Prevalence of risk factors for obesity, diabetes mellitus and hypertension in high school children and screening of high-risk children for glycosuria: A cross-sectional study in Dharwad District, India

Introduction

Diabetes mellitus (DM) prevalence is on a rise worldwide, predominantly in the middle- and low-income countries (Dunachie & Chamnan, 2019). India, in particular, is viewed as the capital of DM by the world. The prevalence of type 2 DM is increasing alarmingly amongst adults from 26 million in 1990 to 65 million in 2016 (Tandon et al., 2018). This trend is going to continue considering that the country is currently undergoing a demographic and epidemiological transition, but surprisingly type 2 DM is appearing even among adolescents and children (Alberti et al., 2004). A cause of major concern is the increasing incidence of childhood obesity, which significantly inflates the chances for development of type 2 DM (Sonya et al., 2010). There is evidence of increasing prevalence of type 2 DM in the U. S, Libya, Australia, Canada, New-Zealand, Singapore, Japan, Taiwan, China and Bangladesh due to increasing obesity and decreased physical activity (Pinhas-Hamiel & Zeitler, 2005). There are reports of type 2 DM in Pima Indians due to westernized lifestyle (Pinhas-Hamiel & Zeitler, 2005). In India, very few studies, conducted mostly in urban areas, have shown occurrence of type 2 DM in children; Bhatia et al., reported 12% (< 18 years) and Ranjani et al., reported 3.7% of glucose intolerance in children (6–19 years) (Bhatia V; IAP National Task Force for Childhood Prevention of Adult Diseases, 2004; Ranjani et al., 2013)

The American Diabetes Association recommendations for screening of type 2 DM in children is as follows: “Overweight (BMI 85th percentile for age and sex, weight for height 85^th percentile, or weight 120% of ideal for height) plus any two of the following risk factors: family history of type 2 diabetes in first- or second-degree relative, race/ethnicity (American Indian, African-American, Hispanic, Asian/Pacific Islander), signs of insulin resistance or conditions associated with insulin resistance (acanthosis nigricans, hypertension, dyslipidaemia, PCOS). The test can be done at 10 years of age or at onset of puberty if puberty occurs at a younger age.” (Alberti et al., 2004; Pinhas-Hamiel & Zeitler, 2005; Prasad, 2011).

The adolescents in the current generation have more access to the digital world which is further adding to a sedentary lifestyle and consumption of an unhealthy diet. Hypertension is a disorder which takes its origin in childhood; prevalence of hypertension in the young is rising, owing to the escalating rate of childhood obesity (Falkner, 2010).

Schools and other educational establishments render significant opportunities to prevent the behaviours and practices that predispose children to Non-Communicable Diseases (Alberti et al., 2004; Prasad, 2011). Hence, the present study was undertaken to assess the prevalence of the risk factors for obesity, hypertension and type 2 DM among high-school children of India, and to screen high-risk children for overt DM.

Methods

Results

A total of 1600 students participated in our study with equal distribution of males (49.2%) and females (50.8%), ranging in age from 11 to 17 years with a mean age of 14.11 ± 0.96 years. The majority belonged to a nuclear family (64.4%), attended government schools (59.88%) and 13.5% belonged to a higher socio-economic class (according to Modified B.G. Prasad socio economic status scale using the consumer price index for the month of September 2016). A detailed description of socio-demographic characteristics of the participants is given in Table 1. The mean weight, height and BMI of the study participants was 40.1 ± 8.7 kg, 153.7 ± 8.5 cm and 16.8 ± 2.9 kg/m² respectively and the mean waist and hip circumference were 69.9 ± 7.1 cm and 78.4 ± 7.4 cm respectively

The family history of gestational diabetes in the mother, DM and hypertension were 2.4%, 20.5% and 26.5% respectively. Of the participants, 12.2% were born through caesarean section, 26.4% had a history of low-birth weight and 26.6% were of birth order >2 (Figure 1).

Figure 1. Percent prevalence of non-modifiable risk factors.

*Family history of diabetes mellitus

It was observed that 1.4% of the study population smoked, 1.5% consumed alcohol, 18.8% were physically inactive, 64% were sleep deprived and 19.4% were in psychological stress. The prevalence of obesity including being overweight was 4.6% and hypertension was 14.8% (Figure 2).

