2026-04-27T01:59:04Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/161052025-05-12T09:41:41Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19610

00925njm 22002777a 4500 dc Rodriguez Martin, Laura author Ottenbros, Ilse author Vogel, Nina author Kolossa-Gehring, Marike author Schmidt, Phillipp author Řiháčková, Katarína author Juliá Molina, Miguel author Varea-Jiménez, Elena author Govarts, Eva author Pedraza-Diaz, Susana author Lebret, Erik author Vlaanderen, Jelle author Luijten, Mirjam author 2023-02-22 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. Toxics. 2023 Feb 22;11(3):204. http://hdl.handle.net/20.500.12105/16105 36976969 10.3390/toxics11030204 2305-6304 Toxics HBM4EU Chemical mixtures Clustering Combined exposure Human biomonitoring Mixture risk assessment Network analysis Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study