Predicting insulin resistance in children: anthropometric and metabolic indicators.

OBJECTIVE: To predict insulin resistance in children based on anthropometric and metabolic indicators by analyzing the sensitivity and specificity of different cutoff points. METHODS: A cross-sectional study was carried out of 109 children aged 7 to 11 years, 55 of whom were obese, 23 overweight and 31 well-nourished, classified by body mass index (BMI) for age. Measurements were taken to determine BMI, waist and hips circumferences, waist circumference/hip circumference ratio, conicity index and body fat percentage (dual emission X-ray absorptiometry). Fasting blood samples were taken to measure triglyceridemia, glycemia and insulinemia. Insulin resistance was evaluated by the glycemic homeostasis method, taking the 90th percentile as the cutoff point. Receiver operating characteristic curves were analyzed to a 95% confidence interval in order to identify predictors of glycemic homeostasis, and sensitivity and specificity were then calculated. RESULTS: After analysis of the area under the receiver operating characteristic curve (confidence interval), indicators that demonstrated the power to predict insulin resistance were, in the following order: insulinemia = 0.99 (0.99-1.00), 18.7 microU mL(-1); body fat percentage = 0.88 (0.81-0.95), 41.3%; BMI = 0.90 (0.83-0.97), 23.69 kg m(2-(1)); waist circumference= 0.88 (0.79-0.96), 78.0 cm; glycemia = 0.71 (0.54-0.88), 88.0 mg dL(-1); triglyceridemia = 0.78 (0.66-0.90), 116.0 mg dL(-1) and conicity index = 0.69 (0.50-0.87), 1.23 for the whole sample; and were: insulinemia = 0.99 (0.98-1.00), 19.54 microU mL(-1); body fat percentage = 0.76 (0.64-0.89), 42.2%; BMI = 0.78 (0.64-0.92), 24.53 kg m(2-(1)); waist circumference = 0.77 (0.61-0.92), 79.0 cm and triglyceridemia = 0.72 (0.56-0.87), 127.0 mg dL(-1), for the obese subgroup. CONCLUSIONS: Anthropometric and metabolic indicators appear to offer good predictive power for insulin resistance in children between 7 and 11 years old, employing the cutoff points with the best balance between sensitivity and specificity of the predictive technique.

Mental Fatigue Alters Cortical Activation and Psychological Responses, Impairing Performance in a Distance-Based Cycling Trial.

Purpose: We sought to verify if alterations in prefrontal cortex (PFC) activation and psychological responses would play along with impairments in pacing and performance of mentally fatigued cyclists. Materials and Methods: Eight recreational cyclists performed two preliminary sessions to familiarize them with the rapid visual information processing (RVP) test, psychological scales and 20 km cycling time trial (TT20km) (session 1), as well as to perform a VO2MAX test (session 2). Thereafter, they performed a TT20km either after a RVP test (30 min) or a time-matched rest control session (session 3 and 4 in counterbalanced order). Performance and psychological responses were obtained throughout the TT20km while PFC electroencephalography (EEG) was obtained at 10 and 20 km of the TT20km and throughout the RVP test. Increases in EEG theta band power indicated a mental fatigue condition. Repeated-measures mixed models design and post-hoc effect size (ES) were used in comparisons. Results: Cyclists completed the trial ~2.7% slower in mental fatigue (34.3 ± 1.3 min) than in control (33.4 ± 1.1 min, p = 0.02, very large ES), with a lower WMEAN (224.5 ± 17.9 W vs. 240.2 ± 20.9 W, respectively; p = 0.03; extremely large ES). There was a higher EEG theta band power during RVP test (p = 0.03; extremely large ES), which remained during the TT20km (p = 0.01; extremely large ES). RPE increased steeper in mental fatigue than in control, together with isolated reductions in motivation at 2th km (p = 0.04; extremely large ES), felt arousal at the 2nd and 4th km (p = 0.01; extremely large ES), and associative thoughts to exercise at the 6th and 16th km (p = 0.02; extremely large ES) of the TT20km.Conclusions: Mentally fatigued recreational cyclists showed impaired performance, altered PFC activation and faster increase in RPE during a TT20km.

Genomic ancestry, self-reported "color" and quantitative measures of skin pigmentation in Brazilian admixed siblings.

