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INTRODUCTION
The NS is the balance between the caloric intake and energy expenditure of an individual, an equilibrium involving physical, genetic, biological, cultural, psycho-socioeconomic, and environmental factors. These factors may cause an insufficient or excessive consumption of nutrients or an inadequate diet 1. For evaluating NS, one of the strategies is to measure the dimensions and composition of the body, thus allowing for a quantitative assessment of the growth and development of children and adolescents 2.
The BMI was proposed by Adolphe Quetelet in 1835 to define and classify obesity, being adopted by the World Health Organization (WHO) for this purpose in 1986. It is closely linked to central obesity, which in turn is associated with alterations in blood lipid levels, hyperinsulinemia and CMR 3,4,5,6. However, the relationship between the BMI and central obesity, and between the latter and CMR can be overestimated. Therefore, the WHO now recommends a set of AIs for the classification of NS to make a more accurate comparison of the prevalence of overweight and obesity in distinct populations 3,4.
Since the BMI is not useful for determining the distribution of body fat or for differentiating adipose from other tissues (or essential body fat from storage body fat), it does not lend itself to assessing adiposity 7. Thus, physicians and epidemiologists have proposed diagnosing obesity with complementary body adipose indexes, such as the WC, WHpI, and WHtI 8. These AIs can be utilized as clinical tools for identifying the risk of metabolic disorders in children and adolescents, representing a non-invasive and economical method that is easy to apply in primary medical care. Indeed, the WHtI has been described as one of the most sensitive AIs for predicting CMR in children and adolescents 9.
Bioelectrical impedance, based on the resistance of tissues to the passage of electrical current, is also an easily applied and non-invasive technique. Through the analysis of bioelectrical impedance, it is possible to distinguish between total body water, body fat, and other tissues, thus allowing the instrument to calculate the BFP 10. This technique depends on some factors linked to the electrical properties of the body, such as the hydration level, age, gender, race, and physical condition 11.
In the State of Chiapas, Mexico, there are no reports, to our knowledge, on the NS and CMR of adolescents after being assessed with multiple AIs. Hence, the present study aimed to analyze the association of five AIs (BMI, WC, WHpI, WHtI, and BFP) in order to evaluate the NS and CMR of a group of Mexican adolescents.
MATERIAL AND METHODS
PARTICIPANTS
A cross-sectional study was carried out from August to December 2017 in three public high schools of Tuxtla Gutiérrez, State of Chiapas, Mexico. Contact was made with 970 adolescents in the first semester of high school, ranging in age from 15 to 17 years. The response rate was 94.6%, with 917 agreeing to take part. Of the participants, 488 were adolescent girls (52.9%) and 429 adolescent boys (47.1%). Informed consent was signed by the corresponding parents or guardians. The lack of desire for a student to get involved in the study was an exclusion criterion. The protocol was reviewed and approved by the National Academic Committee on Bioethics of Mexico, Chiapas Chapter (Comité de la Academia Nacional Mexicana de Bioética, Capítulo Chiapas).
CHARACTERIZATION OF NUTRITIONAL STATUS
Anthropometric measurements were taken by university students, who were in the last year of the Bachelor's program of nutrition. They were previously trained in the techniques recommended by Lohman 12. The participants were examined in an upright position, with shoes removed and in a state of exhalation. Procedures were carried out from 7 to 9 a.m. in the privacy of spaces assigned by the schools. The adolescents were instructed to wear light clothing and have evacuated the bladder. Additionally, they were asked to have fasted and refrained from consuming diuretics or doing exercise during the 12 hour before examination. Individuals were weighed on an electronic scale (Tanita®, model BC-533, Arlington Heights, Illinois, USA; precision, 100 g). Height was determined with an ultrasonic stadiometer (Inkids Inlab; precision, 1 mm). The WC and hip circumference were taken with a latex tape measure (Bodyfit, precision, 1 mm). The tape was placed at the height of the navel for the WC and the greater trochanters for the hip circumference.
