Evidence map›Paper›PMID 38132464›Full record

ReviewCurrent issues in molecular biology2023

A Systematic Review of Lipid-Focused Cardiovascular Disease Research: Trends and Opportunities.

Uchenna Alex Anyaegbunam, Piyush More, Jean-Fred Fontaine, Vincent Ten Cate, Katrin Bauer, Ute Distler, Elisa Araldi, Laura Bindila, Philipp Wild, Miguel A Andrade-Navarro

Open access · goldAbstract readReview
In one paragraph

Review in Current issues in molecular biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 26% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 1 institution in 1 country.

Uchenna Alex AnyaegbunamComputational Biology and Data Mining Group (CBDM), Institute of Organismic and Molecular Evolution (iOME), Johannes Gutenberg University, 55122 Mainz, Germany.
Piyush MoreComputational Biology and Data Mining Group (CBDM), Institute of Organismic and Molecular Evolution (iOME), Johannes Gutenberg University, 55122 Mainz, Germany.ORCID 0000-0002-6232-6124
Jean-Fred FontaineComputational Biology and Data Mining Group (CBDM), Institute of Organismic and Molecular Evolution (iOME), Johannes Gutenberg University, 55122 Mainz, Germany.ORCID 0000-0002-1101-4091
Vincent Ten CatePreventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany.
Katrin BauerPreventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany.
Ute DistlerInstitute of Immunology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany.ORCID 0000-0002-8031-6384
Elisa AraldiPreventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany.
Laura BindilaInstitute of Physiological Chemistry, University Medical Center, 55131 Mainz, Germany.ORCID 0000-0001-5538-2008
Philipp WildPreventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany.ORCID 0000-0003-4413-9752
Miguel A Andrade-NavarroComputational Biology and Data Mining Group (CBDM), Institute of Organismic and Molecular Evolution (iOME), Johannes Gutenberg University, 55122 Mainz, Germany.ORCID 0000-0001-6650-1711
Johannes Gutenberg University Mainz · DE

Funding

Federal Ministry of Education and Research Diasym
6 · The paper itself

Abstract

Lipids are important modifiers of protein function, particularly as parts of lipoproteins, which transport lipophilic substances and mediate cellular uptake of circulating lipids. As such, lipids are of particular interest as blood biological markers for cardiovascular disease (CVD) as well as for conditions linked to CVD such as atherosclerosis, diabetes mellitus, obesity and dietary states. Notably, lipid research is particularly well developed in the context of CVD because of the relevance and multiple causes and risk factors of CVD. The advent of methods for high-throughput screening of biological molecules has recently resulted in the generation of lipidomic profiles that allow monitoring of lipid compositions in biological samples in an untargeted manner. These and other earlier advances in biomedical research have shaped the knowledge we have about lipids in CVD. To evaluate the knowledge acquired on the multiple biological functions of lipids in CVD and the trends in their research, we collected a dataset of references from the PubMed database of biomedical literature focused on plasma lipids and CVD in human and mouse. Using annotations from these records, we were able to categorize significant associations between lipids and particular types of research approaches, distinguish non-biological lipids used as markers, identify differential research between human and mouse models, and detect the increasingly mechanistic nature of the results in this field. Using known associations between lipids and proteins that metabolize or transport them, we constructed a comprehensive lipid-protein network, which we used to highlight proteins strongly connected to lipids found in the CVD-lipid literature. Our approach points to a series of proteins for which lipid-focused research would bring insights into CVD, including Prostaglandin G/H synthase 2 (PTGS2, a.k.a. COX2) and Acylglycerol kinase (AGK). In this review, we summarize our findings, putting them in a historical perspective of the evolution of lipid research in CVD.

Indexed as

cardiovascular diseaseslipid-protein networklipidslipoproteinsresearch trends

Identifiers

PMID38132464
PMCPMC10742042
OpenAlexW4389541952

What Socratic holds

Textmetadata
LicenceCC BY
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.