|Year : 2018 | Volume
| Issue : 3 | Page : 373-378
Prevalence of pediatric metabolic syndrome and associated risk factors among school-age children of 10–16 Years living in District Shimla, Himachal Pradesh, India
Anmol Gupta1, Amit Sachdeva1, Narender Mahajan1, Aakriti Gupta2, Neha Sareen2, Ravindra Mohan Pandey3, Lakshmy Ramakrishnan4, Hem Chandra Sati3, Brij Sharma5, Neetu Sharma6, Umesh Kapil2
1 Department of Community Medicine, Indira Gandhi Medical College, Shimla, Himachal Pradesh, India
2 Department of Human Nutrition, All India Institute of Medical Sciences, New Delhi, India
3 Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
4 Department of Biochemistry, All India Institute of Medical Sciences, New Delhi, India
5 Department of Gastroenterology, Indira Gandhi Medical College, Shimla, India
6 Department of Physiology, Indira Gandhi Medical College, Shimla, Himachal Pradesh, India
|Date of Web Publication||16-Jul-2018|
First Floor Room Number 118, Old OT Block, Department of Human Nutrition, All India Institute of Medical Sciences, New Delhi - 110 029
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Introduction: Recently, an increasing trend in the prevalence of pediatric metabolic syndrome (PMS) among school-age children has been documented in different parts of India. There is lack of data on the prevalence of PMS and its associated risk factors among school-age children living in district Shimla, Himachal Pradesh. Hence, to fill in the gap in the existing knowledge, the present study was conducted. Methodology: A cross-sectional study was conducted during 2015–2016. Thirty clusters (schools) were identified from a list of all schools using population proportionate to size sampling methodology. From each school, 70 children in the age group of 10–16 years were selected. Data was collected on the sociodemographic characteristics, anthropometry, waist circumference, blood pressure, and physical activity. Fasting venous blood samples were collected for estimation of blood glucose, triglycerides, and high-density lipoprotein levels. Results: The prevalence of PMS using International Diabetes Federation classification was 3.3% and using modified-adult treatment panel classification criteria was 3.5%. Risk factors identified to be associated with PMS among school-age children were (i) male gender, (ii) high family monthly income, (iii) sedentary lifestyle, (iv) consumption of evening snack, (v) television/computer viewing, and (vi) motorized transportation for commuting to school. Conclusion: The PMS prevalence was 3.3% in school-age children residing in District Shimla. There is a need to formulate interventions to prevent and correct metabolic syndrome among them for reducing early onset of cardiovascular disease during adulthood.
Keywords: Diabetes, dyslipidemia, Himachal Pradesh, hypertension, obesity, pediatric metabolic syndrome
|How to cite this article:|
Gupta A, Sachdeva A, Mahajan N, Gupta A, Sareen N, Pandey RM, Ramakrishnan L, Sati HC, Sharma B, Sharma N, Kapil U. Prevalence of pediatric metabolic syndrome and associated risk factors among school-age children of 10–16 Years living in District Shimla, Himachal Pradesh, India. Indian J Endocr Metab 2018;22:373-8
|How to cite this URL:|
Gupta A, Sachdeva A, Mahajan N, Gupta A, Sareen N, Pandey RM, Ramakrishnan L, Sati HC, Sharma B, Sharma N, Kapil U. Prevalence of pediatric metabolic syndrome and associated risk factors among school-age children of 10–16 Years living in District Shimla, Himachal Pradesh, India. Indian J Endocr Metab [serial online] 2018 [cited 2019 Apr 20];22:373-8. Available from: http://www.ijem.in/text.asp?2018/22/3/373/236780
| Introduction|| |
Pediatric metabolic syndrome (PMS) in childhood can lead to early onset of diabetes mellitus and cardiovascular diseases in adulthood. PMS among school-age children is considered to be present when at least 3 or more of the following clinical and metabolic abnormalities are present together: (i) abdominal obesity, (ii) low level of high-density lipoprotein (HDL), (iii) high triglyceride, (iv) high fasting blood glucose (FBG), and (v) elevated blood pressure (BP).
