Evidence map›Paper›PMID 38076879›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Profiling the genome and proteome of metabolic dysfunction-associated steatotic liver disease identifies potential therapeutic targets.

Jun Liu, Sile Hu, Lingyan Chen, Charlotte Daly, Cesar Augusto Prada Medina, Tom G Richardson, Matthew Traylor, Niall J Dempster, Richard Mbasu, Thomas Monfeuga and 10 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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, 13 citations in OpenAlex.

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

20 authors at 8 institutions in 2 countries.

Jun LiuNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID 0000-0001-5288-3042
Sile HuGenetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK.
Lingyan ChenGenetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK.
Charlotte DalyDepartment of Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, UK.
Cesar Augusto Prada MedinaAI & Digital Research, Research & Early Development, Novo Nordisk Research Centre Oxford, UK.
Tom G RichardsonGenetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK.ORCID 0000-0002-7918-2040
Matthew TraylorGenetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK.
Niall J DempsterOxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK.
Richard MbasuDepartment of Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, UK.
Thomas MonfeugaAI & Digital Research, Research & Early Development, Novo Nordisk Research Centre Oxford, UK.
Marijana VujkovicCorporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.ORCID 0000-0003-4924-5714
Philip S TsaoVA Palo Alto Health Care System, Palo Alto, CA, USA.
Julie A LynchVA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
Benjamin F VoightCorporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.ORCID 0000-0002-6205-9994
Kyong-Mi ChangCorporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.ORCID 0000-0001-6811-9364
V A MillionNuffield Department of Population Health, University of Oxford, Oxford, UK.
Jeremy F CobboldNIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust and the University of Oxford, Oxford, UK.
Jeremy W TomlinsonOxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK.
Cornelia M van DuijnNuffield Department of Population Health, University of Oxford, Oxford, UK.
Joanna M M HowsonGenetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK.
Novo Nordisk (United Kingdom) · GBChurchill Hospital · GBUniversity of Oxford · GBPhiladelphia VA Medical Center · USTranslational Therapeutics (United States) · USUniversity of Bristol · GBUniversity of Utah · USVA Palo Alto Health Care System · US

Funding

A genomics-based strategy to precision phenotyping and drug repositioning in cardiometabolic diseasesR01DK134575 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI Marijana Vujkovic · 2023 to 2026
$1.4M
Genetics of Cardiometabolic Diseases in the VA PopulationI01BX003362 · VA · VETERANS ADMIN PALO ALTO HEALTH CARE SYS · PI Kyong-Mi Chang, Philip S Tsao · 2017 to 2026
–
BLRD VA I01 BX003362NIDDK NIH HHS R01 DK134575
6 · The paper itself

Abstract

BACKGROUND &

aimsMetabolic dysfunction-associated steatotic liver disease (MASLD) affects over 25% of the population and currently has no effective treatments. Plasma proteins with causal evidence may represent promising drug targets. We aimed to identify plasma proteins in the causal pathway of MASLD and explore their interaction with obesity.

methodsWe analysed 2,941 plasma proteins in 43,978 European participants from UK Biobank. We performed genome-wide association study (GWAS) for all MASLD-associated proteins and created the largest MASLD GWAS (109,885 cases/1,014,923 controls). We performed Mendelian Randomization (MR) and integrated proteins and their encoding genes in MASLD ranges to identify candidate causal proteins. We then validated them through independent replication, exome sequencing, liver imaging, bulk and single-cell gene expression, liver biopsies, pathway, and phenome-wide data. We explored the role of obesity by MR and multivariable MR across proteins, body mass index, and MASLD.

resultsWe found 929 proteins associated with MASLD, reported five novel genetic loci associated with MASLD, and identified 17 candidate MASLD protein targets. We identified four novel targets for MASLD (CD33, GRHPR, HMOX2, and SCG3), provided protein evidence supporting roles of AHCY, FCGR2B, ORM1, and RBKS in MASLD, and validated nine previously known targets. We found that CD33, FCGR2B, ORM1, RBKS, and SCG3 mediated the association of obesity and MASLD, and HMOX2, ORM1, and RBKS had effect on MASLD independent of obesity.

conclusionsThis study identified new protein targets in the causal pathway of MASLD, providing new insights into the multi-omics architecture and pathophysiology of MASLD. These findings advise further therapeutic interventions for MASLD.

Indexed as

drug targetgenomicsmetabolic dysfunction-associated steatotic liver diseasenon-alcoholic fatty liver diseaseproteomics

Identifiers

PMID38076879
PMCPMC10705663
OpenAlexW4389163831

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.