Significance of waist to height ratio as an early predictor in developing metabolic syndrome in children of age group 5-12 years in a tertiary care centre in Trichy: Part I

Dissertation Submitted to the National Board of Examinations, New Delhi

Dr. Nimisha PV*

Department of Pediatrics, KMC Speciality Hospital, Trichy, Tamil Nadu



Background: Prevalence of childhood obesity has increased worldwide. This is associated with significant health problems and is an early risk factor for much of adult morbidity and mortality. BMI is used as a parameter in identifying obesity in children. Other parameters like waist circumference and waist to height ratio also help to estimate risk of occurrence of metabolic syndrome in children.

Objective: To study the significance of waist to height ratio as an early predictor in children prone to developing metabolic syndrome, in the age group 5-12 years in our hospital.

Methods: Children from both In Patient and Out Patient department who met the definition for obese/overweight were included in the study. Children with related syndromes and hypothyroidism were excluded from the study. Based on the prevalence, the calculated sample size was 170. Data was statistically evaluated with IBM SPSS Statistics for Windows.

Results: Among 188 children belonging to Overweight/Obese category, 18 didn’t meet the inclusion criteria. Among 170 children recruited, 70 had metabolic syndrome. The study showed that waist to height ratio more than 95th centile had significant positive prediction and specificity in identifying children with metabolic syndrome with p value <0.000.

Conclusion: Waist to height ratio is a simple and effective parameter for identifying children prone for metabolic syndrome. Further biochemical evaluation and follow up can be planned for them. Specificity and Positive predictive value of Waist to height ratio from the study is 95% and 98% respectively which is better than the current BMI definition of obesity.  Classification with BMI missed every fifth child with metabolic syndrome.


Prevalence of obesity has increased worldwide over the last few decades and has reached alarming rates in developing countries. Childhood has also been affected by this epidemic, which leads into premature health issues [1]. Childhood obesity tends to track into adulthood. Studies have shown that 85% of obese children become obese adults. Obese toddlers have an odds ratio of 1.3 for becoming obese adults, while obese teenagers have an odds ratio of 17.5 [2]. Childhood obesity is associated with significant health problems and is an early risk factor for much of adult morbidity and mortality. While children rarely develop true cardiovascular events, early evidence of accelerated atherogenesis can be detected [2].

Metabolic syndrome features a cluster of cardiovascular risk factors (hypertension, altered glucose metabolism, dyslipidemia, and abdominal obesity) that occur in obese children [3]. However, metabolic syndrome can also occur in lean individuals, suggesting that obesity is a marker for the syndrome, not a cause. Metabolic syndrome is difficult to define, due to its non-uniform classification and reliance on hard cut-offs in the evaluation of disorders with non-Gaussian distributions. Defining the syndrome is even more difficult in children, owing to racial and pubertal differences and lack of cardiovascular events. Multiple environmental factors, in particular a typical western diet, other changes in society, such as stress and sleep deprivation, increase insulin resistance and the propensity for food intake. These culminate in an adverse biochemical phenotype, including development of altered glucose metabolism and early atherogenesis during childhood and early adulthood [4].

Metabolic syndrome is associated with many clinical conditions besides Cardiovascular diseases and Type 2 Diabetes mellitus. They are oxidative stress, hyperuricemia, hyperandrogenism, polycystic ovary syndrome, hepatic steatosis and non-alcoholic fatty liver disease (NAFLD), impaired glucose tolerance, obstructive sleep apnea (OSA), hypogonadism, chronic low-grade inflammation, vascular dementia and Alzheimer’s disease, and certain forms of cancer [5].

The goal then should be to focus on the prevention and treatment of obesity in childhood and young adulthood, since its complications are harmful to health, leading to serious outcomes in later life. So, it is of great importance that the individuals at high risk for overweight and obesity, mainly children and adolescents, are identified early. Attention to their lifestyle is urgent, with regard to the quality of dietary habits and avoiding “obesogenic” environments, encouraging and increasing physical activity in groups, and reducing sedentary behaviour. Understanding and building up an early behaviour of healthy habits would be the basis for a future life with more health and wellness [6].

Early identification of children at risk of developing metabolic syndrome is of paramount importance. In 2007, the International Diabetes Federation developed a straightforward and easy to apply clinical definition of metabolic syndrome in children and adolescents. That definition was built on previous studies that used modified adult criteria to assess the prevalence of metabolic syndrome in children and adolescents and has been widely used. However, an adult definition cannot simply be applied for use in children and adolescents (particularly in toddlers and young children) because drastic changes in body size and proportion occur with age and development. Furthermore, puberty has an effect on fat distribution, insulin sensitivity in the muscle and liver, and insulin secretion by β cells (insulin sensitivity declines by 25-50% during puberty and returns to normal on completion of pubertal development) [4].

