ArticleJournal of inflammation research2025
Inflammatory Biomarkers in Coronary Artery Disease: Insights From Mendelian Randomization and Transcriptomics.
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed.
- Higher Neutrophil-to-Albumin Ratio Is Associated with Greater Coronary Atherosclerotic Burden According to the Gensini Score in an Angiography-Referred Population.Diagnostics (Basel, Switzerland) · 2026Article
- Decoding coronary artery calcification: metabolic reprogramming features and a promising circulating biomarker PXDN.Frontiers in cell and developmental biology · 2026Article
- The Immunometabolic Bridge: Targeting Metainflammation to Prevent the Transition from Obesity to Cardiovascular Disease.Journal of inflammation research · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The identification of inflammatory genes linked to coronary artery disease (CAD) helps to enhance our understanding of the disease's pathogenesis and facilitate the identification of novel therapeutic targets. Methods: Inflammation-related genes (IRGs) were downloaded from the Msigdb database. Differentially expressed genes (DEGs) were determined by comparing CAD group with the control group in the GSE113079 and GSE12288 datasets. Key module genes associated with CAD were identified through weighted gene co-expression network analysis (WGCNA). Differentially expressed IRGs (DE-IRGs) were established by intersecting the DEGs, key module genes, and IRGs. Feature genes were derived using machine learning techniques. Mendelian randomization (MR) analysis was conducted to explore the causal relationship between CAD and the identified feature genes. Subsequently, a logistic regression model and an alignment diagram model were developed to predict the incidence of CAD. Results: In the given datasets, a total of 92 DE-IRGs were identified. Furthermore, twelve feature genes were discerned utilizing four distinct machine learning algorithms. Notably, two pivotal genes, HIF1A (odds ratio (OR) = 1.031, Conclusion: The identification of
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Registered trials
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.