RESUMO
Objetivo: Evaluar la capacidad discriminativa de predicción de diabetes con indicadores antropométricos, bioquímicos y antecedentes. Métodos: Se utilizaron dos modelos, el análisis de las curvas ROC y regresión logística binaria. Resultados: Mediante el análisis de las curvas ROC, el perímetro abdominal obtuvo mayor poder discriminativo de predicción (AUC=0,747; p<0,001; IC: 0,624-0,870), en comparación a la glicemia (AUC=0,749; p<0,001; IC: 0,645-0,852) y el índice de cintura-talla (AUC=0,737; p=0,001; IC: 0,638-0,836). Los antecedentes patológicos personales se incluyen en la ecuación de regresión logística P(Y=1) = (1+e0,693+1,897APP)-1 para predecir el riesgo de tener diabetes en el futuro. Conclusiones: El perímetro abdominal obtuvo mayor poder discriminativo, seguido de los antecedentes patológicos personales.
Objective: To evaluate the discriminative ability to predict diabetes with anthropometric and biochemical indicators and medical history. Methods: The sampling carried out was census and the sample consisted of 104 workers. A longitudinal study was carried out to evaluate the discriminative ability to predict diabetes with the anthropometric, biochemical, and antecedent indicators, using two models, the analysis of the ROC curves and binary logistic regression. Results: By analyzing the ROC curves, the abdominal circumference obtained greater predictive discriminative power (AUC = 0.747; p <0.001; CI: 0.624-0.870), compared to glycemia (AUC=0.749; p <0.001; CI: 0.645-0.852) and the waist-height index (AUC=0.737; p=0.001; CI: 0.638-0.836). Personal medical history is included in the logistic regression equation P(Y=1)=(1+e0,693+1,897APP)-1 to predict the risk of developing diabetes in the future. Conclusions: The abdominal circumference obtained the highest discriminative power, followed by the pathological history.