to our knowledge, there is not any research on the association of the cardio- metabolic risk factors and the status of single-child families Therefore, the present study was designed to examine the associations of the single-child family associated with cardio-metabolic risk factors in Iranian children and adolescents.

Methods

Aim to assess the association between the cardio- metabolic risk factors and the status of single-child family we analyzed the data of comprehensive national survey of CASPIAN-V study was conducted in 2015. Using multistage, stratified cluster sampling method, the study participants selected from, students aged 7–18 years of primary and secondary schools, of urban and rural areas of 30 provinces of Iran. Proportional to size sampling within each province was conducted according to the student’s place of residence (urban or rural) and level of education (primary and secondary) with equal sex ratio. Details on the methodology have been presented before [16], and here we report it in brief.

An expert team of trained health care professionals involved to processes of data gathering. After identifying eligible students, the mission and purpose of the interview was explained. Following informed consent, through interview with students and their parents, specific questionnaires were completed. These questionnaires were extracted from the World Health Organization-Global School Student Health Survey (WHO-GSHS) [17]. Their validity and reliability of Persian-translated questionnaires were confirmed previously [18]. More than demographic information, many aspects of life skills, health behaviors and history of diseases targeted through these questioners [16].

At the next step, by using calibrated instruments, the physical measurements conducted under the standard protocols [17]. During Anthropometric measurements; weight was measured to the nearest 0.1 kg with wearing a light cloth, and height were measured without shoes to the nearest 0.1 cm. Body mass index (BMI) calculated by dividing weight to height squared (m2). Using a non-elastic tape, WC was measured at a point midway between the lower border of the rib cage and the iliac crest at the end of normal expiration to the nearest 0.1 cm. Hip circumference was measured, to the nearest 0.1 cm, at the widest part of the hip at the level of the greater trochanter [18].

Blood pressure measured in sitting position, on the right arm, using a mercury sphygmomanometer with an appropriate cuff size. It was measured 2 times at 5-min intervals and the average was registered [19]. BMI categories considered based on he WHO growth curves; to define underweight as age and sex-specific BMI < 5th, overweight as sex-specific BMI for age of 85th -95th, and obesity as sex-specific BMI for >95th [20]. Abdominal obesity was defined as waist-to-height ratio (WHtR) equal to or more than 0.5 [21]. High fasting blood sugar (FBG) ≥ 100 mg/dl, high triglyceride (TG) ≥ 100 mg/dl, high total cholesterol (TC): > 200 mg/dL, high LDL ≥ 110 mg/dl and low HDL <  40 mg/dl (except than15–19-year- old boys< 45 mg/dl) were considered as abnormal [22]. Elevated BP was defined as either high systolic or diastolic BP (SBP/ DBP ≥ 90th percentile for age, sex and height). MetS was defined according to ATP-III criteria modified for children and adolescents [22].

Physical activity (PA) assessed through a validated questionnaire, through which the information of past week frequency of leisure time physical activity outside the school was collected. Enough physical activity was considered as at least 30 min duration of exercises per day that led to sweating and large increases in breathing or heart rate [23].

The Screen time (ST) evaluation of the children was assessed through the questionnaire that contains the average number of hours/day spent on watching TV/VCDs, personal computer [24], or electronic games (EG) in time of week days and weekends. The total cumulative spent time categorized into two groups; less than 2 h per day (Low), and 2 h per day or more (High) ( [24]).

Aim to assess the socioeconomic status [25] of students, benefiting from principle component analysis (PCA) method related questions including parental education, parents’ job, possessing private car, school type (public/private), and having personal computer were combined as a unique index values were analyzed as tertiles of low; intermediate and high SES [25].

Underweight, Overweight and obesity in parents were defined according to BMI ≤ 18.5 kg/m2, BMI ≥25 kg/m2 and BMI ≥30 kg/m2, respectively. Abdominal obesity in parents was defined as WC ≥95 cm [26].

Statistical analysis

Using Stata package ver. 11.0 (Stata Statistical Software: Release 11. College Station, TX: Stata Corp LP. Package), all statistical measures were estimated by survey data analysis methods. Results provide as mean and standard deviation (SD) for continuous variables, and number (percentage) for categorical variables.

Comparing the mean differences between quantitative variables assessed by Student t-test and association between qualitative variables evaluated through the Pearson Chi-square test. Logistic regression analysis considered for evaluation of the association between single-child family and cardio- metabolic risk factors in Iranian children and their families.

