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 dimension, Single-child family, Cardio-metabolic risk factors, Children, Adolescents Background related risk factors, the backgrounds of childhood NCDs Over the past decade, the global pattern of diseases has is well documented . significantly shifted from communicable diseases to the More than three-quarters of Cardio Vascular Disease non-communicable diseases (NCDs). This concern mainly (CVD) deaths occur in low and middle-income countries rooted in epidemiological transition and rapid changes in . Through past three decades, we were witnessing an lifestyle . Considering the behavioral and biological epidemic of obesity in the world among the children and adolescents  has been reported the significant increase in waist circumference (WC), low density lipoprotein * Correspondence: firstname.lastname@example.org; email@example.com (LDL), triglyceride (TG), blood pressure (BP), metabolic Non-Communicable Diseases Research Center, Alborz University of Medical syndrome (MetS) and the reduction in high density lipo- Sciences, Karaj, Iran 3 protein (HDL) among the adolescents in some countries Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, [5, 6]. In children and teens of developing countries such Iran Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This 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. Kelishadi et al. BMC Cardiovascular Disorders (2018) 18:109 Page 2 of 8 as Iran and Turkey, it has been shown that the most validity and reliability of Persian-translated questionnaires common factors of MetS are high TG and low HDL . were confirmed previously . More than demo- Most of these adverse health outcomes could be pre- graphicinformation,manyaspects of lifeskills, health vented by addressing the environmental risk factors such behaviors and history of diseases targeted through as using tobacco, unhealthy diet and obesity, physical these questioners . activity, alcohol consumption and harms of using broad At the next step, by using calibrated instruments, the population strategies . physical measurements conducted under the standard As another related important point, following the protocols . During Anthropometric measurements; demographic transitions happened in most countries in weight was measured to the nearest 0.1 kg with wearing the world, there has been observed the fertility reduction a light cloth, and height were measured without shoes to and changes in family structures . As a result, the the nearest 0.1 cm. Body mass index (BMI) calculated by numbers of the households have been decreases and the dividing weight to height squared (m ). Using a single-child families have been increased . In Iran, non-elastic tape, WC was measured at a point midway such reduction was observed both in urban area and between the lower border of the rib cage and the iliac rural areas . The impact of family structure on cardio- crest at the end of normal expiration to the nearest metabolic risk factors discussed in many previous at- 0.1 cm. Hip circumference was measured, to the nearest tempts. Results of a study showed that, compared to sin- 0.1 cm, at the widest part of the hip at the level of the gle child, children who are siblings, have more daily greater trochanter . physical activity [10, 11]. The association of some of Blood pressure measured in sitting position, on the cardio-metabolic risk factors assessed through some right arm, using a mercury sphygmomanometer with an scattered studies [12, 13]. appropriate cuff size. It was measured 2 times at 5-min Despite the priority of the problem, yet there is an intervals and the average was registered . BMI cat- evident gap in the related evidence. Many studies have egories considered based on he WHO growth curves; to investigated the association between the cardio- meta- define underweight as age and sex-specific BMI < 5th, bolic risk factors and the family structure [14, 15], but overweight as sex-specific BMI for age of 85th -95th, due to our knowledge, there is not any research on the and obesity as sex-specific BMI for >95th . Abdom- association of the cardio- metabolic risk factors and the inal obesity was defined as waist-to-height ratio (WHtR) status of single-child families Therefore, the present equal to or more than 0.5 . High fasting blood sugar study was designed to examine the associations of the (FBG) ≥ 100 mg/dl, high triglyceride (TG) ≥ 100 mg/dl, single-child family associated with cardio-metabolic risk high total cholesterol (TC): > 200 mg/dL, high LDL ≥ factors in Iranian children and adolescents. 