Evidence map›Paper›PMID 42335150›Full record

ArticlePloS one2026

Proteomics risk scores and mortality in heart failure: Generalizability across populations.

Joseph J Shearer, Jungnam Joo, Carolina G Downie, Kayode O Kuku, Maryam Hashemian, Eric Leifer, Suzette J Bielinski, Véronique L Roger

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Joseph J ShearerHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.ORCID https://orcid.org/0000-0002-1443-7428
Jungnam JooOffice of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
Carolina G DownieHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
Kayode O KukuHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.ORCID https://orcid.org/0000-0003-3366-0702
Maryam HashemianHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.ORCID https://orcid.org/0000-0002-9702-9752
Eric LeiferOffice of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
Suzette J BielinskiDivision of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, United States of America.
Véronique L RogerHeart Disease Phenomics Laboratory, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.ORCID https://orcid.org/0000-0002-9347-7865

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeart failure (HF) is a complex syndrome with high mortality. Proteomics risk scores have shown promise in predicting mortality beyond guideline-recommended clinical tools. It is crucial to understand how risk scores generated by different methods and populations perform, and whether they highlight the same protein targets relevant to outcomes.

methodsTo examine whether the study design impacts proteomics scores designed to predict mortality in HF, we evaluated three published risk scores that used the SomaScan assay to measure plasma proteins in a community cohort, clinical trial, and registry. Each score was assessed in the aforementioned community cohort and Cox models examined the association of a 1-standard deviation increase in score with mortality, with and without adjustment for clinical covariates. Performance of each risk score to predict 5-year mortality risk was assessed using calibration plots and time-dependent area under the curve and compared with a clinical model.

resultsRisk scores were similarly distributed and moderately correlated (Pearson correlation coefficient = 0.59-0.76). A 1-standard deviation increase in each risk score was associated with an increased risk of all-cause mortality: community cohort (HR = 2.70, 95% CI: 2.50-2.91); clinical trial (HR = 1.76, 95% CI: 1.65-1.88); registry (HR = 1.70, 95% CI: 1.6-1.81). Risk remained after adjustment for clinical covariates, although slightly attenuated, and similar across different ejection fraction categories. All risk scores showed strong calibration across the risk levels, alone, with an average expected over observed ratio ranging between 0.96-1.56. Seven proteins were included in at least two risk scores, with renin being included in all three.

conclusionsAll three proteomics risk scores improved risk stratification in HF patients beyond guideline recommended clinical tools, independent of study design and ejection fraction. These results demonstrate that proteomics risk scores can enhance risk stratification across the HF syndrome, even when derived from different methods and populations.

Indexed as

Heart FailureProteomicsAgedFemaleHumansMaleMiddle AgedProportional Hazards ModelsRisk AssessmentRisk Factors

Identifiers

PMID42335150
PMCPMC13289903

What Socratic 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 Socratic graph.