Journal Article

Variability of multi-omics profiles in a population-based child cohort

Urquiza, Jose, Heude, Barbara, Borràs, Eva, Sabidó, Eduard, Chatzi, Leda, Casas, Maribel, Hernández-Ferrer, Carles, Bustamante, Mariona, Coen, Muireann, Maitre, Léa, Estivill, Xavier, Vrijheid, Martine, Basagaña, Xavier, Carracedo, Ángel, Barrera-Gómez, Jose, Gallego-Paüls, Marta, Wright, John, Slama, Remy, Ruiz-Arenas, Carlos, González, Juan R., Keun, Hector C., Siskos, Alexandros P., Grazuleviciene, Regina, Lau, Chung-Ho E., Vives-Usano, Marta

2021

Overview

Background Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood. Methods We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability. Results All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability. Conclusions Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Biomedicine / Body mass / Body mass index / Body size / Child / Children / Children & youth / Cluster analysis / Cohort Studies / Cross-Sectional Studies / Deoxyribonucleic acid / Diabetes / Diabetes mellitus (non-insulin dependent) / Diabetes Mellitus, Type 2 / DNA / DNA Methylation / Environmental stress / Epidemiology / Exposome / Gene expression / Health aspects / Health risks / Humans / Laboratories / Life Sciences / Medicine / Medicine & Public Health / Metabolites / Metabolomics / Methods / MicroRNAs / miRNA / Multi-omics / Network analysis / Parameters / Pediatric research / Population / Population study / Population-based studies / Pregnancy / Proteins / Proteomics / Research Article / Ribonucleic acid / RNA / Sample size / Stability analysis / Urine / Variability