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INTRODUCTION
Worldwide childhood obesity has significantly increased during the last 42 years (1975-2016) in both, girls (from 0.7% to 5.6%) and boys (from 0.9% to 7.8%) 1. Obesity during youth is associated with early development of atherosclerosis and cardiometabolic risk factors 2,3,4; therefore, there is a need to find a correct and accurate measure of obesity in children and adolescents 5. For this end, the waist-to-height ratio (WHtR) is suggested as an alternative tool to the body mass index (BMI) and waist circumference (WC), and its usefulness to identify youths at metabolic risk has gain much attention lately 5,6,7,8,9.
BMI is the most commonly used anthropometric index to evaluate overweight and obesity in adolescents. However, BMI is unable to differentiate whether an excess of body weight reflects an increase in fat mass or fat free mass 10,11. This limitation is highlighted in adolescents who have a great variability in the fat mass content for a given BMI 10,11. WC is a measurement more related to the fat tissue than BMI, particularly abdominal fat tissue 5,6,7,8,9. However, WC is height dependent given that taller children generally have larger WC than shorter ones 12. This feature could led to misevaluation of central obesity in youths 12.
The WHtR, calculated by dividing WC by height, is a practical tool to evaluate obesity in youth. WHtR has shown comparable results to BMI at evaluating adolescents' body composition 13. The use of a single value (WHtR ≥ 0.50) is very practical to identify subjects with higher odds of having cardiometabolic risk factors 12,14. However, the application of one cut-off for all age groups and ethnicities generates controversy 15,16). Differences in optimal cut-offs have been found in adolescents from Korea, United States, and Africa 8,16,17. These differences are probably due, at least in part, to the ethnic variability in patterns of body fat distribution, body segment proportions, and their relations with cardiometabolic risk factors 18,19,20.
There is limited information about the usefulness of WHtR in Colombian adolescents who are a mixture of European, African and Native American 21,22. This study aimed: a) to compare the usefulness of WHtR, BMI and WC to identify adolescents with cardiometabolic risk factors; and b) to explore the utility of a WHtR value corresponding to BMI classification of obesity.
MATERIALS AND METHODS
STUDY DESIGN AND PARTICIPANTS
This is a cross-sectional analytical study. Our study sample included 346 adolescents aged 10 to 18 years living in Medellin-Colombia. They took part in the Medellin's Food and Nutritional Survey (Perfil Alimentario y Nutricional de Medellín) carried out during 2015. The survey included a sample of 3,008 homes representative of the six socio-economic strata of the population, and the rural and urban areas of the city. Three hundred and forty six adolescents living in the selected homes who accepted to provide a blood sample were included in this analysis. This sample size assuming a power of 85%, at the 95% level of confidence, allows to detect a minimum difference of 5.62% in metabolic syndrome components between subjects with WHtR < 0.5 and WHtR ≥ 0.50, previously reported 9. Subjects who were sick at the moment of evaluation or were under treatment with steroids, hormones or medications were excluded. The study was performed according to the Declaration of Helsinki and was approved by the Bioethical Review Board from the Faculty of Dentistry of the University of Antioquia (Act of approval No. 01, February 27th of 2015). Informed consent was obtained from all participants and their guardians.
ANTHROPOMETRIC MEASUREMENTS
Anthropometric measurements were performed by experienced and trained personal following the techniques described by Lohman et al. 23. Adolescents were barefoot and wore a T-shirt and short pants for body measurements. Body weight was measured to the nearest 0.1 kg using a digital scale (Seca(r) 813, California, USA). Height was measured to the nearest 0.1 cm using a wall mounted mechanical measuring tape (Seca(r) 206, California, USA). The WC was measured to the nearest 0.1 cm, midway between the lowest rib margin and the iliac crest, using a flexible tape (Seca(r) 206, California, USA). Anthropometric measurements were done at least by duplicate or by triplicate when the difference between the first and the second values was higher than 0.1 kg in body weight, 0.5 cm in height and 1% in WC. This was done in order to get high quality data as recommended by the Anthropometric Standardization Reference Manual
23.