For each association three models were run; the first one representing the crude association and in second model additionally association was adjusted for age, living area, sex, physical activity and screen time, SES, family history of obesity. The third model additionally adjusted for BMI in all abnormality except weight disorders. Results of logistic regression revealed as odd ratio (OR) and 95% confidence interval (CI).For all measurements p-value of < 0.05 was considered statistically significant.

Results

Overall, 14,274 students and one of their parents completed the survey (participation rate: 99%). From them 14,151 individuals had complete data for analysis in this study (50.7% boys and 71.4% from urban areas); for blood sampling from students, the participation rate was 91.5% (3843 students out of 4200 students selected for blood sampling). The mean (SD) age of participants was 12.3 (3.2) years with no significant difference between girls and boys. Regarding the distribution of sex and resident area, there was no significant difference between two comparing groups. The mean of height of students in single child families significantly was shorter than the other group [(144.07 ± 18.35) vs. (146.75 ± 17.40), p < 0.001] yet the prevalence of abdominal obesity was significantly higher in single child students (26.3% vs. 20.3%, p < 0.001). Given the number of children there was no any detected association between type of families and cardio-metabolic risk factors. Demographic and biochemical characteristics of the participants compared between single/several child families in Table 1. The frequency of MetS components in single child and multiple children families was not statistically different (P-value: 0.16) (Fig. 1).
Table 1

Demographic and biochemical characteristics of the participants according to single-child family: the CASPIAN V study

Variable

 

Single child family

 

Total

Yes

No

p-value

Age (year) a

12.28 ± 3.15

11.80 ± 3.24

12.32 ± 3.14

< 0.001

Living area

 Urban b

10,106 (71.4)

822(75.3)

9284(71.1)

0.003

 Rural b

4045 (28.6)

270(24.7)

3775(28.9)

Sex

 Boy b

7172(50.7)

545(49.9)

6627(50.7)

0.595

 Girl b

6979(49.3)

547(50.1)

6432(49.3)

 Height (cm) a

147.07 ± 17.53

144.07 ± 18.35

146.75 ± 17.40

< 0.001

 Weight (cm) a

41.54 ± 16.93

40.07±16.97

41.5 ± 17.13

0.008

 WC (cm) a

66.63 ± 12.1

66.72 ± 12.88

66.71 ± 12.12

0.978

 BMI (kg/m2) a

18.48 ± 4.69

18.47 ± 4.24

18.51 ± 4.75

0.753

 SBP (mmHg) a

98.72 ± 12.91

99.85 ± 13.59

99.10 ± 13.06

0.079

 DBP (mmHg) a

63.53 ± 10.19

63.88 ± 10.76

63.83 ± 10.41

0.888

 FBG (mg/dL) a

91.66 ± 12.13

92.53 ± 10.00

91.55 ± 12.28

0.119

 TG (mg/dL) a

88.16 ± 45.27

90.74 ± 42.05

87.88 ± 45.49

0.272

 HDL-C (mg/dL) a

46.16 ± 9.98

46.32 ± 11.25

46.16 ± 9.86

0.822

 LDL-C (mg/dL) a

90.06 ± 22.64

90.57 ± 24.06

90.02 ± 22.47

0.710

 TC (mg/dL) a

153.85 ± 27.47

155.04 ± 28.93

153.76 ± 27.27

0.472

Physical activity

 Low b

8160 (58.2)

696(64.4)

7464(57.7)

< 0.001

 High b

5859 (41.8)

385(35.6)

5474(42.3)

Screen Time

  Low b

13,067(92.5)

979(90.0)

12,088(92.7)

0.001

 High b

1065(7.5)

109(10.0)

956(7.3)

SES

 Low b

4496 (33.2)

168(16.9)

4328(34.5)

< 0.001

 Medium b

4510 (33.3)

304(30.5)

4206(33.5)

 High b

4544 (33.5)

525(52.7)

4019(32.0)

 Abdominal obesity b

2950 (21.1)

283(26.3%)

2667(20.6)

< 0.001

 High SBP b

433 (3.1)

50(4.7)

383(3.0)

0.002

 High DBP b

1436 (10.3)

121(11.3)

1315(10.3)

0.293

 High BP b

1589 (11.4)

143(13.3)

1446(11.3)

