Associating multiple mycotoxin exposure and health outcomes: current statistical approaches and challenges

Abstract

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. Nevertheless, previous studies on mycotoxin exposure-health outcome associations have focused on a single or a limited number of exposures. To guide multi-exposure assessment, careful considerations of statistical approaches available are required. 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 generalised linear regressions (GLR) in conjunction with regularisation 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.