Evidence map›Paper›PMID 35466367›Full record

ArticleThe Journal of antimicrobial chemotherapy2022

Antimicrobial resistance genes aph(3')-III, erm(B), sul2 and tet(W) abundance in animal faeces, meat, production environments and human faeces in Europe.

Dongsheng Yang, Dick J J Heederik, Peter Scherpenisse, Liese Van Gompel, Roosmarijn E C Luiken, Katharina Wadepohl, Magdalena Skarżyńska, Eri Van Heijnsbergen, Inge M Wouters, Gerdit D Greve and 11 more

Abstract read
In one paragraph

Article in The Journal of antimicrobial chemotherapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. One Health, One Microbiome.Microbiome · 2025
    Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Recent Trends of Antibiotic Resistance inAntibiotics (Basel, Switzerland) · 2023
    Article
  14. 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

21 authors.

Dongsheng YangInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.ORCID 0000-0001-9473-2744
Dick J J HeederikInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Peter ScherpenisseInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Liese Van GompelInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.ORCID 0000-0002-6185-219X
Roosmarijn E C LuikenInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Katharina WadepohlAußenstelle für Epidemiologie, Tierärztliche Hochschule Hannover, Hannover, Germany.
Magdalena SkarżyńskaDepartment of Microbiology, National Veterinary Research Institute, Pulawy, Poland.
Eri Van HeijnsbergenInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Inge M WoutersInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Gerdit D GreveInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Betty G M Jongerius-GortemakerInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Monique Tersteeg-ZijderveldInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Lützen PortengenInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Katharina JuraschekDepartment of Biological Safety, German Federal Institute for Risk Assessment, Berlin, Germany.
Jennie FischerDepartment of Biological Safety, German Federal Institute for Risk Assessment, Berlin, Germany.
Magdalena ZającDepartment of Microbiology, National Veterinary Research Institute, Pulawy, Poland.ORCID 0000-0002-8155-7614
Dariusz WasylDepartment of Microbiology, National Veterinary Research Institute, Pulawy, Poland.
Jaap A WagenaarDepartment of Infectious Diseases and Immunology, Utrecht University, Utrecht, The Netherlands.
Dik J MeviusDepartment of Infectious Diseases and Immunology, Utrecht University, Utrecht, The Netherlands.
Lidwien A M SmitInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.ORCID 0000-0003-0292-0946
Heike SchmittInstitute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.

Funding

China Scholarships Council 201709110149EFFORT 613754Polish Ministry of Science 3173/7PR/2014/2
6 · The paper itself

Abstract

backgroundReal-time quantitative PCR (qPCR) is an affordable method to quantify antimicrobial resistance gene (ARG) targets, allowing comparisons of ARG abundance along animal production chains.

objectivesWe present a comparison of ARG abundance across various animal species, production environments and humans in Europe. AMR variation sources were quantified. The correlation of ARG abundance between qPCR data and previously published metagenomic data was assessed.

methodsA cross-sectional study was conducted in nine European countries, comprising 9572 samples. qPCR was used to quantify abundance of ARGs [aph(3')-III, erm(B), sul2, tet(W)] and 16S rRNA. Variance component analysis was conducted to explore AMR variation sources. Spearman's rank correlation of ARG abundance values was evaluated between pooled qPCR data and earlier published pooled metagenomic data.

resultsARG abundance varied strongly among animal species, environments and humans. This variation was dominated by between-farm variation (pigs) or within-farm variation (broilers, veal calves and turkeys). A decrease in ARG abundance along pig and broiler production chains ('farm to fork') was observed. ARG abundance was higher in farmers than in slaughterhouse workers, and lowest in control subjects. ARG abundance showed a high correlation (Spearman's ρ > 0.7) between qPCR data and metagenomic data of pooled samples.

conclusionsqPCR analysis is a valuable tool to assess ARG abundance in a large collection of livestock-associated samples. The between-country and between-farm variation of ARG abundance could partially be explained by antimicrobial use and farm biosecurity levels. ARG abundance in human faeces was related to livestock antimicrobial resistance exposure.

Indexed as

Anti-Bacterial AgentsAnti-Infective AgentsAnimalsCattleChickensCross-Sectional StudiesDrug Resistance, BacterialFecesGenes, BacterialHumansLivestockMeatRNA, Ribosomal, 16SSwineAnti-Bacterial AgentsAnti-Infective AgentsRNA, Ribosomal, 16S

Identifiers

PMID35466367
PMCPMC9244224

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LicenceCC BY-NC
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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.