Evidence map›Paper›PMID 34050710›Full record

ArticleESC heart failure2021

Proteomic profiling for detection of early-stage heart failure in the community.

Nicholas Cauwenberghs, František Sabovčik, Alessio Magnus, Francois Haddad, Tatiana Kuznetsova

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

Article in ESC heart failure, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

9 citing papers in PubMed, 14 citations in OpenAlex.

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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

5 authors at 3 institutions in 2 countries.

Nicholas CauwenberghsResearch Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, Leuven, B-3000, Belgium.ORCID 0000-0001-8059-7692
František SabovčikResearch Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, Leuven, B-3000, Belgium.ORCID 0000-0001-8275-6431
Alessio MagnusResearch Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, Leuven, B-3000, Belgium.
Francois HaddadStanford Cardiovascular Institute, Department of Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0002-9648-7671
Tatiana KuznetsovaResearch Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, Leuven, B-3000, Belgium.ORCID 0000-0002-6278-1979
KU Leuven · BEZero to Three · USCardiovascular Institute of the South · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsBiomarkers may provide insights into molecular mechanisms underlying heart remodelling and dysfunction. Using a targeted proteomic approach, we aimed to identify circulating biomarkers associated with early stages of heart failure. METHODS AND

resultsA total of 575 community-based participants (mean age, 57 years; 51.7% women) underwent echocardiography and proteomic profiling (CVD II panel, Olink Proteomics). We applied partial least squares-discriminant analysis (PLS-DA) and a machine learning algorithm [eXtreme Gradient Boosting (XGBoost)] to identify key proteins associated with echocardiographic abnormalities. We used Gaussian mixture modelling for unbiased clustering to construct phenogroups based on influential proteins in PLS-DA and XGBoost. Of 87 proteins, 13 were important in PLS-DA and XGBoost modelling for detection of left ventricular remodelling, left ventricular diastolic dysfunction, and/or left atrial reservoir dysfunction: placental growth factor, kidney injury molecule-1, prostasin, angiotensin-converting enzyme-2, galectin-9, cathepsin L1, matrix metalloproteinase-7, tumour necrosis factor receptor superfamily members 10A, 10B, and 11A, interleukins 6 and 16, and α1-microglobulin/bikunin precursor. Based on these proteins, the clustering algorithm divided the cohort into two distinct phenogroups, with each cluster grouping individuals with a similar protein profile. Participants belonging to the second cluster (n = 118) were characterized by an unfavourable cardiovascular risk profile and adverse cardiac structure and function. The adjusted risk of presenting echocardiographic abnormalities was higher in this phenogroup than in the other (P < 0.0001).

conclusionsWe identified proteins related to renal function, extracellular matrix remodelling, angiogenesis, and inflammation to be associated with echocardiographic signs of early-stage heart failure. Proteomic phenomapping discriminated individuals at high risk for cardiac remodelling and dysfunction.

Indexed as

Heart FailureProteomicsEchocardiographyFemaleHumansMaleMiddle AgedPlacenta Growth FactorVentricular RemodelingPlacenta Growth FactorBiomarkersEarly-stage heart failureEchocardiographyEpidemiologyProteomics

Identifiers

PMID34050710
PMCPMC8318505
OpenAlexW3165020158

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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.