LASSO Regression Analysis: Applications in Dyslipidemia and Cardiovascular Disease Research
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Dyslipidemia and atherosclerosis are major contributors to cardiovascular disease (CVD), necessitating the development of effective risk assessment models. Traditional regression methods often encounter limitations in handling high-dimensional data and multicollinearity, highlighting the need for advanced statistical techniques. This study discusses the theoretical background of least absolute shrinkage and selection operator (LASSO) regression and presents an example of its use with data from the Framingham Heart Study to identify the most predictive clinical variables and construct a robust CVD risk prediction model. Data from patients with dyslipidemia were analyzed, including lipid profiles, inflammatory markers, and additional metabolic indicators. Model performance was evaluated using cross-validation and benchmarked against conventional regression approaches. LASSO regression effectively selected key predictors, such as low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, C-reactive protein, and body mass index. The proposed model exhibited superior predictive accuracy and generalizability compared to traditional methods. LASSO regression is a valuable tool in cardiovascular research, offering improved variable selection and enhanced prediction performance. Its application in dyslipidemia-related CVD risk assessment holds promise for optimizing clinical decision-making and advancing personalized treatment strategies.
Abstract
Dyslipidemia and atherosclerosis are major contributors to cardiovascular disease (CVD), necessitating the development of effective risk assessment models. Traditional regression methods often encounter limitations in handling high-dimensional data and multicollinearity, highlighting the need for advanced statistical techniques. This study discusses the theoretical background of least absolute shrinkage and selection operator (LASSO) regression and presents an example of its use with data from the Framingham Heart Study to identify the most predictive clinical variables and construct a robust CVD risk prediction model. Data from patients with dyslipidemia were analyzed, including lipid profiles, inflammatory markers, and additional metabolic indicators. Model performance was evaluated using cross-validation and benchmarked against conventional regression approaches. LASSO regression effectively selected key predictors, such as low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, C-reactive protein, and body mass index. The proposed model exhibited superior predictive accuracy and generalizability compared to traditional methods. LASSO regression is a valuable tool in cardiovascular research, offering improved variable selection and enhanced prediction performance. Its application in dyslipidemia-related CVD risk assessment holds promise for optimizing clinical decision-making and advancing personalized treatment strategies.