In one paragraphArticle in Hypertension (Dallas, Tex. : 1979), 2026. 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
–field-weighted citation impact
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
9 authors.
Minkwan KimDivision of Cardiology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Republic of Korea (M.K.).ORCID 0000-0002-4079-8219 Hamed TavolinejadPerelman School of Medicine, University of Pennsylvania, Philadelphia (H.T., M.B., J.A.C.).ORCID 0000-0002-0244-5914 Mateo Sarmiento BustamantePerelman School of Medicine, University of Pennsylvania, Philadelphia (H.T., M.B., J.A.C.).
Patrick SegersInstitute of Biomedical Engineering and Technology (IBiTech) (P.S.), Ghent University, Belgium.ORCID 0000-0003-3870-3409 Robbe E NeirynckDepartment of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering (R.E.N., T.D.M.), Ghent University, Belgium.ORCID 0009-0003-2146-1770 Tim De MeyerDepartment of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering (R.E.N., T.D.M.), Ghent University, Belgium.ORCID 0000-0003-2994-9693 Julio A ChirinosPerelman School of Medicine, University of Pennsylvania, Philadelphia (H.T., M.B., J.A.C.).ORCID 0000-0001-9035-5670 Funding
HeartShare DeCODE-HF: Data translation center to Combine Omics, Deep phenotyping, and Electronic health records for Heart Failure subtypes and treatment targetsU54HL160273 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Abel N. Kho, Yuan Luo · 2021 to 2026
$19.0MKeeping fat out of muscle - Role of Branched Amino AcidsAmino Acids in Insulin ResistanceR01DK114103 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI Zoltan P Arany · 2018 to 2026
$4.5MTargeting Skeletal Muscle Perfusion and Oxidative Capacity in HFpEFR01HL155599 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI ZAMANI, PAYMAN · 2021 to 2025
$3.9MCardiovascular Risk, Vascular and Kidney Damage in COVID-19 SurvivorsR01HL157108 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI JULIO ALONSO CHIRINOS MEDINA, Jordana B. Cohen · 2022 to 2026
$3.9MBlockade of calcium channels and beta adrenergic receptors for physiologic abnormalities in heart failure with preserved ejection fraction (BLOCK HFpEF)R01HL153646 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI COHEN, JORDANA B. · 2020 to 2024
$3.2MCardiovascular Consequences of Duchenne and Becker Muscular DystrophinopathiesR01HL155764 · NHLBI · UNIVERSITY OF DELAWARE · PI WITMAN, MELISSA A.H. · 2021 to 2025
$3.1MHeartShare: Next-Generation Phenomics to Define Heart Failure Subtypes and Treatment Targets - Clinical CentersU01HL160277 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI JULIO ALONSO CHIRINOS MEDINA · 2021 to 2026
$1.7MGenetic determinants of thoracic aortic stiffness and remodelingK24AG070459 · NIA · UNIVERSITY OF PENNSYLVANIA · PI CHIRINOS MEDINA, JULIO ALONSO · 2021 to 2025
$975kNHLBI NIH HHS R01 HL153646NHLBI NIH HHS R01 HL155599NHLBI NIH HHS R01 HL155764NHLBI NIH HHS R01 HL157108NHLBI NIH HHS U01 HL160277NHLBI NIH HHS U54 HL160273NIA NIH HHS K24 AG070459NIDDK NIH HHS R01 DK114103
6 · The paper itselfAbstract
backgroundProteomic signatures may enhance the prediction of cardiometabolic diseases for targeted prevention. We evaluated whether a proteomic risk score (ProtRS) improves the prediction of incident hypertension.
methodsWithin the UK Biobank Proteomics data set, participants at risk for incident hypertension were randomly split into derivation (n=25 158) and test (n=10 781) sets. A ProtRS was trained in the derivation cohort using least absolute shrinkage and selection operator penalized Cox regression and evaluated in the test set with sequential adjustment for demographics, clinical risk factors, and a polygenic risk score (PRS). External replication was performed in the Asklepios study (n=793).
resultsIn UK Biobank Proteomics (2312 events), each 1-SD higher ProtRS was associated with incident hypertension after covariate adjustment (hazard ratio, 1.68 [95% CI, 1.59-1.78];
conclusionsA ProtRS improves prediction of incident hypertension beyond demographics, clinical, and genetic information in UK Biobank Proteomics, with supportive external replication. Proteomic information may enhance individualized hypertension risk stratification and provide biologic insights into disease development.
Indexed as
HypertensionProteomicsAgedFemaleGenetic Risk ScoreHumansIncidenceMaleMiddle AgedRisk AssessmentRisk FactorsUK BiobankUnited Kingdombiomarkersgenetic risk scorehypertensionproteomicsrisk assessment
Identifiers
PMID41717706
PMCPMC12931956
What OpenQuestion holds
Textmetadata
LicenceTDM
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