ANTHROPOMETRIC INDICES
BMI was calculated by dividing body weight in kg by height in square meters. Overweight and obesity were assessed using the reference values and cut-offs from the World Health Organization (WHO) 24. WC was classified using the standards and cut-offs suggested by the Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents 25. The WHtR was calculated by dividing WC in cm by height in cm. Two WHtR values were analyzed, the WHtR ≥ 0.50 originally proposed by Ashwell 26 and a WHtR value that matched the proportion of subjects classified with obesity according to WHO cut-offs for BMI 24).
CARDIOMETABOLIC RISK FACTORS
Adolescents were instructed to fast overnight for 10 to 12 hours. Blood was drawn from the antecubital vein in dry tubes. Blood was centrifuged at 3,000 x g for ten minutes to obtain serum. Serum glucose, insulin, total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured by standardized methods using an automatic analyzer (Roche, Cobas(r) c501, Mannheim, Germany). HOMA-IR was calculated as serum glucose (mmol/l) x serum insulin (mU/l)/22.5 27. Adolescents were classified as having high total cholesterol (≥ 200 mg/dl), high triglycerides (≥ 130 mg/dl), high LDL-C (≥ 130 mg/dl), high non-HDL-C (≥ 145 mg/dl) and low HDL-C (≤ 40 mg/dl) according to the Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents 25. HOMA-IR value ≥ 3.1 was considered as high following the criteria previously applied to our population 28,29. Multiple non-WC MetS factors included high HOMA-IR, high triglycerides and low HDL-C.
STATISTICAL ANALYSIS
Normal distribution of data was tested with the Kolmogorov-Smirnov test. Data are presented as means ± standard deviations or medians and interquartile range according to data distribution. Differences between groups were compared using one-way ANOVA with general lineal models using age as a covariate in the normally distributed data, and the Mann-Whitney test in non-normally distributed data. The adjusted R2 was calculated to determine the association between anthropometric indices and cardiometabolic risk factors. Receiver-operating characteristic (ROC) curve analysis was run to test the diagnostic accuracy of the anthropometric indices to identify adolescents with cardiometabolic risk factors. Chi-square and McNemar test were used to compare the proportions of anthropometric indices in adolescents by sex, and by the presence of two or more non-WC MetS factors. Odds ratios (OR) were calculated using logistic regression analysis to measure the association between being at risk according to the anthropometric indices (yes/no) and the presence of multiple non-WC MetS factors. Adolescents with normal body weight were used as the reference group (OR = 1.0). p < 0.05 was considered as statistically significant. The statistical analysis was developed in SPSS 24.
RESULTS
A total of 346 adolescents (166 girls and 180 boys) were included in this study (Table 1). Boys were taller (p < 0.05) than girls (155.9 ± 13.6 vs 152.3 ± 8.6 cm, respectively). Nonetheless, both genders showed similar proportions (p > 0.05) of normal weight (63.3 vs. 63.3%), overweight (13.3 vs. 16.3%), and obesity (4.4 vs. 4.8%). A value of WHtR ≥ 0.55 classified with central obesity the same proportion of adolescents than BMI cut-off for obesity (4.1 vs. 4.6%; p > 0.05); this WHtR value was analyzed as an alternative cut-off (Table 1).
Table I Participant characteristics by gender
Data presented as mean ± standard deviation or medians and interquartile range in parentheses according to data distribution. Nutritional status and non-WC MetS factors are presented as numbers and percentages. *Differences between groups obtained with general linear models adjusted by age. †Differences between groups obtained with the Mann-Whitney test. HOMA-IR: homeostasis model assessment of insulin resistance; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; MetS: metabolic syndrome; WC: waist circumference. BMI: body mass index; WHtR: waist-to-height ratio.
There were not differences (p > 0.05) in the proportion of boys and girls with central obesity according to WC ≥ 90 (2.8 vs. 5.6%), WHtR ≥ 0.50 (11.7 vs. 13.6%) or WHtR ≥ 0.55 (2.8 vs. 5.6%). The glucose levels were higher in boys than in girls (4.61 vs. 4.50 mmol/l, p < 0.05). The girls showed higher values of insulin (92.4 vs. 69.5 pmol/l, p < 0.01) and HOMA-IR (2.69 vs. 2.00, p < 0.01) (Table 1).
Associations between anthropometric indices and cardiometabolic risk factors are shown in Table 2. After adjusting by age and sex, the anthropometric indices correlated (p < 0.05) with insulin, HOMA-IR, total cholesterol, triglycerides, HDL-C and non-HDL-C. Only WC showed a significant correlation with glucose (p < 0.05) and WHtR with LDL-C (p < 0.01) (Table 2).
