Evidence map›Paper›PMID 41393260›Full record

ArticleFrontiers in cardiovascular medicine2025

Identification and validation of key biomarkers of the glycolysis-ketone body metabolism in heart failure based on multi-omics and machine learning.

Na Xiao, Jing Liu, Zhe Chen, Xiaoyong Geng

Abstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 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

What it found

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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.

Na XiaoCardiology, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, China.
Jing LiuCardiology, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, China.
Zhe ChenCardiology, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, China.
Xiaoyong GengCardiology, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Metabolic remodeling, particularly involving glycolysis and ketone body metabolism, is a hallmark of heart failure (HF) pathophysiology. However, the regulatory network linking energy metabolism with immune dysregulation remains poorly understood. Objectives: This study aimed to identify and validate key biomarkers within the glycolysis-ketone body metabolism axis that contribute to the progression of HF, and to explore their association with immune microenvironment alterations. Methods: Transcriptomic data from HF patients were integrated with glycolysis and ketone metabolism gene sets. Differentially expressed genes (DEGs) were identified and analyzed through Weighted Gene Co-expression Network Analysis (WGCNA). Candidate genes were refined using machine learning algorithms (LASSO regression and Boruta), with functional enrichment assessed via Gene Set Enrichment Analysis (GSEA). Immune infiltration was profiled using ssGSEA, and regulatory networks were constructed by integrating miRNA and transcription factor predictions. Experimental validation was conducted in a murine myocardial infarction model using qPCR and cardiac ultrasound imaging. Results: Five candidate genes related to glycolysis and ketone metabolism were identified, among which TIMP1 emerged as the key hub gene. TIMP1 expression was significantly elevated in HF and correlated with enriched pathways including inflammatory signaling and mitochondrial dysfunction. Immune profiling revealed that TIMP1 positively associated with the infiltration of activated CD8⁺ T cells and dendritic cells, potentially mediated by chemokines such as CCL2. Regulatory network analysis suggested that upstream transcription factors and miRNAs may contribute to TIMP1 overexpression. Animal model validation confirmed the upregulation of TIMP1 and other core genes, supporting its central role in HF progression. Conclusion: This study identifies TIMP1 as a central regulator linking glycolysis-ketone metabolic imbalance with immune microenvironment dysregulation in heart failure. These findings offer new mechanistic insights and propose TIMP1 as a potential diagnostic biomarker and therapeutic target in HF.

Indexed as

glycolysisheart failureimmune infiltrationketone body metabolismmachine learning

Identifiers

PMID41393260
PMCPMC12695798

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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.