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Abstract
Background Early identification of overweight and obesity with the help of simple anthropometric tests can prevent from development of metabolic complications in these children. Body mass index (BMI) is the most commonly used parameter but, measurements such as waist circumference (WC), waist-to-height ratio (WHtR) and wrist circumference (WrC) have also been studied and found to have a better correlation with visceral fat.
Objective To correlate WC, WHtR and WrC with BMI among overweight and obese children. The secondary objective was to estimate the proportion of metabolic syndrome among obese and overweight children.
Methods A single-centre, cross-sectional study involving 80 overweight and obese children aged 3–15 years. Anthropometric measures such as WC, WHtR and WrC of the study subjects were correlated with BMI and investigated for metabolic syndrome.
Results Statistically significant and moderate positive correlation was found between BMI and WC, r (80)=0.45 and p<0.001 with WC explaining 20% of the variation of BMI. There was a statistically significant, moderate positive correlation between WHtR and BMI r (80)=0.34 and p<0.001 with 11% of the variation in BMI. There was a statistically significant strong positive correlation between WC and WrC (80)=0.61 and p<0.001, and WrC explains 37.2% of the variation in WC. However, there was no statistically significant correlation between BMI and WrC. Metabolic syndrome was found in 13 (16.25%) children.
Conclusion Alternative anthropometric measurements such as WC and WHtR have a significant correlation with BMI and may be of help in defining overweight and obesity in children. There was a statistically significant strong positive correlation between WC and WrC among obese children. Metabolic syndrome is common in these children.
- Obesity
- Growth
Data availability statement
Data are available on reasonable request.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Body mass index (BMI) is the most widely used tool for the assessment and classification of obesity and overweight in children, but it has its own limitations.
WHAT THIS STUDY ADDS
Alternative measurements such as waist circumference (WC) and waist-to-height ratio correlate significantly with BMI among obese and overweight children. Wrist circumference (WrC) had a strong positive correlation with WC and probably has a role in central obesity.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
WC, waist-to-height and WrC may be used to identify and classify obesity in children.
Introduction
Global obesity and overweight rates are on the rise, impacting both resource-rich and resource-limited nations. The associated complications are also increasing, posing a significant global health challenge.1–3 Approximately 200 million school-aged children worldwide are overweight or obese, with 40–50 million classified as obese.4 Factors contributing to childhood obesity include high-calorie, nutrient-deficient diets and decreased physical activity.5 In India, studies indicate a growing prevalence of overweight and obesity in children, raising concerns about increased risks of non-communicable diseases such as diabetes, hypertension and cardiovascular conditions in adulthood.6–8 Treatment options for children with excess weight encompass lifestyle modifications, pharmacotherapy and bariatric surgeries. Early screening and intervention are crucial steps in managing obesity, emphasising the role of healthcare professionals in identifying and providing appropriate counselling and treatment for overweight and obese children.9 10
Techniques such as air displacement plethysmography, bioelectrical impedance analysis and dual-energy X-ray absorptiometry (DEXA) are used for assessing body composition. While techniques like DEXA are gold standards for body composition assessment, their limited use due to complexity and cost underscores the importance of simpler methods.11 Anthropometric measurements such as body mass index (BMI), waist circumference (WC) and waist-to-height ratio (WHtR) offer cost-effective alternatives.12 BMI correlates with adiposity and also with the complications of obesity and overweight in children.13–17 WC and WHtR are reliable indicators of central obesity, associated with hypertension and abnormal lipid profiles. Studies suggest their effectiveness in assessing visceral fat, with WHtR particularly linked to metabolic and cardiovascular risks in obese children.18
Many studies have suggested WC and WHtR as good predictor tools to assess obesity because they correlate better than other anthropometric measurements with visceral fat.19 WHtR is considered a good predictor of developing metabolic and cardiovascular comorbidities among obese and overweight children. It is more closely associated with central body fat distribution than general adiposity.20 21 Recent exploration of wrist circumference (WrC) reveals its potential as a predictor for obesity-related complications, including insulin resistance and cardiovascular diseases.22 The primary objective of this study was to correlate WC, WHtR and WrC with BMI among overweight and obese children. The secondary objective was to estimate the proportion of metabolic syndrome among obese and overweight children.
Methods
Study design
Cross-sectional study.
Study settings
The study was conducted from 10 June 2020 to 31 August 2021 for a duration of 14 months at a tertiary care Medical Institute of National Importance in eastern India.