A current concern in genetic epidemiology studies in admixed populations is that population stratification can lead to spurious results. The Brazilian census classifies individuals according to self-reported "color", but several studies have demonstrated that stratifying according to "color" is not a useful strategy to control for population structure, due to the dissociation between self-reported "color" and genomic ancestry. We report the results of a study in a group of Brazilian siblings in which we measured skin pigmentation using a reflectometer, and estimated genomic ancestry using 21 Ancestry Informative Markers (AIMs). Self-reported "color", according to the Brazilian census, was also available for each participant. This made it possible to evaluate the relationship between self-reported "color" and skin pigmentation, self-reported "color" and genomic ancestry, and skin pigmentation and genomic ancestry. We observed that, although there were significant differences between the three "color" groups in genomic ancestry and skin pigmentation, there was considerable dispersion within each group and substantial overlap between groups. We also saw that there was no good agreement between the "color" categories reported by each member of the sibling pair: 30 out of 86 sibling pairs reported different "color", and in some cases, the sibling reporting the darker "color" category had lighter skin pigmentation. Socioeconomic status was significantly associated with self-reported "color" and genomic ancestry in this sample. This and other studies show that subjective classifications based on self-reported "color", such as the one that is used in the Brazilian census, are inadequate to describe the population structure present in recently admixed populations. Finally, we observed that one of the AIMs included in the panel (rs1426654), which is located in the known pigmentation gene SLC24A5, was strongly associated with skin pigmentation in this sample.

Comparação entre o critério do CDC e outros indicativos de gordura corporal para avaliação do estado nutricional / Comparison between CDC criteria and other body fat indexes to assess nutritional status

Introdução: A obesidade está aumentando de forma alarmante sendo considerada uma verdadeira epidemia mundial; atinge todas as faixas etárias, especialmente as crianças. Nesse sentido, a utilização de métodos que sejam confiáveis e de fácil aplicação para o diagnostico da obesidade em crianças, torna-se essencial para a adoção de medidas necessárias. Objetivo: Comparar os indicadores de gordura corporal nas diferentes classificações nutricionais através do protocolo utilizado pelo CDC e verificar a associação entre estes indicadores. Metodologia: Foram avaliadas 1550 crianças (776 meninos e 774 meninas) com idades entre 7 e11 anos estudantes do ensino fundamental da cidade satélite de Taguatinga – DF. Para a classificação nutricional foi utilizado o Índice de Massa Corporal (IMC) baseado nos critérios de corte do Center For Disease Control and Prevention (CDC). Os outros indicadores de gordura corporal utilizados foram: as circunferências da cintura (CC) e do quadril (CQ) e a sua relação (RCQ); somatório de dobras cutâneas(SDCtrpa) e o percentual de gordura corporal (%GC). Para as analises estatísticas foram utilizados o teste t para amostras independentes, One way ANOVA, teste de Chi-quadrado (c²) e correlação de Pearson (p d” 0,05). Resultados: Foi encontrada maior prevalência de obesidade nos meninos (c² = 8,383; (1): p = 0,003). As medidas do IMC, CC, CQ, SDCtrpa e %GC apresentaram diferenças significativas para as quatro classificações nutricionais, enquanto que o RCQ só foi estatisticamente diferente entre o sobrepeso e obeso. Além disso, todos os indicadores apresentaram correlações significativas entre si. Conclusão: Tanto o IMC quanto os outros indicadores de gordura corporal apresentaram comportamento semelhantes nas diferentes classificações nutricionais. A escolha do protocolo proposto pelo CDC é adequada para a estratificação das classificações nutricionais na população do presente estudo...

Introduction: Obesity has achieved high levels in recent years. This fact is alarming since anyone can become fatty person, including children. In this way, reliable and available methods which can be used to identify this worldwide epidemic are very important to prevent it. Objective: The aim of this study was to compare different methods to identify nutritional status using CDC protocol as standard method and to verify their association. Methodolgy: a total of 1550 children (776-boys and 774-girls) aged 7-11-years were evaluated. All of them were primary school students in Taguatinga - DF city. Body mass index (BMI) cut-off points from Center for Disease Control and Prevention (CDC) was used to classify their nutritional status. Other methods to measure body fat were: waist circumference (WC); hip circumference (HC); Waist-to-hipratio (WHR); skin-fold thickness some (SSFT) and body fat percentage (%BF). The tests used for statistics analysis were: independent sample t test, One Way ANOVA, Chi-square (c²) and Pearson’s correlation (p d”0.05). Results: Body fat prevalence was higher in boys than in girls (c² = 8,383; (1): p = 0,003). BMI, WC, HC,SSFT and %BF were statistical significant among all nutritional classification, while WHR was only different between overweight and obesity. Furthermore, all methods were statistically correlated between them.

