Indicadores antropométricos na predição de síndrome metabólica em crianças e adolescentes: um estudo de base populacional

Predictive ability of anthropometric indices in identifying metabolic syndrome among US adolescents 10 to 19 years old: Analysis from the National Health and Nutrition Examination Survey 2011 to 2018 data set

OBJECTIVE

The aim of this study was to identify the best anthropometric indices for predicting metabolic syndrome in US adolescents.

METHODS

A cross-sectional study analyzed data of adolescents ages 10 to 19 y using the National Health and Nutrition Examination Survey 2011 to 2018 data. The receiver operating characteristic areas under the curve (AUCs) of waist circumference z score, body roundness index, body mass index, and A Body Shape Index in identifying predicting metabolic syndrome were assessed. Furthermore, sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios of all anthropometric indices were calculated.

RESULTS

A total of 5496 adolescents were included in the analysis. Waist circumference z score had an AUC of 0.90 (95% CI, 0.89-0.91), sensitivity of 95.0% (95% CI, 89.4-98.1), and specificity of 74.8% (95% CI, 73.6, 76.0). Body roundness index had an AUC of 0.88 (95% CI, 0.87-0.89), sensitivity of 96.7% (95% CI, 91.7-99.1), and specificity of 75.2% (95% CI, 74.1-76.4). Body mass index z score had an AUC of 0.83 (95% CI, 0.81-0.85), sensitivity of 97.5% (95% CI, 92.9-99.5), and specificity of 68.2% (95% CI, 66.9-69.4). A Body Shape Index had an AUC of 0.59 (95% CI, 0.56-0.61), sensitivity of 75.0% (95% CI, 66.3-82.5), and specificity of 50.9% (95% CI, 49.5-52.2).

CONCLUSIONS

Our study found waist circumference z score and body roundness index were the best predictors of predicting metabolic syndrome compared with body mass index z score and A Body Shape Index in both boys and girls. We recommend that future studies develop global cutoff points for these anthropometric indices and examine their performance in a multi-country setting.

Predictive Ability of Seven Anthropometric Indices for Cardiovascular Risk Markers and Metabolic Syndrome in Adolescents

PURPOSE

The aim of the study was to evaluate the reliability of 7 anthropometric indices in predicting cardiovascular risk markers (CRMs) and metabolic syndrome (MetS) in Brazilian adolescents.

METHODS

A cross-sectional study conducted with 1,069 participants of the Cardiovascular Risk in Adolescents Study aged 12-17 years. Receiver operating characteristics curves were plotted, and area under curve (AUC) was calculated for body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHR), conicity index (CoI), body shape index (BSI), adjusted BSI for adolescents (adjusted BSI), and body roundness index (BRI).

RESULTS

In girls, reliability of BMI, WC, WHR, and BRI was sufficient (≥.6 AUC <.7) only to predict high blood pressure. Among boys, reliability of BMI, WC, WHR, BRI, and adjusted BSI ranged from good to sufficient (≥6 AUC <.8) to predict insulin resistance and high blood pressure, but poor to sufficient (≥.5 AUC <.7) for high total cholesterol, triglycerides, and low-density lipoprotein. For both sexes, CoI and BSI presented AUC ≤.5 for all CRM. A majority of the anthropometric indices showed AUC ≥.9 for MetS.

CONCLUSION

Reliability of CoI, BSI, adjusted BSI, and BRI is not superior to BMI, WC, and WHR in predicting CRM and MetS. All the anthropometric indices had excellent predictive capacity for MetS, but limited accuracy for CRM. Among the evaluated indices, we recommend the use of cutoff point WHR ≥.55 to screening MetS in girls and boys because of its easy to measure and interpretation.

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

BACKGROUND

Excess body fat has been growing alarmingly among adolescents, especially in low income and middle income countries where access to health services is scarce. Currently, the main method for assessing overweight in adolescents is the body mass index, but its use is criticized for its low sensitivity and high specificity, which may lead to a late diagnosis of comorbidities associated with excess body fat, such as cardiovascular diseases. Thus, the aim of this study was to develop a computational model using linear regression to predict obesity in adolescents and compare it with commonly used anthropometric methods. To improve the performance of our model, we estimated the percentage of fat and then classified the nutritional status of these adolescents.

METHODS

The model was developed using easily measurable socio-demographic and clinical variables from a database of 772 adolescents of both genders, aged 10-19 years. The predictive performance was evaluated by the following metrics: accuracy, sensitivity, specificity, and area under ROC curve. The performance of the method was compared to the anthropometric parameters: body mass index and waist-to-height ratio.

RESULTS

Our model showed a high correlation (R = 0.80) with the body fat percentage value obtained through bioimpedance. In addition, regarding discrimination, our model obtained better results compared to BMI and WHtR: AUROC = 0.80, 0.64, and 0.55, respectively. It also presented a high sensitivity of 92% and low false negative rate (6%), while BMI and WHtR showed low sensitivity (27% and 9.9%) and a high false negative rate (65% and 53%), respectively.

CONCLUSIONS

The computational model of this study obtained a better performance in the evaluation of excess body fat in adolescents, compared to the usual anthropometric indicators presenting itself as a low cost alternative for screening obesity in adolescents living in Brazilian regions where financial resources are scarce.

INDICADORES ANTROPOMÉTRICOS COMO PREDITORES DA SÍNDROME METABÓLICA EM ADOLESCENTES

RESUMO Objetivo: predizer a síndrome metabólica em adolescentes a partir de indicadores antropométricos por análise de sensibilidade e especificidade dos pontos de corte. Método: estudo transversal e descritivo em uma amostra estratificada de 716 adolescentes de 10 a 19 anos selecionados por amostragem aleatória simples em 30 escolas públicas municipais e estaduais localizadas em Picos, Piauí, na Região Nordeste do Brasil, a coleta de dados ocorreu no período de agosto a dezembro de 2014 e março de 2015. Investigaram-se os fatores índice de massa corporal e índice de conicidade. A síndrome metabólica foi identificada a partir dos critérios adaptados para a idade. Resultados: observou-se a prevalência de estudantes do sexo feminino 61,3%, a prevalência de síndrome metabólica foi de 3,2%. A área sobre a curva de Características de Operação do Receptor mostrou que o índice de massa corporal e o índice de conicidade se mostraram eficientes para a predição de síndrome metabólica tanto na amostra total, quanto na estratificação por sexo. Conclusão: os indicadores antropométricos índice de massa corporal e índice de conicidade foram bons preditores da síndrome metabólica nessa população.

ANTHROPOMETRY AND CLUSTERED CARDIOMETABOLIC RISK FACTORS IN YOUNG PEOPLE: A SYSTEMATIC REVIEW

OBJECTIVE

To conduct a systematic review of the literature on the ability of anthropometric indicators to predict clustered cardiometabolic risk factors (CMRF) in children and adolescents.

DATA SOURCE

Studies published from June 1st, 2011 to May 31st, 2016 in the PubMed, SciELO and LILACS databases were analyzed. The research was based on keywords derived from the terms "anthropometric indicators" AND "cardiometabolic risk factors". Observational studies on the ability of anthropometric indicators as predictors of clustered CMRF in children and adolescents in Portuguese, English and Spanish languages were included. Studies with a specific group of obese patients or with other diseases were not included.

DATA SYNTHESIS

Of the 2,755 articles retrieved, 31 were selected for systematic review. Twenty-eight studies analyzed body mass index (BMI) as a predictor of clustered CMRF. Only 3 of the 25 cross-sectional studies found no association between anthropometric indicators and clustered CMRF. The results of six studies that compared the predictive ability of different anthropometric measures for clustered CMRF were divergent, and it was not possible to define a single indicator as the best predictor of clustered CMRF. Only six articles were cohort studies, and the findings suggested that changes in adiposity during childhood predict alterations in the clustered CMRF in adolescence.

CONCLUSIONS

BMI, waist circumference and waist-to-height ratio were predictors of clustered CMRF in childhood and adolescence and exhibited a similar predictive ability for these outcomes. These findings suggest anthropometric indicators as an interesting screening tool of clustered CMRF at early ages.

[Anthropometric indices to identify metabolic syndrome and hypertriglyceridemic waist phenotype: a comparison between the three stages of adolescence]

OBJECTIVE:

To determine the prevalence of metabolic syndrome (MS) and the hypertriglyceridemic waist phenotype (HW) in a representative adolescent sample; as well as to establish which anthropometric indicator better identifies MS and HW, according to gender and adolescent age.

METHODS:

This cross sectional study had the participation of 800 adolescents (414 girls) from 10-19 years old. Anthropometric indicators (body mass index, waist perimeter, waist/stature ratio, waist/hip ratio, and central/peripheral skinfolds) were determined by standard protocols. For diagnosis of MS, the criteria proposed by de Ferranti et al. (2004) were used. HW was defined by the simultaneous presence of increased waist perimeter (>75th percentile for age and sex) and high triglycerides (>100 mg/dL). The ability of anthropometric indicators was evaluated by Receiver Operating Characteristic curve.

RESULTS:

The prevalence of MS was identical to HW (6.4%), without differences between genders and the adolescence phases. The waist perimeter showed higher area under the curve for the diagnosis of MS, except for boys with 17-19 years old, for whom the waist/stature ratio exhibited better performance. For diagnosing HW, waist perimeter also showed higher area under the curve, except for boys in initial and final phases, in which the waist/stature ratio obtained larger area under the curve. The central/peripheral skinfolds had the lowest area under the curve for the presence of both MS and HW phenotype.

CONCLUSIONS:

The waist perimeter and the waist/stature showed a better performance to identify MS and HW in both genders and in all three phases of adolescence.