Surrogate indices of insulin resistance using the Matsuda index as reference in adult men—a computational approach

Background: Overweight and obesity, high blood pressure, hyperglycemia, hyperlipidemia, and insulin resistance (IR) are strongly associated with non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular disease, stroke, and cancer. Different surrogate indices of IR are derived and validated with the euglycemic–hyperinsulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cutoff value and diagnosis accuracy for surrogate indices in non-diabetic young adult men. Methods: A cross-sectional descriptive study was carried out with 93 young men (ages 18–31). Serum levels of glucose and insulin were analyzed in the fasting state and during an oral glucose tolerance test (OGTT). Additionally, clinical, biochemical, hormonal, and anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cutoff value using difference parameters was examined, as well as other statistical tools to make the output robust. Results: The highest sensitivity and specificity at the optimal cutoff value, respectively, were established for the Homeostasis model assessment of insulin resistance index (HOMA-IR) (0.91; 0.98; 3.40), the Quantitative insulin sensitivity check index (QUICKI) (0.98; 0.96; 0.33), the triglyceride-glucose (TyG)-waist circumference index (TyG-WC) (1.00; 1.00; 427.77), the TyG-body mass index (TyG-BMI) (1.00; 1.00; 132.44), TyG-waist-to-height ratio (TyG-WHtR) (0.98; 1.00; 2.48), waist-to-height ratio (WHtR) (1.00; 1.00; 0.53), waist circumference (WC) (1.00; 1.00; 92.63), body mass index (BMI) (1.00; 1.00; 28.69), total body fat percentage (TFM) (%) (1.00; 1.00; 31.07), android fat (AF) (%) (1.00; 0.98; 40.33), lipid accumulation product (LAP) (0.84; 1.00; 45.49), leptin (0.91; 1.00; 16.08), leptin/adiponectin ratio (LAR) (0.84; 1.00; 1.17), and fasting insulin (0.91; 0.98; 16.01). Conclusions: The computational approach was used to determine the diagnosis accuracy and the optimal cutoff value for IR to be used in preventive healthcare.Fil: Malagón Soriano, Víctor Antonio. Universidad Nacional de Colombia; ColombiaFil: Ledezma Forero, Andres Julian. Universidad Nacional de Colombia; ColombiaFil: Espinel Pachon, Cristian Felipe. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Burgos Cárdenas, Álvaro Javier. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Garces, Maria Fernanda. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Ortega Ramírez, Gustavo Eduardo. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Franco Vega, Roberto. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Peralta Franco, Jhon Jairo. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Maldonado Acosta, Luis Miguel. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Rubio Romero, Jorge Andres. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Mercado Pedroza, Manuel Esteban. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Caminos Cepeda, Sofia Alexandra. Universitat Pompeu Fabra; EspañaFil: Lacunza, Ezequiel. Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Centro de Investigaciones Inmunológicas Básicas y Aplicadas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Rivera Moreno, Carlos Armando. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Darghan Contreras, Aquiles Enrique. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Ruiz Parra, Ariel Iván. Universidad Nacional de Colombia. Facultad de Medicina; ColombiaFil: Caminos, Jorge E.. Universidad Nacional de Colombia. Facultad de Medicina; Colombi

Surrogate indices of insulin resistance using the Matsuda index as reference in adult men—a computational approach

BackgroundOverweight and obesity, high blood pressure, hyperglycemia, hyperlipidemia, and insulin resistance (IR) are strongly associated with non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular disease, stroke, and cancer. Different surrogate indices of IR are derived and validated with the euglycemic–hyperinsulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cutoff value and diagnosis accuracy for surrogate indices in non-diabetic young adult men.MethodsA cross-sectional descriptive study was carried out with 93 young men (ages 18–31). Serum levels of glucose and insulin were analyzed in the fasting state and during an oral glucose tolerance test (OGTT). Additionally, clinical, biochemical, hormonal, and anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cutoff value using difference parameters was examined, as well as other statistical tools to make the output robust.ResultsThe highest sensitivity and specificity at the optimal cutoff value, respectively, were established for the Homeostasis model assessment of insulin resistance index (HOMA-IR) (0.91; 0.98; 3.40), the Quantitative insulin sensitivity check index (QUICKI) (0.98; 0.96; 0.33), the triglyceride-glucose (TyG)-waist circumference index (TyG-WC) (1.00; 1.00; 427.77), the TyG-body mass index (TyG-BMI) (1.00; 1.00; 132.44), TyG-waist-to-height ratio (TyG-WHtR) (0.98; 1.00; 2.48), waist-to-height ratio (WHtR) (1.00; 1.00; 0.53), waist circumference (WC) (1.00; 1.00; 92.63), body mass index (BMI) (1.00; 1.00; 28.69), total body fat percentage (TFM) (%) (1.00; 1.00; 31.07), android fat (AF) (%) (1.00; 0.98; 40.33), lipid accumulation product (LAP) (0.84; 1.00; 45.49), leptin (0.91; 1.00; 16.08), leptin/adiponectin ratio (LAR) (0.84; 1.00; 1.17), and fasting insulin (0.91; 0.98; 16.01).ConclusionsThe computational approach was used to determine the diagnosis accuracy and the optimal cutoff value for IR to be used in preventive healthcare