Table II Associations between antropometric indices and cardiometabolic risk factors*
*Adjusted by sex and age. HOMA-IR: homeostasis model assessment of insulin resistance; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol.
The anthropometric indices showed similar performance detecting adolescents with cardiometabolic risk factors (Table 3). The AUC for BMI, WC and WHtR were alike to identify adolescents with high HOMA-IR (AUC = 0.686, 0.694 and 0.641, respectively), low HDL-C (AUC = 0.623, 0.652 and 0.572, respectively), and multiple non-WC MetS factors (AUC = 0.694, 0.715 and 0.688, respectively). None of the anthropometric indices showed capacity to detect adolescents with high total cholesterol, high LDL-C or high non-HDL-C (Table 3).
Table III Area under the ROC curve of anthropometric indices to predict cardiometabolic risk factors
*p < 0.05; †p < 0.01; ‡p < 0.001. ROC: receiver operating characteristic; AUC: area under the curve; CI: confidence intervals; HOMA-IR: homeostasis model assessment of insulin resistance; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; MetS: metabolic syndrome; WC: waist circumference.
The proportion of adolescents with overweight, obesity or central obesity having multiple non-WC MetS factors is shown in Figure 1. Overweight adolescents represented 14.7% (n = 51), 4.6% had obesity (n = 16), 4.1% (n = 14) had WC ≥ P90, 12.7% (n = 43) had WHtR ≥ 0.50 and 4.1% (n = 14) had WHtR ≥ 0.55. The proportions of adolescents with multiple non-WC MetS factors were similar in overweight (33.3%) and those with a WHtR ≥ 0.5 (53.5%). The proportion of adolescents with multiple non-WC MetS factors was lower (p < 0.05) in the overweight (33.3%) than in the obese (75.0%) or those having a WC ≥ P90 (85.7%) or a WHtR ≥ 0.55 (78.6%). There were no significant differences in the proportion of adolescents with multiple non-WC MetS factors among the obese (75.0%), those having a WC ≥ P90 (85.7%), a WHtR ≥ 0.50 (78.6%) and a WHtR ≥ 0.55 (78.6%) (Fig. 1).
Figure 1 Proportion of adolescents with overweight, obesity or central obesity having multiple non-waist circumference metabolic syndrome factors. Different capital letters indicate differences (p < 0.05) in the proportions of overweight, obesity or central obesity, using McNemar test. Different small letters indicate differences (p < 0.05) in the proportions of having multiple non-waist circumference metabolic syndrome factors, using McNemar test (MetS: metabolic syndrome; WC: waist circumference; WHtR: waist-to-height ratio).
The OR for having multiple non-WC MetS factors in adolescents with overweight, obesity or abdominal obesity are presented in Table 4. Adolescents with normal body weight were used as a reference group. Compared to the reference group: a) adolescents with obesity were 9.88 (95% CI: 3.1-31.7) times more likely of having multiple non-WC MetS factors; b) adolescents with WC ≥ P90 were 18.30 (95% CI: 4.01-83.47) times more likely of having multiple non-WC MetS factors; and c) youths with WHtR ≥ 0.50 or WHtR ≥ 0.55 were 3.76 (95% CI: 1.95-7.25) and 11.00 (95% CI 3.0-40.0) times more likely of having multiple non-WC MetS factors, respectively. In overweight adolescents, those with WC ≥ P90 or WHtR ≥ 0.55 significantly increased their chances to have multiple non-WC MetS factors (Table 4). In obese participants, those with WC ≥ P90, WHtR ≥ 0.50 or WHtR ≥ 0.55 did show higher chances of having multiple non-WC MetS factors (Table 4).
Table IV Odds ratio (OR) for two or more non-waist circumference metabolic syndrome factors across anthropometric indices*
*Adolescents with normal body weight were used as the reference group (OR = 1.0). WC: waist circumference; WHtR: waist-to-height ratio; BMI: body mass index; P: percentile; CI: confidence intervals.
DISCUSSION
The purpose of this study was to compare the utility of BMI, WC and WHtR to identify adolescents with cardiometabolic risk factors. The main finding was the anthropometric indices have similar capacity to identify adolescents with high HOMA-IR, low HDL-C and multiple non-WC MetS factors. None of the anthropometric indices showed capacity to detect adolescents with high total cholesterol, high LDL-C or high non-HDL-C. Thus, it does not appear to exist advantage of using either BMI, WC or WHtR to identify youths with cardiometabolic risk factors, though the practicality of the WHtR could be a plus in epidemiological studies.