Earlier studies have documented the prevalence of PMS in different parts of the country in the range of 1.5%–9.9%.,,,,, An increasing trend in the prevalence of PMS is being observed possibly due to the increase in the prevalence of obesity in school-age children. This may lead to an increase in cholesterol, triglyceride level, impaired fasting glucose, and high BP among them.,,,,,,,,
Earlier studies on the prevalence of PMS have been conducted in the plain regions of the country.,, There was lack of data on the prevalence of PMS and associated risk factors among school-age children residing in District Shimla, Himachal Pradesh. Hence, to fill the gap in the existing knowledge, the present study was conducted.
| Methodology|| |
A school-based cross-sectional study was conducted during 2015–2016. A list of all schools catering to high-income populations in District Shimla was prepared. Thirty clusters (schools) were identified using population proportionate to size sampling methodology. Since the school enrollment was >90% (as per the secondary data available with the Department of Education, Himachal Pradesh), the children studying in the school were considered as a proxy for school-age children residing in the area.
In each school, a complete list of all school-age children in the age group of 10–16 years was developed. With the help of random number table, 70 children were selected from each custer. If the requisite sample of school-age children was not available in the identified cluster, the nearest school was included to complete the sample size. A total of 2100 school-age children in the age group of 10–16 years were enrolled. A written informed consent was obtained from the parents of each participant. Written assent from each child was also obtained. The research protocol was approved by Institution Ethical Committee.
The following exclusion criterion was adopted: (i) Participants having deformity of the vertical column, (ii) Participants suffering from a disease which could change their dietary intake, (iii) Participants who were either fasting or feasting, and (iv) Participants who had migrated from plain areas within last 5 years of the study.
Data collection parameters
A team of three field investigators and one research assistant were trained in methods of data collection by the first author. This training was conducted for 2 weeks.
A questionnaire was administered to each child to obtain information on sociodemographic profile such as name, sex, age, family monthly income, occupation, and educational qualification.
Assessment of dietary pattern
Information regarding dietary pattern (vegetarian or nonvegetarian), consumption of fast foods, junk foods, and evening snacks was collected.
Assessment of physical activity
The physical activity undertaken by a child during last 24 h was collected by administering a detailed questionnaire. Domain-wise metabolic equivalents (MET) scores were calculated for light, moderate, and vigorous activity. The 24-h energy expenditure was computed by adding all the MET values for the various activities in a day. Physical activity level (PAL) was calculated as the ratio of the 24 h energy expenditure to the basal metabolic rate. The PAL values were categorized into three categories, namely, physically active lifestyle (PAL 1.75 and above), limited physically active lifestyle (PAL 1.55–1.74), and sedentary lifestyle (PAL <1.54).
Information regarding mode of commuting to school and number of hours spent on viewing television (TV)/computer was also collected.
Assessment of abdominal obesity
Waist circumference (WC) was measured using a SECA 203 nonstretchable fibreglass tape, to the nearest of 0.1 cm. WC was measured midway between the superior border of the iliac crest and the lowermost margin of ribs at the end of normal expiration. School-age children with a WC of ≥90th percentile  or adult cutoffs  were considered as having abdominal obesity.
Assessment of blood pressure
BP was measured using mercury sphygmomanometer. Appropriate cuff size was used which covered 80%–100% of the circumference of the arm., The stethoscope was placed over the brachial artery pulse, proximal and medial to the cubital fossa, and below the bottom edge of the cuff (i.e., about 2 cm above the cubital fossa). The systolic BP was defined as the appearance of the first sound (Korotkoff phase 1), and diastolic BP was defined as the disappearance of the sound (Korotkoff phase 5) during deflation of the cuff at a 2–3 mm/s decrement rate of the mercury column. The mean of two measurements was considered to be the participant's BP. Hypertension was defined as systolic (SBP) and diastolic BP (DBP) greater than 90th percentile for age and sex , as per the seventh report of the Joint National Committee on Evaluation, Diagnosis, Treatment, and Prevention of BP (JNC7) classification criteria.,
Fasting venous blood samples were taken from all the participants. The samples were centrifuged at the field and stored at–80° Celsius till the time of analysis. Samples were transported from the field to the central laboratory for analysis in a thermocol box with ice within 3 h of blood collection.