Developmental changes are also associated with physiological changes in blood pressure and lipid levels. Another limitation was the International Diabetes Federation’s suggestion that metabolic syndrome should not be diagnosed in children younger than age 10 years, but that a strong message for weight reduction should be delivered for those with abdominal obesity. Additionally, children who are younger than 6 years were excluded from the definition because of insufficient data for this age-group. Nonetheless, the main factor contributing to the absence of a consensus definition in children is the lack of reference values for some of the components of metabolic syndrome [7].

Over the past years, it has become clear that obesity-related complications are highly prevalent in prepubertal and preschool children. This high prevalence highlights the urgent need to develop a new definition of metabolic syndrome that could allow early diagnosis and timely action to prevent long-term metabolic and cardiovascular consequences [7]. In 2014, a definition of metabolic syndrome for prepubertal children in Europe was proposed by investigators of the Identification and prevention of dietary- and lifestyle-induced health effects in children and infants (IDEFICS) study, which addressed the limitations of previous definitions in children and the need for early diagnosis. The IDEFICS study used reference values from their study of 18745 children in eight European countries to establish age-specific and sex-specific (and height-specific in the case of blood pressure) percentiles, which were then used to identify cut-offsi for the components of metabolic syndrome in children aged 2-11 years [4] According to this definition, children would require close monitoring if three or more of these risk factors exceed the 90th percentile (or <=10th percentile for HDL cholesterol); intervention is deemed appropriate if three or more of these risk factors exceed the 95th percentile (or <= 5th percentile for HDL cholesterol). Although the IDEFICS definition still has limitations (for example, the lipids are affected by toddlers’ diet, particularly the lipid and carbohydrate content), they concluded that it could be used in prepubertal children worldwide, provided that cut-offs for each parameter and long-term outcomes are well defined in each population. Various studies have been done all over the world regarding the risk factors and early detection of paediatric metabolic syndrome, however there is still a room for future studies to investigate how obesity in children could be better defined, e.g., using weight/height, waist circumference, and BMI [8].

Review of Literature

The worldwide prevalence of overweight and obesity among children and young adults has increased over the years. In children under 5 years, the prevalence of overweight and obesity in 1990 was 4.2 %, increasing to 6.7 % in 2010, and in 2020, it could reach 9.1 % [9]. Among adolescents, the rate of increase of obesity was about 12 % (from 1980 to 2000) [10]. The scenario recently changed because, in low middle-income countries, the prevalence of childhood overweight and obesity seems to rise quickly, especially in urban areas, reaching about 30% more when compared with developed countries [11].

In 2005, the International Diabetes Federation (IDF) defined metabolic syndrome in adults “as a cluster of risk factors for cardiovascular diseases and type 2 diabetes mellitus, including abdominal obesity, atherogenic dyslipidemia (high TGs and low HDL-cholesterol), impaired glucose tolerance and hypertension”. Metabolic syndrome could also be defined as a grouping of abnormalities resulting from insulin resistance and the excess of abdominal obesity [12]. Thus, two potential causative factors in the pathogenesis of metabolic syndrome stand out; namely insulin resistance and abdominal obesity.

Concerning the paediatric population, based on previous studies, the IDF suggested modified adult criteria to be applied in children and adolescents [13,14]. In addition, metabolic syndrome should not be diagnosed in children younger than 10 years, but in those with abdominal obesity (90th percentile as a cut-off for waist circumference), they should “work on weight reduction,” with healthy changes on lifestyle. For children between age 10 and 16 years, metabolic syndrome can be determined by the presence of abdominal obesity and two or more clinical risk factors such as high triglycerides (≥150 mg/dl), low HDL-cholesterol (<40 mg/dl), high blood pressure (95th percentile), or high fasting plasma glucose (>100 mg/dl) [13]. Thereafter different studies were conducted across the globe to define metabolic syndromes and its associated factors.

Annelie Lindholm et al have conducted a population-based longitudinal birth cohort study of 1540 children in Sweden, following children from 0 to 5 years with nine measurement points [15]. The children were classified as having waist to height ratio standard deviation scores (waist to height ratio SDS) <1 or >1 at 5 years. Student’s t-tests and Chi-square tests were used in the analysis and the study found that BMI classification missed every second child with waist to height ratio SDS >1 at 5 years of age, suggesting that waist to height ratio adds value in identifying children with abdominal adiposity and may need further investigation regarding cardio-metabolic risk factors.

A population study was done by Qiang Lu et al, to evaluate the relationship between waist-to-height ratio (WHtR) and glucose and lipid metabolism in adolescents aged 13-15 years. The study was conducted on 1665 adolescents aged 13-15 years. Measurements included Height, Weight, Waist circumference, Fasting blood sugar, Triglyceride and HDL Cholesterol. The subjects were divided into two groups according to WHtR. This study showed that waist-to-height ratio was an appropriate tool to assess dyslipidemic-diabetic adolescents and should be used to guide early intervention with the aim of future prevention of these linked diseases [16].

