FLEXiGUT: Rationale for exposomics associations with chronic low-grade gut inflammation. (January 2022)

• Record Type: Journal Article
• Authors: Pero-Gascon, Roger; Hemeryck, Lieselot Y.; Poma, Giulia; Falony, Gwen; Nawrot, Tim S.; Raes, Jeroen; Vanhaecke, Lynn; De Boevre, Marthe; Covaci, Adrian; De Saeger, Sarah
• Is Part Of: Environment international. Volume 158(2022)
• Publication Date: 2022-01
• Subjects: Exposome -- Gut inflammation -- Environmental and food contaminants -- Metabolomics -- Microbiome -- Chronic exposure; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2021.106906
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: FLEX i GUT is the first large-scale exposomics study focused on chronic low-grade inflammation. It aims to characterize human life course environmental exposure to assess and validate its impact on gut inflammation and related biological processes and diseases. The cumulative influences of environmental and food contaminants throughout the lifespan on certain biological responses related to chronic gut inflammation will be investigated in two Flemish prospective cohorts, namely the "ENVIRONAGE birth cohort", which provides follow-up from gestation to early childhood, and the "Flemish Gut Flora Project longitudinal cohort", a cohort of adults. The exposome will be characterised through biomonitoring of legacy and emerging contaminants, mycotoxins and markers of air pollution, by analysing the available metadata on nutrition, location and activity, and by applying state-of-the-art -omics techniques, including metagenomics, metabolomics and DNA adductomics, as well as the assessment of telomere length and measurement of inflammatory markers, to encompass both exposure and effect. Associations between exposures and health outcomes will be uncovered using an integrated -omics data analysis framework comprising data exploration, pre-processing, dimensionality reduction and data mining, combined with machine learning-based pathway analysis approaches. This is expected to lead to a more profound insight in mechanisms underlying disease progression (e.g. metabolic disorders, food allergies, gastrointestinal cancers) and/or accelerated biological ageing.