Assessment of fasting blood glucose
FBG was estimated by the glucose oxidase peroxide (God-Pod) method using Randox kit.
Assessment of triglycerides and high-density lipoprotein
Serum samples were estimated for triglycerides (TG) and HDL using automated analyzer (Hitachi-902 fully automated biochemistry analyzer, Roche, Mannheim, Germany) using Roche kits.
Criteria for assessing pediatric metabolic syndrome
The school-age children were identified to be suffering from PMS based on 2 classifications:
- International Diabetes Federation classification: A child was identified to be suffering with PMS when he/she along with abdominal obesity had any of the 2 following parameters were present: (i) TG ≥150 mg/dL, (ii) HDL <40 mg/dL, (iii) FBG ≥100 mg/dL, and (iv) BP ≥SBP 130/DBP 85 mmHg)
- Modified adult treatment panel-III (modified-ATP) classification: A child was identified as suffering with PMS when any of the following three parameters were present concomitantly (i) WC ≥90th percentile, (ii) TG ≥150 mg/dL, (iii) HDL <40 mg/dL, (iv) FBG ≥100 mg/dL, and (v) hypertension (>95th percentile for SBP or DBP).
The collected data were entered into Microsoft Excel worksheets. Statistical Package for the Social Sciences (SPSS) version 20.0 was utilized for conducting the statistical analysis of the data (IBM SPSS statistics for windows. version 20. Armonk, NY: IBM corporation). Data were presented as mean ± standard deviation or percentages. Chi-square test was applied with 95% confidence interval (CI) to assess the association of various parameters with PMS. Univariate and multivariate analysis was conducted to identify the risk factors associated with PMS. The P < 0.05 was considered for statistical significance.
Sample Size Calculation: Assuming a 10% prevalence of obesity, the desired sample size using the formula is 1276 with z = 1.96 at 0.05 level of significance and 5% CI. Considering design effect of 1.5 and 10% noncompliance, the total sample size of 2105 school-age children was calculated. However, we included 2100 school-age children from the district.
| Results|| |
A total of 2100 school-age children (1149 males, 951 females) in the age group of 10–16 years were included. The mean age of the males and females was 13.4 ± 1.8 years and 13.5 ± 1.8 years, respectively. The mean WC, WHR, SBP, DBP, FBG, HDL, and PAL were significantly (P< 0.001) higher in males as compared to females [Table 1].
The prevalence of abdominal obesity, hypertension, dyslipidemia, and impaired fasting glucose among school-age children has been depicted in [Table 2]. The prevalence of PMS using International Diabetes Federation (IDF) classification and modified-ATP classification criteria among school-age children was found to be 3.3% (n = 69) and 3.5% (n = 74), respectively.
|Table 2: Distribution of participants according to the presence of metabolic indicators of pediatric metabolic syndrome|
Click here to view
The risk factors of PMS in school-age children were found to be similar both by IDF classification and modified ATP classification [Table 3].
|Table 3: Distribution of study participants according to the presence of pediatric metabolic syndrome (International Diabetes Federation, classification) (n=2100)|
Click here to view
Risk factors associated with pediatric metabolic syndrome
Males (73.9%) had a significantly higher prevalence of PMS as compared to females (26.1%) (P = 0.001). High prevalence of PMS was observed among school-age children whose parents were graduates or above (79.7%) (P = 0.018). The high family monthly income of >Rs. 36,017 (P = 0.015) was also found to be significantly associated with PMS. We found that school-age children with PMS had a significantly lower PAL (1.2 ± 0.2) than children without PMS (1.5 ± 0.3) (P< 0.001). In addition, children with TV/computer viewing hours of >2 h (P = 0.033) and who commuted to school through motorized transport (P = 0.049) had a significantly high prevalence of PMS [Table 3].
