| Revista: | lRevista médica de Chile |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000454865 |
| ISSN: | 0034-9887 |
| Autores: | Contreras, Patricio H1 Bernal, Yanara A2 Vigil, Pilar1 |
| Instituciones: | 1Fundación Médica San Cristóbal, Santiago de Chile. Chile 2Instituto de Investigación en Salud Reproductiva, Santiago de Chile. Chile 3Universidad Católica de Chile, Vicerrectoría Comunicaciones, Santiago de Chile. Chile |
| Año: | 2020 |
| Periodo: | Abr |
| Volumen: | 148 |
| Número: | 4 |
| Paginación: | 436-443 |
| País: | Chile |
| Idioma: | Español |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en español | Background An instrument to help clinicians to evaluate the oral glucose tolerance test (OGTT) at-a-glance is lacking. Aim To generate a program written in HTML squeezing relevant information from the OGTT with glucose and insulin measurements. Material and Methods We reanalyzed a database comprising 90 subjects. All of them had both an OGTT and a pancreatic suppression test (PST) measuring insulin resistance directly. Thirty-seven of the 90 studied participants were insulin resistant (IR). Receiver operating characteristic (ROC) curves and Bayesian analyses delineated the diagnostic performances of four predictors of insulin resistance: HOMA, QUICKI, ISI-OL (Matsuda-DeFronzo) and I0*G60. We validated a new biochemical predictor, the Percentual Relative Insulin Sensitivity (%RIS), and calculated the Percentual Relative Beta Cell Function (%RBCF). Results The best diagnostic performance of the five predictors were those of the I0*G60 and the %RIS. The poorest diagnostic performances were those of the HOMA and QUICKI. The ISI-OL’s performance was in between. The %RIS of participants with and without IR was 44.4 ± 7.3 and 101.1 ± 8.8, respectively (p < 0.05). The figures for % RBCF were 55.8 ± 11.8 and 90.8 ± 11.6, respectively (p < 0.05). Mathematical modeling of the relationship between these predictors and the Steady State Plasma Glucose Value from the PST was performed. We developed a program with 10 inputs (glucose and insulin values) and several outputs: I0*G60, HOMA, QUICKI, ISI-OL, Insulinogenic Index, Disposition Index, %RBCF, %RIS, and metabolic categorization of the OGTT (ADA 2003). Conclusions The OGTT data permitted us to write successfully an HTML program allowing the user to fully evaluate at-a-glance its metabolic information |
| Disciplinas: | Medicina |
| Palabras clave: | Metabolismo y nutrición, Diagnóstico, Pruebas diagnósticas, Tolerancia a la glucosa, Resistencia a la insulina, Síndrome metabólico |
| Keyword: | Metabolism and nutrition, Diagnosis, Diagnostic tests, Glucose tolerance, Insulin resistance, Metabolic syndrome |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
