Evidence map›Paper›PMID 39386745›Full record

ArticleFrontiers in medicine2024

Causal effects of lipid-lowering drugs on skin diseases: a two-sample Mendelian randomization study.

Yong Liu, Hui Liu, Queqiao Bian

Abstract read
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Yong LiuTianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Department of Dermatology and STD, The Third Central Hospital of Tianjin, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, Tianjin, China.
Hui LiuTianjin Institute of Hepatobiliary Disease, The Third Central Hospital of Tianjin, Tianjin, China.
Queqiao BianTianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Department of Dermatology and STD, The Third Central Hospital of Tianjin, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although previous studies have indicated an association between low-density lipoprotein (LDL) and skin diseases, their causal effects remain inconclusive. This study aimed to assess the causal relationship between genetically proxied lipid-lowering drugs and skin cancers and psoriasis. Methods: Two-sample Mendelian randomization (MR) analysis was performed using single-nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS). The inverse-variance weighted (IVW) method was used to determine causal relationships. The "leave-one-out" sensitivity test, Cochran's Q-statistic and MR-Egger intercept were used to assess heterogeneity and horizontal pleiotropy. Results: We identified 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) and proprotein convertase subtilisin-kexin type 9 (PCSK9) as genetically proxied lipid-lowering drugs. Genetically proxied inhibition of HMGCR (stains) was causally associated with reduced risk of nonmelanoma skin cancer (OR 0.982, 95% CI 0.967-0.997, Conclusion: This study revealed the existing HMGCR inhibitors (stains) might be protective for reducing nonmelanoma skin cancer risk, and HMGCR inhibitors (stains) and PCSK9 inhibitor (alirocumab) might be promising for reducing psoriasis risk in the European population.

Indexed as

genome-wide association studieslipid-lowering drugsmelanoma skin cancerMendelian randomization studynonmelanoma skin cancerpsoriasissingle-nucleotide polymorphisms

Identifiers

PMID39386745
PMCPMC11461303

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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.