110 mg/dl and low HDL < 40 mg/dl (except than15– 19-year- old boys< 45 mg/dl) were considered as abnor- Methods mal . Elevated BP was defined as either high systolic Aim to assess the association between the cardio- meta- or diastolic BP (SBP/ DBP ≥ 90th percentile for age, sex bolic risk factors and the status of single-child family we and height). MetS was defined according to ATP-III cri- analyzed the data of comprehensive national survey of teria modified for children and adolescents . CASPIAN-V study was conducted in 2015. Using multi- Physical activity (PA) assessed through a validated stage, stratified cluster sampling method, the study par- questionnaire, through which the information of past ticipants selected from, students aged 7–18 years of week frequency of leisure time physical activity outside primary and secondary schools, of urban and rural areas the school was collected. Enough physical activity was of 30 provinces of Iran. Proportional to size sampling considered as at least 30 min duration of exercises per within each province was conducted according to the day that led to sweating and large increases in breathing student’s place of residence (urban or rural) and level of or heart rate . education (primary and secondary) with equal sex ratio. The Screen time (ST) evaluation of the children was Details on the methodology have been presented before assessed through the questionnaire that contains the , and here we report it in brief. average number of hours/day spent on watching TV/ An expert team of trained health care professionals in- VCDs, personal computer , or electronic games volved to processes of data gathering. After identifying eli- (EG) in time of week days and weekends. The total gible students, the mission and purpose of the interview cumulative spent time categorized into two groups; was explained. Following informed consent, through inter- less than 2 h per day (Low), and 2 h per day or more view with students and their parents, specific question- (High)(). naires were completed. These questionnaires were Aim to assess the socioeconomic status  of stu- extracted from the World Health Organization-Global dents, benefiting from principle component analysis School Student Health Survey (WHO-GSHS) . Their (PCA) method related questions including parental Kelishadi et al. BMC Cardiovascular Disorders (2018) 18:109 Page 3 of 8 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) 12.28 ± 3.15 11.80 ± 3.24 12.32 ± 3.14 < 0.001 Living area Urban 10,106 (71.4) 822(75.3) 9284(71.1) 0.003 Rural 4045 (28.6) 270(24.7) 3775(28.9) Sex Boy 7172(50.7) 545(49.9) 6627(50.7) 0.595 Girl 6979(49.3) 547(50.1) 6432(49.3) Height (cm) 147.07 ± 17.53 144.07 ± 18.35 146.75 ± 17.40 < 0.001 Weight (cm) 41.54 ± 16.93 40.07±16.97 41.5 ± 17.13 0.008 WC (cm) 66.63 ± 12.1 66.72 ± 12.88 66.71 ± 12.12 0.978 2 a BMI (kg/m ) 18.48 ± 4.69 18.47 ± 4.24 18.51 ± 4.75 0.753 SBP (mmHg) 98.72 ± 12.91 99.85 ± 13.59 99.10 ± 13.06 0.079 DBP (mmHg) 63.53 ± 10.19 63.88 ± 10.76 63.83 ± 10.41 0.888 FBG (mg/dL) 91.66 ± 12.13 92.53 ± 10.00 91.55 ± 12.28 0.119 TG (mg/dL) 88.16 ± 45.27 90.74 ± 42.05 87.88 ± 45.49 0.272 HDL-C (mg/dL) 46.16 ± 9.98 46.32 ± 11.25 46.16 ± 9.86 0.822 LDL-C (mg/dL) 90.06 ± 22.64 90.57 ± 24.06 90.02 ± 22.47 0.710 TC (mg/dL) 153.85 ± 27.47 155.04 ± 28.93 153.76 ± 27.27 0.472 Physical activity Low 8160 (58.2) 696(64.4) 7464(57.7) < 0.001 High 5859 (41.8) 385(35.6) 5474(42.3) Screen Time Low 13,067(92.5) 979(90.0) 12,088(92.7) 0.001 High 1065(7.5) 109(10.0) 956(7.3) SES Low 4496 (33.2) 168(16.9) 4328(34.5) < 0.001 Medium 4510 (33.3) 304(30.5) 4206(33.5) High 4544 (33.5) 525(52.7) 4019(32.0) Abdominal obesity 2950 (21.1) 283(26.3%) 2667(20.6) < 0.001 High SBP 433 (3.1) 50(4.7) 383(3.0) 0.002 