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Using machine learning to estimate the calendar age based on autonomic cardiovascular function.

Andy Schumann1, Christian Gaser2,3, Rassoul Sabeghi1

  • 1Lab for Autonomic Neuroscience, Imaging and Cognition (LANIC), Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Jena, Germany.

Frontiers in Aging Neuroscience
|February 9, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning accurately estimates chronological age using cardiovascular metrics in healthy adults. This method shows potential as a marker for cardiovascular aging, especially in individuals with obesity.

Keywords:
agingbaroreflexblood pressure variabilityheart rate variabilitypulse pressure

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Area of Science:

  • Cardiovascular Physiology
  • Biomedical Engineering
  • Machine Learning Applications

Background:

  • Aging alters cardiovascular regulation, measurable through various physiological indices.
  • Autonomic cardiovascular indices offer a non-invasive approach to assess biological aging.

Purpose of the Study:

  • To employ machine learning models for estimating chronological age based on autonomic cardiovascular indices.
  • To evaluate the performance of different regression models in age prediction using cardiovascular data.

Main Methods:

  • Analysis of resting-state electrocardiogram and continuous blood pressure recordings from 884 healthy volunteers.
  • Calculation of 29 cardiovascular indices, including heart rate variability, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval.
  • Application of four regression models: relevance vector regression (RVR), Gaussian process regression (GPR), support vector regression (SVR), and linear regression (LR).
  • Validation using a separate cohort of 72 obese participants, with age estimation compared to normal-weight controls.

Main Results:

  • Gaussian Process Regression (GPR) demonstrated the best performance, achieving a correlation of r=0.81 and a mean absolute error (MAE) of 5.6 years.
  • Age estimation error was slightly lower in men (MAE=5.4 years) than in women (MAE=6.0 years).
  • Obese participants showed significantly overestimated ages by GPR and SVR compared to controls, indicating advanced cardiovascular aging (up to 5.7 years).

Conclusions:

  • Machine learning effectively estimates age from cardiovascular function in healthy individuals, aligning with biological aging models.
  • The machine-learned age may serve as a comprehensive marker for cardiovascular health and function.
  • Further research is warranted to determine the predictive value of this estimated age for cardiovascular risk.