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Exposure assessment in early life: it is about time for multi-omics approaches
BMC Medicine ( IF 7.0 ) Pub Date : 2021-08-27 , DOI: 10.1186/s12916-021-02088-0
Jantje Goerdten ₁ , Anna Floegel ₁

Affiliation

Multi-omics data, such as epigenomics, transcriptomics, proteomics, and metabolomics, are increasingly used to detect molecular responses to environmental exposures [1]. The integration of several omics layers can inform changes occurring in the structure, function, and dynamics of the body on a cellular level [2]. The correct identification of the effects of environmental exposures on the body is crucial in understanding disease development and progression [3]. Early life, i.e., pregnancy and childhood, is regarded as the most crucial periods of the developmental stages [4]. Environmental exposures in early life can have far reaching influences on the wellbeing and health of the individual [4]. Hence, the identification of these exposures and effects on the organism becomes critical.

Variability of omics biomarkers in epidemiological studies

Omics biomarkers can pose objective measures of exposure, as they might be able to depict “true” exposure in individuals, i.e., the average exposure over a month or year [5]. Exposure biomarkers measure the extrinsic variables individuals are exposed to, for example, diet, tobacco smoke, pesticides, and air pollution. However, the technical and biological variability of omics profiles needs to be assessed. Epidemiological studies predominantly rely on single measurements [6]; hence, the biological variability of omics profiles should be known to interpret changes and classify individuals correctly. High variability can lead to biased results, namely misclassification bias which leads to incorrect effect estimates [7]. Variability can be influenced by the circadian rhythm, season, or individual characteristics and can be categorized into within-individual variability and between-individual variability [5]. Thereby, between-individual variability is desired to be higher than within-individual variability, so the investigated changes are due to differences between the subjects. Another important source of variability that has to be considered in omics studies is the technical variability that is derived from the laboratory methods and procedures [5]. This becomes a crucial issue when measuring numerous compounds with omics technologies in a large set of samples as in epidemiological studies. Technical variability in omics data includes random measurement errors that reduce statistical power [5], but also systematic measurement errors, such as batch effects, that lead to biased results. Technical variability needs to be addressed, e.g., by running quality controls, standardized procedures, normalization of the data, replication of the analysis, statistical adjustment, and proper randomization of the samples according to the study design [8].

Studying variability of multi-omics layers in early life

In the present study of Gallego-Paüls et al. [9], the variability of six omics layers, namely blood DNA methylation, gene expression, miRNA, proteins, and serum and urine metabolites, from 156 children are evaluated. These children from six European cohort studies participate in the Human Early-Life Exposome (HELIX) project and have been sampled at two time points around 6 months apart. This study comprehensively assesses the within-individual and between-individual variability of each omics layer, evaluates the interrelationships between the variability in the omics layers, and analyzes the influence of several factors on the variability. The authors report large heterogeneity between the omics layers and their variability. Age, body-mass-index (BMI), and hour of collection are the characteristics with the highest influence on the variability, while variability due to cohort membership is small. DNA methylation and serum metabolites are the most stable features, with high between-individual variability and lower within-individual variability. Proteins and urinary metabolites present somewhat more variability; however, they show great heterogeneity between the features. Lastly, gene expression presents the highest variability and is therefore the least stable omics layer.

The HELIX project set out to establish the early life exposome which includes the complete set of exposures individuals are exposed to during pregnancy and childhood [3]. To correctly measure all exposures during early life, the uncertainty in exposure estimates needs to be assessed. Therefore, each omics layer should be reliable, i.e., present small variability over time in an individual. These results show the various variability profiles of the omics layers and their features in European children. The discovered influences on variability advance the understanding and interpretation of individual omics changes in children. Nevertheless, these results are based on a small sample of children and need to be replicated in larger and broader populations of children. The great strengths of this study are the inclusion of healthy children from different European countries and the evaluation of several omics layers. On the other hand, only a targeted analysis was performed for proteomics and metabolomics, and the use of untargeted analysis would result in a broader picture of all features belonging in the different layers. Furthermore, no technical replicate samples were analyzed in the laboratory; hence, the technical variability was only to some extent controlled for. Last but not the least, dietary intake on the day before sample collection was not controlled for; however, it may particularly impact urinary metabolites and may be one reason for the lower reliability observed here.

