ArticlePeerJ2025
Identification of diabetes related phenotype and diagnostic biomarkers in coronary artery disease.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Diabetes mellitus (DM) and coronary artery disease (CAD) are closely interrelated clinical conditions. However, the combination analysis based on DM related CAD diagnostic model remains a gap. The primary objective of this study was to identify diagnostic models and diagnostic markers for CAD based on the association of diabetic phenotypes and attempt to explore them further in a mouse model. Methods: We used data integration as well as multiple datasets for both coronary artery disease and diabetes to exclude bias as well as to improve reliability. We employed the least absolute shrinkage and selection operator (LASSO) regression algorithms to construct the CAD diagnostic model. Furthermore, we established mouse CAD model (low-density lipoprotein receptor deficient mice with high fat diet) to explore the crosstalk between the screened biomarkers and severe CAD progress. Results: The intersecting genes from differential analysis and weighted correlation network analysis (WGCNA) results yielded 32 diabetes-related biomarkers. We then identified two diabetes-related phenotypes through the consensus clustering in CAD patients. Microenvironmental analysis revealed that phenotype 1 exhibited higher expression of most cytokines, inflammatory factors, interleukins, and related receptors. Immune cell composition in phenotype 1 showed increased infiltration compared to phenotype 2. The LASSO regression identified 16 diabetes-related genes and we further constructed a diagnostic model based on these genes, which the area under the curve (AUC) reached 0.8. Additionally, single cell immune analysis exhibited the location of these genes. KCNQ1, ATP6V1B1, MTDH, and ITPK1 were predominantly located in macrophages, indicating their potential in regulating macrophage during myocardial injury. Furthermore, We elucidated that KCNQ1 and ITPK1 exhibited high expression level in mouse CAD model in tissue level. exhibited similar expression trends with macrophage biomarkers (CD31 and CD68). The result of qPCR also indicated the elevated level of KCNQ1 and ITPK1, which exhibited crosstalk with CD31 and CD68 in mouse CAD model. Conclusion: This study delves into the microenvironmental characteristics of diabetes-related phenotypes in CAD, constructing an optimal diagnostic model and validated the significance of diagnostic markers in mouse CAD model, which may offer insights that could be beneficial for clinical management in the near future.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.