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Updated: Sep 11, 2025
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Multimodal Computational Approach for Forecasting Cardiovascular Aging Based on Immune and Clinical-Biochemical
Madina Suleimenova1, Kuat Abzaliyev2, Ainur Manapova3
1Department of Big Data and Artificial Intelligence, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
Insights
This study developed a novel model using machine learning to predict cardiovascular disease risk by analyzing clinical, immunological, and biochemical markers. The findings aid in early detection and personalized prevention strategies for cardiovascular aging.
Area of Science:
- Cardiology
- Immunology
- Biochemistry
- Data Science
Background:
- Cardiovascular disease (CVD) risk prediction is enhanced by integrating clinical, immunological, and biochemical markers.
- A wide array of biomarkers including immune cells (e.g., CD14, CD16), cytokines (e.g., IL-10), inflammatory markers (e.g., CRP), and organ function indices (e.g., GFR, NT-proBNP) were analyzed.
- Clinical factors such as arterial hypertension, diabetes mellitus, and lifestyle choices were also incorporated.
Purpose of the Study:
- To develop an innovative predictive model for cardiovascular disease (CVD) risk.
- To enable early detection of predisposition to CVDs and their complications.
- To create personalized recommendations for CVD prevention and management.
Main Methods:
- Utilized mathematical modeling and machine learning techniques for risk prediction.
- Included 52 patients aged 65 and older.
- Employed numerical methods like Runge-Kutta, Adams-Bashforth, and backward-directed Euler for accurate modeling of biomarker dynamics.
Main Results:
- HLA-DR (50%), CD14 (41%), and CD16 (38%) demonstrated the strongest association with aging processes.
- Body Mass Index (BMI) correlated with placental growth factor (PGF) (37%).
- Glomerular filtration rate (GFR) showed positive association with physical activity (47%), while superoxide dismutase (SOD) activity showed negative correlation (48%), indicating reduced antioxidant defense.
Conclusions:
- The study improves the accuracy of cardiovascular risk prediction.
- Results facilitate personalized recommendations for preventing and managing cardiovascular disease.
- The integrated approach highlights the importance of diverse biomarkers in assessing cardiovascular health.
Abstract:
Background: This study presents an innovative approach to cardiovascular disease (CVD) risk prediction based on a comprehensive analysis of clinical, immunological and biochemical markers using mathematical modelling and machine learning methods. Baseline data include indices of humoral and cellular immunity (CD59, CD16, IL-10, CD14, CD19, CD8, CD4, etc.), cytokines and markers of cardiovascular disease, inflammatory markers (TNF, GM-CSF, CRP), growth and angiogenesis factors (VEGF, PGF), proteins involved in apoptosis and cytotoxicity (perforin, CD95), as well as indices of liver function, kidney function, oxidative stress and heart failure (albumin, cystatin C, N-terminal pro B-type natriuretic peptide (NT-proBNP), superoxide dismutase (SOD), C-reactive protein (CRP), cholinesterase (ChE), cholesterol, and glomerular filtration rate (GFR)). Clinical and behavioural risk factors were also considered: arterial hypertension (AH), previous myocardial infarction (PICS), aortocoronary bypass surgery (CABG) and/or stenting, coronary heart disease (CHD), atrial fibrillation (AF), atrioventricular block (AB block), and diabetes mellitus (DM), as well as lifestyle (smoking, alcohol consumption, physical activity level), education, and body mass index (BMI). Methods: The study included 52 patients aged 65 years and older. Based on the clinical, biochemical and immunological data obtained, a model for predicting the risk of premature cardiovascular aging was developed using mathematical modelling and machine learning methods. The aim of the study was to develop a predictive model allowing for the early detection of predisposition to the development of CVDs and their complications. Numerical methods of mathematical modelling, including Runge-Kutta, Adams-Bashforth and backward-directed Euler methods, were used to solve the prediction problem, which made it possible to describe the dynamics of changes in biomarkers and patients' condition over time with high accuracy. Results: HLA-DR (50%), CD14 (41%) and CD16 (38%) showed the highest association with aging processes. BMI was correlated with placental growth factor (37%). The glomerular filtration rate was positively associated with physical activity (47%), whereas SOD activity was negatively correlated with it (48%), reflecting a decline in antioxidant defence. Conclusions: The obtained results allow for improving the accuracy of cardiovascular risk prediction, and form personalised recommendations for the prevention and correction of its development.
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