Study participants
Eighty obese and overweight children (aged 3–15 years) from the paediatric endocrine clinic were included.
Sample size estimation
A study conducted by Divya Krishnan et al, among adolescents from two different schools in Kerala, India. The study showed a strong correlation between WC and BMI with r=0.78 and p<0.001 and WHtR and BMI with r=0.74 and p<0.001.21 In another study conducted by Karki et al, WrC showed a strong positive correlation with WC with r=0.58 in males and r=0.64 in females and a positive Pearson coefficient with BMI was found with r=0.62 in males and r=0.60 in females with p<0.001.22 We assumed a moderate correlation coefficient of 0.5 between WC and BMI in our study. Taking power of study at 90% and significance level at 5%, the required sample size was 38. Assuming 10% as non-participation rate, the estimated sample size was 42. However, more samples were included during the study period considering it a thesis project and no major issue of resources.
Standard definitions and criteria
Overweight and obesity were defined using WHO MGRS 2006 and the Indian Academy of Pediatrics’ 2015 growth charts. Pathological obesity included patients with delayed growth, delayed development, dysmorphic features or visual complaints, indicating underlying issues like hypothyroidism, growth hormone deficiency, cortisol excess or genetic causes. This encompasses conditions such as Prader-Willi syndrome, Alstrom syndrome and leptin deficiency. Obesity secondary to drugs or hypothalamic issues was also considered. Obese patients with normal growth and development, lacking features of secondary causes, were categorised as having physiological or exogenous obesity. The proportion of metabolic syndrome was calculated among the study participants using Cook et al’s modified ATP-III criteria, where the metabolic syndrome is defined as WC above or equal to 90th percentile (abdominal obesity) for age and gender plus any two of the following: fasting blood sugar (FBS) more than or equal to 110 mg/dL, high-density lipids (HDL) less than or equal to 40 mg/dL, triglycerides more than or equal to 110 mg/dL and elevated blood pressure (systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) ≥90th percentile for age, gender and height).23
Study procedure
The first author, assisted by trained paramedical staff, conducted anthropometric measurements. Each measurement was taken thrice, and the average was considered final. Non-stretchable measuring tape was used. For WC, participants stood with raised clothes; a line was drawn above the right ilium, and the tape was placed snugly at the marked level. Results were recorded at the end of normal expiration. Participants were categorised using Indian children’s WC percentile chart. WC>90 th percentile indicated abdominal obesity. WHtR was calculated; a ratio ≤0.5 was normal, and >0.5 was obese. WrC, measured at Lister’s tubercle, was recorded on both wrists, and the average was calculated. Age-specific and gender-specific percentiles were calculated for WrC based on a reference centile chart for children aged 3–18 years and the 70th percentile was used as cut-off for risk of hypertension in children. Patients’ blood pressure was measured seated, with the cubital fossa at heart level, using a mercury sphygmomanometer and appropriately sized cuff after a 5 min rest. SBP and DBP were determined by the first and fifth Korotkoff sounds, respectively. Three consecutive readings were averaged for analysis, and elevated values were verified after 10 min. The 2017 American Academy of Pediatrics definitions for paediatric blood pressure categories were applied. The children were not on any drugs that could affect their blood pressure parameters. Blood samples for FBS and lipid profile were taken after overnight fasting and analysed at the Institute’s lab. All the investigations were standardised.
Statistical analysis
Statistical Product and Service Solutions (SPSS V.20.0) software was used for data analysis. Descriptive statistics were used to describe the demographic details and baseline characteristics of the study group. Pearson’s correlation was applied to estimate the correlation between different anthropometric measurements like WC, WHtR and WrC with BMI.
Results
Out of 80 overweight and obese children, the maximum patients belonged to the age group of 11–15 years (58.8%), followed by 6–10 years (30.0%) and 3–5 years (11.3%). The majority of the patients were males (80%) against females (20%) and most (66.7%) belonged to urban areas. The fathers or guardians of a majority of the patients (35%) were working as professionals and most of the mothers (76.7%) were unemployed despite sharing almost equal educational status as fathers. Most of them (61.7%) belonged to the nuclear family than a joint family (38.3%). As per Indian socioeconomic class, modified B G Prasad classification 2021 showed that the majority of the patients belonged to class I (upper class) and class II (upper middle class) with 26.7% each. Five per cent of the patients belonged to class V (lower class).24
The distribution of anthropometry revealed the mean (SD) of height, weight, BMI, WC, WHtR and WrC as 136.54 cm (18.43), 47.70 kg (16.27), 24.91 kg/m2 (4.47), 78.30 cm (13.33), 0.57 (0.09) and 15.10 cm (1.45), respectively. The majority of the patients, that is, 96.7% had WrC more than or equal to 70th percentile for age and gender (table 1).
