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Related Concept Videos

Aging01:26

Aging

29
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
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Psychoneuroimmunology: Cardiovascular Disease01:27

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Psychoneuroimmunology (PNI) is a multidisciplinary field that examines how psychological factors, particularly stress, interact with the immune system and impact physical health. Research in PNI has shown that chronic or traumatic stress can disrupt both the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. These disruptions contribute to serious health conditions, including cardiovascular diseases.
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Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning.

Madina Suleimenova1, Kuat Abzaliyev2, Madina Mansurova1

  • 1Department of Big Data and Artificial Intelligence, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.

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Machine learning models predict cardiovascular aging by analyzing immune markers like CD14+ and HLA-DR, alongside clinical data. The XGBoost model achieved 91% accuracy, identifying early aging risks in the elderly.

Keywords:
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Area of Science:

  • Gerontology and immunology
  • Cardiovascular disease research
  • Machine learning applications in healthcare

Background:

  • Aging and immune mechanisms are critical in cardiovascular disease (CVD) development, particularly with chronic inflammation.
  • Early detection of aging in the elderly is crucial for proactive health management.
  • Machine learning offers a novel approach to analyze complex health data for prognostic modeling.

Purpose of the Study:

  • To develop a machine learning-based prognostic model for predicting aging rate and CVD risk in individuals over 60.
  • To identify key clinical, immunological, and lifestyle factors associated with cardiovascular aging.
  • To assess the role of systemic inflammation in the pathogenesis of aging and related diseases.

Main Methods:

  • Analysis of relationships between immunological markers (CD14+, HLA-DR, IL-10, CD8+), clinical parameters (BMI, CVD history, hypertension, diabetes), and lifestyle factors.
  • Development and comparison of machine learning models: random forest, logistic regression, k-nearest neighbors, and XGBoost.
  • Evaluation of model performance using accuracy, ROC-AUC, and F1-score metrics.

Main Results:

  • Significant correlations found between immune markers (CD14+, HLA-DR, IL-10, CD8+) and clinical/behavioral factors.
  • CD14+ correlated with cardiosclerosis (37%), HLA-DR with BMI (39%), and IL-10 showed a negative association with BMI (-52%).
  • The XGBoost model demonstrated superior performance with 91% accuracy and 0.8333 AUC, effectively predicting cardiovascular aging risks.

Conclusions:

  • Immunological markers and clinical parameters are significantly correlated, enabling assessment of individual risks for premature cardiovascular aging.
  • Machine learning models, particularly XGBoost, show high efficacy in predicting aging rates and CVD risk.
  • The findings highlight the central role of systemic inflammation in aging and cardiovascular health, paving the way for targeted interventions.