State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field

Can Self-Determination Explain Dietary Patterns Among Adults at Risk of or with Type 2 Diabetes? : A Cross-Sectional Study in Socio-Economically Disadvantaged Areas in Stockholm

Type 2 Diabetes (T2D) is a major health concern in Sweden, where prevalence rates have been increasing in socioeconomically disadvantaged areas. Self-Determination Theory (SDT) is posited as an optimal framework to build interventions targeted to improve and maintain long-term healthy habits preventing and delaying the onset of T2D. However, research on SDT, T2D and diet has been widely overlooked in socio-economically disadvantaged populations. This study aims to identify the main dietary patterns of adults at risk of and with T2D from two socio-economically disadvantaged Stockholm areas and to determine the association between those patterns and selected SDT constructs (relatedness, autonomy motivation and competence). Cross-sectional data of 147 participants was collected via questionnaires. Exploratory Factor Analysis was used to identify participants' main dietary patterns. Multiple linear regressions were conducted to assess associations between the SDT and diet behaviours, and path analysis was used to explore mediations. Two dietary patterns (healthy and unhealthy) were identified. Competence construct was most strongly associated with healthy diet. Autonomous motivation and competence mediated the effect of relatedness on diet behaviour. In conclusion, social surroundings can promote adults at high risk of or with T2D to sustain healthy diets by supporting their autonomous motivation and competence