Evidence map›Paper›PMID 31173660›Full record

ArticleRisk analysis : an official publication of the Society for Risk Analysis2019

A Novel Approach to Chemical Mixture Risk Assessment-Linking Data from Population-Based Epidemiology and Experimental Animal Tests.

Carl-Gustaf Bornehag, Efthymia Kitraki, Antonios Stamatakis, Emily Panagiotidou, Christina Rudén, Huan Shu, Christian Lindh, Joelle Ruegg, Chris Gennings

Abstract read
In one paragraph

Article in Risk analysis : an official publication of the Society for Risk Analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
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

9 authors.

Carl-Gustaf BornehagPublic Health Sciences, Karlstad University, Karlstad, Sweden.
Efthymia KitrakiNational and Kapodistrian University of Athens, Athens, Greece.
Antonios StamatakisNational and Kapodistrian University of Athens, Athens, Greece.
Emily PanagiotidouNational and Kapodistrian University of Athens, Athens, Greece.
Christina RudénStockholm University, Stockholm, Sweden.
Huan ShuStockholm University, Stockholm, Sweden.
Christian LindhLund University, Lund, Sweden.
Joelle RueggKarolinska Institutet, Stockholm, Sweden.
Chris GenningsIcahn School of Medicine at Mount Sinai, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Humans are continuously exposed to chemicals with suspected or proven endocrine disrupting chemicals (EDCs). Risk management of EDCs presents a major unmet challenge because the available data for adverse health effects are generated by examining one compound at a time, whereas real-life exposures are to mixtures of chemicals. In this work, we integrate epidemiological and experimental evidence toward a whole mixture strategy for risk assessment. To illustrate, we conduct the following four steps in a case study: (1) identification of single EDCs ("bad actors")-measured in prenatal blood/urine in the SELMA study-that are associated with a shorter anogenital distance (AGD) in baby boys; (2) definition and construction of a "typical" mixture consisting of the "bad actors" identified in Step 1; (3) experimentally testing this mixture in an in vivo animal model to estimate a dose-response relationship and determine a point of departure (i.e., reference dose [RfD]) associated with an adverse health outcome; and (4) use a statistical measure of "sufficient similarity" to compare the experimental RfD (from Step 3) to the exposure measured in the human population and generate a "similar mixture risk indicator" (SMRI). The objective of this exercise is to generate a proof of concept for the systematic integration of epidemiological and experimental evidence with mixture risk assessment strategies. Using a whole mixture approach, we could find a higher rate of pregnant women under risk (13%) when comparing with the data from more traditional models of additivity (3%), or a compound-by-compound strategy (1.6%).

Indexed as

Environmental ExposureAnimalsComplex MixturesEndocrine DisruptorsEnvironmental PollutantsFemaleHumansInfantPregnancyRisk AssessmentComplex MixturesEndocrine DisruptorsEnvironmental PollutantsChemical exposuremixturesrisk assessmentsexual development

Identifiers

PMID31173660
PMCPMC6973107

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.