Figure 2. Percent prevalence of modifiable risk factors.

The binary logistic regression analysis (Table 2) done to compare the prevalence of risk factors amongst obese/overweight and normal/underweight participants showed birth order more than 2 (p = 0.039), family history of diabetes (p = 0.044), school type (p < 0.005), psychological stress (p < 0.005) and high socioeconomic status (p < 0.005), to be statistically significant among obese/overweight. Also, acanthosis nigricans (p < 0.005) and hypertension (p < 0.005) showed statistically significant prevalence in the obese/overweight category. Multivariate logistic regression (Table 3) was done for variables found significant on univariate analysis. This showed higher odds ratio for family history of diabetes (OR = 1.14, CI: 0.58–2.23), private school (OR = 1.5, CI:0.75–2.96), high socio-economic status (OR = 2.0, CI: 0.95–4.30), hypertension (OR = 2.0, CI: 0.95–4.30) and acanthosis nigricans (OR = 219, CI: 71.49–672.42).

Similarly, binary logistic regression analysis (Table 4) done for comparison of risk factors among hypertensives and normotensives depicted a statistically significant relationship with high socioeconomic status (p < 0.005), private school (p < 0.005), inadequate sleep (p = 0.012), psychological stress (p < 0.005), obesity and acanthosis nigricans (p < 0.005). Multivariate logistic regression (Table 5) was done for variables found significant on univariate analysis which showed higher odds ratio for inadequate sleep (OR = 1.4, CI: 1.00–1.87), private school (OR = 1.32, CI: 0.96–1.81), high SES (OR = 1.43, CI: 0.95–2.14), psychological stress (OR = 1.97, CI: 1.42–2.73) and obesity (OR = 6.98, CI: 3.75–12.98).

The mean (SD) Z score for BMI for age was -1.18 (1.18) with -0.96 (1.1) and -1.41 (1.22) respectively for females and males. The mean (SD) Z score for height for age was -0.93(0.92) with -0.87(0.83) and -0.99(0.99) for females and males respectively. Tables 6 and 7 show mean (SD) Z score and distribution of study participants according to the WHO (Kilombero District, n.d.) classification for BMI for age and height for age.

Discussion

The study was conducted in 35 schools across seven talukas of Dharwad district; 1600 high school students were recruited. Anthropometric measurements were taken, blood pressure was measured, and a general physical examination was done to look for signs of insulin resistance. Those who had overweight plus two risk factors were screened for glucosuria.

In the present study, 16.5% of students were severely thin, 22.4% were thin, 56.5% were normal, 4% were overweight and 0.6% were obese. The mean ± standard deviation of underweight, normal, overweight, obese students was 14.42 ± 1.20 kg/m², 17.89 ± 1.76 kg/m², 24.20 ± 1.44 kg/m², 28.60 ± 1.82 kg/m² respectively. There was significant difference in the mean of these BMI categories (p = 0.0001). Among males, 3.5% were overweight and 0.6% obese. Among females, 4.4% were overweight and 0.6% obese. A study by Laxmaiah et al., in Hyderabad reported overweight and obesity prevalence of 5.1% and 1.0% respectively amongst boys and 6.6% and 1.6% amongst girls and in Siddiqui et al., prevalence in boys was 6.81% and girls was 8.16% both of which are slightly higher than our study. However, prevalence in girls is higher than boys in both the studies, which is similar to our study (Laxmaiah et al., 2007; Siddiqui & Bose, 2012). In our study, the prevalence of students being overweight, and obesity was 2.7% in government and 7.5% in private schools, whereas the study done by Jagadesan et al., in urban Chennai showed 4% in government and 22.5% in private schools (Sonya, Ranjani, Priya, Ranjit & Mohan, 2014). The higher prevalence in their study might be because it was carried out in urban Chennai, which is a metropolitan city and rice is their staple food, and different scales were used to classify BMI. Our study was done in a non-metropolitan city including rural as well as urban areas, jowar is the staple diet of this population and WHO Anthroplus software 1.0.4 was used to classify BMI.