The study by Gallego-Paüls et al. demonstrates that single measurements of DNA methylation and serum metabolites can be used to depict exposure in children over 6 months. In contrast, proteins, urinary metabolites, and gene expression are rather used to depict a short-term exposure in children. However, for proteins and urinary metabolites, it is also highly dependent on the individual compounds as some of them are more reliable than others. The variability of the omics profiles can be reduced by adjusting for age and BMI and by following standard protocols for sample collection. Despite some limitations, the present study reports on an important topic, i.e., the omics-based exposure assessment in children, which has been understudied. It further paves the road for future large epidemiological studies that plan to include multiple omics layers.

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Gallego-Paüls M, Hernández-Ferrer C, Bustamente M, Basagaña X, Barrera-Gómez J, Lau CHE, et al. Variability of multi-omics profiles in a population-based child cohort. BMC Med. 2021;19(1):166. https://doi.org/10.1186/s12916-021-02027-z.
CAS Article PubMed PubMed Central Google Scholar

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The research of Jantje Goerdten and Anna Floegel is currently funded by the German Research Foundation (DFG FL 884/3-1) and the Agence Nationale de La Recherche. The funding bodies had no influence on the writing of the commentary.

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Unit Molecular Epidemiology, Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Achterstraße 30, 28359, Bremen, Germany
Jantje Goerdten & Anna Floegel

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JG and AF wrote the commentary. Both authors read and approved the final manuscript.

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Correspondence to Anna Floegel.

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Cite this article

Goerdten, J., Floegel, A. Exposure assessment in early life: it is about time for multi-omics approaches. BMC Med 19, 210 (2021). https://doi.org/10.1186/s12916-021-02088-0

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Received: 06 August 2021
Accepted: 06 August 2021
Published: 27 August 2021
DOI: https://doi.org/10.1186/s12916-021-02088-0

Keywords

Omics
Reliability
Variability
Exposure assessment
Children
DNA methylation
Gene expression
miRNA
Proteins
Metabolites

中文翻译：

生命早期的暴露评估：是时候采用多组学方法了

表观基因组学、转录组学、蛋白质组学和代谢组学等多组学数据越来越多地用于检测对环境暴露的分子反应[1]。多个组学层的整合可以在细胞水平上告知身体结构、功能和动力学发生的变化[2]。正确识别环境暴露对身体的影响对于了解疾病的发展和进展至关重要 [3]。早期，即妊娠和儿童期，被认为是发育阶段中最关键的时期[4]。生命早期的环境暴露会对个人的福祉和健康产生深远的影响 [4]。因此，确定这些暴露和对生物体的影响变得至关重要。

流行病学研究中组学生物标志物的变异性

组学生物标志物可以提出客观的暴露量度，因为它们可能能够描述个体的“真实”暴露量，即一个月或一年的平均暴露量[5]。暴露生物标志物测量个体暴露于例如饮食、烟草烟雾、杀虫剂和空气污染的外在变量。然而，需要评估组学特征的技术和生物学变异性。流行病学研究主要依赖于单一测量[6]；因此，应该知道组学特征的生物学变异性，以正确解释变化和对个体进行分类。高可变性会导致有偏差的结果，即错误分类偏差会导致不正确的效果估计 [7]。昼夜节律、季节、或个体特征，可分为个体内变异性和个体间变异性[5]。因此，希望个体间变异性高于个体内变异性，因此所研究的变化是由于受试者之间的差异。在组学研究中必须考虑的另一个重要变异来源是源自实验室方法和程序的技术变异[5]。在流行病学研究中，当使用组学技术在大量样本中测量大量化合物时，这成为一个关键问题。组学数据的技术变异性包括降低统计功效的随机测量误差 [5]，以及导致结果有偏差的系统测量误差，例如批次效应。

