Publikation:
Identification of real-life mixtures using human biomonitoring data: a proof of concept study

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dc.contributor.authorMartin, Laura Rodriguez
dc.contributor.authorKolossa-Gehring, Marike
dc.contributor.authorOttenbros, Ilse
dc.contributor.authorSchmidt, Phillipp
dc.contributor.authorVogel, Nina
dc.date.accessioned2024-06-16T12:53:48Z
dc.date.available2024-06-16T12:53:48Z
dc.date.issued2023
dc.description.abstractHuman 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. © 2023 by the authorsen
dc.format.extent1 Online-Ressource (31 Seiten)
dc.format.extent15,0 MB
dc.format.mediumonline resource
dc.identifier.doihttps://doi.org/10.60810/openumwelt-855
dc.identifier.urihttps://openumwelt.de/handle/123456789/2070
dc.language.isoeng
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectHuman-Biomonitoring
dc.subjectNetzplantechnik
dc.titleIdentification of real-life mixtures using human biomonitoring data: a proof of concept study
dc.typeWissenschaftlicher Artikel
dc.type.dcmitext
dc.type.mediumcomputer
dspace.entity.typePublication
local.bibliographicCitation.journalTitleToxics
local.bibliographicCitation.originalDOI10.3390/toxics11030204
local.bibliographicCitation.volume11 (2023), Heft 3
local.collectionAufsätze
local.contributor.authorId02191913
local.contributor.authorId02191914
local.identifier.catalogId02497358
local.ingest.leader05641naa a2200000uu 4500
local.jointTitleIDENTIFICATION OF REALLIFE MIXTURES USING HUMAN BIOMONITORING DATA A PROOF OF CONCEPT STUDY
local.reviewtrue
local.sourcecatalog
local.source.urihttp://creativecommons.org/licenses/by/4.0/
local.staffPublicationtrue
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