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整合多组学与环境数据进行精准健康研究:基于产前汞诱导儿童脂肪肝疾病的新型分析框架及案例研究。

Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease.

机构信息

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

出版信息

Environ Int. 2024 Aug;190:108930. doi: 10.1016/j.envint.2024.108930. Epub 2024 Aug 3.


DOI:10.1016/j.envint.2024.108930
PMID:39128376
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11620538/
Abstract

BACKGROUND: Precision Health aims to revolutionize disease prevention by leveraging information across multiple omic datasets (multi-omics). However, existing methods generally do not consider personalized environmental risk factors (e.g., environmental pollutants). OBJECTIVE: To develop and apply a precision health framework which combines multiomic integration (including early, intermediate, and late integration, representing sequential stages at which omics layers are combined for modeling) with mediation approaches (including high-dimensional mediation to identify biomarkers, mediation with latent factors to identify pathways, and integrated/quasi-mediation to identify high-risk subpopulations) to identify novel biomarkers of prenatal mercury induced metabolic dysfunction-associated fatty liver disease (MAFLD), elucidate molecular pathways linking prenatal mercury with MAFLD in children, and identify high-risk children based on integrated exposure and multiomics data. METHODS: This prospective cohort study used data from 420 mother-child pairs from the Human Early Life Exposome (HELIX) project. Mercury concentrations were determined in maternal or cord blood from pregnancy. Cytokeratin 18 (CK-18; a MAFLD biomarker) and five omics layers (DNA Methylation, gene transcription, microRNA, proteins, and metabolites) were measured in blood in childhood (age 6-10 years). RESULTS: Each standard deviation increase in prenatal mercury was associated with a 0.11 [95% confidence interval: 0.02-0.21] standard deviation increase in CK-18. High dimensional mediation analysis identified 10 biomarkers linking prenatal mercury and CK-18, including six CpG sites and four transcripts. Mediation with latent factors identified molecular pathways linking mercury and MAFLD, including altered cytokine signaling and hepatic stellate cell activation. Integrated/quasi-mediation identified high risk subgroups of children based on unique combinations of exposure levels, omics profiles (driven by epigenetic markers), and MAFLD. CONCLUSIONS: Prenatal mercury exposure is associated with elevated liver enzymes in childhood, likely through alterations in DNA methylation and gene expression. Our analytic framework can be applied across many different fields and serve as a resource to help guide future precision health investigations.

摘要

背景:精准健康旨在通过利用多个组学数据集(多组学)中的信息来彻底改变疾病预防。然而,现有的方法通常不考虑个性化的环境风险因素(例如,环境污染物)。

目的:开发并应用一种精准健康框架,该框架将多组学整合(包括早期、中期和晚期整合,分别代表组学层用于建模的组合的连续阶段)与中介方法(包括高维中介来识别生物标志物、具有潜在因子的中介来识别途径,以及综合/拟中介来识别高危亚群)相结合,以识别产前汞诱导的代谢功能障碍相关脂肪性肝病(MAFLD)的新型生物标志物,阐明产前汞与儿童 MAFLD 之间的分子途径,并基于综合暴露和多组学数据识别高危儿童。

方法:这项前瞻性队列研究使用了来自人类早期生活外显子组(HELIX)项目的 420 对母婴对的数据。在妊娠期间从母体或脐带血中测定汞浓度。在儿童期(6-10 岁)的血液中测量了角质蛋白 18(CK-18;MAFLD 的生物标志物)和五个组学层(DNA 甲基化、基因转录、microRNA、蛋白质和代谢物)。

结果:产前汞每增加一个标准差,CK-18 就会增加 0.11 [95%置信区间:0.02-0.21]个标准差。高维中介分析确定了 10 个连接产前汞和 CK-18 的生物标志物,包括 6 个 CpG 位点和 4 个转录本。潜在因子介导分析确定了连接汞和 MAFLD 的分子途径,包括细胞因子信号改变和肝星状细胞激活。综合/拟中介根据暴露水平、组学谱(由表观遗传标记驱动)和 MAFLD 的独特组合,确定了高危儿童亚组。

结论:产前汞暴露与儿童期肝酶升高有关,可能是通过 DNA 甲基化和基因表达的改变。我们的分析框架可以应用于许多不同的领域,并作为资源帮助指导未来的精准健康研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8b7/11620538/c7d2f45883db/nihms-2036061-f0006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8b7/11620538/f0291c2c9566/nihms-2036061-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8b7/11620538/700bd4360ecf/nihms-2036061-f0003.jpg
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本文引用的文献

[1]
A precision environmental health approach to prevention of human disease.

Nat Commun. 2023-4-28

[2]
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Nat Commun. 2022-11-21

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J Am Stat Assoc. 2022

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PLoS Genet. 2022-5

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