Evidence map›Paper›PMID 33039031›Full record

ReviewPlacenta2020

Exploring the causes and consequences of maternal metabolic maladaptations during pregnancy: Lessons from animal models.

Amanda N Sferruzzi-Perri, Jorge Lopez-Tello, Tina Napso, Hannah E J Yong

Open access · greenAbstract readReview
In one paragraph

Review in Placenta, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
3.2field-weighted citation impact, top 6% 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

34 citing papers in PubMed, 55 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Amanda N Sferruzzi-PerriCentre for Trophoblast Research, Department of Physiology, Development and Neuroscience, Downing Street, University of Cambridge, Cambridge, CB2 3EG, UK. Electronic address: ans48@cam.ac.uk.
Jorge Lopez-TelloCentre for Trophoblast Research, Department of Physiology, Development and Neuroscience, Downing Street, University of Cambridge, Cambridge, CB2 3EG, UK.
Tina NapsoCentre for Trophoblast Research, Department of Physiology, Development and Neuroscience, Downing Street, University of Cambridge, Cambridge, CB2 3EG, UK.
Hannah E J YongCentre for Trophoblast Research, Department of Physiology, Development and Neuroscience, Downing Street, University of Cambridge, Cambridge, CB2 3EG, UK.
University of Cambridge · GB

Funding

Trophoblast mTOR: a critical hub linking maternal nutrient supply to placental function, fetal growth and fetal islet functionR01HD068370 · NICHD · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI JANSSON, THOMAS, ROSARIO-JOSEPH, FREDRICK · 2011 to 2024
$4.1M
Placenta Association of the Americas Conference GrantR13HD084096 · NICHD · UNIVERSITY OF COLORADO DENVER · PI JANSSON, THOMAS · 2015 to 2024
$74k
Medical Research Council MR/R022690/1NICHD NIH HHS R01 HD068370NICHD NIH HHS R13 HD084096
6 · The paper itself

Abstract

Pregnancy is a remarkable physiological state, during which the metabolic system of the mother adapts to ensure that nutrients are made available for transfer to the fetus for growth and development. Adaptations of maternal metabolism during pregnancy are influenced by the metabolic and nutritional status of the mother and the production of endocrine factors by the placenta that exert metabolic effects. Insufficient or inappropriate adaptations in maternal metabolism during pregnancy may lead to pregnancy complications with important short- and long-term effects for both the health of the child and mother. This is very evident in gestational diabetes, which is marked by greater glucose intolerance and insulin resistance above that expected of a normal pregnancy. Gestational diabetes is associated with increased fetal weight and/or increased adiposity, higher instrumented delivery rates and greater risks for both mother and child of developing type 2 diabetes in the long-term. However, despite the negative health impacts of such metabolic imbalances during pregnancy, the precise mechanisms responsible for orchestrating these changes remain largely unknown. The present review describes the dynamic pregnancy-specific changes that occur in the metabolic system of the mother during pregnancy. It also discusses findings using surgical, pharmacological, genetic and dietary methods in experimental animals that highlight the role of pathways in maternal tissues that lead to metabolic dysfunction, with a particular focus on gestational diabetes. Finally, it summarises the work largely employing gene targeting and hormone administration in rodents that have illuminated the involvement of placental endocrine function in driving maternal metabolic adaptations. While current animal models may not fully replicate what is observed in humans, these have been instrumental in showing that there is a dynamic interplay between changes in maternal metabolic physiology and the placental production of endocrine factors that govern the availability of nutrients to the growing fetus. However, more work is required to specifically identify the placenta-driven changes in maternal metabolic physiology that ensure the appropriate level of insulin production and action during pregnancy. In doing so, these studies may pave the way to understanding the development of pregnancy complications like gestational diabetes, as well as further our understanding of type-2 diabetes and the control of metabolic physiology more broadly.

Indexed as

Disease Models, AnimalAdaptation, PhysiologicalAnimalsDiabetes, GestationalFemalePlacentaPregnancyFetal growthGestational diabetesHormonesMetabolismNutrient partitioningPlacentaPregnancy

Identifiers

PMID33039031
PMCPMC7548399
OpenAlexW3003570789

What OpenQuestion holds

Textmetadata
LicenceTDM
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.