Nambiar S et al conducted a study validating the waist height ratio and developing centiles for use amongst children and adolescents in Australia 2017 [17]. Height and Waist circumference were measured in 3597 children from grades 1 (5-7 years), 5 (9-11 years) and 10 (15-17 years). Log regression analyses using waist circumference and height were performed to determine appropriate powers (p) to raise height, to completely adjust the index for height, by sex and grade. Correlations between WHtR and height were assessed. Statistically, the WHtR was only valid for use among grade 1 boys and girls (p = 1.09 [95% CI 0.95-1.23] and p = 1.07 [95% CI 0.92-1.22], respectively) and grade 10 girls p = 0.85 (95% CI 0.62-1.08). However, the error (0.25-1.85%), associated with the use of this index, in all ages and both sexes was clinically and biologically acceptable. The study concluded that WHtR is a clinically and biologically valid index to use among Australian children and adolescents.

Weili Y et al (2007) conducted a study aimed to evaluate the accuracy of the index of waist-to-height ratio (WHtR), and proposed the optimal thresholds of WHtR in the definition of childhood overweight and obesity in a bi-ethnic Chinese school-aged population [18]. Overweight and obese were identified by BMI for age and gender in a random sample school-aged child (8 to 18 years old). WHtR was calculated. Receiver operating characteristic (ROC) curve analysis was performed to assess the accuracy of WHtR as a diagnostic test for childhood overweight and obesity, compared with Waist circumference. The optimal thresholds of WHtR for defining overweight and obesity were recommended respectively by gender. The correlation between WHtR and age was analysed and compared with BMI and concluded that WHtR is a simple, easy, accurate, and non-age-dependent index with high applicability to screening overweight and obesity in children and adolescents.

Study done by Rebecca Kuriyan et al developed waist circumference and waist for height percentiles in urban south Indian children aged 3-16 years [19]. Participants were 9060 children (5172 boys and 3888 girls) in the age group of 3-16 years. They formulated tables for waist and waist height ratio percentiles for Indian children which could be used as reference values for urban Indian children. They suggested that the 75th percentile of waist circumference from this study be used as an “action point” for Indian children to identify obesity (as a tautological argument), while retaining the cut-off of 0.5 for the waist to height ratio; however, this underlines the need to derive biologically rational cut-offs that would relate to different levels of risk for adult cardiovascular disease.

Manu Raj et al conducted a study to determine blood pressure distribution in school children and to derive population specific reference values appropriate for age, gender and height status for South Indians. Stratified random cluster sampling method was used to select the children. Blood pressure and anthropometric data were collected from 20,263 students of 5-16 years age and they developed a table for blood pressure percentile in relation to age and height percentile [20]. This was taken as the reference value for BP in the current study population.

The Princeton lipid research clinics follow up study was done to assess the association of metabolic syndrome in childhood with adult cardiovascular disease 25 years later. They used data from the National Heart, Lung, and Blood Institute Lipid Research Clinics Princeton Prevalence Study (1973-1976) and the Princeton Follow-up Study (2000-2004). They used BMI as the obesity measure in childhood, because waist circumference was not measured in the Lipid Research Clinics study. The adult cardiovascular disease status of participants and their parents was obtained through participant report. A logistic analysis was used to predict adult cardiovascular disease. Paediatric metabolic syndrome, age, gender, race, and parental history of cardiovascular disease were potential explanatory variables identified. Ages ranged from 6 to 19 years in the Lipid Research Clinics study and from 30 to 48 years in the Princeton Follow-up Study. They concluded that evaluating children for metabolic syndrome could identify patients at increased risk of adult cardiovascular disease, making targeted interventions possible [21].

Li et al conducted another study in china. This study was aimed to assess the accuracy of body mass index (BMI) percentile, waist circumference (WC) percentile, waist-height ratio, and waist-hip ratio for identifying cardio-metabolic risk factors in Chinese children and adolescents stratified by sex and BMI categories [22]. They measured anthropometric indices, fasting plasma glucose, lipid profile and blood pressure for 15698 participants aged 6-17 in a national survey between September and December 2013. The predictive accuracy of anthropometric indices for cardio-metabolic risk factors was examined using receiver operating characteristic (ROC) analyses. The DeLong test and Z test were used for the comparisons of areas under ROC curves (AUCs). They concluded that anthropometric indices were more predictive of dyslipidemia, hypertension and clustered risk factors in overweight/obese group compared to their normal BMI peers.

