Evidence map›Paper›PMID 30093686›Full record

ArticleScientific reports2018

Resistance to retinopathy development in obese, diabetic and hypertensive ZSF1 rats: an exciting model to identify protective genes.

Vincenza Caolo, Quentin Roblain, Julie Lecomte, Paolo Carai, Linsey Peters, Ilona Cuijpers, Emma Louise Robinson, Kasper Derks, Jurgen Sergeys, Agnès Noël and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Animal models of diabetic retinopathy.Annals of translational medicine · 2021
    Review
  3. Review
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

13 authors at 4 institutions in 2 countries.

Vincenza CaoloDepartment of Cardiovascular Sciences, Centre for Molecular and Vascular Biology, KU Leuven, Belgium. Vincenza.caolo@kuleuven.be.ORCID 0000-0001-6215-2702
Quentin RoblainDepartment of Cardiology, CARIM School for Cardiovascular Diseases Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Julie LecomteLaboratory of Tumor and Development Biology, GIGA-Cancer, University of Liège, Liège, Belgium.
Paolo CaraiDepartment of Cardiovascular Sciences, Centre for Molecular and Vascular Biology, KU Leuven, Belgium.
Linsey PetersDepartment of Cardiology, CARIM School for Cardiovascular Diseases Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Ilona CuijpersDepartment of Cardiovascular Sciences, Centre for Molecular and Vascular Biology, KU Leuven, Belgium.
Emma Louise RobinsonDepartment of Cardiology, CARIM School for Cardiovascular Diseases Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0002-4866-731X
Kasper DerksDepartment of Genetics and Cell Biology, CARIM School for Cardiovascular Diseases Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Jurgen SergeysLaboratory of Neural Circuit Development and Regeneration, Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium.ORCID 0000-0002-6418-7359
Agnès NoëlLaboratory of Tumor and Development Biology, GIGA-Cancer, University of Liège, Liège, Belgium.
Elizabeth A V JonesDepartment of Cardiovascular Sciences, Centre for Molecular and Vascular Biology, KU Leuven, Belgium.
Lieve MoonsLaboratory of Neural Circuit Development and Regeneration, Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium.ORCID 0000-0003-0186-1411
Stephane HeymansDepartment of Cardiovascular Sciences, Centre for Molecular and Vascular Biology, KU Leuven, Belgium.
Maastricht University · NLUniversity of Liège · BEVIB-KU Leuven Center for Cancer Biology · BEKU Leuven · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic retinopathy (DR) is one of the major complications of diabetes, which eventually leads to blindness. Up to date, no animal model has yet shown all the co-morbidities often observed in DR patients. Here, we investigated whether obese 42 weeks old ZSF1 rat, which spontaneously develops diabetes, hypertension and obesity, would be a suitable model to study DR. Although arteriolar tortuosity increased in retinas from obese as compared to lean (hypertensive only) ZSF1 rats, vascular density pericyte coverage, microglia number, vascular morphology and retinal thickness were not affected by diabetes. These results show that, despite high glucose levels, obese ZSF1 rats did not develop DR. Such observations prompted us to investigate whether the expression of genes, possibly able to contain DR development, was affected. Accordingly, mRNA sequencing analysis showed that genes (i.e. Npy and crystallins), known to have a protective role, were upregulated in retinas from obese ZSF1 rats. Lack of retina damage, despite obesity, hypertension and diabetes, makes the 42 weeks of age ZSF1 rats a suitable animal model to identify genes with a protective function in DR. Further characterisation of the identified genes and downstream pathways could provide more therapeutic targets for the treat DR.

Indexed as

Disease Models, AnimalAnimalsBlood GlucoseCrystallinsDiabetes Mellitus, ExperimentalDiabetic NephropathiesDiabetic RetinopathyGene Expression ProfilingHypertensionMaleMetabolic SyndromeNeuropeptide YObesityRatsRetinaBlood GlucoseCrystallinsNeuropeptide Y

Identifiers

PMID30093686
PMCPMC6085379
OpenAlexW2887576502

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.