Early detection of stress is crucial to preventing performance declines, mental and physical health problems, and overall well-being problems. Four stress trials (heat, muscle exertion, sleep deprivation, and psychosocial stress) were conducted to assess the impact of stress on the interrelationships between biological, cognitive, and physical states.
This study is unique in its approach, aiming to elucidate the relationships between gut microbiome, health habits, psychological, cognitive, physiological, and physical aspects. An integrative analytical pipeline was employed, starting with unsupervised machine learning for variable reduction, followed by Redundancy Analysis (RDA) and supervised machine learning to determine which traits explain the most variance in the full omics datasets. Structural Equation Modelling (SEM) was then utilised to explore causal relationships between these traits and the gut microbiome.
This novel approach revealed the importance of physical activity, sex, hormones and nap duration in influencing the gut microbiome. The results also suggest that the microbiome plays a protective role, with a specific taxa cluster contributing to this potential stress-buffering effect. This integrative approach offers new perspectives that complement traditional statistical analyses, and support multi-omics approaches in understanding stress and performance.
These findings have significant implications. They emphasise the importance of integrating diverse data types to understand stress and performance comprehensively. This approach could advance early stress detection while predicting and improving performance.