Evidence map›Paper›PMID 37192969›Full record

ArticleiScience2023

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk.

Adam S Chan, Songhua Wu, Stephen T Vernon, Owen Tang, Gemma A Figtree, Tongliang Liu, Jean Y H Yang, Ellis Patrick

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 31% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 2 institutions in 1 country.

Adam S ChanSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
Songhua WuSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Stephen T VernonKolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia.
Owen TangCharles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
Gemma A FigtreeCharles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
Tongliang LiuSydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia.
Jean Y H YangSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
Ellis PatrickSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
The University of Sydney · AURoyal North Shore Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular disease remains a leading cause of mortality with an estimated half a billion people affected in 2019. However, detecting signals between specific pathophysiology and coronary plaque phenotypes using complex multi-omic discovery datasets remains challenging due to the diversity of individuals and their risk factors. Given the complex cohort heterogeneity present in those with coronary artery disease (CAD), we illustrate several different methods, both knowledge-guided and data-driven approaches, for identifying subcohorts of individuals with subclinical CAD and distinct metabolomic signatures. We then demonstrate that utilizing these subcohorts can improve the prediction of subclinical CAD and can facilitate the discovery of novel biomarkers of subclinical disease. Analyses acknowledging cohort heterogeneity through identifying and utilizing these subcohorts may be able to advance our understanding of CVD and provide more effective preventative treatments to reduce the burden of this disease in individuals and in society as a whole.

Indexed as

Health sciencesHuman physiologyMetabolomics

Identifiers

PMID37192969
PMCPMC10182278
OpenAlexW4364355450

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.