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.
Being part of the Italian Air Force, as in active flight service or ground staff
Age between 26 and 38 years
Consent to collect biological samples and use the wearable device to monitor exposure parameters
Age < 25 years and > 39 years
no signature on informed consent
The study population will consist of high-performance Italian Air Force pilots, compared to Italian Air Force ground staff.
Cohort
Prospective
Biological sample collection
Other interventionCollection of biological samples (blood, urine, saliva) and clinical data
Assessment of flight-related exposure data and molecular modifications
Collection of information on: i) lifestyle, ii) medical examination, iii) previous trauma, iv) cumulative professional exposure to flying, determination of panel of genes and circulating markers to assess prognostic and predictive factors
Assessment of General Health
Recording of general health condition and work stress by General Health Questionnaire by the Effort-Reward Imbalance Questionnaire (ERI)
Assessment of Sleep Quality
Recording of sleep quality by the Sleeping Quality Questionnaire (SQQ)
Assessment of eating habits
Recording of eating habits by Food Frequency Questionnaire (EPIC)
Creation of reliable AI and disease-based models for personalized medicine
Integration of information obtained from anamnesis, questionnaires, biochemical, genomic, epigenomic, proteomic data with the measurement of heart rate, oxygenation, acceleration, external temperature, presence of ultrasound, infrasound and radiation with artificial intelligence algorithm for the creation of reliable models of disease based on personalized medicine
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Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
Lead sponsor
University of Milan
Collaborator
Italian Air Force
Collaborator
A-Tono
Collaborator
Ministry of Defense, Italy
Collaborator
