ABSTRACT
El sobrepeso y la obesidad, representan una preocupación para las autoridades de salud, dado que se ha incrementado en los últimos años; particularmente en poblaciones adultas. Objetivo: Relacionar el porcentaje de grasa corporal con la circunferencia de cintura, el índice cintura/talla y el Índice de Masa Corporal, como indicadores de obesidad en sujetos con diagnóstico de hígado graso no alcohólico. Materiales y Métodos: estudio descriptivo, transversal en una muestra de 137 adultos, de 18 a 70 años. Se midieron las variables: Edad, Talla, Peso, Circunferencia de Cintura (CC), Índice de Masa Corporal (IMC), Índice Cintura/Talla (ICT), Índice peso/circunferencia de cintura (IPCC), Porcentaje de Grasa Corporal (%GC). Se determinaron promedios, desviación, correlaciones y prueba t de student. Resultados: Promedio: edad (47,06±13,71 años), peso (77,94±21,99 kg), talla (160,57±9,20 cm), IMC (30,21±7,92 kg/m2), CC (0,96±0,16 cm), ICT (0,60±0,10), IPCC (0,80±0,11), %GC (34,19±10,88). Según IMC, 34,3% sobrepeso y 39,4% obesos. Según CC, Riesgo elevado 19,0% y Riesgo muy elevado 55,5%. Según ICT, sobrepeso 10,9%, sobrepeso elevado 21,2% y obesidad 51,8%. Según IPCC, 54,0% en riesgo. Según %GC, 13,9% en límite y 52,5% obesidad. Alta correlación entre %GC e IMC (r = 0,85) y entre GC% y CC (r = 0,89). Conclusiones: el IMC es un buen indicador de obesidad, pero es importante conocer la cantidad de grasa del organismo. Se sugiere el %GC como complemento para diagnosticar obesidad; además al agregar CC, como indicador de grasa abdominal, se puede hacer un diagnóstico más preciso de la obesidad para garantizar un adecuado tratamiento y mejorar la calidad de vida en el adulto. Palabras clave: Índice de Masa Corporal, Circunferencia de Cintura, Índice Cintura-Talla, Índice Peso-Circunferencia de cintura, Porcentaje de grasa corporal, Sobrepeso, Obesidad(AU)
Overweight and obesity represent a concern for health authorities, given that it has increased in recent years. Objective: Relate the percentage of body fat with waist circumference, the index size waist and body mass (BMI), as an indicator of obesity. Materials and methods: descriptive, cross-sectional study in a sample of 137 adults, 18 to 70 years. Measured variables: age, height, weight, waist circumference (WC), body mass (BMI), waist height index (WHtR), index weight waist circumference (WWCtR), percentage of body fat (BFP). Determined average, deviation, correlation and test student's t. Results: Average: age (47, 06±13,71 years), weight (77,94±21,99 kg), height (160,57±9,20 cm), BMI (30,21 ± 7,92 kg/m2), WC (0,96±0,16 cm), WHCtR (0,60±0,10), WWCtR (0,80±0,11), BFP(34,19±10,88). According to the BMI, 34.3% obese and 39, 4% overweight. According to WC, high-risk 19.0% and 55.5% very high risk. According to WWCtR, 10, 9% overweight, elevated 21, 2% overweight and obesity 51, 8%. According to WWCtR, 54, 0% at risk. According to BFP, 13, 9% in limit and 52, 5% obesity. High correlation between BMI and BFP (r=0, 85) and between the BFP and WC (r=0, 89). Conclusions: BMI is a good indicator of obesity, but it is important to know the amount of fat in the body. It is suggested the BFP as a complement to diagnose obesity; in addition to add WC, as an indicator of abdominal fat, a more accurate diagnosis of obesity can be to ensure adequate treatment and improve the quality of life in the adult(AU)
Subject(s)
Humans , Male , Female , Adolescent , Adult , Anthropometry/methods , Overweight , Fatty Liver , Obesity , Body Mass Index , Waist-Height RatioABSTRACT
Many indices are available to evaluate adiposity. A new index, body adiposity index (BAIp) (expressed in % fat) for children [Hip circumference (cm)/ Height (m)0.8) – 38] has been developed (El Aarbaoui et al., 2013). The objective of the present study was to use the index in a sample of preschool children to understand the association between BAIp and other anthropometric characteristics estimating adiposity. Methods: The study was cross-sectional and the participants were 2- to 5-year-old preschoolers (505 boys and 463 girls) from Purulia district in West Bengal, India. Anthropometric measurements recorded were height, weight, waist circumference (WC), hip circumference (HC); derived indices were body mass index (BMI) and adiposity measures including waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), conicity index (CI) and BAIp. Results: Mean age of the participants was 4.03 years. Significant sex differences (p< 0.05) of anthropometric characteristics were found with respect to the mean values of body weight, BMI, HC, WHR, CI, and BAIp. Mean value of BAIp was higher in girls (13.0%fat) than in boys (12.28%fat). The BAIp was highly correlated (p< 0.05) to WHtR (r= 0.87 in boys, 0.86 in girls) than to BMI (r= 0.36 in boys, 0.41 in girls) and CI (r= 0.52 in boys, 0.46 in girls). In linear regression models, adiposity measures were observed to be significantly related to BAIp in preschoolers; age and sex were other predictors; coefficient was highest for WHtR (78.89) and least for WC (0.34). Conclusion: The results confirmed the existing hypothesis that BAIp, as an index for the assessment of children’s body fatness, works with acceptable accuracy.