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Decrypting network physiology for clinical practice.

Reece Munson1, Megan Sands2, Sarvesh Srinivasan3

  • 1St Helier Hospital, Carshalton, United Kingdom.

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Network physiology reveals how body systems interact, showing disease disrupts healthy variability. Analyzing continuous signals can improve early disease detection and personalized medicine through wearable technology.

Keywords:
COPDOSA (obstructive sleep apnea)chronic obstructive pulmonary diseaseliver cirrhosisnetwork physiologyoxygen saturation variabilitysepsistransfer entropy

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Area of Science:

  • Physiology
  • Complex Systems Biology
  • Computational Medicine

Background:

  • Network physiology studies information exchange across physiological systems.
  • Clinicians intuitively apply network physiology principles in practice, recognizing patterns in conditions like sepsis and asthma.
  • Loss of physiological variability is a key indicator of strain across various diseases.

Purpose of the Study:

  • To provide a jargon-free overview of network physiology methods and their clinical applications.
  • To highlight the practical interpretation, limitations, and integration pathways for network physiology in healthcare.
  • To explore the potential of network physiology in enhancing risk stratification, early warning systems, and precision medicine.

Main Methods:

  • Analysis of continuous physiological signals, including oxygen saturation and heart rate variability.
  • Network-based analysis to identify patterns of physiological strain and adaptability.
  • Leveraging wearable technology for continuous monitoring in clinical and remote settings.

Main Results:

  • Reduced physiological variability is a marker of disease and physiological strain.
  • Changes in oxygen saturation variability can predict exacerbations in respiratory conditions like Chronic Obstructive Pulmonary Disease (COPD).
  • Network physiology metrics show promise in distinguishing between healthy and diseased states, reflecting shifts from adaptive to regular physiological patterns.

Conclusions:

  • Network physiology offers valuable insights into physiological adaptability and strain.
  • Continuous monitoring and network physiology metrics can support early intervention and personalized treatment.
  • Future integration of network physiology, potentially with digital twins, can advance precision medicine and clinical care.