Dynamical Network Stability Analysis of Multiple Biological Ages Provides a Framework for Understanding the Aging...

Area of Science:

• Gerontology
• Network Science
• Biomarker Discovery

Background:

• Multiple biological age measures exist, but they are univariate and fail to capture the complexity of aging.
• Aging theories emphasize multidimensional, multicausal, and multiscale processes, necessitating a more integrated approach.
• Existing biological age measures offer insights but lack a holistic view of aging dynamics.

Purpose of the Study:

• To develop a multidimensional network representation of biological ages.
• To analyze the interaction network of biological ages for insights into aging processes and interventions.
• To identify novel biomarkers for long-term resilience and age-related health decline.

Main Methods:

• Systematically combined multiple biological ages into a multidimensional network.
• Applied dynamical network stability analysis to explore system behavior.
• Utilized data from the Swedish Adoption/Twin Study of Aging (SATSA) with 8 biological ages.

Main Results:

• Identified physiological age as a central node in the biological age interaction network, linked to cardiometabolic health.
• Discovered a weakly stable direction in the network dynamics, representing slow recovery over a lifespan.
• This slow direction serves as a novel aging biomarker, correlating with chronological age and predicting health decline.

Conclusions:

• A network approach provides a more comprehensive understanding of aging than univariate measures.
• Physiological age is a critical vulnerability in the aging network.
• The identified slow direction biomarker offers a new way to quantify resilience and predict health trajectories.