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Cellular needs and conditions vary from cell to cell and change within individual cells over time. For example, the required enzymes and energetic demands of stomach cells are different from those of fat storage cells, skin cells, blood cells, and nerve cells. Furthermore, a digestive cell works much harder to process and break down nutrients during the time that closely follows a meal compared with many hours after a meal. As these cellular demands and conditions vary, so do the amounts and...
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Updated: Sep 18, 2025

Conducting Maximal and Submaximal Endurance Exercise Testing to Measure Physiological and Biological Responses to Acute Exercise in Humans
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Omics Sciences in Regular Physical Activity.

Rosamaria Militello1, Simone Luti1, Alessandra Modesti1

  • 1Department of Experimental and Clinical Biomedical Sciences, University of Florence, 50134 Florence, Italy.

International Journal of Molecular Sciences
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

Regular physical activity offers numerous health benefits through exercise adaptations. Multiomic approaches help clarify these effects by analyzing proteins, metabolites, lipids, and epigenetic changes, guiding personalized training strategies.

Keywords:
exerciseexercise adaptationmultiomicssystems biology

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Area of Science:

  • Exercise physiology
  • Molecular biology
  • Systems biology

Background:

  • Regular physical activity provides well-established health benefits driven by complex exercise adaptations.
  • Understanding exercise biology is challenging due to organ crosstalk and individual variability.
  • Multiomic strategies offer a comprehensive approach to dissecting these adaptations.

Purpose of the Study:

  • To review omics technologies applied to physical training biology.
  • To identify key molecules associated with exercise adaptations using multiomic data.
  • To discuss future directions integrating bioinformatics and artificial intelligence.

Main Methods:

  • Review of studies published in the last 10 years focusing on omics technologies in physical training.
  • Analysis of multiomic data (proteomics, metabolomics, lipidomics, epigenomics) to identify exercise-induced molecular patterns.
  • Exploration of bioinformatics and artificial intelligence tools for future applications.

Main Results:

  • Multiomic approaches enable large-scale analysis of proteins, metabolites, lipids, and epigenetic modifications.
  • Physical activity stimuli promote re-establishment of dynamic balance in redox homeostasis and metabolic state.
  • Specific training evaluations are crucial for maximizing exercise benefits.

Conclusions:

  • Multiomic technologies are powerful tools for understanding the molecular basis of exercise adaptations.
  • Integrating advanced bioinformatics and AI is essential for future progress in exercise biology.
  • Personalized training strategies can be optimized by understanding individual molecular responses to exercise.