High DBP 1436 (10.3) 121(11.3) 1315(10.3) 0.293 High BP 1589 (11.4) 143(13.3) 1446(11.3) 0.043 High FBG 161 (4.2) 8(2.8) 153(4.3) 0.219 High TG 1060 (27.7) 87(30.5) 973(27.5) 0.275 Low HDL-c 1127 (29.5) 86(30.2) 1041(29.4) 0.793 High TC 187(4.9) 15(5.3) 172(4.9) 0.764 High LDL 670(17.5) 58(20.4) 612(17.3) 0.194 MetS 188 (5.1) 17(6.0) 171(5.0) 0.435 Weight status Underweight 2270(16.2) 144(13.4) 2126(16.4) 0.008 Normal weight 8819(62.9) 685(63.8) 8134(62.8) Overweight 1321(9.4) 96(8.9) 1225(9.5) Obesity 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 Data are presented as mean ± standard deviation Data are presented as number (%) Kelishadi et al. BMC Cardiovascular Disorders (2018) 18:109 Page 4 of 8 education, parents’ job, possessing private car, school study (50.7% boys and 71.4% from urban areas); for type (public/private), and having personal computer blood sampling from students, the participation rate was were combined as a unique index values were analyzed 91.5% (3843 students out of 4200 students selected for as tertiles of low; intermediate and high SES . blood sampling). The mean (SD) age of participants was Underweight, Overweight and obesity in parents were 12.3 (3.2) years with no significant difference between defined according to BMI ≤ 18.5 kg/m2, BMI ≥25 kg/m2 girls and boys. Regarding the distribution of sex and and BMI ≥30 kg/m2, respectively. Abdominal obesity in resident area, there was no significant difference be- parents was defined as WC ≥95 cm . tween two comparing groups. The mean of height of students in single child families significantly was shorter Statistical analysis than the other group [(144.07 ± 18.35) vs. (146.75 ± Using Stata package ver. 11.0 (Stata Statistical Software: 17.40), p < 0.001] yet the prevalence of abdominal obes- Release 11. College Station, TX: Stata Corp LP. Package), ity was significantly higher in single child students all statistical measures were estimated by survey data (26.3% vs. 20.3%, p < 0.001). Given the number of chil- analysis methods. Results provide as mean and standard dren there was no any detected association between type deviation (SD) for continuous variables, and number of families and cardio-metabolic risk factors. Demo- (percentage) for categorical variables. graphic and biochemical characteristics of the partici- Comparing the mean differences between quantitative pants compared between single/several child families in variables assessed by Student t-test and association be- Table 1. The frequency of MetS components in single tween qualitative variables evaluated through the Pearson child and multiple children families was not statistically Chi-square test. Logistic regression analysis considered for different (P-value: 0.16) (Fig. 1). evaluation of the association between single-child family Comparing the characteristics of the two groups of and cardio- metabolic risk factors in Iranian children and study, no significant difference was found between age their families. and anthropometric indices of mothers and fathers of For each association three models were run; the first single/several child families (Table 2). one representing the crude association and in second Although in univariate logistic regression model model additionally association was adjusted for age, living (Model I), single child students had an increased risk of area, sex, physical activity and screen time, SES, family abdominal obesity [OR: 1.37; 95% CI: 1.19–1.58)], high history of obesity. The third model additionally adjusted SBP [OR: 1.58; 95% CI:1.17–2.14)], high BP [OR: 1.21; for BMI in all abnormality except weight disorders. Re- 95% CI:1.01–1.45)] and generalized obesity [OR: 1.27; sults of logistic regression revealed as odd ratio (OR) and 95% CI:1.06–1.52)], in multiple logistic regression model, 95% confidence interval (CI).For all measurements p-value only association of single child family with abdominal of < 0.05 was considered statistically significant. obesity remained statistically significant [OR: 1.28; 95% CI:1.1–1.50)]. Results In multivariate logistic regression model, for every in- Overall, 14,274 students and one of their parents com- crease