Prevalence of Glucose Intolerance Among Children and Adolescents in Urban South India (ORANGE-2)

Abstract

Aim:

This study was designed to determine the prevalence of glucose intolerance (prediabetes or diabetes) in children and adolescents in urban South India.

Subjects and Methods:

Children (6–11 years old) and adolescents (12–19 years old) (n=1,519; 777 boys and 742 girls) were randomly selected from residential apartments representing the 10 corporation zones of Chennai city. Height, weight, waist circumference, body fat percentage, and blood pressure were measured using standardized techniques. Investigations included oral glucose tolerance test (OGTT), lipid profile, and fasting insulin. Insulin resistance (IR) was assessed by homeostasis model assessment (HOMA-IR).

Results:

The overall prevalence of glucose intolerance was 3.7% but was higher in girls compared with boys (4.2% vs. 3.2%, P<0.001) and increased to 12.7% in girls with abdominal obesity. On univariate regression, the following risk factors showed significant association with glucose intolerance in girls: adolescent age group (odds ratio [OR] 2.94; confidence interval (CI) 1.12, 7.76), waist circumference (OR 4.45; CI 1.95, 10.14), body mass index (OR 2.73; CI 1.32, 5.65), acanthosis nigricans (OR 2.35; CI 1.14, 4.83), family history of diabetes (OR 2.52; CI 1.07, 5.92), and HOMA-IR (OR 9.30; CI 3.59, 24.12). On multivariate analysis, only family history of diabetes (OR 4.11; CI 1.28, 13.22; P=0.018) and HOMA-IR (OR 11.22; CI 4.19, 30.05; P<0.001) remained significant. In boys only HOMA-IR (OR 5.19; CI 1.54, 17.44; P=0.008) was associated with glucose intolerance.

Conclusions:

The prevalence of glucose intolerance is high in Asian Indian adolescents, particularly in girls with abdominal obesity.

Introduction

T ype 2 diabetes mellitus (T2DM), once considered a disease of older adults, is now being reported in children and adolescents worldwide, with the highest prevalence in people of American Indian, Hispanic, African American, and Asian children compared with those of European descent.^1

–5 This increase in prevalence of diabetes is attributed to the parallel increase in obesity rates among youth.^6
–8 By the age of 15 years, more than 25% of obese adolescents may show glucose intolerance.⁹

Currently screening for glucose intolerance in children and adolescents is not recommended.¹⁰ However, in high-risk populations current guidelines suggest screening children and adolescents by fasting plasma glucose.¹⁰ Some studies, however, have shown that many children with impaired glucose tolerance and diabetes may have normal fasting plasma glucose levels.^6,11 Hence, fasting plasma glucose alone may be inadequate to identify hyperglycemia in some populations and that an oral glucose tolerance test (OGTT) would thus be required. As the OGTT is a cumbersome test, strategies to minimize the number of people who should undergo the test have been extensively studied in adults.^12,13 However, there are very few such studies in children and adolescents and indeed very few data on prevalence of glucose intolerance in youth using an OGTT.

India currently has 62 million people with T2DM,¹⁴ and this number is expected to exceed 100 million by 2030.¹⁵ It is well known that the age at onset of T2DM is at least a decade lower among Asian Indians compared with Europeans.^1,14,16 Recently, the occurrence of T2DM among children and adolescents is being increasingly reported from various clinic-based studies from around the world, including India.^17

–20 This report is perhaps the first population-based epidemiological study on the prevalence of glucose intolerance among children and adolescents from India.

Subjects and Methods

The “Obesity Reduction And Non communicable disease awareness through Group Education” (ORANGE) program was begun with the twin aim of creating awareness about obesity and its ill effects among schoolchildren and to screen for obesity and glucose intolerance in children and adolescents, in Chennai, South India. The details of the ORANGE study methodology have been published earlier and are briefly described here.²¹ This study has two components: the school and the colony component. In the school component 20,000 children from private and government schools were screened, to determine the prevalence rates of overweight and obesity in this population. This component also aimed at developing standardized age- and gender-specific cut points for height, weight, waist circumference, body mass index (BMI), body fat percentage, and blood pressure in children and adolescents. The colony component consisted of a house-to-house epidemiological survey to study the prevalence of glucose intolerance (prediabetes or diabetes) and metabolic syndrome in children (defined as 6–11 years old) and adolescents (defined as 12–19 years old). This report will deal with the colony component of the ORANGE study.