The characterization of NS was made with the cut-off points of the BMI established by the WHO for adolescents: < -2 standard deviation (SD), underweight; -2 to +0.99 SD, normal weight; 1 to 1.99 SD, overweight; and > 2 SD, obesity 13. In the evaluation of WC, obesity was considered at values > 75th percentile (for males, 73.6-76.5 cm; for females, 73.0-74.1 cm) 14. The WHtI standard employed for diagnosing overweight and obesity was > 0.47 and > 0.50 for males while being > 0.48 and > 0.51 for females, respectively 15. Regarding the WHpI, the cut-off point for diagnosing obesity was set at > 0.80 for females and > 0.95 for males 16. Moreover, a value ≥ 0.55 for the weight/height index was the criterion for estimating CMR 9. Finally, BFP was measured by bioimpedance, classifying the adolescents by percentiles based on gender: low body fat (P3), healthy (P10-P75), high body fat (P90) and obesity (P97) 17,18.
STATISTICAL ANALYSIS
The calculation was made of central tendency, location, and distribution. The Kolmogorov-Smirnov test was used to verify the normal distribution of quantitative variables (p > 0.05). Since the majority did not fit a normal distribution, an analysis was performed with non-parametric statistics. The Mann-Whitney U test was employed to compare the average values of the distinct AIs. With the Chi-squared test, an examination was made of the association between gender and the prevalence of overweight and obesity shown by each of the parameters, and to establish the correlation between obesity and CMR. In all cases, a significant difference was considered at p < 0.05. Finally, to explore the relationship between variables, the Pearson correlation coefficient ® was computed, and dispersion graphs were constructed. The r-value > 0.8 was regarded as significant. The quantitative relation of the distinct AIs was calculated with the coefficient of determination (R2). All statistical analyses were performed on the Statistical Package for Social Science® software, version 22 (SPSS; Chicago, IL, USA).
RESULTS
For the participating students, the average age was 15.58 ± 0.6 years, weight 60.5 ± 12.9 kg, height 161 ± 8.3 cm, WC 77.4 ± 9.7 cm, and hip circumference 95.7 ± 8.8 cm. The average values were significantly higher in males for weight, height, WC, and WHpI, and in females for BMI, WHtI, and BFP (Table I).
Table I. Comparison of the anthropometric variables of the study participants by gender
Data are expressed as the mean (X) ± standard deviation (σ) of weight in kg, height in cm, waist circumference (WC) in cm, body mass index (BMI), waist/hip index (WHpI), waist/height index (WHtI) and the body fat percentage (BFP), using the Mann-Whitney U test (males vs females; z-score).
*p < 0.05.
Among the five AIs used (Table II), the WHtI showed the highest prevalence of obesity (31%), followed by the WHpI (25.1%), BFP (21%) and WC (18.3%). Surprisingly, the BMI exhibited the lowest level of obesity (7%). The NS was gender-dependent for four of the five AIs (WC, BMI, WHpI, and BFP), according to the Chi-squared test.
Overweight was diagnosed in more females than males, based on the BMI (24% vs 20%) and the WHtI (19% vs 12%). Likewise, obesity was also more prevalent among the females, judging by the BMI, WHpI, and WHtI (Table II). For males versus females, on the other hand, the BMI demonstrated a highest percentage of underweight (15.2% vs 8.2%) and low body fat (8.6% vs 0.6%) (Fig. 1).
Table II. Comparison of the nutritional status of study participants by gender, according to five anthropometric indicators
Data show the partial number (n) or total number (N) of adolescents grouped in a classification of nutritional status (NS) by gender (females ♀ or males ♂). The NS is diagnosed by five anthropometric indicators (AIs). WC: waist circumference; BMI: body mass index; WHpI: waist/hip index; WHtI: waist/height index; BFP: body fat percentage. Chi-squared test (χ2).
*p < 0.05.