Predição da resistência à insulina em crianças: indicadores antropométricos e metabólicos / Predicting insulin resistance in children: anthropometric and metabolic indicators

OBJETIVO: Predizer a resistência à insulina em crianças a partir de indicadores antropométricos e metabólicos por análise de sensibilidade e especificidade dos pontos de corte. MÉTODOS: Estudo transversal foi realizado em 109 crianças de 7 a 11 anos, sendo 55 obesas, 23 sobrepesadas e 31 eutróficas, classificadas pelo índice de massa corporal (IMC) para idade. Foram medidos IMC, circunferências da cintura e quadril, razão circunferência da cintura/circunferência do quadril, índice de conicidade e percentual de gordura (absortometria de raio X de dupla energia). Coleta sangüínea em jejum foi realizada para mensuração da trigliceridemia, glicemia e insulinemia. A resistência à insulina foi avaliada pelo método homeostase glicêmica, considerando-se o percentil 90 como ponto de corte. Na identificação dos preditores de homeostase glicêmica, foi adotada a análise das curvas receiver operating characteristic com intervalo de confiança de 95 por cento, calculando-se posteriormente a sensibilidade e especificidade. RESULTADOS: Os indicadores com poder de predição da resistência à insulina analisando a área sob a curva receiver operating characteristic (intervalo de confiança), com respectivos pontos de corte, foram, nesta ordem: insulinemia = 0,99 (0,99-1,00), 18,7 µU×mL-1; percentual de gordura = 0,88 (0,81-0,95), 41,3 por cento; IMC = 0,90 (0,83-0,97), 23,69 kg×m²-¹; circunferência da cintura = 0,88 (0,79-0,96), 78,0 cm; glicemia = 0,71 (0,54-0,88), 88,0 mg×dL-1; trigliceridemia = 0,78 (0,66-0,90), 116,0 mg×dL-1 e índice de conicidade = 0,69 (0,50-0,87), 1,23 para amostra total; e insulinemia = 0,99 (0,98-1,00), 19,54 µU×mL-1; percentual de gordura = 0,76 (0,64-0,89), 42,2 por cento; IMC = 0,78 (0,64-0,92), 24,53 kg×m²-¹; circunferência da cintura = 0,77 (0,61-0,92), 79,0 cm e trigliceridemia = 0,72 (0,56-0,87), 127,0 mg×dL-1 para os obesos. CONCLUSÕES: Indicadores antropométricos e metabólicos...

OBJECTIVE: To predict insulin resistance in children based on anthropometric and metabolic indicators by analyzing the sensitivity and specificity of different cutoff points. METHODS: A cross-sectional study was carried out of 109 children aged 7 to 11 years, 55 of whom were obese, 23 overweight and 31 well-nourished, classified by body mass index (BMI) for age. Measurements were taken to determine BMI, waist and hips circumferences, waist circumference/hip circumference ratio, conicity index and body fat percentage (dual emission X-ray absorptiometry). Fasting blood samples were taken to measure triglyceridemia, glycemia and insulinemia. Insulin resistance was evaluated by the glycemic homeostasis method, taking the 90th percentile as the cutoff point. Receiver operating characteristic curves were analyzed to a 95 percent confidence interval in order to identify predictors of glycemic homeostasis, and sensitivity and specificity were then calculated. RESULTS: After analysis of the area under the receiver operating characteristic curve (confidence interval), indicators that demonstrated the power to predict insulin resistance were, in the following order: insulinemia = 0.99 (0.99-1.00), 18.7 µU×mL-1; body fat percentage = 0.88 (0.81-0.95), 41.3 percent; BMI = 0.90 (0.83-0.97), 23.69 kg×m2-¹; waist circumference= 0.88 (0.79-0.96), 78.0 cm; glycemia = 0.71 (0.54-0.88), 88.0 mg×dL-1; triglyceridemia = 0.78 (0.66-0.90), 116.0 mg×dL-1 and conicity index = 0.69 (0.50-0.87), 1.23 for the whole sample; and were: insulinemia = 0.99 (0.98-1.00), 19.54 µU×mL-1; body fat percentage = 0.76 (0.64-0.89), 42.2 percent; BMI = 0.78 (0.64-0.92), 24.53 kg×m2-¹; waist circumference = 0.77 (0.61-0.92), 79.0 cm and triglyceridemia = 0.72 (0.56-0.87), 127.0 mg×dL-1, for the obese subgroup. CONCLUSIONS: Anthropometric and metabolic indicators appear to offer good predictive power for insulin resistance in children...