Evidence map›Paper›PMID 39327534›Full record

ArticleNature metabolism2024

Mapping biological influences on the human plasma proteome beyond the genome.

Julia Carrasco-Zanini, Eleanor Wheeler, Burulça Uluvar, Nicola Kerrison, Mine Koprulu, Nicholas J Wareham, Maik Pietzner, Claudia Langenberg

Abstract read
In one paragraph

Article in Nature metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. A trans-omics gene-smoking interaction study of lung cancer based on consortium data.American journal of respiratory and critical care medicine · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Long-term temporal stability of circulating proteins in older adults.bioRxiv : the preprint server for biology · 2025
    Article
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.

Julia Carrasco-ZaniniPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK.
Eleanor WheelerMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
Burulça UluvarComputational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0001-8143-6989
Nicola KerrisonMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.ORCID http://orcid.org/0000-0002-1471-953X
Mine KopruluMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.ORCID http://orcid.org/0000-0001-6870-4539
Nicholas J WarehamMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.ORCID http://orcid.org/0000-0003-1422-2993
Maik PietznerPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK.ORCID http://orcid.org/0000-0003-3437-9963
Claudia LangenbergPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK. claudia.langenberg@qmul.ac.uk.ORCID http://orcid.org/0000-0002-5017-7344

Funding

Cancer Research UK (CRUK) C864/A14136RCUK | Medical Research Council (MRC) MC_PC_13046RCUK | Medical Research Council (MRC) MC_UU_00006/1RCUK | Medical Research Council (MRC) MR/N003284/1Wellcome TrustWellcome Trust (Wellcome) 220044/Z/19/Z
6 · The paper itself

Abstract

Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors. For over a third (N = 1,575) of protein targets, joint genetic and non-genetic factors explain 10-77% of the variation in plasma (median 19.88%, interquartile range 14.01-31.09%), independent of technical factors (median 2.48%, interquartile range 0.78-6.41%). Together with genetically anchored causal inference methods, our map highlights potential causal associations between modifiable risk factors and plasma proteins for hundreds of protein-disease associations, for example, COL6A3, which possibly mediates the association between reduced kidney function and cardiovascular disease. We provide a map of biological and technical influences on the human plasma proteome to help contextualize findings from proteomic studies.

Indexed as

Blood ProteinsProteomeProteomicsGenome, HumanGenome-Wide Association StudyHumansMaleBlood ProteinsProteome

Identifiers

PMID39327534
PMCPMC11496106

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.