Evidence map›Paper›PMID 41270070›Full record

ArticlePloS one2025

A plasma-based protein signature association with all-cause mortality.

Natalia Koziar, Anthony D Whetton, Nophar Geifman

Abstract read
In one paragraph

Article in PloS one, 2025. 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
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

3 authors.

Natalia KoziarSchool of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
Anthony D WhettonSchool of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
Nophar GeifmanSchool of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.ORCID https://orcid.org/0000-0003-2956-6676

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circulating plasma proteins play key roles in measuring and reflecting states of disease and health. Developments in protein metrology allow for over 2,900 proteins to be quantified in a single sample. In major epidemiological studies, this allows for profound insights into protein expression in liquid biopsies, mortality, and morbidity. Here, we have investigated the relationship between peripheral blood protein profiles and non-accident all-cause mortality within 5- and 10-year timeframes, using data on 38,150 participants from the UK Biobank. Adjusting for lifestyle and health covariates, we identified 392 proteins associated with an increase in risk for death within 5-years, and 377 associated with an increase in risk within 10-years. Proteomic signatures of cause-specific mortality (cardiovascular, cancer, all-other causes) were also identified, with 19 proteins found to overlap across those. Using logistic regression modelling, we constructed a parsimonious predictive protein panel for each respective all-cause mortality timeframe, including markers such as adrenomedullin, SERPINA1 and PLAUR. When compared to models inclusive of standalone traditional risk criteria, such as demographic and lifestyle factors, models utilising the protein panels modestly improve prediction for 5 and 10-year mortality (from AUC 0.49-0.57 to AUC of 0.62-0.68). Our results demonstrate the potential of the plasma proteome in risk stratification for all-cause mortality.

Indexed as

Blood ProteinsMortalityAgedBiomarkersCardiovascular DiseasesCause of DeathFemaleHumansMaleMiddle AgedNeoplasmsProteomicsRisk FactorsUnited KingdomBiomarkersBlood Proteins

Identifiers

PMID41270070
PMCPMC12637995

What OpenQuestion 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 OpenQuestion graph.