Impacto de la diabetes en el riesgo cardiovascular en pacientes con dislipidemia / Impact of diabetes on cardiovascular risk in patients with dyslipidemia

Resumen Objetivo: Determinar el impacto de la diabetes en el riesgo cardiovascular en pacientes con dislipidemia. Método: Estudio observacional, transversal y comparativo, en el que se determinó el riesgo cardiovascular en 100 pacientes con dislipidemia, de los cuales 50 eran diabéticos, sin complicaciones crónicas. Resultados: Ambos grupos tenían características similares en cuanto a edad, presión arterial, índice de masa corporal, niveles de c-HDL y c-LDL. Sin embargo, al comparar el porcentaje de riesgo cardiovascular, observamos que el grupo de diabéticos tenía casi el doble de riesgo cardiovascular, 13.7 contra 7.9 (p = 0.014), y la edad del corazón calculada también fue mayor en los pacientes con diabetes, 80 contra 66 años (p = 0.003). Incluso, en los pacientes diabéticos la diferencia entre la edad real y la edad del corazón fue mayor, 24 años contra 15 años (p = 0.000). Conclusión: Padecer diabetes y dislipidemia duplica el riesgo cardiovascular. En la población estudiada se encontró poco control metabólico, lo que aumenta significativamente las complicaciones en edades tempranas y la carga económica al sistema de salud y a las familias de los pacientes; por tanto, es necesario replantear las estrategias de tratamiento para mejorar el control metabólico y el pronóstico del paciente a largo plazo.

Abstract Objective: To determine the impact of diabetes on cardiovascular risk in patients with dyslipidemia. Method: Observational, cross-sectional and comparative study in which cardiovascular risk was determined at 10 years in 100 patients with dyslipidemia, of these, 50 non-diabetic patients and 50 diabetic patients. Results: Both groups had similar characteristics in terms of age, blood pressure figures, average body mass index, and HDL and LDL levels. It was observed that the diabetic group has almost double the risk compared to the dyslipidemia group, 13.7 vs. 7.9 (p = 0.014), and the calculated heart age is also higher in patients with diabetes, 80 vs. 66 years (p = 0.003). Even in patients with diabetes there is a greater difference between the real age and the age of the heart, 24 years vs. 15 years of patients without diabetes (p = 0.000). Conclusion: Having diabetes and dyslipidemia doubles the cardiovascular risk of patients. Little metabolic control was found in the population studied, which significantly increases complications at an early age and the economic burden on the health system and the families of patients, so it is necessary to rethink treatment strategies to improve metabolic control and with it the prognosis for the patient in the long term.

[Impact of diabetes on cardiovascular risk in patients with dyslipidemia]. / Impacto de la diabetes en el riesgo cardiovascular en pacientes con dislipidemia.

Objective: To determine the impact of diabetes on cardiovascular risk in patients with dyslipidemia. Method: Observational, cross-sectional and comparative study in which cardiovascular risk was determined at 10 years in 100 patients with dyslipidemia, of these, 50 non-diabetic patients and 50 diabetic patients. Results: Both groups had similar characteristics in terms of age, blood pressure figures, average body mass index, and HDL and LDL levels. It was observed that the diabetic group has almost double the risk compared to the dyslipidemia group, 13.7 vs. 7.9 (p = 0.014), and the calculated heart age is also higher in patients with diabetes, 80 vs. 66 years (p = 0.003). Even in patients with diabetes there is a greater difference between the real age and the age of the heart, 24 years vs. 15 years of patients without diabetes (p = 0.000). Conclusion: Having diabetes and dyslipidemia doubles the cardiovascular risk of patients. Little metabolic control was found in the population studied, which significantly increases complications at an early age and the economic burden on the health system and the families of patients, so it is necessary to rethink treatment strategies to improve metabolic control and with it the prognosis for the patient in the long term.

Objetivo: Determinar el impacto de la diabetes en el riesgo cardiovascular en pacientes con dislipidemia. Método: Estudio observacional, transversal y comparativo, en el que se determinó el riesgo cardiovascular en 100 pacientes con dislipidemia, de los cuales 50 eran diabéticos, sin complicaciones crónicas. Resultados: Ambos grupos tenían características similares en cuanto a edad, presión arterial, índice de masa corporal, niveles de c-HDL y c-LDL. Sin embargo, al comparar el porcentaje de riesgo cardiovascular, observamos que el grupo de diabéticos tenía casi el doble de riesgo cardiovascular, 13.7 contra 7.9 (p = 0.014), y la edad del corazón calculada también fue mayor en los pacientes con diabetes, 80 contra 66 años (p = 0.003). Incluso, en los pacientes diabéticos la diferencia entre la edad real y la edad del corazón fue mayor, 24 años contra 15 años (p = 0.000). Conclusión: Padecer diabetes y dislipidemia duplica el riesgo cardiovascular. En la población estudiada se encontró poco control metabólico, lo que aumenta significativamente las complicaciones en edades tempranas y la carga económica al sistema de salud y a las familias de los pacientes; por tanto, es necesario replantear las estrategias de tratamiento para mejorar el control metabólico y el pronóstico del paciente a largo plazo.

Rapid automatic identification of parameters of the Bergman Minimal Model in Sprague-Dawley rats with experimental diabetes for adaptive insulin delivery.

Glucose-Insulin regulation models can be used to individualize insulin therapy. However, the experimental techniques currently used to identify the appropriate parameter sets of an individual are expensive, time consuming, and very unpleasant for the patient. Since there is a wide range of intrapersonal parameter variability, the identified parameters in a laboratory setting (at rest) are not optimal for dynamic conditions of daily activities. In this study we propose a methodology to identify three parameters of Bergman's Minimal Model in streptozotocin-induced diabetic rats from the experimental data of the glucose response to exogenous insulin doses, based on a genetic algorithm (GA). The algorithm requires glucose measurements from a continuous subcutaneous sensor once every 5 min and the amount of injected insulin. The model parameters of 20 in vivo experiments (from 19 rats) were identified with high accuracy and the average root-mean squared (RMS) error between predicted and measured glucose concentration was 17.6 mg/dl. Since the algorithm requires a relatively short (60-120 min) observation time it can be used for real-time parameter identification to optimize insulin infusion systems. Model parameter changes due to experimental settings like drug testing or in natural lifestyle changes should be calculable, on-the-fly, using data from only the glucose sensor and the amount of insulin delivered.

A discrete-time recurrent neurofuzzy network for black-box modeling of insulin dynamics in diabetic type-1 patients.

In this work we present a data-driven modeling of the insulin dynamics in different in silico patients using a recurrent neural network with output feedback. The inputs for the identification is the rate of insulin (microU/dl/min) applied to the patient, and blood glucose concentration. The output is insulin concentration (microU/ml) present in the blood stream. Once completed the off-line modeling, this model could be used for on-line monitoring of the insulin concentration for a better treatment. The learning law of the recurrent neural network is inspired by adaptive observer theory, and proven to be convergent in the parameters and stable in the Lyapunov sense, even with only 13 samples available. Simulation results are shown to validate the presented modeling.