Evidence map›Paper›PMID 37544351›Full record

ArticleAmerican journal of obstetrics and gynecology2024

Embryonic statistical analyses reveal 2 growth phenotypes in mouse models of Down syndrome.

April D Adams, Jielu Lin, Diana W Bianchi, Lauren Bishop, Taisuke Sato, Laura L Baxter, Victoria Hoffmann, Laura Koehly, Faycal Guedj

Open access · bronzeAbstract read
In one paragraph

Article in American journal of obstetrics and gynecology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

9 authors at 1 institution in 1 country.

April D AdamsSection on Prenatal Genomics and Fetal Therapy, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD; Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX.
Jielu LinSection on Social Network Methods, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.
Diana W BianchiSection on Prenatal Genomics and Fetal Therapy, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD; Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD. Electronic address: Diana.Bianchi@nih.gov.
Lauren BishopEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD; Department of Obstetrics and Gynecology, Columbia University School of Medicine, New York, NY.
Taisuke SatoSection on Prenatal Genomics and Fetal Therapy, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.
Laura L BaxterSection on Prenatal Genomics and Fetal Therapy, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.
Victoria HoffmannDivision of Veterinary Resources, Office of the Director, National Institutes of Health, Bethesda, MD.
Laura KoehlySection on Social Network Methods, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.
Faycal GuedjSection on Prenatal Genomics and Fetal Therapy, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.
National Institutes of Health · US

Funding

Prenatal Treatment of Down Syndrome to Improve Brain Development and NeurocognitionZIAHG200399 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI BIANCHI, DIANA · 2017 to 2025
$11.1M
Intramural NIH HHS ZIA HG200399
6 · The paper itself

Abstract

backgroundDown syndrome is associated with several comorbidities, including intellectual disability, growth restriction, and congenital heart defects. The prevalence of Down syndrome-associated comorbidities is highly variable, and intellectual disability, although fully penetrant, ranges from mild to severe. Understanding the basis of this interindividual variability might identify predictive biomarkers of in utero and postnatal outcomes that could be used as endpoints to test the efficacy of future therapeutic interventions.

objectiveThe main objective of this study was to examine if antenatal interindividual variability exists in mouse models of Down syndrome and whether applying statistical approaches to clinically relevant measurements (ie, the weights of the embryo, placenta, and brain) could define cutoffs that discriminate between subgroups of trisomic embryos. STUDY

designThree commonly used mouse models of Down syndrome (Dp(16)1/Yey, Ts65Dn, and Ts1Cje) and a new model (Ts66Yah) were used in this study. Trisomic and euploid littermate embryos were used from each model with total numbers of 102 for Ts66Yah, 118 for Dp(16)1/Yey, 92 for Ts65Dn, and 126 for Ts1Cje. Placental, embryonic, and brain weights and volumes at embryonic day 18.5 were compared between genotypes in each model. K-mean clustering analysis was applied to embryonic and brain weights to identify severity classes in trisomic embryos, and brain and placental volumetric measurements were compared between genotypes and classes for each strain. In addition, Ts66Yah embryos were examined for malformations because embryonic phenotypes have never been examined in this model.

resultsReduced body and brain weights were present in Ts66Yah, Dp(16)1/Yey, and Ts65Dn embyos. Cluster analysis identified 2 severity classes in trisomic embryos-mild and severe-in all 4 models that were distinguishable using a putative embryonic weight cutoff of <0.5 standard deviation below the mean. Ts66Yah trisomic embryos develop congenital anomalies that are also found in humans with Down syndrome, including congenital heart defects and renal pelvis dilation.

conclusionStatistical approaches applied to clinically relevant measurements revealed 2 classes of phenotypic severity in trisomic mouse models of Down syndrome. Analysis of severely affected trisomic animals may facilitate the identification of biomarkers and endpoints that can be used to prenatally predict outcomes and the efficacy of treatments.

Indexed as

Down SyndromeHeart Defects, CongenitalIntellectual DisabilityAnimalsBiomarkersDisease Models, AnimalFemaleHumansMiceMice, Inbred C57BLPhenotypePlacentaPregnancyBiomarkersbiomarkersDown syndromefetal growthmouse modelsphenotype severityplacental developmentTs66Yah

Identifiers

PMID37544351
PMCPMC10841273
OpenAlexW4385606287

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.