of a child in the family the risk of abdominal pleted the survey (participation rate: 99%). From them obesity [OR: 0.95; 95% CI: 0.91–0.97), high SBP [OR: 14,151 individuals had complete data for analysis in this 0.88; 95% CI: 0.81–0.95)] and generalized obesity Fig. 1 Frequency of metabolic syndrome components according to type of families Kelishadi et al. BMC Cardiovascular Disorders (2018) 18:109 Page 5 of 8 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) 87.75 ± 14.29 86.90 ± 14.56 87.78 ± 14.21 0.076 2 a BMI (kg/m ) 26.74 ± 5.02 26.52 ± 4.65 26.75 ± 5.05 0.143 Age (year) 38.11 ± 6.45 36.33 ± 6.83 38.25 ± 6.37 < 0.001 Weight status Underweight 451(4.0) 14(1.5) 437(4.2) < 0.001 Normal weight 3951(34.7) 387(42.0) 3564(34.1) Overweight 4224(37.1) 316(34.3) 3908(37.4) Obesity 2749(24.2) 204(22.1) 2545(24.3) abdominal obesity 3446(30.3) 245(26.4) 3201(30.70) 0.007 Father WC (cm) 87.03 ± 16.65 89.20 ± 17.96 86.97 ± 16.49 0.162 2 a BMI (kg/m ) 25.11 ± 4.07 25.66 ± 4.35 25.08 ± 4.05 0.135 Age (year) 44.19 ± 7.05 41.96 ± 6.74 44.25 ± 7.01 < 0.001 Weight status Underweight 172(6.8) 3(2.2) 169(7.1) 0.011 Normal weight 1032(40.7) 64(46.4) 968(40.4) Overweight 1062(41.9) 49(35.5) 1013(42.3) Obesity 269(10.6) 22(15.9) 247(10.3) Abdominal obesity 886(35.5) 50(37.0) 836(35.4) 0.695 2 2 2 Parental underweight: BMI ≤18.5 kg/m ; Parental overweight: BMI ≥25 kg/m ; parental general obesity: BMI ≥30 kg/m ; parental abdominal obesity: waist circumference ≥ 95 cm BMI body mass index, WC waist circumference Data are presented as mean ± standard deviation Data are presented as number (%) [OR: 0.95; 95% CI: 0.91–0.99)], decreased significantly In the logistic model, there is no significant association (Table 3). 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 Discussion model, we investigate the significant association between Based on our knowledge this is the first investigation on the the numbers of children the decreased risk of high SBP association between the single-child and cardio-metabolic (OR: 0.88, CI: 0.81, 0.95). risk factors in national representative data. The results of Based on the evidence; boys in single-child families, studyhaveshown that there isa significant statistical associ- compare with their counterpart in numerous child fam- ation between the single-child family and the obesity among ilies; significantly spent more time for watching TV  children and adolescents. It is considerablethat, therewas and less time for physical activities. The physical activ- not significant association between the single-child and ities shown the inverse association with the levels of other cardio-metabolic risk factors. LDL and TC . Increasing screen time during a week Thereissomeevidenceonthe family structureand with, discussed as a predisposing factor of obesity, over- itsassociation with theNCDsortheir corespound weight, diabetes, CVD and MetS [31–33]. risk factors. The association of single child dimension There is some discussion that shown single children of family with increased risk of obesity have been because their sense of loneliness, mostly spend more confirmed in previous investigations . In another time for watching TV. This face them with increased study, it has been shown that, compared to the single risk of cardiometabolic risk factors. Consumption junk children, the students who have a sister or a brother food is one of the probable related factors for insulin re- are less likely to be obese . Another research sistance and high risks of SBP . On the other hand, shown the association between more siblings and less junk food intake is positively associated with levels of risk for obesity . BMI, WC, and TG level . Kelishadi et al. BMC Cardiovascular Disorders (2018) 18:109 Page 6 of 8 Table 3 Association of Single child family with cardio-metabolic Table 3 Association of Single child family with cardio-metabolic risk factors in logistic regression analysis: the CASPIAN V study risk factors in logistic regression analysis: the CASPIAN V study (Continued) Variable Single child family (yes/ no) Number of children Variable Single child family (yes/ no) Number of children OR 95% CI OR 95% CI OR 95% CI