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. 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. 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. 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.

中文翻译：

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基于人群的儿童队列中多组学概况的变异性


多种组学技术越来越多地应用于检测对环境压力源的早期、微妙的分子反应，以预防未来的疾病风险。然而，迫切需要进一步评估健康个体（尤其是儿童时期）组学特征的稳定性和变异性。我们的目的是评估多组学特征（血液 DNA 甲基化、基因表达、miRNA、蛋白质以及血清和尿液代谢物）的内部、个体间和队列变异性，这些变异性对来自五个欧洲国家的 156 名健康儿童进行了 6 个月的测量。我们进一步进行了多组学网络分析，以建立共变组学特征的聚类，并评估关键变量（包括生物性状和样本收集参数）对组学变异性的贡献。根据每个组学特征，所有组学都显示出大范围的个体内和个体间变异性，尽管所有组学都呈现出最高的中位个体内变异性。 DNA 甲基化是最稳定的（个体间变异中位数为 37.6%），而基因表达最不稳定（6.6%）。在最不稳定的特征中，我们发现了 CpG 和代谢物之间 1% 的跨组学共变（例如，葡萄糖和与肥胖和 2 型糖尿病相关的 CpG）。包括年龄和体重指数 (BMI) 在内的解释变量可以解释高达 9% 的血清代谢变异性。甲基化和靶向血清代谢组学是在大型横断面研究中单时间点测量中实施的最可靠的组学。就代谢组学而言，样本收集和个体性状（例如BMI）是在研究设计或分析阶段控制以提高可比性的重要参数。 这项研究对于流行病学研究的设计和解释非常有价值，这些研究旨在将组学特征与疾病、环境暴露或两者联系起来。