Evidence map›Paper›PMID 31148414›Full record

ArticleESC heart failure2019

The metabolites urobilin and sphingomyelin (30:1) are associated with incident heart failure in the general population.

Markus Stenemo, Andrea Ganna, Samira Salihovic, Christoph Nowak, Johan Sundström, Vilmantas Giedraitis, Corey D Broeckling, Jessica E Prenni, Per Svensson, Patrik K E Magnusson and 4 more

Open access · goldAbstract read
In one paragraph

Article in ESC heart failure, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 20% 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 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

14 authors at 7 institutions in 3 countries.

Markus StenemoDepartment of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, EpiHubben, MTC-huset, 75185, Uppsala, Sweden.
Andrea GannaAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
Samira SalihovicDepartment of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, EpiHubben, MTC-huset, 75185, Uppsala, Sweden.
Christoph NowakDivision of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden.
Johan SundströmDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.
Vilmantas GiedraitisDepartment of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden.
Corey D BroecklingProteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA.
Jessica E PrenniProteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA.
Per SvenssonDepartment of Clinical Science and Education, Department of Cardiology, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
Patrik K E MagnussonDepartment of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet, Stockholm, Sweden.
Lars LindDepartment of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, Sweden.
Erik IngelssonDepartment of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, EpiHubben, MTC-huset, 75185, Uppsala, Sweden.
Johan ÄrnlövDivision of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden.
Tove FallDepartment of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, EpiHubben, MTC-huset, 75185, Uppsala, Sweden.
Uppsala University · SEKarolinska Institutet · SEColorado State University · USMassachusetts General Hospital · USScience for Life Laboratory · SEStockholm South General Hospital · SEThe George Institute for Global Health · AU

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Hjärt-LungfondenHorizon 2020 Framework Programme 634869NIDDK NIH HHS P30 DK116074Vetenskapsrådet 2012-02215Vetenskapsrådet 2017-00641
6 · The paper itself

Abstract

aimsWe aimed to investigate whether metabolomic profiling of blood can lead to novel insights into heart failure pathogenesis or improved risk prediction. METHODS AND

resultsMass spectrometry-based metabolomic profiling was performed in plasma or serum samples from three community-based cohorts without heart failure at baseline (total n = 3924; 341 incident heart failure events; median follow-up ranging from 4.6 to 13.9 years). Cox proportional hazard models were applied to assess the association of each of the 206 identified metabolites with incident heart failure in the discovery cohorts Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) (n = 920) and Uppsala Longitudinal Study of Adult Men (ULSAM) (n = 1121). Replication was undertaken in the independent cohort TwinGene (n = 1797). We also assessed whether metabolites could improve the prediction of heart failure beyond established risk factors (age, sex, body mass index, low-density and high-density lipoprotein cholesterol, triglycerides, lipid medication, diabetes, systolic and diastolic blood pressure, blood pressure medication, glomerular filtration rate, smoking status, and myocardial infarction prior to or during follow-up). Higher circulating urobilin and lower sphingomyelin (30:1) were associated with incident heart failure in age-adjusted and sex-adjusted models in the discovery and replication sample. The hazard ratio for urobilin in the replication cohort was estimated to 1.29 per standard deviation unit, 95% confidence interval (CI 1.03-1.63), and for sphingomyelin (30:1) to 0.72 (95% CI 0.58-0.89). Results remained similar after further adjustment for established heart failure risk factors in meta-analyses of all three cohorts. Urobilin concentrations were inversely associated with left ventricular ejection fraction at baseline in the PIVUS cohort (β = -0.70, 95% CI -1.03 to -0.38). No major improvement in risk prediction was observed when adding the top 2 metabolites (C-index 0.787, 95% CI 0.752-0.823) or nine Lasso-selected metabolites (0.790, 95% CI 0.754-0.826) to a modified Atherosclerosis Risk in Communities heart failure risk score model (0.780, 95% CI 0.745-0.816).

conclusionsOur metabolomic profiling of three community-based cohorts study identified associations of circulating levels of the haem breakdown product urobilin, and sphingomyelin (30:1), a cell membrane component involved in signal transduction and apoptosis, with incident heart failure.

Indexed as

AgedFemaleHeart FailureHumansIncidenceLongitudinal StudiesMaleMiddle AgedProspective StudiesRisk AssessmentSphingomyelinsUrobilinSphingomyelinsUrobilinBiomarkersEpidemiologyHeart failureMetabolomicsRisk prediction

Identifiers

PMID31148414
PMCPMC6676274
OpenAlexW2947571055

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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.