Chennai (formerly known as Madras) city has a population of around 6 million people. The Chennai Corporation has 145 wards, of which 56 wards were randomly selected for this study. The wards were selected as to represent all the 10 corporation zones of Chennai. This sampling framework can be accessed from our Web site (www.mdrf.in/misc/ORANGE/orange%20chennai%20camp%20zone.pdf). Within each ward, one residential colony (comprising a group of houses belonging to residents of similar socioeconomic background) was randomly selected. Permission was obtained from the president and members of the selected colony before the study was conducted. A door-to-door demographic survey was first done, and all children and adolescents in the age group of 6–19 years (n=2,065) were invited to participate in the study. Phone calls were made before the day of testing, and both parents and the participants were informed about the blood test procedures and other measurements to be carried out the next day.

The study was conducted according to the ethical guidelines laid down by the Indian Council of Medical Research, and the study protocol was approved by the Institutional Ethics Committee of the Madras Diabetes Research Foundation. Informed consent was obtained from one of the parents in the case of children below 18 years of age along with the child's assent, while for those 18 years and above, the consent was obtained from the participants themselves.

Clinical and anthropometric measurements

Participants were asked to wear light clothing without any heavy items like mobile phone, wallet, or keys in their pockets. Anthropometric (height, weight, body fat percentage, and waist measurement) and blood pressure measurements were obtained using standardized techniques. Height was measured in centimeters using a stadiometer, and weight was measured in kilograms by an electronic weighing machine. BMI was calculated using the following formula: weight (in kg)/height (in m²). Waist circumference was measured by using a nonstretchable measuring tape. The participants were asked to stand upright with both feet together on a flat surface; one layer of clothing was accepted. Waist girth was measured as the smallest horizontal girth between the costal margins and the iliac crests at minimal respiration. Body fat percentage was measured using a bioelectric impedance body fat analyzer (model BF60; Beurer, Ulm, Germany), which has been validated in our population.²² Blood pressure was recorded with the subject in the sitting position in the left arm to the nearest 1 mm Hg using an electronic Omron machine (Omron Corp., Tokyo, Japan). Two readings were taken 5 min apart, and the mean of the two was taken as the blood pressure. All the participants were examined for the following clinical markers of insulin resistance (IR) and obesity with a help of a physician: acanthosis nigricans, skin tags, double chin, and hirsutism (in female participants).

Fasting venous blood samples were drawn between 7:00 and 8:00 a.m., after a minimum of 8 h of fasting. A questionnaire was administered to the participants and their parents followed by anthropometric and blood pressure measurements. After the fasting samples were obtained, glucose (Glucon-D; Heinz India Pvt. Ltd., Mumbai, India) was given at a dose of 1.75 g/kg of body weight (up to a maximum of 75 g) for participants <12 years of age; for participants above 12 years of age, 75 g of anhydrous glucose was administered.

Biochemical analysis

Plasma glucose (hexokinase method) was measured on a Hitachi 912 autoanalyzer (Hitachi, Mannheim, Germany) using kits supplied by Roche Diagnostics (Mannheim). Serum cholesterol (cholesterol oxidase-peroxidase-amidopyrine method), serum triglycerides (glycerol phosphate oxidase-peroxidase-amidopyrine method), and high-density lipoprotein cholesterol (direct method; polyethylene glycol-pretreated enzymes) were measured using the Hitachi 912 autoanalyzer. Low-density lipoprotein cholesterol was calculated using the formula of Friedewald et al.²³ Glycated hemoglobin was estimated by high-pressure liquid chromatography using the Variant™ machine (Bio-Rad, Hercules, CA). Complete hemogram was estimated using a Sysmex (Kobe, Japan) XT-1800 i analyzer. Fasting serum insulin concentration was estimated using the electrochemiluminescence method (COBAS E 411 immunology analyzer; Roche Diagnostics). The intra- and inter-assay coefficient of variation for the biochemical assays ranged between 3.1% and 7.6%.

All measurements were carried out at Dr. Mohan's Diabetes Specialities Centre Laboratory, which is certified by the College of American Pathologists and the National Accreditation Board for Testing and Calibration of Laboratories.

