Evidence map›Paper›PMID 39143741›Full record

ArticleESC heart failure2024

Non-alcoholic fatty liver disease and heart failure: A comprehensive bioinformatics and Mendelian randomization analysis.

Yayun Zhang, Lu Feng, Xin Guan, Zixiong Zhu, Yubin He, Xuewen Li

Abstract read
In one paragraph

Article in ESC heart failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

6 authors.

Yayun ZhangShanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China.
Lu FengShanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China.
Xin GuanThird Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Zixiong ZhuThird Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Yubin HeThird Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Xuewen LiShanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China.ORCID 0000-0003-1610-9812

Funding

Four 'Batches' Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province 2023XM021National Natural Science Foundation of China 82170398
6 · The paper itself

Abstract

aimsHeart failure (HF) and non-alcoholic fatty liver disease (NAFLD) are significant global health issues with a complex interrelationship. This study investigates their shared biomarkers and causal relationships using bioinformatics and Mendelian randomization (MR) approaches.

methodsWe analysed NAFLD and HF datasets from the Gene Expression Omnibus (GEO). The GSE126848 dataset included 57 liver biopsy samples [14 healthy individuals, 12 obese subjects, 15 NAFL patients and 16 non-alcoholic steatohepatitis (NASH) patients]. The GSE24807 dataset comprised 12 NASH samples and 5 healthy controls. The GSE57338 dataset included 313 cardiac muscle samples [177 HF patients (95 ischaemic heart disease patients and 82 idiopathic dilated cardiomyopathy patients) and 136 healthy controls]. The GSE84796 dataset consisted of 10 end-stage HF patients and 7 healthy hearts procured from organ donors. We identified differentially expressed genes (DEGs) and constructed a protein-protein interaction (PPI) network. Functional pathways were elucidated through enrichment analyses using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and GeneMANIA annotation. Single nucleotide polymorphism (SNP) data for HF and NAFLD were sourced from genome-wide association studies (GWAS). The HF dataset included 486 160 samples (14 262 experimental and 471 898 control), and the NAFLD dataset comprised 377 988 samples (4761 experimental and 373 227 control). MR analysis investigates the causal interrelations.

resultsOur analysis revealed 4032 DEGs from GSE126848 and 286 DEGs from GSE57338. The top 10 hub genes (CD163, VSIG4, CXCL10, FCER1G, FPR1, C1QB, CCR1, C1orf162, MRC1 and CD38) were significantly enriched in immune response, calcium ion concentration regulation and positive regulation of monocyte chemotaxis. CIBERSORT analysis indicated associations between these hub genes and natural killer (NK) cells and macrophages. Transcription factor (TF) target prediction for CD38, CXCL10 and CCR1 highlighted related TFs. A two-sample MR analysis confirmed a bidirectional causal relationship between NAFLD and HF. The main method [inverse variance weighted (IVW)] demonstrated a significant positive causal relationship between NAFLD and HF [P = 0.037; odds ratio (OR) = 1.024; 95% confidence interval (CI): 1.001 to 1.048]. Similarly, HF was associated with an increase in the risk of NAFLD (P < 0.001; OR = 1.117; 95% CI: 1.053 to 1.185).

conclusionsOur findings reveal novel molecular signatures common to NAFLD and HF and confirm their bidirectional causality, highlighting the potential for targeted therapeutic interventions and prompting further investigation into their intricate relationship.

Indexed as

Computational BiologyHeart FailureMendelian Randomization AnalysisNon-alcoholic Fatty Liver DiseaseGene Expression ProfilingGenome-Wide Association StudyHumansMalePolymorphism, Single Nucleotideco‐expressed geneGene Expression Omnibusheart failureMendelian randomizationnon‐alcoholic fatty liver disease

Identifiers

PMID39143741
PMCPMC11631243

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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.