Immune fingerprints as signs of environmental exposure Other
Conditions
Interventions
The observational study will include quantification of exposure (chemical, biological and physical agents), measurement of high-dimensional immune cell signatures and soluble proteins, genetic-epigene
Sponsors
European Commission
Eligibility
Sex/Gender
All
Age
18 Years to 100 Years
Inclusion criteria
Inclusion criteria: General Adult population 1. At least 18 years old 2. Written informed consent must be obtained before any assessment is performed Birth cohort 1. Participants are recruited at the birth of their child. Enrollment of eligible women is performed during all four seasons. Occupational study population 1. Occupational exposure to one of the exposures: mineral dust and/or organic solvents at selected workplaces (UMFST – Romania); Waste workers with a high diversity of microorganisms (NRCWE – Denmark); Park workers in the city of Barcelona (VHIR – Spain) Control group of office workers, matched to age and gender. 1. At least 18 years old 2. Written informed consent must be obtained before any assessment is performed Disease cohorts 1. Clinically active cases of sarcoidosis or Systemic sclerosis (KU Leuven), Systemic sclerosis, Systemic Lupus Erythematosus and Rheumatoid arthritis (UCL), active hypersensitivity pneumonitis (VHIR) or AID (Note: patients are recruited in the following hospitals: University hospital of Leuven, University hospital of Louvain, Vall d’Hebron University Hospital) Control group for disease cohorts 1. At least 18 years old 2. With or without treatment 3. Written informed consent must be obtained before any assessment is performed
Exclusion criteria
Exclusion criteria: General Adult population: 1. Immunomodulator / immunosuppressive treatments Birth cohort: 1. Not being able to fill out a questionnaire 2. Not a singleton pregnancy 3. Having a planned caesarean section Occupational study population 1. Younger than 18 2. Pregnancy 3. Unwillingness or inability to wear air samplers, and fill-out questionnaire-based health surveys 4. Immunomodulator / immunosuppressive treatments 5. Less than 1 year of employment to the selected workplaces Disease cohorts 1. Younger than 18 2. Specific criteria may include wearers of metal prostheses (sarcoidosis), or Previous diagnosis in case of hypersensitivity pneumonitis
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Objective 1: delineating the exposome Type of data: - Environmental sampling 1.1 Air pollution measures (including Volatile organic compounds (VOC)) 1.2 Air pollution data from monitor stations 1.3 Dust measures (mineral and metal dust, textile dust) 1.4 Chemicals in occupational environment (solvents, Pesticides) 1.5 Organics (microplastics, polymers, mycobacteria, fungi) - Questionnaires 1.6 Questionnaires about living habits (smoking, food intake, ...) and other demographic information (sex, age, employment, education, geographic,…) 1.7 Questionnaires about current and past exposures (at work and at home) - (Personal) Biomonitoring Sampling 1.8 Biological samples (blood cells and plasma, urine (black carbon, metals, VOC), ...) Source of data: - Environmental sampling of current exposures in different cohorts (feasible in all cohorts except for DOC*X and LifeLines cohort; the focus will be on occupational cohorts) - Modelling of environmental exposure (all cohorts) - Biomonitoring on biological samples from different cohorts (patients and controls, waste workers (Denmark + Romania), LifeLines Cohort, ENVIRONAGE cohort) - Clinical interviews about current and past exposures (All disease cohorts and their controls) - Questionnaires about current and past exposures (feasible in all cohorts except for DOC*X and LifeLines cohort) - Information about traffic indicators and residential greenness, temperature, humidity, UV, sunlight (ENVIRONAGE cohort) - Hyperspectral images acquired from environmental sampling and its data processing. This consists of raw ENVI files for hyperspectral images, classification images (.png) and statistical data about the spectral data and the machine learning models generated. Objective 2A: immunomics Type of data: 2.1 Biological samples: whole blood, mononuclear blood cells, blood plasma, urine samples, bronchoalveolar lavage (BAL), lymph node cells, DNA extracted from blood cells, buccal cells, nails, saliva 2.2 Clinical data: clinical in | — |
Secondary
| Measure | Time frame |
|---|---|
| There are no secondary outcome measures | — |
Countries
Belgium, Denmark, Romania, Spain
Contacts
Public ContactKatrijn Broothaerts
Outcome results
None listed