RESUMEN
The experimental study and transplantation of pancreatic islets requires their isolation from the surrounding tissue, and therefore, from the vasculature. Under these conditions, avascular islets rely on the diffusion of peripheral oxygen and nutrients to comply with the requirements of islet cells while responding to changes in body glucose. As a complement to the experimental work, computational models have been widely used to estimate how avascular islets would be affected by the hypoxic conditions found both in culture and transplant sites. However, previous models have been based on simplified representations of pancreatic islets which has limited the reach of the simulations performed. Aiming to contribute with a more realistic model of avascular human islets, in this work we used architectures of human islets reconstructed from experimental data to simulate the availability of oxygen for α, ß and δ-cells, emulating culture and transplant conditions at different glucose concentrations. The modeling approach proposed allowed us to quantitatively estimate how the loss of cells due to severe hypoxia would impact interactions between islet cells, ultimately segregating the islet into disconnected subnetworks. According to the simulations performed, islet encapsulation, by reducing the oxygen available within the islets, could severely compromise cell viability. Moreover, our model suggests that even without encapsulation, only microislets composed of less than 100 cells would remain viable in oxygenation conditions found in transplant sites. Overall, in this article we delineate a novel modeling methodology to simulate detailed avascular islets in experimental and transplant conditions with potential applications in the field of islet encapsulation.
RESUMEN
Background: Prevalence of diabetes in Mexico has constantly increased since 1993. Since type 2 diabetes may remain undiagnosed for many years, identification of subjects at high risk of diabetes is very important to reduce its impact and to prevent its associated complications. Objective: To develop easily implementable screening models to identify subjects with undiagnosed diabetes based on the characteristics of Mexican adults. Subjects and methods: Screening models were developed using datasets from the 2006 and 2012 National Health and Nutrition Surveys (NHNS). Variables used to develop the multivariate logistic regression models were selected using a backward stepwise procedure. Final models were validated using data from the 2000 National Health Survey (NHS). Results: The model based on the 2006 NHNS included age, waist circumference, and systolic blood pressure as explanatory variables, while the model based on the 2012 NHNS included age, waist circumference, height, and family history of diabetes. The sensitivity and specificity values obtained from the external validation procedure were 0.74 and 0.62 (2006 NHNS model) and 0.76 and 0.55 (2012 NHNS model) respectively. Conclusions: Both models were equally capable of identifying subjects with undiagnosed diabetes (∼75%), and performed satisfactorily when compared to other models developed for other regions or countries
Antecedentes: En México, la prevalencia de diabetes se ha incrementado consistentemente desde 1993. Dado que la diabetes tipo 2 puede mantenerse sin diagnóstico por muchos años, es de suma importancia la identificación temprana de los sujetos con alto riesgo de tener la enfermedad con la finalidad de reducir su impacto y prevenir así las complicaciones asociadas. Objetivo: Desarrollar mecanismos de fácil implementación para la detección de sujetos con diabetes no diagnosticada con base en las características de la población adulta mexicana. Sujetos y métodos: Los modelos fueron desarrollados usando datos de las Encuestas Nacionales de Salud y Nutrición (NHNS) 2006 y 2012. Las variables utilizadas para desarrollar los modelos de regresión logística multivariada fueron seleccionadas mediante un procedimiento de pasos hacia atrás. Los modelos finales se validaron usando datos de la Encuesta Nacional de Salud (NHS) 2000. Resultados: El modelo obtenido de la NHNS 2006 incluyó edad, circunferencia de cintura y presión arterial sistólica como variables explicativas, mientras que el modelo NHNS 2012 incluyó edad, circunferencia de cintura, estatura e historia familiar de diabetes. La sensibilidad y la especificidad obtenidas del proceso de validación externo fueron 0,74, 0,62 (modelo NHNS 2006) y 0,76, 0,55 (modelo NHNS 2012), respectivamente. Conclusiones: Ambos modelos desarrollados fueron igualmente capaces de identificar sujetos con diabetes no diagnosticada (∼75%), y mostraron un desempeño satisfactorio en comparación con otros modelos desarrollados para otras regiones y países