Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00131149" target="_blank" >RIV/00216224:14310/23:00131149 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2305-6304/11/3/204" target="_blank" >https://www.mdpi.com/2305-6304/11/3/204</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/toxics11030204" target="_blank" >10.3390/toxics11030204</a>
Alternative languages
Result language
angličtina
Original language name
Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study
Original language description
Human health risk assessment of chemical mixtures is complex due to the almost infinite number of possible combinations of chemicals to which people are exposed to on a daily basis. Human biomonitoring (HBM) approaches can provide inter alia information on the chemicals that are in our body at one point in time. Network analysis applied to such data may provide insight into real-life mixtures by visualizing chemical exposure patterns. The identification of groups of more densely correlated biomarkers, so-called "communities", within these networks highlights which combination of substances should be considered in terms of real-life mixtures to which a population is exposed. We applied network analyses to HBM datasets from Belgium, Czech Republic, Germany, and Spain, with the aim to explore its added value for exposure and risk assessment. The datasets varied in study population, study design, and chemicals analysed. Sensitivity analysis was performed to address the influence of different approaches to standardise for creatinine content of urine. Our approach demonstrates that network analysis applied to HBM data of highly varying origin provides useful information with regards to the existence of groups of biomarkers that are densely correlated. This information is relevant for regulatory risk assessment, as well as for the design of relevant mixture exposure experiments.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Toxics
ISSN
2305-6304
e-ISSN
2305-6304
Volume of the periodical
11
Issue of the periodical within the volume
3
Country of publishing house
CH - SWITZERLAND
Number of pages
31
Pages from-to
1-31
UT code for WoS article
000958423400001
EID of the result in the Scopus database
2-s2.0-85151152404