Periodic Reporting for period 1 - HUMYCO (Investigating the Human Mycobolome through Uniting Large-scale Epidemiological and Mechanistic Poly-omic Designs)
Reporting period: 2020-12-01 to 2022-05-31
Mycotoxin contamination is a global challenge to food safety and population health. A diversity of adverse effects in human health such as organ damage, immunity disorders and carcinogenesis are attributed to acute and chronic exposure to mycotoxins. While there is a high likelihood of mycotoxin co-occurrence in the daily diet, multiple mycotoxin exposures represent a considerable challenge in understanding the accumulative effects of groups of exposures on health outcomes. Chronic intake of multiple mycotoxins are hypothesized to be associated with an increased risk of developing gastro-intestinal carcinomas. The fundamental evidence-based research of HUMYCO offers further opportunities to focus on dietary human health prevention, and new research-fields will benefit by exploring mycotoxin mitigation strategies. Four strategic objectives are detailed in the HUMYCO project. First, the human toxicokinetic & dynamic profile of mycotoxins is unravelled by identifying and validating new biomarkers of exposure and effect. Next, the metabolomic profile of mycotoxins is unravelled using UHPLC-HRMS through insight on the identification of human mycotoxin metabolites by in vitro liver microsomal phase I and II- metabolisation. Associative analyses are investigated through an external and internal exposure assessment approaches by using large-scale epidemiological cohorts in both Europe as Africa. To further unravel the paradigm of association and causality, new insights into the role of mycotoxins in the aetiology and development of human gastro-intestinal carcinomas are investigated by experimentally determining acquired genome-wide mutation spectra with focus on neglected mycotoxins.
Previous studies on mycotoxin exposure-health outcome associations have focused on a single or a limited number of exposures. In the first months of the project, we aimed to guide multi-exposure assessment, and make careful considerations of statistical approaches available. In addition, the issue of multicollinearity in high-dimensional settings of multiple exposure analysis underlies the controversy surrounding the reliability and consistency of statistical conclusions about the exposure-health outcome associations. Conventional approaches such as generalized linear regressions (GLR) in conjunction with regularization methods, including ridge regression, lasso and elastic net, offer some clear advantages in terms of results’ interpretation and model selection. However, when highly-correlated variables are observed, these methods have shown a low specificity in variable selection. Principal component analysis (PCA) that has been widely used as a dimensionality reduction technique also has the limitation to identify important predictor variables as this approach may overlook the associations between certain components and health outcomes. Recently, some alternative approaches have been introduced to address the issues of high dimensionality and highly-correlated data in the context of epidemiological and environmental research. Two of the noticeable approaches are weighted quantile sum regression (WQSR) and Bayesian kernel machine regression (BKMR). Combining different methods of inference allows us to interpret the role of certain exposures, their interactions and the combined effects on human health under diverse statistical perspectives, which ultimately facilitate the construction of the toxicological profile of multiple mycotoxins’ exposure. These results were recently published in an opinion paper in World Mycotoxin Journal. The human toxicokinetic trials are currently ongoing and results regarding new mycotoxin biomarkers of exposure will be reported soon.