Distribution of anthropometric measurements among the children
Among physical signs, acanthosis nigricans was present in 35.4% of the patients. Almond-shaped eyes, dysmorphism, round facies, buried penis, undescended testes, small hands and feet, delayed puberty and microorchidism were present in 3 (6.3%), 2 (4.2%), 1 (2.1%), 12 (25%), 3 (6.3%), 2 (4.2%), 1 (2.1%) and 1 (2.1%) patients respectively. The blood pressure was normal, elevated and hypertension in 65%, 20% and 15% of the patients, respectively (table 2).
Distribution of clinical signs among the children
The laboratory profile distribution showed mean (SD) of total cholesterol, LDL, HDL, triglycerides and FBS as 152.10 (43.63), 97.63 (40.84), 37.69 (9.70), 134.07 (62.11) and 91.02 (14.97), respectively. In our study, the lipid profile showed that among the total patients, 10% had high total cholesterol levels, 18.3% had high LDL levels, 51.7% had high triglyceride levels and 56.7% had low HDL levels. Metabolic syndrome was found in 13 (16.25%) children (table 3).
Distribution of components of metabolic syndrome
A statistically significant and moderate positive correlation was found between BMI and WC, r (80)=0.45 and p<0.001 with WC explaining 20% of the variation in BMI (figure 1). There was a statistically significant, moderate positive correlation between WHtR and BMI r (80)=0.34 and p<0.001 with 11% of the variation in BMI. There was a statistically significant strong positive correlation between WC and WrC (80)=0.61 and p<0.001, and WrC explains 37.2% of the variation in WC (figure 2). However, there was no statistically significant correlation between BMI and WrC (figure 3, table 4).
Correlation matrix of various anthropometric measurements and BMI
Scatter plot of correlation between BMI and waist circumference. BMI, body mass index.
Scatter plot of correlation between waist circumference and wrist circumference.
Scatter plot of correlation between BMI and wrist circumference. BMI, body mass index.
Discussion
Our study mainly focuses on determining the correlation between the anthropometric measurements in obese and overweight children. BMI is the most common anthropometric measurement used in assessing obesity in general. We found the alternative anthropometric measurements statistically correlating with BMI. Both WC and WHtR had a moderate positive correlation with BMI. There was a statistically significant strong positive correlation between WC and WrC. However, there was no statistically significant correlation between WrC and BMI. MetS was found in 13 (16.25%) of the children.
Brannsether et al conducted a cross-sectional study among 5725 children in the age group of 4–18 years in Norway to establish the reference values for WC and WHtR for Norwegian children. There was a strong positive correlation between WC and BMI with r=0.907, p<0.01.25 Ramírez-Vélez et al conducted a cross-sectional study in Columbia among 7954 healthy school children to obtain smoothed centile chart and Learning Management System tables for WC and WHtR among Columbian children and adolescents and also to evaluate the utility of these parameters as predictors of overweight and obesity. A moderate positive correlation was observed between WC and BMI with r=0.756 and p<0.01 and between WHtR and BMI with r=0.604 and p<0.01.26 In our study, Pearson’s product-moment correlation was applied to assess the relationship between WC and BMI among obese and overweight children. A statistically significant and moderate positive correlation was found between BMI and WC, with WC explaining 20% of the variation in BMI.
Castro et al did a study among 1803 adolescents from the state of Rio Grande do Sul, Brazil to describe the distribution of WC and WHtR among Kaingang indigenous adolescents to estimate the correlation between WC and WHtR and BMI for age. The correlation coefficients of WHtR for BMI were r=0.76 and r=0.80 respectively for boys and girls.27 Dimala et al conducted a study among 200 HIV/AIDS patients to assess the extent of agreement between parameters such as WC, WHtR and WHtR and BMI as anthropometric parameters and in classifying cardiometabolic risk in HIV/AIDS patients. After adjusting for gender and HAART (highly active antiretroviral therapy) status, there was a significant linear association between WHtR and BMI of r=0.65 and p<0.001.28 In our study, Pearson’s product-moment correlation was run to assess the relationship between the WHtR and BMI. Preliminary analyses showed the relationship to be linear with both variables and normally distributed. We found a statistically significant, moderate positive correlation between WHtR and BMI with 11% of the variation in BMI in our study. The difference in correlation coefficients may be due to the smaller sample size in our study and also, in our study we considered only overweight and obese children.
