Evidence map›Paper›PMID 40348582›Full record

ArticleESC heart failure2025

GEO combined with quantitative protein trait loci identify causative proteins in hypertrophic cardiomyopathy.

Bo Li, Xu Zhao, Yan Ding, Yi Zhang

Abstract read
In one paragraph

Article in ESC heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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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

2 citing papers in PubMed.

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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

4 authors.

Bo LiDepartment of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, China.ORCID https://orcid.org/0000-0002-9597-1306
Xu ZhaoEmergency and Critical Care Center, Renmin Hospital, Hubei University of Medicine, No. 37 Chaoyang Middle Road, Shiyan, Hubei, China.
Yan DingHubei Key Laboratory of Embryonic Stem Cell Research, Hubei Provincial Clinical Research Center for Umbilical Cord Blood Hematopoietic Stem Cells, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Yi ZhangDepartment of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsHypertrophic cardiomyopathy (HCM) is a rare genetic heart disease characterized by a limited patient population and scarce research and treatment resources. This study aimed to identify HCM-associated proteins by integrating cardiac tissue data from the Gene Expression Omnibus (GEO) database with the latest protein quantitative trait locus (pQTL) dataset. METHODS AND

resultsWe analysed data from the GEO database. The GSE36961 dataset included 106 HCM samples and 39 healthy controls. The GSE180313 dataset included 13 HCM samples and 7 healthy controls. pQTL data were obtained from the plasma of 54 000 UK Biobank participants, covering 1463 proteins. HCM genome-wide association study (GWAS) data were sourced from the FinnGen study, which included 1125 HCM cases and 411 056 controls. We analysed the GEO dataset of cardiac tissue from HCM patients to identify differentially expressed genes (DEGs). These DEGs were compared with pQTL data to identify protein phenotypes suitable for Mendelian randomization (MR) analysis. A two-sample MR analysis was performed to assess the causal association between these protein phenotypes and HCM. The robustness of the study results was further assessed through sensitivity analysis of heterogeneity and horizontal pleiotropy tests. Two proteins were identified as causally associated with HCM risk: carbonic anhydrase 3 (CA3) [inverse variance weighted (IVW): odds ratio (OR) = 1.292, 95% confidence interval (CI) = 1.021-1.636, P = 0.033] and serpin family E member 1 (SERPINE1) [IVW: OR = 1.313, 95% CI = 1.063-1.621, P = 0.011]. Both proteins were associated with increased HCM risk, with no significant heterogeneity (P > 0.05) or evidence of horizontal pleiotropy (P > 0.05).

conclusionsCA3 and SERPINE1 proteins may exert causal effects on HCM and may serve as characteristic markers and therapeutic targets for this condition.

Indexed as

Cardiomyopathy, HypertrophicQuantitative Trait LociFemaleGenome-Wide Association StudyHumansMaleMendelian Randomization AnalysisPhenotypeCausal effectGene Expression Omnibus (GEO)Hypertrophic cardiomyopathyProtein quantitative trait loci (pQTL)

Identifiers

PMID40348582
PMCPMC12287861

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.