A Data Science Approach to Precision Medicine: Allostatic Load as a Predictor of Cardiovascular Disease

Introduction: Precision medicine, tailored to individual genetic and environmental attributes, has emerged as a practical approach in healthcare. Allostatic load (AL), reflecting the body's response to stressors, holds promise in predicting susceptibility to chronic illnesses like cardiovascular disease (CVD). This study explored the relationship between CVD and AL, employing big data analytics and machine learning to enhance prediction-accuracy. Objectives: 1. Develop a predictive model using AL measures for CVD prediction. 2. Create a clinical AL tool for personalized medicine. 3. Demonstrate the relationship between AL scores and CVD. Methods: Data from the National Center for Health Statistics encompassed demographic, health, and clinical variables. AL was computed using a count-based method, considering factors like blood pressure, body mass index, glucose, lipids, etc. Machine learning (ML) algorithms, including random forest and logistic regression, were employed to predict CVD based on AL factors. Missing data were managed using imputation methods tailored to variable distributions. Results: The count-based AL method correlated more with age than the weighted method. Both methods showed significant correlations with age, emphasizing the association between AL and aging. AL was positively correlated with CVD, with an 82% accuracy rate in predicting CVD using the random forest algorithm. Black/African American populations displayed the highest correlation between AL and CVD. Conclusions: This study highlights the potential of AL as a predictor for CVD and supports its integration into clinical practice. AL provides a comprehensive view of physiological health and empowers healthcare providers to make informed decisions, particularly in vulnerable populations. Big data analytics and ML enhance prediction accuracy and offer insights into the complex factors contributing to CVD risk. The convergence of healthcare, statistics, and computer programming is essential for harnessing the power of data in precision medicine and improving patient outcomes.