Cardiovascular Risk Factor, Discogenic Pain, Epigenetic Changes, LONGEVITY 1, Neuroplasticity, NGS, Oxidative Injury, Space Maintenance, Stress Physiological
Conditions
Keywords
pilots, air force, epigenetic change, environmental exposure
Brief summary
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.
Detailed 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.
Interventions
OTHERBiological sample collection
Collection of biological samples (blood, urine, saliva) and clinical data
Sponsors
University of Milan
Italian Air Force
A-Tono
Ministry of Defense, Italy
Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
26 Years to 38 Years
Healthy volunteers
No
Inclusion criteria
* 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
Exclusion criteria
* Age \< 25 years and \> 39 years * no signature on informed consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Assessment of flight-related exposure data and molecular modifications | Through study completion, an average of 3 year | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Assessment of General Health | Through study completion, an average of 3 year | Recording of general health condition and work stress by General Health Questionnaire by the Effort-Reward Imbalance Questionnaire (ERI) |
| Assessment of Sleep Quality | Through study completion, an average of 3 year | Recording of sleep quality by the Sleeping Quality Questionnaire (SQQ) |
| Assessment of eating habits | Through study completion, an average of 3 year | Recording of eating habits by Food Frequency Questionnaire (EPIC) |
| Creation of reliable AI and disease-based models for personalized medicine | Through study completion, an average of 3 year | 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 |
Countries
Italy
Contacts
Primary ContactGiovanni Marfia, MD, PhD
Backup ContactLaura Guarnaccia, PhD
Outcome results
None listed