0.043

 High FBG b

161 (4.2)

8(2.8)

153(4.3)

0.219

 High TG b

1060 (27.7)

87(30.5)

973(27.5)

0.275

 Low HDL-c b

1127 (29.5)

86(30.2)

1041(29.4)

0.793

 High TC b

187(4.9)

15(5.3)

172(4.9)

0.764

 High LDL b

670(17.5)

58(20.4)

612(17.3)

0.194

 MetS b

188 (5.1)

17(6.0)

171(5.0)

0.435

Weight status

 Underweight b

2270(16.2)

144(13.4)

2126(16.4)

0.008

 Normal weight b

8819(62.9)

685(63.8)

8134(62.8)

 Overweight b

1321(9.4)

96(8.9)

1225(9.5)

 Obesity b

1606(11.5)

149(13.9)

1457(11.3)

0.010

Overweight: BMI; 85th–95th; obesity, BMI > 95th; low HDL: < 40 mg/dL (except in boys 15–19 y old, that cut-off was < 45 mg/dL); high LDL: > 110 mg/dL; high TG: 150 mg/dL; high TC: > 200 mg/dL; elevated FBS > 100 mg/dL; high blood pressure: > 90th (adjusted by age, sex, height); MetS: ATP-III criteria

SES socioeconomic status, SBP systolic blood pressure, DBP diastolic blood pressure, BP blood pressure, FBG fasting blood glucose, TG triglycerides, HDL high density lipoprotein, LDL low density lipoprotein, TC total cholesterol, MetS metabolic syndrome, BMI body mass index, WC waist circumference

a Data are presented as mean ± standard deviation

b Data are presented as number (%)

Figure 1
Fig. 1

Frequency of metabolic syndrome components according to type of families

Comparing the characteristics of the two groups of study, no significant difference was found between age and anthropometric indices of mothers and fathers of single/several child families (Table 2).
Table 2

Parental characteristics of participants according to single child family: the CASPIAN V study

Variable

 

Single child family

p-value

Total

Yes

No

Mother

 WC (cm) a

87.75 ± 14.29

86.90 ± 14.56

87.78 ± 14.21

0.076

 BMI (kg/m2) a

26.74 ± 5.02

26.52 ± 4.65

26.75 ± 5.05

0.143

 Age (year) a

38.11 ± 6.45

36.33 ± 6.83

38.25 ± 6.37

< 0.001

Weight status

 Underweight b

451(4.0)

14(1.5)

437(4.2)

< 0.001

 Normal weight b

3951(34.7)

387(42.0)

3564(34.1)

 Overweight b

4224(37.1)

316(34.3)

3908(37.4)

 Obesity b

2749(24.2)

204(22.1)

2545(24.3)

 abdominal obesity b

3446(30.3)

245(26.4)

3201(30.70)

0.007

Father

 WC (cm) a

87.03 ± 16.65

89.20 ± 17.96

86.97 ± 16.49

0.162

 BMI (kg/m2) a

25.11 ± 4.07

25.66 ± 4.35

25.08 ± 4.05

0.135

 Age (year) a

44.19 ± 7.05

41.96 ± 6.74

44.25 ± 7.01

< 0.001

Weight status

 Underweight b

172(6.8)

3(2.2)

169(7.1)

0.011

 Normal weight b

1032(40.7)

64(46.4)

968(40.4)

 Overweight b

1062(41.9)

49(35.5)

1013(42.3)

 Obesity b

269(10.6)

22(15.9)

247(10.3)

 Abdominal obesity b

886(35.5)

50(37.0)

836(35.4)

0.695

Parental underweight: BMI ≤18.5 kg/m2; Parental overweight: BMI ≥25 kg/m2; parental general obesity: BMI ≥30 kg/m2; parental abdominal obesity: waist circumference ≥ 95 cm

BMI body mass index, WC waist circumference

aData are presented as mean ± standard deviation

bData are presented as number (%)

Although in univariate logistic regression model (Model I), single child students had an increased risk of abdominal obesity [OR: 1.37; 95% CI: 1.19–1.58)], high SBP [OR: 1.58; 95% CI:1.17–2.14)], high BP [OR: 1.21; 95% CI:1.01–1.45)] and generalized obesity [OR: 1.27; 95% CI:1.06–1.52)], in multiple logistic regression model, only association of single child family with abdominal obesity remained statistically significant [OR: 1.28; 95% CI:1.1–1.50)].

