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  3. Quantifying Individual Health Status From Multi-omics Data By Health State Manifold.
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  3. Quantifying Individual Health Status From Multi-omics Data By Health State Manifold.

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Quantifying Individual Health Status from Multi-omics Data by Health State Manifold.

Xinyan Zhang1, Chengming Zhang2, Yanpu Wu1

  • 1Key Laboratory of Systems Biology, Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024 China.

Phenomics (Cham, Switzerland)
|February 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Quantifying individual health using multi-omics data is crucial for disease prevention. The new Health State Manifold (HSM) method accurately measures health status and resilience, aiding personalized and preventive medicine.

Keywords:
Dynamic networkHomeostatic resilienceMetabolic healthPersonalized health

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Area of Science:

  • Integrative biology and systems medicine
  • Computational biology and bioinformatics

Background:

  • Accurate quantification of individual health status from multi-omics data is critical for disease prevention and intervention.
  • Current methods often focus on classification, lacking a dynamical systems perspective.
  • Individual health can be viewed as a non-equilibrium steady state with homeostatic and phenotypic potential.

Purpose of the Study:

  • To develop a novel method for quantifying individual health status using multi-omics data.
  • To characterize health status by homeostatic and phenotypic potential.
  • To enable robust and accurate health status quantification for personalized and preventive medicine.

Main Methods:

  • Proposed the Health State Manifold (HSM) method.
  • Derived HSM from dynamic network biomarker and diffusion map theory.
  • Applied HSM to multi-omics data for health status quantification.

    • Main Results:

    • The HSM method successfully quantified individual health status.
    • Demonstrated effectiveness in characterizing disease progression and recovery in diabetes mellitus (rat) and Roux-en-Y Gastric Bypass (human) studies.
    • Validated the robustness and accuracy of the HSM approach.

    Conclusions:

    • The HSM method provides a powerful tool for understanding and quantifying individual health dynamics.
    • Offers potential for advancing personalized medicine and preventive strategies.
    • Highlights the utility of dynamical systems approaches in health and disease research.