Definitions and calculations

Diabetes and prediabetes

Based on the American Diabetes Association¹⁰ and International Diabetes Federation (IDF)⁸ criteria, prediabetes was defined as participants with impaired fasting glucose (fasting plasma glucose 100–125 mg/dL) and/or impaired glucose tolerance (2-h post-glucose load 140–199 mg/dL). Diabetes mellitus was defined as fasting plasma glucose ≥126 mg/dl and/or 2-h post-glucose load ≥200 mg/dL.

Overweight

Overweight categorization was based on the World Health Organization age- and gender-specific BMI cut points, whereby generalized obesity (based on BMI) was diagnosed if the child's weight was greater than or equal to the age- and gender-specific 85^th percentile given by the World Health Organization standardized BMI percentile cut points.²⁴

Abdominal obesity

Waist circumference cut points to define abdominal obesity were determined on the basis of the IDF consensus report on metabolic syndrome in children and adolescents.⁸ To classify abdominal obesity, for children in the age group of 6–11 years waist circumference ≥90^th percentile, for adolescents in the age group 12–16 years waist circumference ≥90^th percentile or adult cut points if lower, and for those between 16 and 19 years adult cut points were used.

Insulin Resistance (IR)

IR was calculated using the Homeostasis model assessment (HOMA) formula: ([fasting insulin (in μIU/mL)×fasting glucose (in mg/dL)/18.01]/22.5).²⁵ Subjects were categorized as IR based on the ≥90^th percentile for HOMA-IR in our population (≥3.56).

Other definitions

Family history of diabetes was classified as positive if the child's parents and/or grandparents had diabetes. Questionnaire data included questions on physical activity and dietary habits of the participants. The physical activity questions included presence or absence of physical training class in school and greater than 1 h of physical or sedentary activity per day. The dietary questions included consumption of fruits and vegetables, eating breakfast, and frequency of use of junk food or eating outside food.

Statistical analysis

For the conversion of insulin and glucose values from μIU/mL and mg/dL to Système International units, factors of 6.945 and 0.0555, respectively, were used. Independent-sample t test was used to compare continuous variables, and the χ² test was used to compare proportions between groups. Descriptive statistics were expressed as either n (%) or mean (SD) for normally distributed variables and geometric mean (SE) for non-normal variables such as fasting insulin, HOMA-IR, and dietary variables. Logistic regression (univariate) analysis was done with glucose intolerance as the dependent variable and various risk factors as independent variables. For the multivariate analysis, the stepwise forward Wald method was used. Using variables found significant on the univariate analysis, odds ratio (ORs) and 95% confidence intervals (CIs) were included. All analyses were done using the Windows-based SPSS Statistical Package (version 15.0; SPSS, Chicago, IL), and values of P<0.05 were considered statistically significant.

Results

Participants' profile

In total, 1,519 of the 2,065 children participated in the study (response rate, 74%). This included 777 (51%) boys and 742 (48.8%) girls. Table 1 compares the clinical, biochemical, physical activity, and dietary characteristics of the boys and girls. There were no significant differences in age. The girls had significantly higher BMI (19.2 vs. 18.3 kg/m², P<0.001), body fat (22.2% vs. 13.2%, P<0.001), fasting blood glucose (4.83 vs. 4.77 mmol/L, P=0.019), 2-h post-glucose load (5.33 vs. 5.17 mmol/L, P<0.006), total serum cholesterol (8.50 vs. 8.21 mmol/L, P<0.001), low-density lipoprotein cholesterol (5.06 vs. 4.83 mmol/L, P<0.004), serum triglycerides (4.44 vs. 4.17 mmol/L, P<0.025), fasting insulin (58.34 vs. 45.15 pmol/L, P<0.001), and HOMA-IR (1.8 vs. 1.4, P<0.001) compared with boys. 12.5% of girls had an abnormal HOMA-IR of 3.56 (≥90^th percentile) compared with 7.8% of boys (P=0.012). However, compared with girls, the boys were taller (151.2 vs. 146.7 cm, P<0.001) and had significantly higher waist circumference (66.1 vs. 64.1 cm, P<0.001), systolic blood pressure (112 vs. 107 mm Hg, P<0.001), and hemoglobin (13.8 vs. 12.9 g%, P<0.001). Boys ate outside food more frequently (18% vs. 11%, P<0.001) but also played outdoors more than girls (43% vs. 25%, P<0.001).