Obstetricia integral siglo XXI. Tomo II

El libro Obstetricia Integral siglo XXI, Tomo II, es una publicación virtual de la Facultad de Medicina, se trata de la continuación sobre el análisis detallado de los principales tópicos en el área de la obstetricia, realizada por un grupo interdisciplinario de investigadores comprometidos con el mejoramiento de la calidad en el cuidado de la salud de la mujer gestante. Se ha procurado un balance entre los aspectos básicos de fisiopatología y las guías de atención clínica soportadas en evidencias científicas, con el ánimo de brindarle al lector un equilibrio entre las bases biopsico-sociales de la salud y la enfermedad y los aspectos prácticos de la atención clínica.Vargas Fiesco, Diana Carolina and Rubio Romero, Jorge Andrés and Ruiz Parra, Ariel Iván and Rodríguez, Luis Martín and Aragón, Miguel Eduardo and Arteaga Díaz, Clara Eugenia and Riaño, Jorge Enrique and Arenas Gamboa, Jaime and Ramírez Martínez, Javier Andrés and Amaya Guío, Jairo and Gaitán , Magda Alexandry and Gallego Arbeláez, Jaime and Cortés Díaz, Daniel Otálvaro and Ángel Müller, Edith and Bracho Ch., Alcides C. and Bautista Charry, Alejandro and Rodríguez Ramos, Marcela and Navarro Milanés, Alfonso and Díaz Cruz, Luz Amparo and Mercado Pedroza, Manuel Esteban and Gaitán Duarte, Hernando and Gómez Sánchez, Pio Iván and Peña, Diana Marcela and Calvo Gómez, José Manuel and Parra Pineda, Mario Orlando and Cárdenas Muñoz, María Luisa (2010) Obstetricia integral siglo XXI. Tomo II. Facultad de Medicina, Universidad Nacional de Colombia, Bogotá. ISBN 978958447618

Presentation_1_Surrogate indices of insulin resistance using the Matsuda index as reference in adult men—a computational approach.pdf

BackgroundOverweight and obesity, high blood pressure, hyperglycemia, hyperlipidemia, and insulin resistance (IR) are strongly associated with non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular disease, stroke, and cancer. Different surrogate indices of IR are derived and validated with the euglycemic–hyperinsulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cutoff value and diagnosis accuracy for surrogate indices in non-diabetic young adult men.MethodsA cross-sectional descriptive study was carried out with 93 young men (ages 18–31). Serum levels of glucose and insulin were analyzed in the fasting state and during an oral glucose tolerance test (OGTT). Additionally, clinical, biochemical, hormonal, and anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cutoff value using difference parameters was examined, as well as other statistical tools to make the output robust.ResultsThe highest sensitivity and specificity at the optimal cutoff value, respectively, were established for the Homeostasis model assessment of insulin resistance index (HOMA-IR) (0.91; 0.98; 3.40), the Quantitative insulin sensitivity check index (QUICKI) (0.98; 0.96; 0.33), the triglyceride-glucose (TyG)-waist circumference index (TyG-WC) (1.00; 1.00; 427.77), the TyG-body mass index (TyG-BMI) (1.00; 1.00; 132.44), TyG-waist-to-height ratio (TyG-WHtR) (0.98; 1.00; 2.48), waist-to-height ratio (WHtR) (1.00; 1.00; 0.53), waist circumference (WC) (1.00; 1.00; 92.63), body mass index (BMI) (1.00; 1.00; 28.69), total body fat percentage (TFM) (%) (1.00; 1.00; 31.07), android fat (AF) (%) (1.00; 0.98; 40.33), lipid accumulation product (LAP) (0.84; 1.00; 45.49), leptin (0.91; 1.00; 16.08), leptin/adiponectin ratio (LAR) (0.84; 1.00; 1.17), and fasting insulin (0.91; 0.98; 16.01).ConclusionsThe computational approach was used to determine the diagnosis accuracy and the optimal cutoff value for IR to be used in preventive healthcare.</p