In multivariate logistic regression model, for every increase of a child in the family the risk of abdominal obesity [OR: 0.95; 95% CI: 0.91–0.97), high SBP [OR: 0.88; 95% CI: 0.81–0.95)] and generalized obesity [OR: 0.95; 95% CI: 0.91–0.99)], decreased significantly (Table 3).
Table 3

Association of Single child family with cardio-metabolic risk factors in logistic regression analysis: the CASPIAN V study

Variable

Single child family (yes/ no)

Number of children

OR

95% CI

OR

95% CI

Abdominal obesity

 Model I

1.37

1.19–1.58*

0.93

0.90–0.95*

 Model II

1.28

1.1–1.50*

0.94

0.91–0.97*

High SBP

 Model I

1.58

1.17–2.14*

0.87

0.81–0.93*

 Model II

1.35

0.96–1.90

0.88

0.81–0.95*

 Model III

1.34

0.95–1.90

0.88

0.81–0.95*

High DBP

 Model I

1.11

0.91–1.35

1.00

0.96–1.03

 Model II

1.04

0.83–1.30

1.00

0.96–1.05

 Model III

1.02

0.81–1.28

1.01

0.97–1.06

High BP

 Model I

1.21

1.01–1.45*

0.98

0.94–1.01

 Model II

1.15

0.93–1.41

0.98

0.94–1.02

 Model III

1.13

0.92–1.39

0.98

0.94–1.03

High TG

 Model I

1.15

0.89–1.50

1.02

0.98–1.07

 Model II

1.3

0.97–1.72

1.02

0.97–1.08

 Model III

1.31

0.98–1.74

1.03

0.97–1.08

Lowe HDL-c

 Model I

1.03

0.79–1.34

1.02

0.98–1.06

 Model II

1.16

0.87–1.56

0.99

0.93–1.04

 Model III

1.17

0.87–1.57

0.99

0.94–1.04

High FBG

 Model I

0.63

0.31–1.31

0.98

0.89–1.08

 Model II

0.51

0.22–1.19

1.04

0.92–1.18

 Model III

0.51

0.22–1.18

1.04

0.92–1.18

MetS

 Model I

1.22

0.73–2.05

0.96

0.88–1.06

 Model II

1.14

0.63–2.06

0.94

0.84–1.06

 Model III

1.08

0.59–1.99

0.95

0.84–1.06

High LDL-c

 Model I

1.22

0.90–1.65

1.03

0.98–1.08

 Model II

1.29

0.93–1.79

1.03

0.97–1.10

 Model III

1.30

0.93–1.80

1.03

0.97–1.10

High TC

 Model I

1.08

0.63–1.86

0.93

0.85–1.03

 Model II

0.97

0.53–1.75

0.97

0.86–1.09

 Model III

0.99

0.54–1.79

0.98

0.87–1.10

Overweight

 Model I

0.94

0.75–1.16

0.97

0.94–1.01

 Model II

0.86

0.68–1.10

0.98

0.94–1.03

Obesity

 Model I

1.27

1.06–1.52*

0.91

0.88–0.94*

 Model II

1.15

0.94–1.41

0.95

0.91–0.99*

Model I: without adjustment

Model II: adjusted for age, living area, sex, physical activity and screen time, SES, family history of obesity

Model III: additionally adjusted for BMI in all abnormality except weight disorders

Overweight: BMI; 85th–95th; obesity, BMI > 95th; excess weight, BMI > 85th; low HDL: < 40 mg/dL (except in boys 15–19 y old, that cut-off was < 45 mg/dL); high LDL: > 110 mg/dL; high TG: 150 mg/dL; high TC: > 200 mg/dL; elevated FBS > 100 mg/dL; high blood pressure: > 90th (adjusted by age, sex, height); MetS: ATP-III criteria;

SBP systolic blood pressure, DBP diastolic blood pressure, BP blood pressure, FBG fasting blood glucose, TG triglycerides, HDL high density lipoprotein, LDL low density lipoprotein, TC total cholesterol, MetS metabolic syndrome

* p-value ˂ 0.05

Discussion

Based on our knowledge this is the first investigation on the association between the single-child and cardio-metabolic risk factors in national representative data. The results of study have shown that there is a significant statistical association between the single-child family and the obesity among children and adolescents. It is considerable that, there was not significant association between the single-child and other cardio-metabolic risk factors.

