Evidence mapPaperPMID 40726539Full record

ReviewJournal of molecular and cellular cardiology plus2025

Current status and challenges of multi-omics research using animal models of atherosclerosis.

Tijana Mitić, Adriana Georgescu, Nicoleta Alexandru-Moise, Michael J Davies, Cecile Vindis, Susana Novella, Eva Gerdts, Georgios Kararigas, Stephanie Bezzina Wettinger, Melissa M Formosa and 12 more

Abstract readReview
In one paragraph

Review in Journal of molecular and cellular cardiology plus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Review
  6. 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

22 authors.

Tijana MitićCentre for Cardiovascular Science (CVS), Queen's Medical Research Institute (QMRI), University of Edinburgh, Edinburgh BioQuarter, United Kingdom.
Adriana GeorgescuDepartment of Pathophysiology and Cellular Pharmacology, Institute of Cellular Biology and Pathology 'Nicolae Simionescu', Bucharest, Romania.
Nicoleta Alexandru-MoiseDepartment of Pathophysiology and Cellular Pharmacology, Institute of Cellular Biology and Pathology 'Nicolae Simionescu', Bucharest, Romania.
Michael J DaviesDepartment of Biomedical Sciences, Panum Institute, University of Copenhagen, Copenhagen, Denmark.
Cecile VindisCARDIOMET, Center for Clinical Investigation 1436 (CIC1436)/INSERM, Toulouse, France.
Susana NovellaDepartment of Physiology, University of Valencia - INCLIVA Biomedical Research Institute, Valencia, Spain.
Eva GerdtsDepartment of Clinical Science, University of Bergen, Bergen, Norway.
Georgios KararigasDepartment of Physiology, Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
Stephanie Bezzina WettingerDepartment of Applied Biomedical Science, Faculty of Health Sciences and Centre for Molecular Medicine and Biobanking, University of Malta, Malta.
Melissa M FormosaDepartment of Applied Biomedical Science, Faculty of Health Sciences and Centre for Molecular Medicine and Biobanking, University of Malta, Malta.
Brenda R KwakDepartment of Pathology and Immunology and Geneva Centre for Inflammation Research, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Filippo MolicaDepartment of Pathology and Immunology and Geneva Centre for Inflammation Research, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Nuria AmigoDepartment of Basic Medical Sciences, Universitat Rovira i Virgili, Reus, Spain.
Andrea CaporaliCentre for Cardiovascular Science (CVS), Queen's Medical Research Institute (QMRI), University of Edinburgh, Edinburgh BioQuarter, United Kingdom.
Fernando de la CuestaDepartment of Pharmacology and Therapeutics, School of Medicine, Universidad Autónoma de Madrid, Spain.
Ignacio Fernando HallCentre for Cardiovascular Science (CVS), Queen's Medical Research Institute (QMRI), University of Edinburgh, Edinburgh BioQuarter, United Kingdom.
Angeliki ChroniInstitute of Biosciences and Applications, National Center for Scientific Research "Demokritos", Athens, Greece.
Fabio MartelliMolecular Cardiology Laboratory, IRCCS Policlinico San Donato, Milan, Italy.
Johannes A SchmidInstitute of Vascular Biology and Thrombosis Research, Center for Physiology and Pharmacology, Medical University of Vienna, Austria.
Paolo MagniDepartment of Pharmacological and Biomolecular Sciences 'Rodolfo Paoletti', Università degli Studi di Milano, Milan, Italy.
Dimitris KardassisIRCCS MultiMedica, Milan, Italy.
COST Action AtheroNET (CA21153)

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atherosclerosis is an underlying cause of cardiovascular diseases (CVD) which account for most deaths worldwide. Use of diverse preclinical models of atherosclerosis has been implemental in understanding the underlying mechanisms, the implicated cell types, the genes and the molecules at play in the onset and progression of atherosclerotic plaques. Although significant research advancements have been made, further research is necessary to delve into factors influencing plaque types, site preference within the vasculature, interactions with adjacent tissues (liver, pancreas and perivascular adipose tissue), inflammation and sex-based disparities, among others. The conventional low throughput methodologies which concentrate on individual cells, genes or metabolites are inadequate to tackle the complex and heterogeneous nature of atherosclerosis. With recent advancement in multi-omics and bioinformatics, research approaches have illuminated a clearer understanding of atherosclerosis. Consequently, these advancements pave the path to design novel therapeutics to complement currently approved lipid-lowering and other effective treatments. In this article, we summarize and critically evaluate the findings derived from recent high throughput single- or multi-omic studies conducted in animal models of atherosclerosis. We also delve into the challenges associated with using experimental animals to model human atherosclerosis and contemplate the essential enhancements needed to better mimic human conditions. We further discuss the requirement of establishing a structured multi-omic database for atherosclerosis research, enabling broader access and utilisation within the scientific community.

Indexed as

Animal modelsArtificial intelligenceAtheroNETAtherosclerosisCardiovascular diseasesLipidomicsMachine learningMetabolomicsProteomicsTranscriptomics

Identifiers

PMID40726539
PMCPMC12301843

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.