OR 95% CI Abdominal obesity Obesity Model I 1.37 1.19–1.58* 0.93 0.90–0.95* Model I 1.27 1.06–1.52* 0.91 0.88–0.94* Model II 1.28 1.1–1.50* 0.94 0.91–0.97* Model II 1.15 0.94–1.41 0.95 0.91–0.99* High SBP Model I: without adjustment Model I 1.58 1.17–2.14* 0.87 0.81–0.93* Model II: adjusted for age, living area, sex, physical activity and screen time, SES, family history of obesity Model II 1.35 0.96–1.90 0.88 0.81–0.95* Model III: additionally adjusted for BMI in all abnormality except Model III 1.34 0.95–1.90 0.88 0.81–0.95* weight disorders Overweight: BMI; 85th–95th; obesity, BMI > 95th; excess weight, BMI > 85th; High DBP low HDL: < 40 mg/dL (except in boys 15–19 y old, that cut-off was < 45 mg/ Model I 1.11 0.91–1.35 1.00 0.96–1.03 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, Model II 1.04 0.83–1.30 1.00 0.96–1.05 height); MetS: ATP-III criteria; SBP systolic blood pressure, DBP diastolic blood pressure, BP blood pressure, Model III 1.02 0.81–1.28 1.01 0.97–1.06 FBG fasting blood glucose, TG triglycerides, HDL high density lipoprotein, LDL High BP low density lipoprotein, TC total cholesterol, MetS metabolic syndrome p-value ˂ 0.05 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 Studies shown that, smaller size families mostly demand Model III 1.13 0.92–1.39 0.98 0.94–1.03 for processed outdoors foods. Such nutritional habits High TG could increase the levels of TG and the risk of cardio- Model I 1.15 0.89–1.50 1.02 0.98–1.07 metabolic diseases . Some studies also emphasized on Model II 1.3 0.97–1.72 1.02 0.97–1.08 the link of fast food consumption with increased levels of serum fat and calorie intake in children obesity . Model III 1.31 0.98–1.74 1.03 0.97–1.08 However, we could not found any significant associ- Lowe HDL-c ation between single child situation and the majority of Model I 1.03 0.79–1.34 1.02 0.98–1.06 metabolic cardiovascular risk factors, but the role of a Model II 1.16 0.87–1.56 0.99 0.93–1.04 healthy lifestyle including physical activity and nutrition Model III 1.17 0.87–1.57 0.99 0.94–1.04 in cardio- metabolic risk factors emphasized. High FBG One of the strengths of the present study was its national representative large sample of children and ado- Model I 0.63 0.31–1.31 0.98 0.89–1.08 lescents. Considering the nature of study design, the Model II 0.51 0.22–1.19 1.04 0.92–1.18 cross-sectional study limit us in causality inference of Model III 0.51 0.22–1.18 1.04 0.92–1.18 variables. On the other hand recalling bias should be MetS mentioned as another limitation. 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 Conclusion The findings of present study provide the confirmatory Model III 1.08 0.59–1.99 0.95 0.84–1.06 evidence on the association of cardio-metabolic risk High LDL-c factors with single-child family in national sample of Model I 1.22 0.90–1.65 1.03 0.98–1.08 children and adolescents. As a considerable point the Model II 1.29 0.93–1.79 1.03 0.97–1.10 mean of height of students in single child families sig- Model III 1.30 0.93–1.80 1.03 0.97–1.10 nificantly was shorter than the other group. The findings High TC of study could be used for better health planning and more evidence-based policy making. The achievements Model I 1.08 0.63–1.86 0.93 0.85–1.03 also highlighted the path of complementary research. 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 Abbreviations BMI: Body Mass Index; BP: blood pressure; CI: Confidence Interval; Overweight CVD: Cardio Vascular Disease; DBP: diastolic blood pressure; EG: Electronic Games; FBG: fasting blood glucose; HDL: high density lipoprotein; LDL: low Model I 0.94 0.75–1.16 0.97 0.94–1.01 density lipoprotein; MetS: metabolic syndrome; NCDs: Non-Communicable Model II 0.86 0.68–1.10 0.98 0.94–1.03 Diseases; OR: Odd Ratio; PA: Physical Activity; PCA: Principle Component Analysis; SBP: systolic blood pressure; SD: Standard Deviation; Kelishadi et al. BMC Cardiovascular Disorders (2018) 18:109 Page 7 of 8 SES: socioeconomic status; ST: Screen Time; TC: total cholesterol; TFR: Total 4. De Onis M, Blössner M, Borghi E. Global prevalence and trends of Fertility Rate; TG: triglycerides; WC: Waist Circumference; WHO-GSHS: World overweight and obesity among preschool children. Am J Clin Nutr. 2010; Health Organization-Global School Student Health Survey; WHtR: Waist-to- 92(5):1257–64. Height Ratio 5. Okosun IS, Seale JP, Boltri JM, et al. Trends and clustering of cardiometabolic risk factors in American adolescents from 1999 to 2008. J Acknowledgments Adolesc Health. 