The prevalence of hypertension was 14.8% in this study, 8.8% had isolated systolic blood pressure, 3.2% had isolated diastolic blood pressure. The prevalence of hypertension was 14.21% and 15.39% in males and females respectively and there was no statistically significant difference between males and females (p = 0.796). There was a statistically significant relationship between increase in BMI with systolic (p = 0.0001) and diastolic blood pressure (p = 0.0001). In the study by Veena Kamath et al., among the paediatric population of coastal India in the year 2010, the overall prevalence of hypertension was 2.2%, 2.1% and 2.4% in males and females respectively. The prevalence in our study is comparatively higher. It may be because of the geographical difference, age group 5–16 years in their study, increasing urbanization and lifestyle changes among adolescents and also because their evaluation of hypertension was based on the 1987 Task Force Report on High Blood Pressure in Children and Adolescents (National High Blood Pressure Education Program Working Group on Hypertension Control in Children and Adolescents, 1996). The prevalence was highest in the 14–16 years age group, whereas in our study it was the 13–14 years age group. There was no significant difference in prevalence between males and females in their study which is similar to ours. The systolic BP increased with increase in BMI but not with diastolic BP (p = 0.041 and p = 0.131 respectively), whereas in our study both systolic and diastolic BP increased with increase in BMI (p = 0.0001) (Kamath G. et al., 2010). A study done by Jasmine Sundar et al., among adolescents of Chennai city, reported the prevalence of hypertension as 21.5%. There was a statistically significant relationship between hypertension and gender, class, body mass index, family history of hypertension and waist hip ratio (p < 0.001). In our study, there was no significant difference with gender, physical activity, food habits but a significant relationship with grade, body mass index, socio-economic status and family history of mother with hypertension (Sundar, 2013). A similar study done by Amit Vasant Deshpande on hypertension in adolescents in Central India, which included 57.8% males and 42.2% females, showed the prevalence of pre-hypertension and hypertension as 15.9% and 13.9% respectively. Age wise distribution of hypertension among study subjects showed the prevalence of hypertension increases with age and there was no significant difference between gender, which is comparable to our study results (Deshpande, 2014).

A total of 4.6 % of students had an overweight plus 2 risk factors; they underwent a urine glucose strip test which showed a negative result in all students. This might be due to the fact that glucose is detected only when renal threshold for blood glucose exceeds 180 mg/dl (Na et al., 2012). In the study done by Asha Bai et al., consisting of 56.4% males and 43.6% females, the prevalence of glycosuria was 0.09% (Bai et al., 1995). A study done by Bassey et al., showed the prevalence of glycosuria at 0.7%; among males it was 1.2% and among females 0.2% (Na et al., 2012). In the study done by Harish Ranjani et al., the participants underwent an oral glucose tolerance test, and the prevalence of glucose intolerance was 3.7%, which included 0.3% with diabetes and 3.4% with prediabetes (Ranjani et al., 2013).

Conclusions and recommendations

The prevalence of obesity/overweight students was 4.6%, 18.8% were physically inactive, 19.4% were in psychological stress, 64% were sleep deprived and 92% consumed an unhealthy diet. We recommend that the Body Mass Index should be measured in schools on a regular basis. It is a simple cost-effective screening tool to look for malnutrition i.e., either obesity or under-nutrition. The effective implementation of nutrition programs in schools is highly recommended. Compulsory physical education (PE) and games classes should be a part of the curriculum in schools with a focus on female students especially in rural areas. Yoga classes were being conducted in some government schools included in the study; this could be extended to all government schools and even private schools. Health education on consumption of healthy and nutritious food should be given to the students by teachers as unhealthy diet consumption was noticed in a significant percentage of participants. The frequency of intake of vegetables and fruits should increase and that of bakery products, cool-drinks, junk and fast food and restaurant/street food should decrease. Of all the participants, 14.8% were hypertensive. The measurement of blood pressure in the routine school health check-ups is strongly recommended and tracking of blood pressure can be done in schools. A family history of DM and hypertension was found in 20.5% and 26.5% of students respectively, 2.4% had a mother with gestational diabetes, 12.2% had a history of caesarean delivery, 26.4% had low birth weight and 26.6% had high birth order (>2). The school health team can maintain a record of family history of DM and hypertension and using the guidelines for screening of type 2 DM used in our study (overweight plus two risk factors) screening tests can be performed.

Data availability

Consent

Written informed consent for publication of the participants’ details was obtained from the participants and the parents of the participants.