Multi-omics footprinting in health and disease: Complexity and interpretation
More details
Hide details
Department of Nutritional Sciences and Dietetics, School of Health Sciences, International Hellenic University, Thessaloniki, Greece
Publication date: 2022-05-27
Public Health Toxicol 2022;2(Supplement 1):A67
The revolutionary multi-omics technology attempts to bridge the gap between genotype and phenotype providing new insights into an integrated understanding of health and disease by creating avenues for precision medicine, nutrition and exercise. Prevention and management of metabolic disorders require a thorough evaluation of cellular stresses (metabolic, inflammatory and oxidative) assessing complex molecular profiles such as metabolomics, proteomics, lipidomics, genomics, nutrigenomics, epigenomics and transcriptomics. Changes in omics illustrate the interplay between biological processes and pathways based on regulated targets known as footprints. Although there is no reliable method for integrating omics data with clinical data, the clinical relevance of these complex molecular profiles and the development of useful diagnostic and prognostic biomarkers must be the driving force in detecting subclinical conditions for better interventions in management and control of diseases. The ultimate goal is to provide a user-friendly analytical tool for targeted interventions. However, the interpretation of multi-omics data at an individual level still remains a difficult task for decision-making in every day clinical practice. Interestingly, in an effort to deal with the unavoidable uncertainty of the extensive and complex information in omics data sets, a recent study explores the value of exercise inflammatory/oxidative signatures in insulin resistance.
Yamada R, Okada D, Wang J, Basak T, Koyama S. Interpretation of omics data analyses. J Hum Genet. 2021;66(1):93-102. doi:10.1038/s10038-020-0763-5
Contrepois K, Wu S, Moneghetti KJ, et al. Molecular Choreography of Acute Exercise. Cell. 2020;181(5):1112-1130.e16. doi:10.1016/j.cell.2020.04.043
Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. doi:10.1186/s13059-017-1215-1
Siopi A, Deda O, Manou V, et al. Effects of Different Exercise Modes on the Urinary Metabolic Fingerprint of Men with and without Metabolic Syndrome. Metabolites. 2017;7(1):5. doi:10.3390/metabo7010005