Evidence map›Paper›PMID 37235224›Full record

ArticleToxics2023

Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept.

Miranda M Loh, Phillipp Schmidt, Yvette Christopher de Vries, Nina Vogel, Marike Kolossa-Gehring, Jelle Vlaanderen, Erik Lebret, Mirjam Luijten

Open access · goldAbstract read
In one paragraph

Article in Toxics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.1field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 3 countries.

Miranda M LohInstitute of Occupational Medicine-IOM, Edinburgh EH14 4AP, UK.ORCID 0000-0002-4285-0375
Phillipp SchmidtGerman Environment Agency (UBA), 14195 Berlin, Germany.ORCID 0000-0002-3058-0582
Yvette Christopher de VriesInstitute of Occupational Medicine-IOM, Edinburgh EH14 4AP, UK.
Nina VogelGerman Environment Agency (UBA), 14195 Berlin, Germany.ORCID 0000-0003-0295-9277
Marike Kolossa-GehringGerman Environment Agency (UBA), 14195 Berlin, Germany.
Jelle VlaanderenInstitute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands.
Erik LebretInstitute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands.
Mirjam LuijtenCenter for Health Protection, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands.ORCID 0000-0002-5277-1443
German Environment Agency · DEInstitute of Occupational Medicine · GBUtrecht University · NLNational Institute for Public Health and the Environment · NL

Funding

European Union 733032
6 · The paper itself

Abstract

Chemical mixture risk assessment has, in the past, primarily focused on exposures quantified in the external environment. Assessing health risks using human biomonitoring (HBM) data provides information on the internal concentration, from which a dose can be derived, of chemicals to which human populations are exposed. This study describes a proof of concept for conducting mixture risk assessment with HBM data, using the population-representative German Environmental Survey (GerES) V as a case study. We first attempted to identify groups of correlated biomarkers (also known as 'communities', reflecting co-occurrence patterns of chemicals) using a network analysis approach (

Indexed as

biomonitoring equivalent (BE)chemical mixtureshazard index (HI)hazard quotient (HQ)HBM4EUhealth-based guidance value (HBGV)human biomonitoring (HBM)mixture risk assessmenttoxicity weighting

Identifiers

PMID37235224
PMCPMC10223739
OpenAlexW4367051323

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.