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Introduction: Introduction: perceived body weight refers to the subjectively assessed weight, which may not correspond to the objectively measured weight. Statistics show that 14 % to 83 % of parents misperceive their children's weight status, with a propensity for underestimation. Objective: we compared the accuracy of the visual versus the categorical method. We also identified factors and feeding practices associated with excess weight underestimation. Material and methods: we carried out a cross-sectional study in five states of Mexico with 1,845 mother-child dyads of children aged 2-12 years. The mothers were interviewed about weight perception with two methods, visual and categorical. The Child Feeding Questionnaire identified maternal feeding practices. Actual weight and height were categorized according to WHO criteria. Analysis consisted of Cohen's kappa estimation, multivariate logistic regression, and Mann-Whitney tests. Results: more mothers correctly identified the weight of their children with the categorical than with the visual method (68 % vs 42 %, p < 0.0001). The excess weight underestimation was lower (49 % vs 82 %, p < 0.0001) and the degree of agreement was higher with the categorical method (kappa, 0.39 and 0.08). The better results remained regardless of age. Age 2-5 years increased the odds of overweight/obesity underestimation. Feeding practices differed by weight perception category, child's age, and method of assessment. Conclusions: the categorical method was more accurate. Recognition of correct weight perception is one of the first actions required for controlling childhood overweight/obesity.

Introducción: Introducción: la percepción del peso corporal se refiere al peso evaluado subjetivamente, que puede no corresponder al peso medido objetivamente. Las estadísticas muestran que entre el 14 % y el 83 % de los padres tienen una percepción inadecuada del estado del peso de sus hijos, predominando la subestimación. Objetivo: comparar la precisión del método categórico y la del visual e identificar los factores y prácticas de alimentación asociados a la subestimación del exceso de peso. Material y métodos: se realizó un estudio transversal en cinco estados de México con 1,845 díadas madre-hijo de niños de 2-11 años. Las madres fueron entrevistadas sobre su percepción del peso con un método visual y uno categórico. El Cuestionario de Alimentación Infantil identificó las prácticas de alimentación. El peso y la altura se clasificaron con los criterios de la OMS. Se estimó la kappa de Cohen y se utilizaron la regresión logística multivariada y las pruebas de Mann-Whitney. Resultados: más madres identificaron correctamente el peso de sus hijos con el método categórico que con el visual (68 % vs. 42 %, p < 0,0001). La subestimación del exceso de peso fue menor (49 % vs 82 %, p <0,0001) y el grado de concordancia mayor con el método categórico (kappa: 0,39 y 0,08). Los resultados se mantuvieron independientemente de la edad. La edad de 2-5 años aumentó la probabilidad de subestimar el sobrepeso/obesidad. Las prácticas de alimentación difirieron por categoría de percepción del peso, edad del niño y método de evaluación. Conclusiones: el método categórico fue más preciso. El reconocimiento de la correcta percepción del peso constituye una de las primeras acciones para el control del sobrepeso/obesidad infantil.

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OBJECTIVE: The aim of this study was to explore the relationship between body frame size (BFS) and body image, self-esteem, and health-related quality of life (HRQL) in Mexican schoolchildren. METHODS: This cross-sectional study included children aged 6 to 11 years. Body image, self-esteem, and HRQL were evaluated through interviews. Two frame-size measures, biacromial and bitrochanteric diameters, were collected and summed for categorizing BFS as small, medium, or large. Height and weight were also measured. Spearman's correlations were determined and adjusted by sex, age, and body mass index (BMI). Multiple logistic regression analyses were performed with the psychological measure as the binary dependent variable, the categories of BFS as the independent variable, and sex, age, and BMI as control variables. RESULTS: The correlation between BFS and body image was 0.15 (P < .01) and after BMI adjustment was 0.07 (P > .05). BFS did not correlate with self-esteem nor HRQL (P > .05). Of the children, 79% were dissatisfied with their body image, 20% had a low self-esteem, and 31.8% had a poorly perceived HRQL; there were no differences by BFS. The multivariate analysis showed that a large BFS was not associated with body image dissatisfaction (OR 1.2, 95% CI 0.6-2.3), low self-esteem (OR 1.3, 95% CI 0.7-2.6), or poor HRQL (OR 1.3, 95% CI 0.8-2.2). CONCLUSIONS: BFS was not correlated with body image, self-esteem, or HRQL. A high self-esteem and a good level of HRQL prevailed, but a high proportion of children were dissatisfied with their body image. School interventions should promote an appropriate body image and a healthy lifestyle.