Independent risk factors of pediatric metabolic syndrome
After controlling for confounding factors, we found that the odds of having PMS were significantly higher among school-age children who were (i) males (adjusted odds ratio (AOR) = 2.3; 95% CI: 1.3–4.0, P = 0.003), (ii) high family monthly income (AOR for Rs. 36017 and above = 2.0; 95% CI: 1.1–3.6, P = 0.032), (iii) sedentary lifestyle (AOR for PAL <1.5 = 5.0; 95% CI: 2.3–11.0, P < 0.001), and (iv) consumption of evening snack (AOR = 2.5; 95% CI: 1.0–5.8, P = 0.038) [Table 3].
| Discussion|| |
In the present study, we found that the 3.3% school-age children had PMS using IDF classification and 3.5% using modified-ATP classification. Earlier studies conducted in Jammu and Kashmir and Delhi reported similar prevalence of 1.5% and 3.0%, respectively, using IDF classification. Studies conducted in Chandigarh, Delhi, Maharashtra, Jammu and Kashmir have also reported similar prevalence of PMS of 4.2%, 4.3%, 9.9%, 3.5%, and 3.8%, respectively, using modified-ATP classification.
We found that males had 2.3 times higher risk of developing PMS than females. A recent systematic review of studies conducted in different countries also found that males (5.1%) had a higher prevalence of PMS compared to females (3.0%) (P< 0.001). Higher tendency of boys to accumulate fat around the waist as compared to females, possibly makes them more prone to PMS.,,,
In the present study, we found that the prevalence of PMS was significantly higher in school-age children with sedentary lifestyle (89.9%) and who commuted to school by motorized transport (56.5%) as compared to children without PMS. Physically active lifestyle has been associated with lower systolic and diastolic BP, HDL, triglyceride, glucose and WC among school-age children.,, Physical exercise improves insulin sensitivity,, lowers BP, raises HDL-C, lowers TG concentrations , and is inversely associated with PMS.,,
TV viewing has been suggested as a measure of sedentary behavior in school-age children., Eating snacks while viewing TV has been documented to increase the energy intake among them. We observed that the prevalence of PMS was significantly higher (P = 0.033) in school-age children who watched TV/played on the computer for >2h in a day. Similar observations have also documented in earlier studies.,,,,,,
Limitation of the study
Information on dietary pattern, consumption of snacks, fast foods and junk foods provided by school-age children in the age group of 10–11-year-old may not be reliable.
In the present study, the prevalence of 3.3% of PMS found among school-age children residing district Shimla, Himachal Pradesh, was similar to plain areas in the country. Symptoms of the metabolic indicators of PMS are not explicit in school-age children, and hence, there is a need for undertaking regular health check-ups in the schools with the aim to diagnose risk factors of PMS so that early intervention can be undertaken for their prevention and control.
We are extremely grateful to Indian Council of Medical Research, Government of India (vide letter no: GIA/30/2014-DHR) for providing us the financial grant for conducting this study.
All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Singh R, Bhansali A, Sialy R, Aggarwal A. Prevalence of metabolic syndrome in adolescents from a North Indian population. Diabet Med 2007;24:195-9.
Tandon N, Garg MK, Singh Y, Marwaha RK. Prevalence of metabolic syndrome among urban Indian adolescents and its relation with insulin resistance (HOMA-IR). J Pediatr Endocrinol Metab 2013;26:1123-30.
Bhalavi V, Deshmukh PR, Goswami K, Garg N. Prevalence and correlates of metabolic syndrome in the adolescents of rural Wardha. Indian J Community Med 2015;40:43-8.
] [Full text]
Bhat R, Paray I, Zargar S, Ganie A, Khan I. Prevalence of the metabolic syndrome among North Indian adolescents using Adult Treatment Panel III and pediatric International Diabetic Federation definitions. Arch Med Health Sci 2015;3:44. [Full text]
Andrabi SM, Bhat MH, Andrabi SR, Kamili MM, Imran A, Nisar I, et al.
