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Journal article

Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation

Abstract:
Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Genetically predicted effects were generally consistent among drug targets, which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the MR estimates on all 249 metabolic traits for genetically predicted inhibition of LDL cholesterol lowering targets HMGCR and PCSK9 was r2 = 0.91. In contrast, comparisons between drug classes that were designed to modify discrete lipoprotein traits typically had very different effects on metabolic signatures (for instance, HMGCR versus each of the 4 triglyceride targets all had r2 < 0.02). Furthermore, we highlight this discrepancy for specific metabolic traits, for example, finding that LDL cholesterol lowering therapies typically had a weak effect on glycoprotein acetyls, a marker of inflammation, whereas triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, despite largely consistent effects on risk of CAD, with potential implications for biomarkers in clinical development and measuring treatment response.
Publication status:
Published
Peer review status:
Peer reviewed

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Role:
Author
ORCID:
0000-0002-7918-2040
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Role:
Author
ORCID:
0000-0002-6024-4950
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Institution:
University of Oxford
Division:
MSD
Department:
Nuffield Department of Population Health
Sub department:
Clinical Trial Service Unit
Role:
Author
ORCID:
0000-0001-8100-4852
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Role:
Author
ORCID:
0000-0001-6892-8786
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Role:
Author
ORCID:
0000-0001-7328-4233


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Funder identifier:
10.13039/501100000274
Grant:
FS/18/23/33512
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Funder identifier:
10.13039/501100000265
Grant:
MC_UU_00011/1


Publisher:
Public Library of Science
Journal:
PLoS Biology More from this journal
Volume:
20
Issue:
2
Pages:
e3001547-e3001547
Publication date:
2022-02-25
DOI:
EISSN:
1545-7885
ISSN:
1544-9173


Language:
English
Keywords:
Pubs id:
1244013
Local pid:
pubs:1244013
Source identifiers:
W4214506665
Deposit date:
2026-04-10
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

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