There is some evidence on the family structure and its association with the NCDs or their cores pound risk factors. The association of single child dimension of family with increased risk of obesity have been confirmed in previous investigations [27]. In another study, it has been shown that, compared to the single children, the students who have a sister or a brother are less likely to be obese [12]. Another research shown the association between more siblings and less risk for obesity [28].

In the logistic model, there is no significant association between the dimension of family and the risks of high SBP, high DBP, high BP, high TG, low HDL-c, high FBS, MetS, high LDL-c and high TC. When we run the linear model, we investigate the significant association between the numbers of children the decreased risk of high SBP (OR: 0.88, CI: 0.81, 0.95).

Based on the evidence; boys in single-child families, compare with their counterpart in numerous child families; significantly spent more time for watching TV [29] and less time for physical activities. The physical activities shown the inverse association with the levels of LDL and TC [30]. Increasing screen time during a week with, discussed as a predisposing factor of obesity, overweight, diabetes, CVD and MetS [3133].

There is some discussion that shown single children because their sense of loneliness, mostly spend more time for watching TV. This face them with increased risk of cardiometabolic risk factors. Consumption junk food is one of the probable related factors for insulin resistance and high risks of SBP [34]. On the other hand, junk food intake is positively associated with levels of BMI, WC, and TG level [35].

Studies shown that, smaller size families mostly demand for processed outdoors foods. Such nutritional habits could increase the levels of TG and the risk of cardio- metabolic diseases [36]. Some studies also emphasized on the link of fast food consumption with increased levels of serum fat and calorie intake in children obesity [37].

However, we could not found any significant association between single child situation and the majority of metabolic cardiovascular risk factors, but the role of a healthy lifestyle including physical activity and nutrition in cardio- metabolic risk factors emphasized.

One of the strengths of the present study was its national representative large sample of children and adolescents. Considering the nature of study design, the cross-sectional study limit us in causality inference of variables. On the other hand recalling bias should be mentioned as another limitation.

Conclusion

The findings of present study provide the confirmatory evidence on the association of cardio-metabolic risk factors with single-child family in national sample of children and adolescents. As a considerable point the mean of height of students in single child families significantly was shorter than the other group. The findings of study could be used for better health planning and more evidence-based policy making. The achievements also highlighted the path of complementary research.

Abbreviations

BMI: 

Body Mass Index

BP: 

blood pressure

CI: 

Confidence Interval

CVD: 

Cardio Vascular Disease

DBP: 

diastolic blood pressure

EG: 

Electronic Games

FBG: 

fasting blood glucose

HDL: 

high density lipoprotein

LDL: 

low density lipoprotein

MetS: 

metabolic syndrome

NCDs: 

Non-Communicable Diseases

OR: 

Odd Ratio

PA: 

Physical Activity

PCA: 

Principle Component Analysis

SBP: 

systolic blood pressure

SD: 

Standard Deviation

SES: 

socioeconomic status

ST: 

Screen Time

TC: 

total cholesterol

TFR: 

Total Fertility Rate

TG: 

triglycerides

WC: 

Waist Circumference

WHO-GSHS: 

World Health Organization-Global School Student Health Survey

WHtR: 

Waist-to-Height Ratio

Declarations

Acknowledgments

The authors thank from cooperation of all of participants of the medical sciences universities who have made this experience.

Authors’ contribution

Study concept and design: SD, RH, MQ, MEM, AMG and RK; drafting of the manuscript, and critical revision of the manuscript: SD, MQ, HZ, FR, GSH, FO and RK; Statistical Analysis: TA, MQ, AMG and MT. All of the authors have given final approval of the version to be published.

Availability of data and materials

The dataset of this article is accessible on reasonable request from the corresponding author.