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OBJECTIVES: The main aim of this study was to test the hypothesis that body frame size is related to the amount of fat in different adipose tissue depots and to fat distribution in schoolchildren. METHODS: Children aged between 5 and 10 years were included in this cross-sectional study (n = 565). Body frame size, adiposity markers (anthropometric, skinfolds thickness, and ultrasound measures), and fat distribution indices were analyzed. Correlation coefficients adjusted by reliability were estimated and analyzed by sex; the significance of the difference between two correlation coefficients was assessed using the Fisher z-transformation. RESULTS: The sample included primarily urban children; 58.6% were normal weight, 16.1% overweight, 19.6% obese, and the rest were underweight. Markers of subcutaneous adiposity, fat mass and fat-free mass, and preperitoneal adiposity showed higher and significant correlations with the sum of the biacromial + bitrochanteric diameter than with the elbow diameter, regardless of sex. The fat distribution conicity index presented significant but weak correlations; and visceral adipose tissue, hepatic steatosis, and the waist-for-hip ratio were not significantly correlated with body frame size measures. CONCLUSIONS: Body frame size in school children was related to the amount of adipose tissue in different depots, but not adipose distribution. More studies are needed to confirm this relationship and its importance to predict changes in visceral fat deposition during growth.

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OBJECTIVE: Determining the prevalence and estimating the risk of obesity for dyslipidemia and hyperinsulinemia in adolescents. The existence of a linear association betweenanthropometric measures, lipids and insulin was also evaluated. MATERIAL AND METHODS: A comparative study was carried out amongst obese (body mass index=BMI >95th percentile for age and gender; n=120) and non-obese adolescents (BMI <85th percentile for age and sex; n=120) aged 10-19. A structured questionnaire was used for collecting anthropometric and demographic data. Glucose, insulin and lipid profiles were obtained for each adolescent. RESULTS: Prevalence of at least one dyslipidemia was 56,6 % among obese adolescents and 20,8 % amongst non-obese ones (p<.001). The former registered 50 % prevalence of hyperinsulinemia, the latter 4 % (p<.001). Obesity increased hyperinsulinemia risk having a 23 odds ratio (8.3-68.9 95 % CI) and for at least one dyslipidemia (OR=5,0; 2,7-9,2 95 % CI). Insulin level significantly correlated with BMI (r=0,57), triglycerides (r=0,57), VLDL (r=0,57), HDL (-0,37), waist-hip circumference index (r=0,29), cholesterol (r=0,22), and LDL (r=0,13). CONCLUSIONS: Obesity can be considered to be a risk factor for developing metabolic disorders in adolescents. In fact, there was a linear relationship between anthropometric measurement, lipids and insulin. Prevention should focus on improving predisposing environments for obesity amongst families having children and teenagers. Emphasising life-styles and healthy behaviour is essential, as well as training and treatment options for complete care of individuals in this age-group.

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Objetivo: Determinar la prevalencia y estimar el riesgo de obesidad para dislipidemias e hiperinsulinemia en adolescentes. Asimismo, evaluar la asociación lineal de medidas antropométricas y lípidos con insulina. Material y Métodos: Se realizó un estudio transversal comparativo entre adolescentes obesos (índice de masa corporal=IMC>percentil 95 para edad y sexo; n=120) y no obesos (IMC< percentil 85 para edad y sexo; n=120) de 10-19 años. Se aplicó una encuesta estructurada con datos sociodemográficos y antropométricos y se determinaron glucosa, insulina y perfil de lípidos. Resultados: La prevalencia de una o más dislipidemias fue de 56,6 por ciento en adolescentes c/obesidad, en comparación con 20,8 por ciento en adolescentes s/obesidad (p<.001). La hiperinsulinemia se presentó en el 50 por ciento del primer grupo mientras que en el segundo, en 4 por ciento (p<.001). La obesidad incrementó el riesgo de hiperinsulinemia con una razón de momios de (RM) de 23 (IC 95 por ciento: 8,3-68,9) y de por lo menos una dislipidemia (RM=5,0; IC95 por ciento: 2,7-9,2). El nivel de insulina se correlacionó significativamente con IMC (r=0,57), triglicéridos (r=0,57), VLDL (r=0,57), HDL (-0,37), relación cintura cadera (r=0,29), colesterol (r=0,22), y LDL (r=0,13). Conclusiones: Se evidenció a la obesidad en adolescentes como factor predisponente para el desarrollo de trastornos metabólicos y la asociación lineal de medidas antropométricas y lípidos con insulina.