Figure 1. Analysis of the correlation of diverse anthropometric indicators by the Pearson correlation coefficient ® and the coefficient of determination (R2)
A significant and positive relation was found, according to the Pearson correlation coefficient and the coefficient of determination between the BMI and WHtI (r = 0.917, R2 = 0.840; p < 0.05), the BMI and WC (r = 0.889, R2 = 0.790; p < 0.05), the BFP and WHtI (r = 0.917, R2 = 0.840; p < 0.05), and the BFP and WC (r = 0.889, R2 = 0.790; p < 0.05). A weak association existed between the BFP and BMI (r = 0.775, R2 = 0.600; p < 0.05, data not graphed), while no significant relation existed between the BMI and WHpI (r = 0.390, R2 = 0.152; p > 0.05) or the BFP and WHpI (r = 0.390; R2 = 0.152; p > 0.05). Finally, each of the five AIs showed a significant association between NS and the estimate of CMR (Table III).
Table III. Relationship of nutritional status with cardiometabolic risk
The diagnosis of nutritional status (NS) is given for each anthropometric indicator (AI), along with the association of this parameter to cardiometabolic risk. WC: waist circumference; BMI: body mass index; WHpI: waist/hip index; WHtI: waist/height index; BFP: body fat percentage. Chi-squared test (χ2).
*p < 0.05.
DISCUSSION
There was a significant gender difference in the average values of each of the AIs, especially the BFP, height and weight. In previous reports, a significantly higher BFP has been documented for women versus men 19,11, probably due in large part to the distinct distribution of body fat in the two genders 20. Furthermore, three of the five AIs considered presently revealed a higher percentage of obesity in females versus males (the BMI, WHpI, and WHtI). The WHpI depicted the higher difference in obesity between females and males. This evaluation is based on the level of intra-abdominal fat, which coincides with gynecoid-type obesity 20.
Among the five AIs used herein, BMI reflected the lowest level of obesity, in agreement with previous studies that found this parameter able to estimate body composition but unable to correctly assesses obesity 21,22. This disadvantage of the BMI could owe itself to multiple factors, such as the influence of race and gender on its value in young people 23,24, as well as its inability to distinguish storage adipose tissue from essential adipose tissue and other lean body mass 25.
Compared to the WHpI and WHtI, the BMI is at a relative disadvantage in its ability to distinguish central from gynecoid obesity 15,26.
The prevalence of obesity calculated by the WC (18.3%), WHpI (25.1%) and BFP (21%) was like that existing in the total sample. It may be due in part to the ability of these variables to indirectly measure central adiposity 27 and estimate CMR 28,29,30. Additionally, the WHtI demonstrated a higher capacity to detect obesity (31%) than the other AIs, perhaps because of its direct connection with the WC, which in turn indirectly determines the quantity of abdominal fat 21,26.
On the other hand, the substantial percentage of adolescents at low weight (11%) in both genders is alarming, since underweight is known to be linked to cognitive alterations 31 and susceptibility to infections 32.
A strong association was established between the BMI and WHtI, as well as between the BFP and WC. These results are congruent with the reports by Beck on the efficacy of the WC and WHtI for predicting high blood pressure in children and adolescents 33,34. Moreover, each of the five AIs included in the current contribution showed a significant correlation between NS and CMR.
The disadvantage of the present parameters is their incapacity to distinguish gynecoid from central obesity or detect a higher distribution of body fat at the thoracic level. Furthermore, it is indispensable to consider height as a possible factor of bias when comparing data on obesity between populations of European origin and those in Latin America. This factor may skew data when diagnosing obesity with the WHtI, the BMI, o both 35.
In the present cross-sectional study, consideration should be given to some factors that may have influenced the results and their interpretation, such as the lack of an inferential sample. Moreover, there are no published tables with the percentiles of the key AIs related to overweight and obesity for the population of the state of Chiapas, a deficiency that should be considered in the design of future research. A set of anthropometric criteria for establishing the prevalence of NS and CMR needs to be developed and standardized.
In conclusion, the WHtI represents an adequate tool for the diagnosis of obesity associated with CMR in adolescents, compared to the other AIs presently studied. It is important to continue investigating the efficacy of estimating CMR with AIs, some of which could be effectively combined with the gold standards, especially the lipid profile and fasting glucose.
