The Exposome in the Era of the Quantified Self.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2021-07-20 DOI:10.1146/annurev-biodatasci-012721-122807
Xinyue Zhang, Peng Gao, Michael P Snyder
{"title":"The Exposome in the Era of the Quantified Self.","authors":"Xinyue Zhang,&nbsp;Peng Gao,&nbsp;Michael P Snyder","doi":"10.1146/annurev-biodatasci-012721-122807","DOIUrl":null,"url":null,"abstract":"<p><p>Human health is regulated by complex interactions among the genome, the microbiome, and the environment. While extensive research has been conducted on the human genome and microbiome, little is known about the human exposome. The exposome comprises the totality of chemical, biological, and physical exposures that individuals encounter over their lifetimes. Traditional environmental and biological monitoring only targets specific substances, whereas exposomic approaches identify and quantify thousands of substances simultaneously using nontargeted high-throughput and high-resolution analyses. The quantified self (QS) aims at enhancing our understanding of human health and disease through self-tracking. QS measurements are critical in exposome research, as external exposures impact an individual's health, behavior, and biology. This review discusses both the achievements and the shortcomings of current research and methodologies on the QS and the exposome and proposes future research directions.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Biomedical Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1146/annurev-biodatasci-012721-122807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 10

Abstract

Human health is regulated by complex interactions among the genome, the microbiome, and the environment. While extensive research has been conducted on the human genome and microbiome, little is known about the human exposome. The exposome comprises the totality of chemical, biological, and physical exposures that individuals encounter over their lifetimes. Traditional environmental and biological monitoring only targets specific substances, whereas exposomic approaches identify and quantify thousands of substances simultaneously using nontargeted high-throughput and high-resolution analyses. The quantified self (QS) aims at enhancing our understanding of human health and disease through self-tracking. QS measurements are critical in exposome research, as external exposures impact an individual's health, behavior, and biology. This review discusses both the achievements and the shortcomings of current research and methodologies on the QS and the exposome and proposes future research directions.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
自我量化时代的暴露。
人类健康是由基因组、微生物组和环境之间复杂的相互作用调节的。虽然对人类基因组和微生物组进行了广泛的研究,但对人类暴露体知之甚少。暴露包括个人一生中所接触的化学、生物和物理的全部暴露。传统的环境和生物监测仅针对特定物质,而暴露学方法使用非靶向高通量和高分辨率分析同时识别和量化数千种物质。量化自我(QS)旨在通过自我跟踪来增强我们对人类健康和疾病的了解。QS测量在暴露研究中至关重要,因为外部暴露会影响个人的健康、行为和生物学。本文综述了目前QS和暴露点的研究成果和方法的不足,并提出了未来的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
期刊最新文献
The Evolutionary Interplay of Somatic and Germline Mutation Rates. Centralized and Federated Models for the Analysis of Clinical Data. Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication. Data Science Methods for Real-World Evidence Generation in Real-World Data. Graph Artificial Intelligence in Medicine.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1