Table 1.

Profile of Study Participants by Gender

Variable	Boys (n=777)	Girls (n=742)	P value
Clinical profile
Age (years)	12.8 (3.8)	13.1 (3.8)	0.223
BMI (kg/m²)	18.3 (4.2)	19.2 (4.5)	<0.001
Waist (cm)	66.1 (12.6)	64.1 (11.0)	0.002
Height (cm)	151.2 (19.3)	146.7 (14.8)	<0.001
Weight (kg)	43.9 (18.1)	42.7 (15.5)	0.173
Body fat (%)	13.2 (7.2)	22.2 (7.3)	<0.001
Blood pressure (mm Hg)
Systolic	112 (13)	107 (11)	<0.001
Diastolic	68 (9)	68 (9)	0.444
Biochemical profile
Plasma glucose (mmol/L)
Fasting	4.77 (0.62)	4.83 (0.39)	0.019
2-h	5.17 (1.22)	5.33 (1.11)	0.006
Serum cholesterol (mmol/L)	8.21 (1.50)	8.50 (1.50)	<0.001
High-density lipoprotein (mmol/L)	2.44 (0.56)	2.50 (0.56)	0.328
Low-density lipoprotein (mmol/L)	4.83 (1.22)	5.06 (1.33)	0.004
Serum triglycerides (mmol/L)^a	4.17 (0.08)	4.44 (0.08)	0.025
Fasting insulin assay (pmol/L)^a	45.15 (2.02)	58.34 (2.50)	<0.001
Hemoglobin (g%)	13.8 (1.4)	12.9 (1.4)	<0.001
HOMA-IR^a	1.4 (0.06)	1.8 (0.08)	<0.001
Abnormal (≥3.56; 90^th percentile) HOMA-IR^b	37 (7.8)	55 (12.5)	0.012
Physical activity^b
Have PT class in school	654 (86.5)	610 (84.3)	NS
Play outdoors more than 1 h/day	313 (42.9)	176 (25.4)	<0.001
Sedentary activity >1 h/day	444 (60.8)	413 (61.8)	NS
Dietary profile^b
Eat breakfast regularly (4–7 days/week)	651 (83.8)	594 (80.1)	NS
Eating outside food (≥1/week)	140 (18.0)	84 (11.3)	<0.001
Fruits and vegetable consumption (recommended ≥5 servings/day)	90 (11.9)	83 (11.4)	NS

Data are expressed as mean (SD) values unless indicated otherwise.

Data are expressed as geometric mean (SE) values.

Data are expressed as n (%).

BMI, body mass index; HOMA-IR, homeostasis model assessment of insulin resistance; NS, not significant; PT, physical training.

Prevalence of glucose intolerance

Of the total of 1,519 participants who underwent an OGTT, 56 (3.7%) had glucose intolerance. This included four (0.3%) with diabetes and 52 (3.4%) with prediabetes (impaired fasting glucose and/or impaired glucose tolerance). As the number of participants with diabetes was too small for statistics, individuals with overall glucose intolerance (diabetes and prediabetes) were considered for all further analysis. Prevalence of glucose intolerance was significantly higher in girls compared with boys (4.2 vs. 3.2%, P<0.001).

Table 2 shows the gender-wise breakdown of glucose intolerance in relation to the presence or absence of various risk factors. Among boys, except for abnormal HOMA-IR (OR 5.19; CI 1.54, 17.44) there was no significant difference in glucose intolerance when stratified based on age or presence or absence of acanthosis nigricans, family history of diabetes, overweight, abdominal obesity, or HOMA-IR. However, among girls, the prevalence of diabetes was higher among the following groups: adolescent age (OR 2.94; CI 1.12, 7.76), overweight (OR 2.73; CI 1.32, 5.65), those with abdominal obesity (OR 4.45; CI 1.95, 10.14), acanthosis nigricans (OR 2.35; CI 1.14, 4.83), family history of diabetes (OR 2.52; CI 1.07, 5.92), and abnormal HOMA-IR (OR 9.30; CI 3.59, 24.12).