Prevalence of metabolic syndrome in 8-18-year-old school-going children of Srinagar city of Kashmir India. Indian J Endocrinol Metab 2013;17:95-100.
Singh N, Parihar RK, Saini G, Mohan SK, Sharma N, Razaq M, et al.
Prevalence of metabolic syndrome in adolescents aged 10-18 years in Jammu, J and K. Indian J Endocrinol Metab 2013;17:133-7.
Cali AM, Caprio S. Obesity in children and adolescents. J Clin Endocrinol Metab 2008;93:S31-6.
Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al.
Obesity and the metabolic syndrome in children and adolescents. N
Engl J Med 2004;350:2362-74.
Srinivasan SR, Myers L, Berenson GS. Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood: The Bogalusa Heart Study. Diabetes 2002;51:204-9.
Chen W, Srinivasan SR, Li S, Xu J, Berenson GS. Metabolic syndrome variables at low levels in childhood are beneficially associated with adulthood cardiovascular risk: The Bogalusa Heart Study. Diabetes Care 2005;28:126-31.
Jung UJ, Choi MS. Obesity and its metabolic complications: The role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int J Mol Sci 2014;15:6184-223.
Jensen MD. Role of body fat distribution and the metabolic complications of obesity. J Clin Endocrinol Metab 2008;93:S57-63.
Saha AK, Sarkar N, Chatterjee T. Health consequences of childhood obesity. Indian J Pediatr 2011;78:1349-55.
Kaushik JS, Narang M, Parakh A. Fast food consumption in children. Indian Pediatr 2011;48:97-101.
Puri S, Bhatia H, Swami S, Rai C, Mangat C. Impact of a diet and nutrition related education package on the awareness and practices of school children of Chandigarh. Internet J Epidemiol 2008;6:1-7.
Kurpad AV, Swaminathan S, Bhat S. IAP national task force for childhood prevention of adult diseases: The effect of childhood physical activity on prevention of adult diseases. Indian Pediatr 2004;41:37-62.
Khadilkar A, Ekbote V, Chiplonkar S, Khadilkar V, Kajale N, Kulkarni S, et al.
Waist circumference percentiles in 2-18 year old Indian children. J Pediatr 2014;164:1358-62.e2.
World Health Organization. Waist circumference and waist-hip ratio: Report of a WHO expert consultation. World Health Organ 2008;12:8-11.
U.S. Department of Health and Human Services National Institute of Health National Heart Lung and BI. Fourth Report on Diagnosis, Evaluation and Treatment of High Blood Pressure in Children and Adolescents. Vol. 4; 2005. Available from: https://www.nhlbi.nih.gov/files/docs/resources/heart/hbp_ped.pdf
. [Last accessed on 2017 Dec 08].
Gómez-Marín O, Prineas RJ, Råstam L. Cuff bladder width and blood pressure measurement in children and adolescents. J Hypertens 1992;10:1235-41.
National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 2004;114:555-76.
Lenfant C, Chobanian AV, Jones DW, Roccella EJ, Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Seventh report of the joint national committee on the prevention, detection, evaluation, and treatment of high blood pressure (JNC 7): Resetting the hypertension sails. Hypertension 2003;41:1178-9.
Alberti G, Zimmet P, Kaufman F, Tajima N, Silink M, Arslanian S, et al
. The IDF consensus definition of the metabolic syndrome in children and adolescents. Pediatr Diabetes 2007;8:299-306.
Jolliffe CJ, Janssen I. Development of age-specific adolescent metabolic syndrome criteria that are linked to the adult treatment panel III and international diabetes federation criteria. J Am Coll Cardiol 2007;49:891-8.
Friend A, Craig L, Turner S. The prevalence of metabolic syndrome in children: A systematic review of the literature. Metab Syndr Relat Disord 2013;11:71-80.
DuBose KD, McKune AJ, Brophy P, Geyer G, Hickner RC. The relationship between physical activity and the metabolic syndrome score in children. Pediatr Exerc Sci 2015;27:364-71.