Similar capacities were shown among BMI, WC and WHtR to identify adolescents with cardiometabolic risk factors, which is in agreement with previous studies 8,17,30. In adults, by the contrary, measurements of central adiposity as WC and WHtR have shown superiority over BMI at identifying subjects with cardiometabolic alterations 31,32. To this point, there are not clear explanations for the dissimilar results between adults and adolescents. It has been suggested that a threshold level of visceral and abdominal subcutaneous fat exists; when it is exceeded, these fat depots are more likely to be associated with cardiometabolic risk factors 33,34. Thus, it could be that the adolescents of this study who had a low prevalence of abdominal obesity (4.1%) did not reach the abdominal obesity threshold. Under these conditions, the central adiposity indices (WC and WHtR) and BMI related similarly to cardiometabolic risk factors in this young population.
The WHtR ≥ 0.50 cut-off was proposed by Ashwell to identify people at risk for cardiovascular disease, who should be aware and do follow-ups 14. In this study, the WHtR ≥ 0.50 cut-off classified similar number of adolescents with central obesity than the BMI category for overweight. Furthermore, the subjects with a WHtR ≥ 0.50 or classified as overweight showed comparable OR of having multiple non-WC MetS factors. Thus, the WHtR ≥ 0.50 behaved similarly to the BMI classification for overweight in this population, and it allowed providing a similar warning health message. Nonetheless, controversy remains about the use of a single WHtR cut-off for all ages and both sexes 15,16. Borderlines lower than WHtR ≥ 0.50 have been reported in youths from Asia (0.41-0.44) and Africa (0.47) 8,16,35. Applying a lower value than 0.50 in this population will decrease the WHtR specificity and will probably increase the number of individuals at risk compared to those classified with overweight. This situation could be overwhelming for health systems of countries like Colombia, where overnutrition has increased in the last years and coexists with the undernutrition problem 36.
People with a WHtR ≥ 0.60 are at higher risk for cardiometabolic factors than those with a WHtR ≥ 0.50, and they should take corrective actions 14. In this study, few people showed a WHtR ≥ 0.60 (n = 6; 1.7%) and further analysis with this group was ruled out. Instead, a WHtR ≥ 0.55 that matched the proportions of subjects classified with obesity by BMI was explored. The proportion of adolescents with a WHtR ≥ 0.55 who had multiple non-WC MetS factors was high (78.6%) and similar to those classified with obesity by BMI and WC. Also, the odds of having multiple non-WC MetS factors among those with WHtR ≥ 0.55 were high (OR = 11.0) and comparable to youths with obesity according to BMI or WC. These results suggest that WHtR ≥ 0.55 could be used as an alternative to the BMI and WC classification for obesity to identify adolescents with non-WC MetS factors. Similar results have been reported 8,17,30, and support the use of the WHtR as an alternative to BMI and WC, given the following advantages: a) the use of a single cut-off for all ages, sexes and ethnicities allowing comparison worldwide; b) it is more practical since it does not require the use of reference values according to sex and age; and c) it is easier to obtain than BMI 14,37.
The study had some strengths and limitations. Among the strengths: a) the comparison of the WHtR cut-offs with BMI categories of overweight and obesity; b) the analysis of having multiple non-WC MetS factors, since the presence of one factor might be due to day-to-day changes (e.g., triglycerides); and c) the OR analysis combining the anthropometric indices. Among the limitations: a) this is a cross-sectional study and it does not allow to establish cause-effect relationships; b) the study model does not provide information about the anthropometric indices ability for predicting future health outcomes; and c) the lack of blood pressure data limiting the analysis of this MetS component.
In conclusion, BMI, WC and WHtR showed similar capacities to identify Colombian adolescents with cardiometabolic risk factors. The WHtR cut-offs of 0.50 and 0.55 behave similarly to the BMI classification of overweight and obesity for identifying Colombian adolescents with cardiometabolic risk factors. Although, the use of WHtR as an alternative to BMI and WC is promising, more research is needed comparing the performance of BMI, WC and WHtR cut-offs in adolescents.
