Evidence map›Paper›PMID 42384936›Full record

ReviewBiology of reproduction2026

Developmental programming in dogs.

Sylvie Chastant

Abstract readReview
In one paragraph

Review in Biology of reproduction, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Sylvie ChastantUniversité Paris-Saclay, UVSQ, INRAE, BREED, F-78350, Jouy-en-Josas, France.ORCID 0000-0003-0790-6377

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developmental programming in dogs, although less studied than in other species, encompasses both nutritional and behavioural influences during gestation and early life. The most critical window corresponds to the first 120 days (pregnancy followed but the first two months of dog's life), considered the canine equivalent of the human "first 1000 days of life." Low birth weight, result from the intra-uterine growth, is one of the most documented examples of developmental programming in this species. Its consequences extend long beyond the neonatal period: while early effects include disproportionate head development and increased mortality during the first three weeks, long-term effects include a greater predisposition to overweight in adulthood. Gestational programming is modulated by the dam's diet, which can affect birth weight, neonatal and paediatric health, and particularly inflammatory status. However, the postnatal period provides opportunities to counteract prenatal influences: the risk of neonatal mortality in low-birth-weight puppies that achieve adequate early growth during the first two days of life becomes similar to that of normal-birth-weight littermates. Conversely, excessive growth during the first weeks of life is suspected to increase the likelihood of adult overweight, highlighting the need for precise management of neonatal and paediatric growth to ensure healthy adult trajectories. Early-life behavioural programming, through maternal behaviour and environmental exposures, is also essential in shaping dogs for their future societal roles. Overall, developmental programming appears to be a key determinant of lifelong physical and mental health in dogs.

Indexed as

Fetal DevelopmentAnimalsBirth WeightDevelopmental Origins of Health and DiseaseDogsFemalePregnancyPrenatal Exposure Delayed EffectsbehaviourbreedDoHaDintra-uterine growth restrictionlow-birth-weightplacentapost-natal growth

Identifiers

PMID42384936
PMCPMC13482281

What OpenQuestion holds

Textmetadata
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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.