Table 2.

Prevalence of Risk Factors Contributing to Glucose Intolerance in Boys and Girls (n=1,519)

Risk factors	n (%)	P value	Exp (B)	95% CI
Prevalence of glucose intolerance in boys ( n= 25) (3.2%)
Age group
<12 years (n=308)	8 (2.6)	NS	1.41	0.60–3.31
12–19 years (n=469)	17 (3.6)
Acanthosis nigricans
No (n=633)	22 (3.5)	NS	0.59	0.17–2.00
Yes (n=144)	3 (2.1)
Family history
No (n=349)	11 (3.2)	NS	1.04	0.47–2.32
Yes (n=428)	14 (3.3)
Overweight (body mass index)
No (n=601)	18 (3)	NS	1.34	0.55–3.27
Yes (n=176)	7 (4)
Abdominal obesity (waist circumference)
No (n=680)	20 (2.9)	NS	1.92	0.70–5.24
Yes (n=91)	5 (5.5)
Abnormal HOMA-IR
No (n=438)	10 (2.3)	0.018	5.19	1.54–17.44^a
Yes (n=37)	4 (10.8)
Prevalence of glucose intolerance in girls ( n= 31) (4.2%)
Age group
<12 years (n=262)	5 (1.9)	0.022	2.94	1.12–7.76^a
12–19 years (n=480)	26 (5.4)
Acanthosis nigricans
No (n=524)	16 (3.1)	0.026	2.35	1.14–4.83^a
Yes (n=218)	15 (6.9)
Family history
No (n=308)	7 (2.3)	0.039	2.51	1.07–5.92^a
Yes (n=434)	24 (5.5)
Overweight (body mass index)
No (n=563)	17 (3.0)	0.009	2.73	1.32–5.65^a
Yes (n=179)	14 (7.8)
Abdominal obesity (waist circumference)
No (n=665)	21 (3.2)	0.001	4.45	1.95–10.14^a
Yes (n=71)	9 (12.7)
Abnormal HOMA-IR
No (n=386)	9 (2.3)	0.001	9.30	3.59–24.12^a
Yes (n=55)	10 (18.2)

Indicates Exp (B) or odds ratio is significant.

HOMA-IR, homeostasis model assessment of insulin resistance; NS, not significant.

Multivariate analysis was carried out separately in girls and boys. In boys, it was seen that HOMA-IR (OR 5.16; CI 1.54, 17.36) remained a significant contributor to glucose intolerance even after adjustment for all other risk factors. In girls, family history of diabetes (OR 4.11; CI 1.28, 13.22; P=0.018) and HOMA-IR (OR 11.22; CI 4.19, 30.05; P<0.001) were independent contributors to glucose intolerance after adjustment of other risk factors identified in the univariate analysis.

Discussion

Current American Diabetes Association and IDF guidelines recommend screening children and youth for diabetes and metabolic syndrome if they are 10 years and above, are overweight (BMI >85^th percentile), and have two of the following risk factors: family history of diabetes, belong to a high-risk ethnic group, show signs of IR like acanthosis nigricans, or have conditions associated with IR (such as hypertension, dyslipidemia, polycystic ovary syndrome, low birth weight).^8,10 The IDF position statement explicitly points to the lack of population-based prevalence rates of diabetes in young people and stresses the need for studies on glucose intolerance in the young.²⁶ A recent editorial states that well-planned population-based studies need to be done in children and adolescents, specifically in the age group of 15–19 years.²⁷ This study attempts to at least partly address this unmet need in an Asian Indian population that is at high risk for diabetes.¹⁵

Until now, data on glucose intolerance from India have been in adults 20 years old and above. Studies reporting glucose intolerance and their risk factors in children have been mostly clinic based and reported prevalence rates of glucose intolerance varying from 5% to 50% in highly selected cohorts of obese children and adolescents.^{2,3,9

–12,17

–20,28
–30} At our diabetes center also, a secular trend of increase in the prevalence of youth-onset T2DM was noted over the last decade.^19,20 However, whether this reflects an increasing incidence of youth-onset T2DM or is simply a reflection of referral bias is unclear. Only a population-based study can answer this.