Andersen LB, Harro M, Sardinha LB, Froberg K, Ekelund U, Brage S, et al.
Physical activity and clustered cardiovascular risk in children: A cross-sectional study (The European Youth Heart Study). Lancet 2006;368:299-304.
Kelishadi R, Ardalan G, Gheiratmand R, Adeli K, Delavari A, Majdzadeh R, et al.
Paediatric metabolic syndrome and associated anthropometric indices: The CASPIAN study. Acta Paediatr 2006;95:1625-34.
Duncan GE, Perri MG, Theriaque DW, Hutson AD, Eckel RH, Stacpoole PW, et al.
Exercise training, without weight loss, increases insulin sensitivity and postheparin plasma lipase activity in previously sedentary adults. Diabetes Care 2003;26:557-62.
Lee S, Bacha F, Gungor N, Arslanian S. Comparison of different definitions of pediatric metabolic syndrome: Relation to abdominal adiposity, insulin resistance, adiponectin, and inflammatory biomarkers. J Pediatr 2008;152:177-84.
Carroll S, Dudfield M. What is the relationship between exercise and metabolic abnormalities? A review of the metabolic syndrome. Sports Med 2004;34:371-418.
Raitakari OT, Taimela S, Porkka KV, Telama R, Välimäki I, Akerblom HK, et al.
Associations between physical activity and risk factors for coronary heart disease: The Cardiovascular Risk in Young Finns Study. Med Sci Sports Exerc 1997;29:1055-61.
Marshall DA, Vernalis MN, Remaley AT, Walizer EM, Scally JP, Taylor AJ, et al.
The role of exercise in modulating the impact of an ultralow-fat diet on serum lipids and apolipoproteins in patients with or at risk for coronary artery disease. Am Heart J 2006;151:484-91.
Brage S, Wedderkopp N, Ekelund U, Franks PW, Wareham NJ, Andersen LB, et al.
Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: The European Youth Heart Study (EYHS). Diabetes Care 2004;27:2141-8.
Mcmurray RG, Andersen LB. The influence of exercise on metabolic syndrome in youth: A review. Am J Lifestyle Med 2009;4:176-86.
Ekelund U, Anderssen SA, Froberg K, Sardinha LB, Andersen LB, Brage S, et al.
Independent associations of physical activity and cardiorespiratory fitness with metabolic risk factors in children: The European Youth Heart Study. Diabetologia 2007;50:1832-40.
Ekelund U, Brage S, Froberg K, Harro M, Anderssen SA, Sardinha LB, et al.
TV viewing and physical activity are independently associated with metabolic risk in children: The European Youth Heart Study. PLoS Med 2006;3:e488.
Gore SA, Foster JA, DiLillo VG, Kirk K, Smith West D. Television viewing and snacking. Eat Behav 2003;4:399-405.
Dunstan DW, Salmon J, Owen N, Armstrong T, Zimmet PZ, Welborn TA, et al.
Associations of TV viewing and physical activity with the metabolic syndrome in Australian adults. Diabetologia 2005;48:2254-61.
Nang EE, Salim A, Wu Y, Tai ES, Lee J, Van Dam RM, et al.
Television screen time, but not computer use and reading time, is associated with cardio-metabolic biomarkers in a multiethnic Asian population: A cross-sectional study. Int J Behav Nutr Phys Act 2013;10:70.
Stamatakis E, Hamer M. The extent to which adiposity markers explain the association between sedentary behavior and cardiometabolic risk factors. Obesity (Silver Spring) 2012;20:229-32.
Fung TT, Hu FB, Yu J, Chu NF, Spiegelman D, Tofler GH, et al.
Leisure-time physical activity, television watching, and plasma biomarkers of obesity and cardiovascular disease risk. Am J Epidemiol 2000;152:1171-8.
Hu FB, Li TY, Colditz GA, Willett WC, Manson JE. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA 2003;289:1785-91.
[Table 1], [Table 2], [Table 3]