2012;50(2):132–9. The authors thank from cooperation of all of participants of the medical 6. Pérez CM, Ortiz AP, Fuentes-Mattei E, et al. High prevalence of sciences universities who have made this experience. cardiometabolic risk factors in Hispanic adolescents: correlations with adipocytokines and markers of inflammation. J Immigr Minor Health. 2014; Authors’ contribution 16(5):865–73. Study concept and design: SD, RH, MQ, MEM, AMG and RK; drafting of the 7. Kelishadi R. Childhood overweight, obesity, and the metabolic syndrome in manuscript, and critical revision of the manuscript: SD, MQ, HZ, FR, GSH, FO developing countries. Epidemiol Rev. 2007;29(1):62–76. and RK; Statistical Analysis: TA, MQ, AMG and MT. All of the authors have 8. Goli S, Arokiasamy P. Demographic transition in India: an evolutionary given final approval of the version to be published. interpretation of population and health trends using ‘change-point analysis’. PLoS One. 2013;8(10):e76404. Availability of data and materials 9. Abbasi-Shavazi MJ. Fertility revolution in Iran. Population and For Soc. 2001; The dataset of this article is accessible on reasonable request from the 373:1–4. corresponding author. 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. 8 Ethics approval and consent to participate 11. Davison KK. Activity-related support from parents, peers, and siblings and Study protocols and ethical consideration guide were reviewed and adolescents’ physical activity: are there gender differences? J Phys Act approved by the Research and Ethics council of Isfahan University of Medical Health. 2004;1(4):363–76. Sciences approved the study (Project number: 194049). After complete 12. Chen AY, Escarce JJ. Family structure and childhood obesity, early explanation of the study objectives and protocols, written informed consent childhood longitudinal study—kindergarten cohort. Prev Chronic Dis. 2010; and verbal consent were obtained from the parents and students, 7:3. respectively. Participation in the study was voluntarily. 13. Pingali P. Westernization of Asian diets and the transformation of food systems: implications for research and policy. Food Policy. 2007;32(3):281– Competing interests The authors declare that they have no competing interests. 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) Publisher’sNote 15. Hajian-Tilaki K, Heidari B. Childhood obesity, overweight, socio-demographic Springer Nature remains neutral with regard to jurisdictional claims in and life style determinants among preschool children in Babol, northern published maps and institutional affiliations. Iran. Iranian J Pub Health. 2013;42:1283–91. 16. Motlagh ME, Ziaodini H, Qorbani M, et al. Methodology and early findings Author details 1 of the fifth survey of childhood and adolescence surveillance and Pediatrics Department, Child Growth and Development Research Center, prevention of adult noncommunicable disease: the Caspian-v study. Int J Research Institute for Primordial Prevention of Non-communicable Disease, 2 Prev Med. 2017;8 Isfahan University of Medical Sciences, Isfahan, Iran. Non-Communicable 17. Kelishadi R, Majdzadeh R, Motlagh M-E, et al. Development and evaluation Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran. 3 of a questionnaire for assessment of determinants of weight disorders Chronic Diseases Research Center, Endocrinology and Metabolism among children and adolescents: the Caspian-IV study. Int J Prev Med. 2012; Population Sciences Institute, Tehran University of Medical Sciences, Tehran, 4 3(10):699. Iran. Department of Social Medicine, Medical School, Jahrom University of 18. Group WMGRS. WHO child growth standards based on length/height, Medical Sciences, Jahrom, Iran. Pediatrics Department, Ahvaz Jundishapur weight and age, vol. 450. Oslo, Norway: Acta Paediatrica; 1992Supplement University of Medical Sciences, Ahvaz, Iran. Deputy of Research and 2006. p. 76. Technology, Ministry of Health and Medical Education, Tehran, Iran. Bureau 19. Knowles K, Paiva L, Sanchez S, et al. Waist circumference, body mass index, of Health and Fitness, Ministry of Education and Training, Tehran, Iran. Office and other measures of adiposity in predicting cardiovascular disease risk of Adolescents and School Health, Ministry of Health and Medical Education, factors among Peruvian adults. Int J Hypertens. 