Evidence map›Paper›PMID 35995802›Full record

ArticleNPJ biofilms and microbiomes2022

Phenotypic correlates of the working dog microbiome.

Hillary A Craddock, Anastasia Godneva, Daphna Rothschild, Yair Motro, Dan Grinstein, Yuval Lotem-Michaeli, Tamar Narkiss, Eran Segal, Jacob Moran-Gilad

Open access · goldAbstract read
In one paragraph

Article in NPJ biofilms and microbiomes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

22 citing papers in PubMed, 31 citations in OpenAlex.

  1. Review
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  6. The Role of Olfaction in Dogs: Evolution, Biology, and Human-Oriented Work.Animals : an open access journal from MDPI · 2026
    Review
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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 at 3 institutions in 1 country.

Hillary A Craddock *MAGICAL Group, Department of Health Policy and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID 0000-0002-0104-3139
Anastasia Godneva *Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
Daphna RothschildDepartment of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.ORCID 0000-0002-0872-8624
Yair MotroMAGICAL Group, Department of Health Policy and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID 0000-0003-1289-6919
Dan GrinsteinMAGICAL Group, Department of Health Policy and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Yuval Lotem-MichaeliMAGICAL Group, Department of Health Policy and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Tamar NarkissIsrael Defense Forces Medical Corps, Tel Hashomer, Ramat Gan, Israel.
Eran SegalDepartment of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
Jacob Moran-GiladMAGICAL Group, Department of Health Policy and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel. giladko@post.bgu.ac.il.
Ben-Gurion University of the Negev · ILWeizmann Institute of Science · ILIsrael Defense Forces Medical Corps · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dogs have a key role in law enforcement and military work, and research with the goal of improving working dog performance is ongoing. While there have been intriguing studies from lab animal models showing a potential connection between the gut microbiome and behavior or mental health there is a dearth of studies investigating the microbiome-behavior relationship in working dogs. The overall objective of this study was to characterize the microbiota of working dogs and to determine if the composition of the microbiota is associated with behavioral and performance outcomes. Freshly passed stools from each working canine (Total n = 134) were collected and subject to shotgun metagenomic sequencing using Illumina technology. Behavior, performance, and demographic metadata were collected. Descriptive statistics and prediction models of behavioral/phenotypic outcomes using gradient boosting classification based on Xgboost were used to study associations between the microbiome and outcomes. Regarding machine learning methodology, only microbiome features were used for training and predictors were estimated in cross-validation. Microbiome markers were statistically associated with motivation, aggression, cowardice/hesitation, sociability, obedience to one trainer vs many, and body condition score (BCS). When prediction models were developed based on machine learning, moderate predictive power was observed for motivation, sociability, and gastrointestinal issues. Findings from this study suggest potential gut microbiome markers of performance and could potentially advance care for working canines.

Indexed as

Gastrointestinal MicrobiomeMicrobiotaAnimalsDogsMetagenomeMetagenomicsWorking Dogs

Identifiers

PMID35995802
PMCPMC9395329
OpenAlexW4292553621

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.