Objective: Determining the prevalence and estimating the risk of obesity for dyslipidemia and hyperinsulinemia in adolescents. The existence of a linear association betweenanthropometric measures, lipids and insulin was also evaluated. Material and Methods: A comparative study was carried out amongst obese (body mass index=BMI >95th percentile for age and gender; n=120) and non-obese adolescents (BMI <85th percentile for age and sex; n=120) aged 10-19. A structured questionnaire was used for collecting anthropometric and demographic data. Glucose, insulin and lipid profiles were obtained for each adolescent. Results: Prevalence of at least one dyslipidemia was 56,6 percent among obese adolescents and 20,8 percent amongst non-obese ones (p<.001). The former registered 50 percent prevalence of hyperinsulinemia, the latter 4 percent (p<.001). Obesity increased hyperinsulinemia risk having a 23 odds ratio (8.3-68.9 95 percent CI) and for at least one dyslipidemia (OR=5,0; 2,7-9,2 95 percent CI). Insulin level significantly correlated with BMI (r=0,57), triglycerides (r=0,57), VLDL (r=0,57), HDL (-0,37), waist-hip circumference index (r=0,29), cholesterol (r=0,22), and LDL (r=0,13). Conclusions: Obesity can be considered to be a risk factor for developing metabolic disorders in adolescents. In fact, there was a linear relationship between anthropometric measurement, lipids and insulin. Prevention should focus on improving predisposing environments for obesity amongst families having children and teenagers. Emphasising life-styles and healthy behaviour is essential, as well as training and treatment options for complete care of individuals in this age-group.

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OBJECTIVE: Evidence is provided to clinicians and decision makers on the validity of ADA and WHO tests based on NDDG criteria for gestational diabetes. MATERIALS AND METHODS: During 18 months, all pregnant women attending a University Hospital underwent a 50-g, 1-h NDDG GCT for universal screening (n = 1092). The following appointment consisted of a 75-g, 2-h GTT (WHO test), independently of the prior result. Women with an abnormal 50-g and/or an abnormal 75-g, received a 100-g, 3-h GTT; subjects with only one abnormal 3-h GTT value were not included in the accuracy analysis. Women whose diagnosis followed NDDG criteria received treatment. Obstetricians were not aware of ADA/WHO results. RESULTS: ADA sensitivity was 100% (95%CI 98.6, 100) and specificity, 98.1% (95%CI 97.6, 98.6), whereas WHO sensitivity was 57.6% (95%CI 55.9, 59.2) and specificity, 85.1% (95%CI 84.7, 85.6). ADA results remained similar independently of obesity and age, but performed better with a family history of diabetes. Accuracy of WHO improved under selective screening, still the false negative rate ranged 40-56%. CONCLUSIONS: We recommend replicating this validation process in other health service settings. Although WHO test is easier and less expensive than NDDG or ADA, caution is needed before deciding employment of such criteria in pregnant women.

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Objetivo. Determinar la magnitud y jerarquizar la necesidad de salud satisfecha del diabético tipo 2, usuario del primer nivel de atención. Material y métodos. Se realizó un estudio transversal comparativo con representatividad rural y urbana, en el Instituto Mexicano del Seguro Social, en Nuevo León, en 1999. Fueron entrevistados, al azar, 256 diabéticos tipo 2 con dos o más años de diagnóstico. La información fue complementada con el expediente clínico y encuesta al proveedor. Se evaluaron cinco áreas y cuatro determinantes de salud, con base en estándares de la Norma Oficial Mexicana y la Asociación Americana de Diabetes. El plan de análisis consistió en estadística descriptiva y estimación de puntajes z. Resultados. Se registró una media de satisfacción global de necesidades de salud de 48.8 por ciento. En la zona rural la media de satisfacción fue menor que en la urbana (36.8 por ciento vs 53.3 por ciento, p<.01). El área de la salud nutricional resultó ser la más afectada (puntaje z=-6), seguida por la conductual de práctica de ejercicio (puntaje z=-1), la metabólica (puntaje z=+1), la conductual de no tabaquismo (puntaje z=+2), la prevención y detección oportuna de compli-caciones (puntaje z=+2), y la cognoscitiva (puntaje z=+3). El determinante de salud con mayor requerimiento fue la utilización (puntaje z=-5), seguido por disponibilidad (puntaje z=-4), necesidad sentida (puntaje z= +4) y barreras al acceso (puntaje z= +6). Conclusiones. La medición de las necesidades de salud permite la evaluación del impacto de intervenciones vigentes, así como la identificación de rubros con mayor necesidad o requerimiento de salud, fortaleciendo así el análisis y la toma de decisiones orientadas a encontrar opciones específicas de solución. El texto completo en inglés de este artículo está disponible en: http://www.insp.mx/salud/index.html

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