Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

Abstract

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.

Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

“…Furthermore, there is a demand for standardized and calibrated analytical procedures and new analytical methods to characterize the steroid metabolome [84,93,94]. The integration of multidimensional omics data has achieved considerable success, and there is still room for improvement, especially in the construction of multi-omics prediction models [95,96,97].…”

An Integrative Approach to Assessing Diet–Cancer Relationships

“…To date, many studies have focused on single nutrients or foods versus dietary patterns which is a disconnect with the reality that diet as a whole is more than the sum of its parts [65]. With respect to omics approaches, choice of the source(s) of biological samples, sample preparation methods, analytic platform (e.g., nuclear magnetic resonance or liquid chromatography-mass spectroscopy for metabolomics), impose various strengths and limitations on the measurement and interpretation of data as summarized previously [66]. The omics approach also needs to be matched to the study aim.…”

“…The integration of different omics data types is an ongoing challenge [66]. Temporality, that is assumptions about the direction of temporal effects, need to be made, with the exception of genetics where it is reasonable to expect genetic variation precedes most other omic measurements.…”

The nuts and bolts of omics for the clinical allergist

“…54 Integrative omic approaches are now used to evaluate multiple forms of omic data together and more recently have become a focus in allergybased research. 42,54,55 Statistical methods to integrate multiomic data are emerging, and multiple review articles have summarized these approaches 1,56 and provide additional insight into important considerations when analyzing multiomic data in epidemiological studies. In the field of allergy and immunology multiomic integration is starting to provide important insights into disease pathophysiology.…”