Ethics approval and consent to participate

Study protocols and ethical consideration guide were reviewed and approved by the Research and Ethics council of Isfahan University of Medical Sciences approved the study (Project number: 194049). After complete explanation of the study objectives and protocols, written informed consent and verbal consent were obtained from the parents and students, respectively. Participation in the study was voluntarily.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Pediatrics Department, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
(2)
Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran
(3)
Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
(4)
Department of Social Medicine, Medical School, Jahrom University of Medical Sciences, Jahrom, Iran
(5)
Pediatrics Department, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
(6)
Deputy of Research and Technology, Ministry of Health and Medical Education, Tehran, Iran
(7)
Bureau of Health and Fitness, Ministry of Education and Training, Tehran, Iran
(8)
Office of Adolescents and School Health, Ministry of Health and Medical Education, Tehran, Iran
(9)
Student Research Committee, Alborz University of Medical Sciences, Karaj, Iran
(10)
Department of Basic and Clinical Research, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
(11)
Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

References

  1. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;15, 380(9859):2095–128.View ArticleGoogle Scholar
  2. Kavey R, Allada V, Daniels S, et al. Cardiovascular risk reduction in high-risk pediatric patients: a scientific statement from the American Heart Association expert panel on population and prevention science; the councils on cardiovascular disease in the young, epidemiology and prevention, nutrition, physical activity and metabolism, high blood pressure research. Cardiovascular nursing, and the kidney in Heart Dis. 2006;114(24):2710–38.Google Scholar
  3. Barbero U, D'Ascenzo F, Nijhoff F, et al. Assessing risk in patients with stable coronary disease: when should we intensify care and follow-up? Results from a meta-analysis of observational studies of the COURAGE and FAME era. Scientifica (Cairo). 2016;2016:3769152.Google Scholar
  4. De Onis M, Blössner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr. 2010;92(5):1257–64.View ArticlePubMedGoogle Scholar
  5. Okosun IS, Seale JP, Boltri JM, et al. Trends and clustering of cardiometabolic risk factors in American adolescents from 1999 to 2008. J Adolesc Health. 2012;50(2):132–9.View ArticlePubMedGoogle Scholar
  6. Pérez CM, Ortiz AP, Fuentes-Mattei E, et al. High prevalence of cardiometabolic risk factors in Hispanic adolescents: correlations with adipocytokines and markers of inflammation. J Immigr Minor Health. 2014;16(5):865–73.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Kelishadi R. Childhood overweight, obesity, and the metabolic syndrome in developing countries. Epidemiol Rev. 2007;29(1):62–76.View ArticlePubMedGoogle Scholar
  8. Goli S, Arokiasamy P. Demographic transition in India: an evolutionary interpretation of population and health trends using ‘change-point analysis’. PLoS One. 2013;8(10):e76404.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Abbasi-Shavazi MJ. Fertility revolution in Iran. Population and For Soc. 2001;373:1–4.Google Scholar
  10. Bagley S, Salmon J, Crawford D. Family structure and children's television viewing and physical activity. Med Sci Sports Exerc. 2006;38(5):910. 8View ArticlePubMedGoogle Scholar
  11. Davison KK. Activity-related support from parents, peers, and siblings and adolescents’ physical activity: are there gender differences? J Phys Act Health. 2004;1(4):363–76.View ArticleGoogle Scholar
  12. Chen AY, Escarce JJ. Family structure and childhood obesity, early childhood longitudinal study—kindergarten cohort. Prev Chronic Dis. 2010;7:3.Google Scholar
  13. Pingali P. Westernization of Asian diets and the transformation of food systems: implications for research and policy. Food Policy. 2007;32(3):281–98.View ArticleGoogle Scholar
  14. Kelishadi R, Heshmat R, Shahsanai A, et al. Determinants of tobacco and hookah smoking in a nationally representative sample of Iranian children and adolescents: the Caspian-IV study. Iran Red Crescent Med J. 2016;18(8)Google Scholar
  15. Hajian-Tilaki K, Heidari B. Childhood obesity, overweight, socio-demographic and life style determinants among preschool children in Babol, northern Iran. Iranian J Pub Health. 2013;42:1283–91.Google Scholar
