Evidence map›Paper›PMID 37371497›Full record

ArticleBiomolecules2023

Lipidomics Profiling and Risk of Coronary Artery Disease in the BioHEART-CT Discovery Cohort.

Dantong Zhu, Stephen T Vernon, Zac D'Agostino, Jingqin Wu, Corey Giles, Adam S Chan, Katharine A Kott, Michael P Gray, Alireza Gholipour, Owen Tang and 6 more

Abstract read
In one paragraph

Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Review
  12. 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

16 authors.

Dantong ZhuSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.
Stephen T VernonKolling Institute of Medical Research, The University of Sydney, Sydney, NSW 2065, Australia.
Zac D'AgostinoSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.
Jingqin WuBaker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.ORCID 0000-0002-2052-3875
Corey GilesBaker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.ORCID 0000-0002-6050-1259
Adam S ChanSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.
Katharine A KottKolling Institute of Medical Research, The University of Sydney, Sydney, NSW 2065, Australia.ORCID 0000-0001-9923-2722
Michael P GrayKolling Institute of Medical Research, The University of Sydney, Sydney, NSW 2065, Australia.ORCID 0000-0003-1154-3011
Alireza GholipourCharles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.
Owen TangKolling Institute of Medical Research, The University of Sydney, Sydney, NSW 2065, Australia.ORCID 0000-0001-5878-1311
Habtamu B BeyeneBaker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.ORCID 0000-0001-7075-6629
Ellis PatrickSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.
Stuart M GrieveCharles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.
Peter J MeikleBaker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.ORCID 0000-0002-2593-4665
Gemma A FigtreeKolling Institute of Medical Research, The University of Sydney, Sydney, NSW 2065, Australia.
Jean Y H YangSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (

Indexed as

Coronary Artery DiseaseMyocardial InfarctionPlaque, AtheroscleroticBiomarkersCoronary AngiographyHumansLipidomicsLipidsRisk AssessmentTomography, X-Ray ComputedBiomarkersLipidsCADimaging technologylipidomicstraditional risk factor

Identifiers

PMID37371497
PMCPMC10296370

What OpenQuestion 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 OpenQuestion graph.