TORNADO-Omics Techniques and Neural Networks for the Development of Predictive Risk Models

Recruiting

• Conditions: Stress Physiological; Discogenic Pain; Cardiovascular Risk Factor; Space Maintenance; Oxidative Injury; LONGEVITY 1; Neuroplasticity; NGS; Epigenetic Changes
• Interventions: Other: Biological sample collection

• Registration Number: NCT06372054
• Lead Sponsor: Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico

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.

Study Timeline

• Study Start: 2024-02-05
• Status Verified: 2024-03
• Study First Submitted: 2024-03-21
• Study First Submitted QC: 2024-04-16
• Last Update Submitted: 2024-04-16
• Study First Posted: 2024-04-17
• Last Update Posted: 2024-04-17
• Primary Completion: 2025-02-05
• Study Completion: 2027-02-05

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 200

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Italian Air Force ground staff	Biological sample collection	This cohort of Italian Air Force ground personnel will be used as a control group to compare data from the pilot cohort.
High-performance Italian Air Force Pilots	Biological sample collection	The primary study cohort is represented by "super-healthy" high-performance Italian Air Force Pilots, aged between 26 and 38 years, in active flight service. Intervention: not applicable

Primary Outcome Measures

Name	Time	Method
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 Outcome Measures

Name	Time	Method
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

Trial Locations

Locations (1)

CeMATA - Joint Center for Aerospace Medicine and Advanced Therapy; 🇮🇹 Milan, Italy