Evidence map›Paper›PMID 34016094›Full record

ArticleCardiovascular diabetology2021

Metabolic syndrome and the plasma proteome: from association to causation.

Mohamed A Elhadad, Rory Wilson, Shaza B Zaghlool, Cornelia Huth, Christian Gieger, Harald Grallert, Johannes Graumann, Wolfgang Rathmann, Wolfgang Koenig, Moritz F Sinner and 6 more

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in Cardiovascular diabetology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

16 citing papers in PubMed, 34 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

16 authors at 8 institutions in 4 countries.

Mohamed A ElhadadResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. mohamed.elhadad@helmholtz-muenchen.de.ORCID 0000-0002-6758-8369
Rory WilsonResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Shaza B ZaghloolWeill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar.
Cornelia HuthInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Christian GiegerResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Harald GrallertResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Johannes GraumannBiomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, 61231, Bad Nauheim, Germany.
Wolfgang RathmannGerman Center for Diabetes Research (DZD), München-Neuherberg, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
Wolfgang KoenigGerman Research Center for Cardiovascular Disease (DZHK), Partner site Munich Heart Alliance, Munich, Germany.
Moritz F SinnerGerman Research Center for Cardiovascular Disease (DZHK), Partner site Munich Heart Alliance, Munich, Germany.
Kristian HveemK.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
Karsten SuhreWeill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar.
Barbara ThorandInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Christian JonassonK.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
Melanie WaldenbergerResearch Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. waldenberger@helmholtz-muenchen.de.
Annette PetersInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. peters@helmholtz-muenchen.de.
Helmholtz Zentrum München · DENorwegian University of Science and Technology · NOWeill Cornell Medical College in Qatar · QADeutsches Diabetes-Zentrum e.V. · DEGerman Centre for Cardiovascular Research · DEMax Planck Institute for Heart and Lung Research · DEUniversität Ulm · DEZimmer Biomet (Netherlands) · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe metabolic syndrome (MetS), defined by the simultaneous clustering of cardio-metabolic risk factors, is a significant worldwide public health burden with an estimated 25% prevalence worldwide. The pathogenesis of MetS is not entirely clear and the use of molecular level data could help uncover common pathogenic pathways behind the observed clustering.

methodsUsing a highly multiplexed aptamer-based affinity proteomics platform, we examined associations between plasma proteins and prevalent and incident MetS in the KORA cohort (n = 998) and replicated our results for prevalent MetS in the HUNT3 study (n = 923). We applied logistic regression models adjusted for age, sex, smoking status, and physical activity. We used the bootstrap ranking algorithm of least absolute shrinkage and selection operator (LASSO) to select a predictive model from the incident MetS associated proteins and used area under the curve (AUC) to assess its performance. Finally, we investigated the causal effect of the replicated proteins on MetS using two-sample Mendelian randomization.

resultsPrevalent MetS was associated with 116 proteins, of which 53 replicated in HUNT. These included previously reported proteins like leptin, and new proteins like NTR domain-containing protein 2 and endoplasmic reticulum protein 29. Incident MetS was associated with 14 proteins in KORA, of which 13 overlap the prevalent MetS associated proteins with soluble advanced glycosylation end product-specific receptor (sRAGE) being unique to incident MetS. The LASSO selected an eight-protein predictive model with an (AUC = 0.75; 95% CI = 0.71-0.79) in KORA. Mendelian randomization suggested causal effects of three proteins on MetS, namely apolipoprotein E2 (APOE2) (Wald-Ratio = - 0.12, Wald-p = 3.63e-13), apolipoprotein B (APOB) (Wald-Ratio = - 0.09, Wald-p = 2.54e-04) and proto-oncogene tyrosine-protein kinase receptor (RET) (Wald-Ratio = 0.10, Wald-p = 5.40e-04).

conclusionsOur findings offer new insights into the plasma proteome underlying MetS and identify new protein associations. We reveal possible casual effects of APOE2, APOB and RET on MetS. Our results highlight protein candidates that could potentially serve as targets for prevention and therapy.

Indexed as

ProteomeProteomicsAdultAgedAged, 80 and overApolipoprotein B-100Apolipoprotein E2BiomarkersBlood ProteinsCardiometabolic Risk FactorsCross-Sectional StudiesFemaleGermanyHumansIncidenceMaleAPOB protein, humanApolipoprotein B-100Apolipoprotein E2BiomarkersBlood ProteinsMAS1 protein, humanProteomeProto-Oncogene MasProto-Oncogene Proteins c-retRET protein, humanBlood proteinsCardiovascular diseaseDiabetes mellitusMendelian randomization analysisMetabolic syndromeProteomicsRisk factorstype 2

Identifiers

PMID34016094
PMCPMC8138979
OpenAlexW3163082444

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LicenceCC BY
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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.