Comparación de índices antropométricos para agregación de múltiples factores de riesgo en adultos de Cuenca, Ecuador
No hay miniatura disponible
Fecha
2018
Autores
Torres, Maritza
Ortiz, Rina
Sigüencia, Wilson
Ordoñez, Maria
Alcántara, Víctor
Salazar, Juan
Añez, Roberto
Rojas, Joselyn
Bermudez, Valmore
Título de la revista
ISSN de la revista
Título del volumen
Editor
Instituto Nacional de Salud
Resumen
Objetivos. El objetivo de este estudio es comparar la capacidad predictiva de diferentes índices antropométricos en la
determinación de la agregación de múltiples factores de riesgo (AMFR) en la población adulta de la ciudad de Cuenca,
Ecuador. Materiales y métodos. Se realizó un estudio descriptivo transversal con un muestreo aleatorio multietápico en
318 sujetos adultos a quienes se les realizó una evaluación clínica, antropométrica y de laboratorio; siendo la circunferencia
abdominal, índice de masa corporal (IMC) e índice cintura altura (ICA) los índices evaluados. La AMFR se definió como
la presencia de ≥ dos componentes del síndrome metabólico (excluyendo circunferencia abdominal). Se realizaron curvas
COR para determinar el área bajo la curva (ABC) para cada índice. Resultados. De los 318 individuos, un 54,1% (n=172)
presentaron AMFR. Según los resultados obtenidos por curvas COR, la mayor capacidad predictiva en mujeres se observó
con el IMC y el ICA (ABC: 0,751 y 0,750, respectivamente) mientras que en hombres la circunferencia abdominal y el ICA
mostraron una capacidad predictiva similar (ABC=0,762). El análisis multivariante ajustado por sexo y edad mostró que el
ICA elevado (OR: 2,53; IC95%: 1,12-5,71; p=0,026) fue el mejor predictor de AMFR, seguido por el IMC (OR: 2,15; IC95%:
1,19-3,88; p=0,010). Conclusiones. La capacidad predictiva de los índices antropométricos está influenciada por el sexo, no
obstante, el ICA es el mejor predictor de la AMFR en la población de Cuenca.
Objective.The aim of this study is to compare the predictive capacity of different anthropometric indices in multiple risk factors aggregation (MRFA) determination in the adult population from Cuenca city, Ecuador. Materials and Methods. A crosssectional descriptive study was performed with a random multi-stage sampling in 318 adult subjects who underwent a clinical, anthropometric and laboratory evaluation; being the abdominal circumference, body mass index (BMI) and waist height index (WHtR) evaluated. MRFA was defined as the presence of ≥2 components of the metabolic syndrome (excluding abdominal circumference). ROC curves were plotted to determine the area under the curve (AUC) for each index. Results. Of the 318 individuals, 54.1% (n=172) presented MRFA. According to ROC curves, the highest predictive capacity in women was observed with BMI and WHtR (AUC: 0.751 and 0.750, respectively), while in men abdominal circumference and WHtR showed a similar predictive power (AUC: 0.762). The multivariate analysis adjusted for sex and age showed that high WHtR (OR: 2.53, 95% CI: 1.12-5.71, p=0.026) was the best predictor of MRFA, followed by BMI (OR: 2.15, 95% CI: 1.19-3.88, p=0.010). Conclusions. The predictive capacity of the anthropometric indexes is influenced by gender; nevertheless the WHtR is the best predictor of MRFA in our population.
Objective.The aim of this study is to compare the predictive capacity of different anthropometric indices in multiple risk factors aggregation (MRFA) determination in the adult population from Cuenca city, Ecuador. Materials and Methods. A crosssectional descriptive study was performed with a random multi-stage sampling in 318 adult subjects who underwent a clinical, anthropometric and laboratory evaluation; being the abdominal circumference, body mass index (BMI) and waist height index (WHtR) evaluated. MRFA was defined as the presence of ≥2 components of the metabolic syndrome (excluding abdominal circumference). ROC curves were plotted to determine the area under the curve (AUC) for each index. Results. Of the 318 individuals, 54.1% (n=172) presented MRFA. According to ROC curves, the highest predictive capacity in women was observed with BMI and WHtR (AUC: 0.751 and 0.750, respectively), while in men abdominal circumference and WHtR showed a similar predictive power (AUC: 0.762). The multivariate analysis adjusted for sex and age showed that high WHtR (OR: 2.53, 95% CI: 1.12-5.71, p=0.026) was the best predictor of MRFA, followed by BMI (OR: 2.15, 95% CI: 1.19-3.88, p=0.010). Conclusions. The predictive capacity of the anthropometric indexes is influenced by gender; nevertheless the WHtR is the best predictor of MRFA in our population.
Descripción
Palabras clave
Antropometría, Índices, Metabolismo, Obesidad, Curva ROC, Anthropometry, Indexes, Metabolism, Obesity, ROC curves