A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning.

Aging and immune mechanisms play a key role in the development of cardiovascular disease (CVD), especially in the context of chronic inflammation. Therefore, in order to detect early aging in the elderly, we have developed a prognostic model based on clinical and immunological markers using machine learning. This paper analyzes the relationships between immunological markers, clinical parameters, and lifestyle factors in individuals over 60 years of age. A machine learning (ML) model including random forest, logistic regression, k-nearest neighbors, and XGBoost was developed to predict the aging rate and risk of CVD. Correlation anal is revealed significant associations between immune markers (CD14+, HLA-DR, IL-10, CD8+), clinical parameters (BMI, coronary heart disease, hypertension, diabetes), and behavioral factors (physical activity, smoking, alcohol). The results of the study confirm that systemic inflammation, as reflected by markers such as CD14+, HLA-DR, and IL-10, plays a central role in the pathogenesis of aging and related diseases. CD14+ shows a moderate positive correlation with post-infarction cardiosclerosis, accounting for 37%. HLA-DR correlates with body mass index at 39%. A negative association between IL-10 level and BMI was also found, where the correlation reaches 52% (r = -0.52). The level of CD8+ cells shows a negative correlation with smoking and their number, being 40%. Training was performed on clinical and immunological data and models were evaluated using accuracy, ROC-AUC, and F1-score metrics. Among all the trained models, the XGBoost model performed best, achieving an accuracy of 91% and an area under the ROC curve (AUC) of 0.8333. The study reveals significant correlations between immunological markers and clinical parameters, which allows the assessment of individual risks of premature cardiovascular aging. R (version 4.3.0) and specialized libraries for correlation matrix construction and visualization were used for data analysis, and Python (version 3.11.11) was used for model development and training.