Systems medicine of inflammaging

Area of Science:

• Systems Medicine
• Computational Biology
• Biomedical Research

Background:

• Systems Medicine (SM) extends Systems Biology (SB) to clinical and epidemiological research.
• SM adopts a patient-centered framework, integrating biomedical hypotheses, omics data, and computational tools.
• Balancing these three pillars is crucial for effective SM.

Purpose of the Study:

• To demonstrate how computational methods can support the Systems Medicine paradigm.
• To integrate and analyze large datasets combining clinical, environmental, and omics data.
• To develop multidimensional biomarkers for distinguishing pathological conditions.

Main Methods:

• Utilizing omics technologies for experimental data acquisition.
• Employing computational, statistical, and modeling tools for analysis.
• Integrating cross-sectional and longitudinal data from healthy subjects and patients.

Main Results:

• The concept of Inflammaging serves as a unifying biomedical hypothesis.
• An ecological perspective can quantitatively model Inflammaging features.
• Computational methods enable the integration and analysis of diverse large datasets.

Conclusions:

• Systems Medicine offers a powerful framework for understanding complex diseases.
• Inflammaging is a key phenotype linking aging, age-related diseases, and cancer.
• Multidimensional biomarkers derived from integrated data can improve disease state differentiation.