BMI centile charts in various countries are derived from their own health surveys conducted previously and the charts vary accordingly. In Korea, Lee et al did a study aimed to compare the BMI percentiles using Korean, United States Centers for Disease Control and Prevention (US-CDC), and World Health Organization (WHO) charts for their ability to detect MetS in Korean children and examine their associations with the severity of MetS [23]. Among 3094 children (1653 boys, age: 10-16 years) from the Korean National Health and Examination Survey 2011-2016, age and sex-specific BMI percentiles using the above three charts were determined. MetS severity score was derived using age and sex-specific z-scores of individual MetS components. Results showed that BMI percentiles were positively associated with MetS (odds ratio 1.27-1.36).

Khoury et al conducted a Cross-sectional analysis of 5 National Health and Nutrition Examination Surveys from 1999 to 2008 (ages 5 to 18 years of age). The BMI percentile categories (normal, overweight, and obese) were further stratified on the basis of WHtR (<0.5, 0.5 to <0.6, ≥0.6). Outcome measures were lipid and glycemic profiles, C-reactive protein, liver transaminases, prevalence of hypertension, and metabolic syndrome. Data were available for 14,493 subjects. In the study overweight and obese subjects with a WHtR <0.5 had a cardiometabolic risk approaching that of subjects with a normal BMI percentile category. Increasing WHtR was significantly associated with increased cardiometabolic risk in overweight and obese subjects, with the greatest associations observed in the obese population. Of obese subjects with WHtR ≥ 0.6, 26% had elevated non-high-density lipoprotein levels, 18% had elevated C-reactive protein levels, 69% had an elevated HOMA- IR, and 32% had metabolic syndrome [24].

The first study which assessed metabolic syndrome and its components in tribal adolescents in India was done by Mahajan et al. This cross-sectional study was conducted on 296 adolescents (128 boys and 168 girls) aged 14-19 years in Kukana tribe of Valsad district, Gujarat. Anthropometric measurements (height, body weight, waist circumference), systolic and diastolic blood pressure, biochemical tests were recorded. Prevalence of metabolic syndrome was estimated as 3.8%, with sex-wise prevalence found to be 3.9% and 3.6% in boys and girls, respectively [25]. Low High-Density Lipoprotein Cholesterol was the most prevalent individual risk factor observed in the study.

He et al conducted a similar study in adolescents. Data from Penn State Children Cohort study was used to investigate the association between abdominal obesity and metabolic syndrome burden in a population-based sample of 421 adolescents. Dual-energy x-ray absorptiometry (DXA) was used to assess abdominal obesity, as measured by android/gynoid fat ratio (A/G ratio), android/whole body fat proportion (A/W proportion), visceral (VAT) and subcutaneous fat (SAT) areas. Continuous metabolic syndrome score (cMetS), calculated as the sum of the age and sex-adjusted standardized residual (Z-score) of five established MetS components, was used to assess the MetS burden. Study showed that abdominal obesity was associated with higher MetS burden in adolescent population. The association between abdominal obesity and IR measure was the strongest, suggesting the key impact of abdominal obesity on IR in adolescents MetS burden [26-27].

Kelishadi R et al conducted a study among 4811 students (2248 boys and 2563 girls) aged 6-18 years. This was the first study of its kind in Iran. They used two definitions for the MetS: type A was defined based on criteria analogous to ATP III28, and type B was defined according to the cut offs obtained from NHANES III.29The mean (SD) age of students studied was 12.07 ± 3.2 years. In their conclusion they found that, MetS type A was seven times more prevalent than type B (14% vs 2%, respectively, p<0.0001), and had no significant gender difference. The most frequent components of both definitions of the MetS were low high-density lipoprotein cholesterol and high triglyceride. Waist circumference and waist-to-hip ratio had the strongest and weakest associations, respectively, with the MetS [30].

Zhang et al. identified a novel marker- The children’s lipid accumulation product (CLAP) which was an effective indicator associated with MetS in Chinese children and adolescents and was better than BMI and WHtR for predicting MetS. Here CLAP = waist circumference (WC (cm)) × abdominal skinfold thickness (AST (mm)) × triglyceride (TG (mmol/L))/100. The CLAP was divided into two grades (≥75th percentile and <75th percentile) by 75th percentile of CLAP for gender and age. A total of 683 Chinese children aged 8-15 years were recruited using a stratified cluster sampling method in this cross-sectional study. CLAP ≥75th centile was significantly associated with an increased risk of MetS (ORs (95% CIs) were 143.79 (18.78-1101.22), 86.83 (27.19-277.27), 150.75 (20.11-1130.19), respectively for BMI, WHtR, CLAP). The area under the ROC curve (AUC) for the CLAP was higher than that for BMI and WHtR for predicting MetS, with AUC (95% CI) values of 0.944 (0.913-0.975), 0.895 (0.864-0.927), and 0.928 (0.903-0.953), respectively [31].

There is still room for discussion regarding metabolic syndrome in children, and a better definition has to be identified.


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Dr. Nimisha PV