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

Area of Science:

• Environmental epigenetics
• Molecular epidemiology
• Systems biology

Background:

• Multi-omics technologies are crucial for detecting early molecular responses to environmental stressors.
• Evaluating the stability and variability of omics profiles in healthy children is essential for future disease risk prediction.
• Childhood represents a critical window for understanding long-term health trajectories.

Purpose of the Study:

• To assess the intra-, inter-individual, and cohort variability of multi-omics profiles in healthy children.
• To identify co-varying omics features using multi-omics network analysis.
• To determine the influence of biological traits and sample collection on omics variability.

Main Methods:

• Measurement of blood DNA methylation, gene expression, miRNA, proteins, and serum/urine metabolites.
• Data collection from 156 healthy children across five European countries, 6 months apart.
• Multi-omics network analysis and assessment of explanatory variables.

Main Results:

• All omics showed significant intra- and inter-individual variability, with intra-individual variability often being highest.
• DNA methylation exhibited the highest stability (37.6% inter-individual variability), while gene expression was least stable (6.6%).
• Cross-omics co-variation was observed, linking CpGs to metabolites (e.g., glucose and obesity-related CpGs).

Conclusions:

• DNA methylation and targeted serum metabolomics are reliable for single time-point measurements in large cross-sectional studies.
• Controlling for sample collection and individual traits (e.g., BMI) is vital for metabolomics comparability.
• Findings aid in designing and interpreting epidemiological studies linking omics to disease and environmental exposures.