Tatar et al conducted a cross-sectional study among 136 subjects to evaluate the association of neck circumference and WrC with traditional abdominal and general obesity measurement parameters and other laboratory parameters such as blood lipids, fasting glucose, insulin concentration and adiponectin to predict their values as obesity measurement parameters. WrC showed a statistically significant moderate positive correlation with WC with r=0.405 and p<0.001.29 Karki et al conducted a cross-sectional study among 297 participants >18 years intending to find out the correlation between neck circumference and WrC. A strong positive Pearson correlation of neck circumference with WC in both males and females with r=0.64 and 0.86 were noted, respectively. WrC showed a strong positive correlation with WC with r=0.58 in males and r=0.64 in females and a positive Pearson coefficient with BMI was found with r=0.62 in males and r=0.60 in females with p<0.001.22 However, contrary to these studies, there was no statistically significant correlation between BMI and WrC in our study. The reason for this result could be a relatively smaller sample size. We did not include neck circumference in our study.
Gobato et al did a cross-sectional study among 79 adolescents aged 10–18 years old with the objective to verify the prevalence of metabolic syndrome and insulin resistance in obese adolescents and its relationship with different body compositions. The metabolic syndrome was diagnosed in 45.5% of the patients and insulin resistance in 29.9% of the patients. They assessed the following body compositions BMI, body fat percentage, abdominal circumference, and subcutaneous fat.30 The metabolic syndrome was diagnosed according to criteria proposed by Cook et al 23 Springwald et al conducted a retrospective cohort study among 289 obese children treated at a metabolic outpatient clinic to evaluate the incidence of metabolic syndrome to assess the role of insulin resistance in metabolic complications and to know the usefulness of diagnosing metabolic syndrome in children. Diagnosis of metabolic syndrome was based on the International Diabetes Federation 2009 criteria. Metabolic syndrome was diagnosed in 69 children (24%).31 However, in our study, we found only 16.25% of children had metabolic syndrome. This could be due to the difference in the criteria used to diagnose MetS.
To the best of our knowledge, this study is the first of its kind in the state of Bihar and the eastern region of India. We found two alternative anthropometric measurements statistically correlating with BMI. Both WC and WHtR had a moderate positive correlation with BMI. There was a statistically significant strong positive correlation between WC and WrC. Our study has a few limitations. The size of the sample population is relatively small and it is a cross-sectional and single-centre hospital-based study. The study included overweight and obese children but no comparison was made with normal weight children. We also did not analyse the data of exogenous and pathological obesity separately, as the abnormal distribution and height may act as confounders. As this is a hospital-based study, the generalisability is truly limited. Also to note that there is a likelihood of over-reporting of metabolic syndrome in the study population due to hospital setting.
Conclusion
Anthropometric measurement is an important first step in the evaluation of overweight and obese children. BMI is the most common anthropometric measurement used in assessing obesity in general. We found alternative anthropometric measurements statistically correlating with BMI. Both WC and WHtR had a moderate positive correlation with BMI. There was a statistically significant strong positive correlation between WC and WrC. However, there was no statistically significant correlation between WrC and BMI. MetS is common in obese and overweight children.
Data availability statement
Data are available on reasonable request.
Ethics statements
Patient consent for publication
Ethics approval
This study involves human participants and was approved by Institute Ethics Committee of All India Institute of Medical Sciences, Patna, India (reference number: AIIMS/Pat/IEC/PGTh/July19/02 dated 9th June 2020).
Acknowledgments
We are thankful to the children who have participated in the study and the parents who agreed to have their children involved in the study.
References
Footnotes
Collaborators Not applicable.
Contributors PK conceptualised this research. NR was involved in the initial drafting of the protocol. NR and PK were involved in collecting data and drafting the manuscript. NR, PK, SS, LKT and AP were involved in the clinical care of the patients and the review of the manuscript. All authors approved the final draft of the manuscript and conclusion drawing. PK is the guarantor of this study and accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests No, there are no competing interests.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.