Evidence map›Paper›PMID 36943304›Full record

ArticleEuropean journal of preventive cardiology2023

Prediction of coronary artery disease using urinary proteomics.

Dongmei Wei, Jesus D Melgarejo, Lucas Van Aelst, Thomas Vanassche, Peter Verhamme, Stefan Janssens, Karlheinz Peter, Zhen-Yu Zhang

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In one paragraph

Article in European journal of preventive cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

10 citing papers in PubMed, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Dongmei WeiStudies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium.ORCID 0000-0001-7010-468X
Jesus D MelgarejoStudies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium.ORCID 0000-0002-1382-1153
Lucas Van AelstDivision of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium.
Thomas VanasscheDivision of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium.ORCID 0000-0002-7404-8918
Peter VerhammeDivision of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium.ORCID 0000-0001-8698-2858
Stefan JanssensDivision of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium.ORCID 0000-0002-3609-1122
Karlheinz PeterBaker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne VIC 3004, Australia.ORCID 0000-0002-8040-2258
Zhen-Yu ZhangStudies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium.ORCID 0000-0002-3785-7417
KU Leuven · BEBaker Heart and Diabetes Institute · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsCoronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD. METHODS AND

resultsUrinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly).

conclusionA novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.

Indexed as

Coronary Artery DiseaseBiomarkersCollagenHumansInflammationPeptidesProteomeProteomicsBiomarkersCollagenPeptidesProteomeAtherosclerosisCollagen turnoverCoronary artery diseasesProteomicsUrine

Identifiers

PMID36943304
OpenAlexW4328049019

What OpenQuestion holds

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Registered trials

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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.