Background/Objectives: 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. Methods: 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). Results: 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. Conclusions: 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.

Background: The aging population is increasing rapidly, with individuals aged 65 and older now representing more than 15% of the global population. This demographic shift is associated with a rising incidence of age-related cardiovascular diseases (CVDs). Early prediction and prevention of cardiovascular aging are essential to improve health outcomes among elderly patients. Objective: This study aimed to develop and externally validate a mathematical model for predicting cardiovascular aging in individuals aged 65 and older, based on general clinical and behavioral data. Methods: The model was built using data from 800 individuals aged 65+ from Almaty, Kazakhstan. Predictors included sex, marital status, education, smoking, alcohol use, disability, physical activity, total cholesterol, hypertension, BMI, coronary artery disease (CAD), myocardial infarction, diabetes mellitus, and chronic heart failure. A system of ordinary differential equations was used to simulate the dynamic interactions of these factors. Numerical integration was performed using the Runge–Kutta, Adams–Bashforth, and backward Euler methods. The model was verified statistically using Pearson correlation analysis and externally validated on independent age cohorts. In addition, we applied k-means clustering to identify hidden patterns and risk profiles within the dataset. A Random Forest classifier was trained to distinguish between high-risk and low-risk individuals using the same feature set. These machine learning approaches were used as complementary tools to enhance the robustness and interpretability of the modeling results. Results: The model trained on the 65–74 age group achieved an external validation accuracy of 98.8% and an AUC of 0.989 when applied to the 75–89 group. Risk modeling showed that in the 65–74 group, smoking and alcohol increased the risk of myocardial infarction, hypertension, and obesity by up to 53%. In the 75–89 group, these factors increased the likelihood of hypertension by 21%, chronic heart failure by 16%, and CAD by 14%. Among individuals aged 90+, hypercholesterolemia increased the risk of chronic heart failure by 17%, while hypertension increased myocardial infarction risk by 16%. Conclusions: The proposed model demonstrated high accuracy in predicting cardiovascular aging and identifying high-risk individuals across elderly subgroups. The integration of clustering and classification methods (k-means and Random Forest) provided additional insights and confirmed the consistency of the findings. This multi-method approach may serve as a valuable tool for developing personalized prevention strategies in geriatric care and improving healthy life expectancy.

Background: The aging process is accompanied by changes in the immunological status of a person. Immunosenescence is considered a significant cause of the development of cardiovascular diseases (CVD) in elderly people. However, to date, the relationship between immune/inflammatory processes and diseases associated with age is considered quite complex and is not fully understood. Immunophenotyping and the intracellular production of cytokines involved in the processes of inflammatory aging will allow us to identify biomarkers that are associated with cardiovascular diseases in the elderly. Objectives: To identify immunological markers associated with the process of inflammatory aging in older individuals with cardiovascular diseases. Methods: CD-phenotyping and intracellular cytokine analysis of peripheral blood using the flow cytometry method were conducted in 52 people over 60 years of age (group 1 had CVD and group 2 did not). Blood samples were stained with monoclonal antibodies (mAb) using Becton Dickinson (BD) reagents for the staining and binding of surface receptors CD4+, CD8+, CD14+, CD19+, CD16+, CD56+, CD59+, CD95+, and HLA DR+ and intracellular receptors TNF, IL-10, GM-CSF, VEGFR-2, IGF, and perforin. In addition, the following parameters were studied: questionnaire data (gender, age, alcohol consumption, smoking, physical activity, and marital status), clinical data (blood pressure (BP), heart rate (HR), body mass index (BMI)), comorbid conditions, and cardiovascular diseases (coronary heart disease (CHD), chronic heart failure (CHF), arterial hypertension (AH), previous myocardial infarction (PICS), diabetes mellitus (DM), atrial fibrillation (AF), and stroke). Results: The older patients with cardiovascular pathology had high levels of monocytes CD14+ (p = 0.014), low levels of CD8+ lymphocytes (p = 0.046), and low intracellular production of GM-CSF (p = 0.013) compared to the older people without CVD. Conclusions: The revealed differences in the expression of CD14+ monocytes indicate their role in the development of cardiovascular pathology associated with age-related changes. A decrease in cytotoxic CD8+ lymphocytes and intracellular GM-CSF production leads to an increased risk of developing cardiovascular diseases in older individuals. These observed changes with age will not only expand existing knowledge about the aging of the regulatory link of the immune system but also help to obtain data to predict CVD in older people. Thus, the obtained results support the use of these immunological markers to identify the risk of circulatory disease and a personalized approach in geriatric practice.

Previously pathological ageing is known to be mainly associated with the development of cardiovascular disease, which is caused by a combination of socioeconomic, metabolic, behavioural, and environmental risk factors. These include sex differences, age, high blood pressure, obesity, low physical activity, smoking, heavy alcohol consumption, high cholesterol and glucose levels, and diabetes mellitus. All processes occurring in the human body are reflected in biochemical and immunological analyses, which may be markers of early cardiovascular aging. The authors investigated correlations between social factors such as education, work, gender, family life, disability, physical activity, smoking and alcohol, and clinical findings such as coronary heart disease (CHD), postinfarct cardiosclerosis (PICS), chronic heart failure (CHF), diabetes mellitus (DM), body mass index (BMI), glucose levels, total cholesterol (TC) and blood pressure (BP) in older individuals. The authors implemented the basic model using R programming language, RStudio version 2023.09.0+463 on a device running MacBook Air M1, 8 GB RAM to identify the association between clinical data and social life. Also, two mathematical models (1)-(3) were proposed to calculate the correlation in combination with traditional machine learning classifiers between clinical data (biomarkers) and the social life of a patient in the age group of 60-74 years, 75-89 years and 90 and above.

An analysis of the current state of the problem of studying biomarkers of human cardiovascular ageing was carried out based on an analysis of international experience. Ageing is an inevitable, constantly progressive systemic process, which is a significant risk factor for the development of most common chronic human diseases, including cardiovascular diseases. Therefore, studying markers of early cardiovascular ageing is a logical goal for developing measures to prevent the development of cardiovascular disease and toimprove the quality of life and prolong active longevity. the intensive care unit. The application of a multidisciplinary approach to diagnose and treat a life-threatening complication as tracheal rupture in a short time allowed us to stabilize the patient’s condition and avoid the development of further complications.