Evidence map›Paper›PMID 41359645›Full record

ArticlePloS one2025

Molecular mechanisms of lipid metabolism abnormalities driving sepsis and atrial fibrillation: A Systematic study based on bioinformatics and machine learning.

Changze Ou, Haidong Yu, Binbin Chen, Huajun Long

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Changze OuGraduate School, Hunan University of Chinese Medicine, Changsha, Hunan Province, China.ORCID https://orcid.org/0009-0003-2347-1614
Haidong YuGraduate School, Hunan University of Chinese Medicine, Changsha, Hunan Province, China.
Binbin ChenGraduate School, Hunan University of Chinese Medicine, Changsha, Hunan Province, China.
Huajun LongDepartment of Emergency, Hunan Provincial Hospital of Integrated Traditional Chinese and Western Medicine (Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine), Changsha, Hunan Province, China.ORCID https://orcid.org/0000-0002-7489-3943

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSepsis and atrial fibrillation are complex, life-threatening medical conditions affecting approximately 49 million individuals globally, characterized by exceptionally high mortality rates. Lipid metabolism abnormalities play a critical role in the pathogenesis of these diseases, yet their underlying molecular mechanisms remain incompletely understood.

objectiveThis innovative study systematically investigates the shared molecular mechanisms of lipid metabolism abnormalities in sepsis and atrial fibrillation using advanced bioinformatics and machine learning methodologies. Methods: We retrieved two independent research cohorts from the Gene Expression Omnibus database: sepsis-related datasets and atrial fibrillation-related datasets. A multi-dimensional analytical approach was employed, including differential expression analysis, Weighted Gene Co-expression Network Analysis, machine learning models, immune cell infiltration analysis, and gene set enrichment analysis to comprehensively elucidate the molecular mechanisms of lipid metabolism abnormalities in these diseases.

resultsComprehensive analysis identified 13 key candidate genes, with CD81, CKAP4, and DPEP2 emerging as core characteristic genes. Functional enrichment analysis revealed these genes primarily participate in mitochondrial function regulation, complement-coagulation cascade, and cell adhesion molecular pathways. Machine learning models demonstrated exceptional diagnostic performance, with area under the curve values of 0.957 for sepsis and 1.000 for atrial fibrillation datasets. Immune cell infiltration analysis unveiled the critical roles of neutrophils and monocytes in disease progression, and revealed the profound impact of lipid metabolism abnormalities on immune regulation.

conclusionWe discovered that lipid metabolism abnormalities significantly modulate disease progression by influencing mitochondrial function, inflammatory responses, and cell adhesion pathways. Future research necessitates further clinical validation and functional experiments to explore personalized therapeutic strategies based on lipid reprogramming.

Indexed as

Atrial FibrillationComputational BiologyLipid MetabolismMachine LearningSepsisGene Expression ProfilingGene Regulatory NetworksHumans

Identifiers

PMID41359645
PMCPMC12685191

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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.