研究生命早期多组学层的变异性

在目前对 Gallego-Paüls 等人的研究中。[9] 评估了来自 156 名儿童的六个组学层的变异性，即血液 DNA 甲基化、基因表达、miRNA、蛋白质以及血清和尿液代谢物。这些来自六项欧洲队列研究的儿童参与了人类早期生命暴露组 (HELIX) 项目，并在相隔 6 个月左右的两个时间点进行了采样。本研究综合评估各组学层的个体内和个体间变异性，评价组学层间变异性之间的相互关系，分析多个因素对变异性的影响。作者报告了组学层之间的巨大异质性及其可变性。年龄、体重指数 (BMI) 和采集时间是对变异性影响最大的特征，而由于队列成员的变异性很小。DNA甲基化和血清代谢物是最稳定的特征，具有较高的个体间变异性和较低的个体内变异性。蛋白质和尿液代谢物呈现出更多的变异性；然而，它们在特征之间表现出很大的异质性。最后，基因表达具有最高的可变性，因此是最不稳定的组学层。

HELIX 项目着手建立早期生命暴露组，其中包括个人在怀孕和儿童期间暴露的完整组 [3]。为了正确测量生命早期的所有暴露，需要评估暴露估计的不确定性。因此，每个组学层都应该是可靠的，即个体随时间的变化很小。这些结果显示了欧洲儿童组学层的各种变异特征及其特征。发现的对变异性的影响促进了对儿童个体组学变化的理解和解释。然而，这些结果基于一小部分儿童样本，需要在更大范围的儿童群体中进行复制。这项研究的巨大优势在于纳入了来自不同欧洲国家的健康儿童以及对多个组学层的评估。另一方面，仅对蛋白质组学和代谢组学进行了靶向分析，而使用非靶向分析将导致更广泛地了解属于不同层的所有特征。此外，实验室没有分析技术重复样品；因此，仅在一定程度上控制了技术可变性。最后但并非最不重要的是，样本采集前一天的饮食摄入量没有受到控制；然而，它可能特别影响尿液代谢物，并且可能是此处观察到的可靠性较低的原因之一。仅对蛋白质组学和代谢组学进行了靶向分析，使用非靶向分析将导致更广泛地了解属于不同层的所有特征。此外，实验室没有分析技术重复样品；因此，仅在一定程度上控制了技术可变性。最后但并非最不重要的是，样本采集前一天的饮食摄入量没有受到控制；然而，它可能特别影响尿液代谢物，并且可能是此处观察到的可靠性较低的原因之一。仅对蛋白质组学和代谢组学进行了靶向分析，使用非靶向分析将导致更广泛地了解属于不同层的所有特征。此外，实验室没有分析技术重复样品；因此，仅在一定程度上控制了技术可变性。最后但并非最不重要的是，样本采集前一天的饮食摄入量没有受到控制；然而，它可能特别影响尿液代谢物，并且可能是此处观察到的可靠性较低的原因之一。最后但并非最不重要的是，样本采集前一天的饮食摄入量没有受到控制；然而，它可能特别影响尿液代谢物，并且可能是此处观察到的可靠性较低的原因之一。最后但并非最不重要的是，样本采集前一天的饮食摄入量没有受到控制；然而，它可能特别影响尿液代谢物，并且可能是此处观察到的可靠性较低的原因之一。

Gallego-Paüls 等人的研究。表明 DNA 甲基化和血清代谢物的单一测量可用于描述 6 个月以上儿童的暴露情况。相比之下，蛋白质、尿液代谢物和基因表达更常用于描述儿童的短期暴露。然而，对于蛋白质和尿液代谢物，它也高度依赖于单个化合物，因为其中一些比其他化合物更可靠。通过调整年龄和 BMI 以及遵循样本收集的标准协议，可以减少组学特征的可变性。尽管存在一些限制，但本研究报告了一个重要主题，即基于组学的儿童暴露评估，该主题尚未得到充分研究。它进一步为未来计划包括多个组学层的大型流行病学研究铺平了道路。

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Jantje Goerdten 和 Anna Floegel 的研究目前由德国研究基金会 (DFG FL 884/3-1) 和 Agence Nationale de La Recherche 资助。资助机构对评论的撰写没有影响。

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