  16. Motlagh ME, Ziaodini H, Qorbani M, et al. Methodology and early findings of the fifth survey of childhood and adolescence surveillance and prevention of adult noncommunicable disease: the Caspian-v study. Int J Prev Med. 2017;8Google Scholar
  17. Kelishadi R, Majdzadeh R, Motlagh M-E, et al. Development and evaluation of a questionnaire for assessment of determinants of weight disorders among children and adolescents: the Caspian-IV study. Int J Prev Med. 2012;3(10):699.PubMedPubMed CentralGoogle Scholar
  18. Group WMGRS. WHO child growth standards based on length/height, weight and age, vol. 450. Oslo, Norway: Acta Paediatrica; 1992Supplement 2006. p. 76.Google Scholar
  19. Knowles K, Paiva L, Sanchez S, et al. Waist circumference, body mass index, and other measures of adiposity in predicting cardiovascular disease risk factors among Peruvian adults. Int J Hypertens. 2011;2011Google Scholar
  20. Pediatrics AAo. National High Blood Pressure Education Program Working Group on high blood pressure in children and adolescents. Pediatrics. 2004;114(Supplement 2):iv-iv.Google Scholar
  21. Choi DH, Hur YI, Kang JH, et al. Usefulness of the waist circumference-to-height ratio in screening for obesity and metabolic syndrome among Korean children and adolescents: Korea National Health and nutrition examination survey, 2010–2014. Nutrients. 2017;10:256.View ArticleGoogle Scholar
  22. Zimmet P, Alberti G, Kaufman F, et al. The metabolic syndrome in children and adolescents. Lancet (London, England) 2007;369(9579):2059.Google Scholar
  23. Kelishadi R, Qorbani M, Djalalinia S, et al. Physical inactivity and associated factors in Iranian children and adolescents: the weight disorders survey of the CASPIAN-IV study. J Cardiovasc Thorac Res. 2017;9(1):41.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Salmon J, Campbell KJ, Crawford DA. Television viewing habits associated with obesity risk factors: a survey of Melbourne schoolchildren. Med J Aust. 2006;184(2):64.PubMedGoogle Scholar
  25. Caro DH, Cortés D. Measuring family socioeconomic status: an illustration using data from PIRLS 2006. IERI Monograph Series Issues and Methodologies in Large-Scale Assessments. 2012;5:9–33.Google Scholar
  26. Azizi F, Hadaegh F, Khalili D, et al. Appropriate definition of metabolic syndrome among Iranian adults: report of the Iranian National Committee of obesity. Arch Iran Med. 2010;13:426–8.PubMedGoogle Scholar
  27. Chen AY, Escarce JJ. Family structure and childhood obesity: an analysis through 8th grade. Matern Child Health J. 2014;18(7):1772–7.View ArticlePubMedGoogle Scholar
  28. Ochiai H, Shirasawa T, Ohtsu T, et al. Number of siblings, birth order, and childhood overweight: a population-based cross-sectional study in Japan. BMC Public Health. 2012;12(1):766.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Salmon J, Timperio A, Telford A, et al. Association of family environment with children's television viewing and with low level of physical activity. Obesity. 2005;13(11):1939–51.View ArticleGoogle Scholar
  30. Mora S, Lee I-M, Buring JE, et al. Association of physical activity and body mass index with novel and traditional cardiovascular biomarkers in women. JAMA. 2006;295(12):1412–9.View ArticlePubMedGoogle Scholar
  31. Morales-Ruán MC, Hernández-Prado B, Gómez-Acosta LM, et al. Obesity, overweight, screen time and physical activity in Mexican adolescents. Salud Publica Mex. 2009;51:S613–S20.View ArticleGoogle Scholar
  32. Mark AE, Janssen I. Relationship between screen time and metabolic syndrome in adolescents. J Public Health. 2008;30(2):153–60.View ArticleGoogle Scholar
  33. Wilmot EG, Edwardson CL, Achana FA, et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Springer. 2012;55(11):2895–905.Google Scholar
  34. Johnson-Down L, Labonte M, Martin I, Tsuji L, et al. Quality of diet is associated with insulin resistance in the Cree (Eeyouch) indigenous population of northern Quebec. Nutr Metab Cardiovasc Dis. 2015;25(1):85–92.View ArticlePubMedGoogle Scholar
  35. Bogl L, Pietiläinen K, Rissanen A, et al. Association between habitual dietary intake and lipoprotein subclass profile in healthy young adults. Nutr Metab Cardiovasc Dis. 2013;23(11):1071–8.View ArticlePubMedGoogle Scholar
  36. Ryman T, Boyer B, Hopkins S, et al. Associations between diet and cardiometabolic risk among Yup'ik Alaska native people using food frequency questionnaire dietary patterns. Nutr Metab Cardiovasc Dis. 2015;25(12):1140–5.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Fraser LK, Clarke GP, Cade JE, et al. Fast food and obesity: a spatial analysis in a large United Kingdom population of children aged 13–15. Am J Prev Med. 2012;42(5):e77–85.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement