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

Abstract

Objetivos: avaliar a capacidade dos indicadores antropométricos e pontos de corte na predição da síndrome metabólica (SM) em crianças e adolescentes. Métodos: estudo transversal, envolvendo amostra probabilística de 879 crianças e adolescentes de ambos os sexos. Dados metabólicos, antropométricos e sociodemográficos foram coletados. Para diagnóstico da SM, foi utilizada a definição modificada do National Cholesterol Education Program's Adult Treatment Panel III (NCEP-ATP III). A capacidade dos indicadores antropométricos na predição do SM foi avaliada por meio da curva Receiver Operating Characteristic (ROC). Resultados: a prevalência de SM foi de 6,6%. Na predição da SM, a área sob a curva ROC foi de 0,79 (0,72; 0,85) para índice de massa corporal (IMC), de 0,79 (0,73; 0,85) para circunferência da cintura (CC) e de 0,83 (0,78; 0,89) para circunferência da cintura corrigida pela estatura (RCE). O ponto de corte identificado para RCE na predição da SM foi de 0,448 (ambos os gêneros). Conclusões: todos os indicadores antropométricos utilizados, com pequena superioridade da RCE, foram bons preditores da SM. O ponto de corte identificado para RCE na predição da SM aproxima-se daquele proposto por alguns autores como universal. Sugere-se o uso deste índice dado a sua simples operacionalidade em estudos clínicos e epidemiológicos como preditor da SM.

Indicadores Antropométricos Como Preditores Da Síndrome Metabólica Em Adolescentes

“…[4][5][6][7][8][9][10][11][12][13] The heterogeneity of definitions and cut off points for the MS components may explain, at least in part, the different prevalence rates reported in the literature, although the prevalence of this condition has increased both in developed and developing countries. 14 The profile of the adolescents reveals most were girls, which is in agreement with studies reporting that a female public is most frequently found in this investigations. [15][16][17] In regard to excess weight, a significant correlation was found between BMI, CI and MS.…”

“…This type of analysis not only enables the identification of the best cut off point but also provides the area under the curve that reveals an indicator's discriminating power for a given outcome. 22 In this study, the ROC curve showed that BMI and CI were good predictors of MS for the population under study.…”

“…These results show that body fat, especially around the abdominal area, is related to MS, as reported by other studies showing that abdominal fat is a superior measure when detecting cardiovascular risk and metabolic disorders among adolescents; thus, it is a good predictor of MS. [22][23] Note that an excessive increase in body fat, regardless of where it is stored, may disrupt the metabolism of carbohydrates and lipids, as well as lead to exaggerated production of factors that potentiate MS. 22 Excess weight is a factor that predisposes the emergence of insulin resistance and MS among adolescents, a condition that may persist into adulthood. Therefore, preventive measures intended to minimize and avoid the occurrence of cardiovascular diseases later in life are needed.…”

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

“…The body mass index, despite being the most used method in clinical practice and epidemiological studies to assess excess body fat [21,66], is widely criticized for not correlating with body composition and being a poor predictor of body fat [9,12,15,60,67]. In a recent review, it was observed that BMI has high specificity (approximately 92%) and low sensitivity (approximately 50 %) to detect obesity based on body fat percentage [68,69].…”

“…The most commonly used anthropometric indicator in the assessment of adolescent nutritional status is the body mass index (BMI) due to its low cost, simplicity, and high reproducibility. However, BMI has controversies regarding its efficiency, since it has low sensitivity in predicting excess body fat [12,13,14,15,16]. Individuals who have a high percentage of lean mass will have their total weight affected, and on BMI assessment, this individual will be mistakenly classified as obese.…”