Publication: Bioinformática: Inteligencia Artificial para profundizar en el conocimiento de trastornos metabólicos complejos
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2018
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Abstract
El síndrome de ovario poliquístico (SOP), es un desorden endocrino metabólico complejo de etiología heterogénea y poco conocida. Este síndrome definido como la combinación de disfunción ovulatoria, exceso de andrógenos y ovarios poliquísticos, es una causa importante de disfunción ovulatoria, de irregularidades menstruales, infertilidad, hiperandrogenismo clínico y disfunción metabólica en mujeres en edad fértil. Existen numerosas investigaciones relacionadas con este campo, entre las que destacan las de Ricardo Azziz. En este trabajo, se propone crear una aplicación en MATLAB que ayude a identificar y estudiar los factores más importantes implicados en su fisiopatología y su relación con la respuesta a los diferentes macronutrientes de la dieta (glucosa, lípidos y proteínas) a partir de los datos obtenidos de un proyecto de investigación. Este programa permitirá hacer un estudio detallado de cada una de las variables, centrándonos primero en un análisis descriptivo para más tarde, implementar distintos métodos de selección de variables, como son la regresión logística y el método Lasso. En nuestro caso, no hace falta buscar una muestra de individuos para realizar el trabajo ya que nos la proporciona el grupo de investigación del Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) del departamento de Endocrinología y Nutrición del hospital Ramón y Cajal quien ayudará con las conclusiones de la investigación.
The polycystic ovary syndrome (PCOS) is a not well know complex metabolic endocrine disorder of heterogeneous etiology. This syndrome, defined as the combination of ovulatory dysfunction, excess androgens and polycystic ovaries, is an important cause of ovulatory dysfunction, menstrual irregularities, infertility, clinical hyperandrogenism and metabolic dysfunction in women of childbearing age. There are several medical researches related to this field, most of then, published by Ricardo Azziz. In this project, we have developed an application in MATLAB that helps to identify and study the most important factors involved in its pathophysiology and its relationship with the response to the different macronutrients of the diet (glucose, lipids and proteins) through the data obtained from a research project. This application will allow an specific study of each variable, first focused on a descriptive analysis to later implement various methods of variable selection, such as logistic regression and the lasso method. In our case, the sample data needed to complete the study has been provided by CIBERDEM’s research group of the department of Endocrinology and Nutrition of the Ramón y Cajal Hospital, which will help us with the conclusions of the research.
The polycystic ovary syndrome (PCOS) is a not well know complex metabolic endocrine disorder of heterogeneous etiology. This syndrome, defined as the combination of ovulatory dysfunction, excess androgens and polycystic ovaries, is an important cause of ovulatory dysfunction, menstrual irregularities, infertility, clinical hyperandrogenism and metabolic dysfunction in women of childbearing age. There are several medical researches related to this field, most of then, published by Ricardo Azziz. In this project, we have developed an application in MATLAB that helps to identify and study the most important factors involved in its pathophysiology and its relationship with the response to the different macronutrients of the diet (glucose, lipids and proteins) through the data obtained from a research project. This application will allow an specific study of each variable, first focused on a descriptive analysis to later implement various methods of variable selection, such as logistic regression and the lasso method. In our case, the sample data needed to complete the study has been provided by CIBERDEM’s research group of the department of Endocrinology and Nutrition of the Ramón y Cajal Hospital, which will help us with the conclusions of the research.