To our knowledge, there has been only one previous report based on the glucose tolerance test in children from India. That study, also from Chennai, was published in 1995 and was a school-based population study that screened 3,515 schoolchildren in the age group of 5–19 years and reported “zero” prevalence of glucose intolerance.³¹ Three children had renal glycosuria in that study. Thus, the overall glucose intolerance prevalence of 3.7% noted in this study indicates that glucose intolerance is now becoming evident among children and adolescents in urban India. The known risk factors such as adolescent age, BMI ≥90^th percentile, abdominal obesity, IR (HOMA-IR), acanthosis nigricans, and family history of diabetes were all associated with glucose intolerance in Asian Indian girls. The finding in our study that the prevalence of glucose intolerance in obese adolescent girls goes up to 12.7% and that in those with abnormal HOMA-IR it can go as high as 18% in girls and 11% in boys is rather worrisome. Asian Indians are well known as an ethnic group to have increased IR.³² The hormonal changes associated with puberty, which can further reduce insulin sensitivity,³⁰ along with higher BMI and lower physical activity, probably explain the higher prevalence in girls.

Recent studies from India,²⁹ Canada,¹¹ and Europe³³ have reported a higher prevalence of dysglycemia among overweight and obese adolescents. However, it is difficult to compare prevalence rates of glucose intolerance across studies in the young because of the use of different cut points and absence of country-specific normative data to identify children and adolescents who have generalized or abdominal obesity. We used the World Health Organization age- and gender-specific cut points²⁴ as they included Indian children as part of the study population. Family history of diabetes and acanthosis nigricans have been previously shown to be associated with diabetes, more commonly in girls,^{19,28
–30,32} as also seen in this study.

A recent commentary emphasizes the rapid changes in lifestyle, among Indian children and adolescents.³² Our data show that overall only 11% of the participants consumed the daily recommended intake of five or more servings of fruits and vegetables and that only 25% of girls played outdoors for more than 1 h/day compared with 43% of boys, which probably explains the higher BMI in girls and also the lower prevalence of glucose intolerance in boys despite the presence of other risk factors. Because obesity and physical inactivity are strongly related to development of T2DM and cardiometabolic risk factors, increasing physical activity, especially among girls, should begin in childhood.³⁴ Adoption of healthy lifestyle behaviors at an early age holds promise, as shown by the Cardiovascular Risk in Young Finns cohort, which reported that increased fruit consumption and physical activity in childhood improved the cardiometabolic profile later in life.³⁵

Many reports have shown clustering of cardiometabolic risk factors in adolescence and HOMA-IR and waist circumference were important contributors to these cardiometabolic abnormalities within these clusters.^30,36
–38 Vikram et al.³⁹ studied correlates of T2DM in the young in North India and concluded that early identification through simple anthropometric parameters such as waist circumference may be useful for planning primary prevention in this population.

One of the limitations of the study is the small number of cases. This is shown by the wide CIs in the risk factors studied. Another limitation of the study is that, because this was an epidemiological study, it was not possible to obtain the Tanner staging in these children and adolescents. However, the strength of this study is that it is the first report of glucose intolerance in a high-risk population of Asian Indian children and adolescents. This study could thus form the basis for future prevalence studies in this population as the epidemic of T2DM spreads to children and adolescents.

In conclusion, our data suggest that glucose intolerance is becoming a significant health issue in Asian Indian adolescents, particularly in girls with abdominal obesity. This calls for large-scale intervention programs focusing on prevention of obesity and lifestyle modification in this ethnic group.

Footnotes

Acknowledgments

We acknowledge the young participants for their participation and the help of the field and data entry teams of the Translational Research Department for the conduct of the study. We also thank Dr. Sreekumaran Nair, Head, Biostatistics Department, Manipal University, Mangalore, India, for his valuable input in the statistical analysis. This is the second publication from the ORANGE Study. This research was conducted with support from the investigator-initiated study program of Lifescan, Inc., a Johnson & Johnson Company.

Author Disclosure Statement

No competing financial interests exist. V.M. conceived the study and revised all drafts of the article. H.R. checked the integrity and accuracy of results, wrote the first draft of the article, and carried out the corrections in consecutive drafts. H.R. and J.S. coordinated and carried out the study. R.M.A. helped in the interpretive analysis and gave valuable comments in the writing of the article.

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