It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

We hypothesized metabolomic profiling could be utilized to identify children who scored poorly on the communication component of the Ages and Stages Questionnaire (ASQ); which assesses development in childhood, and to provide candidate biomarkers for autism spectrum disorders (ASD). In a population of three-year-old children, 15 plasma metabolites, were significantly (p < 0.05) different between children who were categorized as having communication skills that were “on schedule” (n = 365 (90.6%)) as compared to those “requiring further monitoring/evaluation” (n = 38 (9.4%)) according to multivariable regression models. Five of these metabolites, including three endocannabinoids, were also dysregulated at age one (n = 204 “on schedule”, n = 24 “further monitoring/evaluation”) in the same children. Stool metabolomic profiling identified 11 significant metabolites. Both the plasma and stool results implicated a role for tryptophan and tyrosine metabolism; in particular, higher levels of N-formylanthranilic acid were associated with an improved communication score in both biosample types. A model based on the significant plasma metabolites demonstrated high sensitivity (88.9%) and specificity (84.5%) for the prediction of autism by age 8. These results provide evidence that ASQ communication score and metabolomic profiling of plasma and/or stool may provide alternative approaches for early diagnosis of ASD, as well as insights into the pathobiology of these conditions.