Overview
The goal of this observational study is to define a personalized risk model in the super healthy and homogeneous population of Italian Air Force high-performance pilots. This peculiar cohort conducts dynamic activities in an extreme environment, compared to a population of military people not involved in flight activity. The study integrates the analyses of biological samples (urine, blood, and saliva), clinical records, and occupational data collected at different time points and analyzed by omic-based approaches supported by Artificial Intelligence. Data resulting from the study will clarify many etiopathological mechanisms of diseases, allowing the creation of a model of analyses that can be extended to the civilian population and patient cohorts for the potentiation of precision and preventive medicine.
Description
The high-performance pilots of the Italian Air Force are "super healthy" individuals subjected to particular working conditions, as changes in temperature, pressure, gravity, acceleration, exposure to cosmic rays and radiation, which determine psycho-physical adaptation mechanisms to maintain homeostasis. However, this environmental exposure may potentially affect human health, well-being and performance.
The study aims to collect exposure data, clinical, physiological data through biosensors and molecular parameters (at different time point), to be integrated by an Artificial Intelligence algorithm expressly trained to create reliable risk models.
The final outcome will consist of the identification of significant biomarkers of pathological risk, in order to better understand the etiopathological mechanisms of many human diseases and apply early and personalized countermeasures to maintain and empower workers' health status and performance, avoiding clinical symptom presentation.