Exposure assessment in early life: it is about time for multi-omics approaches

Multi-omics data, such as epigenomics, transcriptomics, proteomics, and metabolomics, are increasingly used to detect molecular responses to environmental exposures [1]. The integration of several omics layers can inform changes occurring in the structure, function, and dynamics of the body on a cellular level [2]. The correct identification of the effects of environmental exposures on the body is crucial in understanding disease development and progression [3]. Early life, i.e., pregnancy and childhood, is regarded as the most crucial periods of the developmental stages [4]. Environmental exposures in early life can have far reaching influences on the wellbeing and health of the individual [4]. Hence, the identification of these exposures and effects on the organism becomes critical.

Variability of omics biomarkers in epidemiological studies
Omics biomarkers can pose objective measures of exposure, as they might be able to depict “true” exposure in individuals, i.e., the average exposure over a month or year [5]. Exposure biomarkers measure the extrinsic variables individuals are exposed to, for example, diet, tobacco smoke, pesticides, and air pollution. However, the technical and biological variability of omics profiles needs to be assessed. Epidemiological studies predominantly rely on single measurements [6]; hence, the biological variability of omics profiles should be known to interpret changes and classify individuals correctly. High variability can lead to biased results, namely misclassification bias which leads to incorrect effect estimates [7]. Variability can be influenced by the circadian rhythm, season, or individual characteristics and can be categorized into within-individual variability and between-individual variability [5]. Thereby, between-individual variability is desired to be higher than within-individual variability, so the investigated changes are due to differences between the subjects. Another important source of variability that has to be considered in omics studies is the technical variability that is derived from the laboratory methods and procedures [5]. This becomes a crucial issue when measuring numerous compounds with omics technologies in a large set of samples as in epidemiological studies. Technical variability in omics data includes random measurement errors that reduce statistical power [5], but also systematic measurement errors, such as batch effects, that lead to biased results. Technical variability needs to be addressed, e.g., by running quality controls, standardized procedures, normalization of the data, replication of the analysis, statistical adjustment, and proper randomization of the samples according to the study design [8].