2011;2011 Tehran, Iran. Student Research Committee, Alborz University of Medical 20. Pediatrics AAo. National High Blood Pressure Education Program Working Sciences, Karaj, Iran. Department of Basic and Clinical Research, Tehran Group on high blood pressure in children and adolescents. Pediatrics. 2004; Heart Center, Tehran University of Medical Sciences, Tehran, Iran. 114(Supplement 2):iv-iv. Endocrinology and Metabolism Research Center, Endocrinology and 21. Choi DH, Hur YI, Kang JH, et al. Usefulness of the waist circumference-to- Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, height ratio in screening for obesity and metabolic syndrome among Tehran, Iran. Korean children and adolescents: Korea National Health and nutrition examination survey, 2010–2014. Nutrients. 2017;10:256. Received: 7 November 2017 Accepted: 22 May 2018 22. Zimmet P, Alberti G, Kaufman F, et al. The metabolic syndrome in children and adolescents. Lancet (London, England) 2007;369(9579):2059. 23. Kelishadi R, Qorbani M, Djalalinia S, et al. Physical inactivity and associated References factors in Iranian children and adolescents: the weight disorders survey of 1. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 the CASPIAN-IV study. J Cardiovasc Thorac Res. 2017;9(1):41. causes of death for 20 age groups in 1990 and 2010: a systematic analysis for 24. Salmon J, Campbell KJ, Crawford DA. Television viewing habits associated the global burden of disease study 2010. Lancet. 2012;15, 380(9859):2095–128. with obesity risk factors: a survey of Melbourne schoolchildren. Med J Aust. 2. Kavey R, Allada V, Daniels S, et al. Cardiovascular risk reduction in high-risk 2006;184(2):64. pediatric patients: a scientific statement from the American Heart Association 25. Caro DH, Cortés D. Measuring family socioeconomic status: an illustration expert panel on population and prevention science; the councils on using data from PIRLS 2006. IERI Monograph Series Issues and cardiovascular disease in the young, epidemiology and prevention, nutrition, Methodologies in Large-Scale Assessments. 2012;5:9–33. physical activity and metabolism, high blood pressure research. Cardiovascular nursing, and the kidney in Heart Dis. 2006;114(24):2710–38. 26. Azizi F, Hadaegh F, Khalili D, et al. Appropriate definition of metabolic 3. Barbero U, D'Ascenzo F, Nijhoff F, et al. Assessing risk in patients with stable syndrome among Iranian adults: report of the Iranian National Committee coronary disease: when should we intensify care and follow-up? Results of obesity. Arch Iran Med. 2010;13:426–8. from a meta-analysis of observational studies of the COURAGE and FAME 27. Chen AY, Escarce JJ. Family structure and childhood obesity: an analysis era. Scientifica (Cairo). 2016;2016:3769152. through 8th grade. Matern Child Health J. 2014;18(7):1772–7. Kelishadi et al. BMC Cardiovascular Disorders (2018) 18:109 Page 8 of 8 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. 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. 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. 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. 32. Mark AE, Janssen I. Relationship between screen time and metabolic syndrome in adolescents. J Public Health. 2008;30(2):153–60. 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. 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. 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. 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. 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.
BMC Cardiovascular Disorders – Springer Journals
Published: Jun 4, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera