FLEXiGUT 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.

弹性我GUT 是第一个专注于慢性低度炎症的大规模暴露组学研究。它旨在表征人类生命过程中的环境暴露，以评估和验证其对肠道炎症和相关生物过程和疾病的影响。将在两个 Flemish 前瞻性队列中研究环境和食物污染物在整个生命周期中对某些与慢性肠道炎症相关的生物反应的累积影响，即“ENVIRONAGE 出生队列”，该队列提供从妊娠到儿童早期的随访，以及“Flemish Gut Flora Project 纵向队列”，一组成年人。通过分析有关营养、位置和活动的可用元数据，将通过对遗留和新出现的污染物、霉菌毒素和空气污染标志物的生物监测来表征暴露组，通过应用最先进的组学技术，包括宏基因组学、代谢组学和 DNA 加合组学，以及端粒长度的评估和炎症标志物的测量，以涵盖暴露和效果。将使用包括数据探索、预处理、降维和数据挖掘的集成组学数据分析框架以及基于机器学习的路径分析方法来揭示暴露与健康结果之间的关联。预计这将导致对疾病进展（例如代谢紊乱、食物过敏、胃肠癌）和/或加速生物老化的机制有更深刻的认识。以及端粒长度的评估和炎症标志物的测量，包括暴露和效果。将使用包括数据探索、预处理、降维和数据挖掘的集成组学数据分析框架以及基于机器学习的路径分析方法来揭示暴露与健康结果之间的关联。预计这将导致对疾病进展（例如代谢紊乱、食物过敏、胃肠癌）和/或加速生物老化的机制有更深刻的认识。以及端粒长度的评估和炎症标志物的测量，包括暴露和效果。将使用包括数据探索、预处理、降维和数据挖掘的集成组学数据分析框架以及基于机器学习的路径分析方法来揭示暴露与健康结果之间的关联。预计这将导致对疾病进展（例如代谢紊乱、食物过敏、胃肠癌）和/或加速生物老化的机制有更深刻的认识。降维和数据挖掘，结合基于机器学习的路径分析方法。预计这将导致对疾病进展（例如代谢紊乱、食物过敏、胃肠癌）和/或加速生物老化的机制有更深刻的认识。降维和数据挖掘，结合基于机器学习的路径分析方法。预计这将导致对疾病进展（例如代谢紊乱、食物过敏、胃肠癌）和/或加速生物老化的机制有更深刻的认识。