Evidence map›Paper›PMID 36823471›Full record

ArticleNature metabolism2023

Proteogenomic links to human metabolic diseases.

Mine Koprulu, Julia Carrasco-Zanini, Eleanor Wheeler, Sam Lockhart, Nicola D Kerrison, Nicholas J Wareham, Maik Pietzner, Claudia Langenberg

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in Nature metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
14.4field-weighted citation impact, top 1% 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

33 citing papers in PubMed, 46 citations in OpenAlex.

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  13. Proteomic prediction of disease largely reflects environmental risk exposure.medRxiv : the preprint server for health sciences · 2025
    Article
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  20. Multi-omic studies on the pathogenesis of Sepsis.Journal of translational medicine · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

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
Julia Carrasco-ZaniniMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.ORCID http://orcid.org/0000-0002-3988-7505
Eleanor WheelerMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
Sam LockhartMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
Nicola D KerrisonMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.ORCID http://orcid.org/0000-0002-1471-953X
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 PietznerMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.ORCID http://orcid.org/0000-0003-3437-9963
Claudia LangenbergMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK. claudia.langenberg@qmul.ac.uk.ORCID http://orcid.org/0000-0002-5017-7344
University of Cambridge · GBQueen Mary University of London · GB

Funding

Medical Research Council MC_PC_21036Medical Research Council MC_UU_00006/1Medical Research Council MC_UU_12015/1Medical Research Council MR/N003284/1Medical Research Council MR/V033867/1Wellcome Trust
6 · The paper itself

Abstract

Studying the plasma proteome as the intermediate layer between the genome and the phenome has the potential to identify new disease processes. Here, we conducted a cis-focused proteogenomic analysis of 2,923 plasma proteins measured in 1,180 individuals using antibody-based assays. We (1) identify 256 unreported protein quantitative trait loci (pQTL); (2) demonstrate shared genetic regulation of 224 cis-pQTLs with 575 specific health outcomes, revealing examples for notable metabolic diseases (such as gastrin-releasing peptide as a potential therapeutic target for type 2 diabetes); (3) improve causal gene assignment at 40% (n = 192) of overlapping risk loci; and (4) observe convergence of phenotypic consequences of cis-pQTLs and rare loss-of-function gene burden for 12 proteins, such as TIMD4 for lipoprotein metabolism. Our findings demonstrate the value of integrating complementary proteomic technologies with genomics even at moderate scale to identify new mediators of metabolic diseases with the potential for therapeutic interventions.

Indexed as

Diabetes Mellitus, Type 2ProteogenomicsBlood ProteinsHumansProteomicsQuantitative Trait LociBlood Proteins

Identifiers

PMID36823471
PMCPMC7614946
OpenAlexW4321618012

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.