Deciphering gut microbial impact in coronary artery disease through multimodal computational approaches

Figures

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  Fig. 1. Graphical abstract illustrating the main steps of our analysis workflow

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  Fig. 2. Gut microbial diversity differs across CAD and healthy individuals. (A) Boxplot comparing the Shannon diversity index of the gut microbiome between CAD patients (green) and healthy controls (blue). The CAD group exhibits a higher median Shannon index, suggesting greater microbial diversity compared to healthy controls. The box represents the interquartile range (IQR), the line within the box indicates the median, and the whiskers extend to 1.5 times the IQR. This observation highlights potential differences in microbial diversity between the two groups, which may reflect alterations in gut microbiome composition associated with coronary artery disease. (B) A PCoA plot visualizing microbial community differences between groups based on beta diversity metrics. Green dots represent the coronary artery disease (CAD) group, while blue dots denote the control group. The axes represent the two principal coordinates (PCo1 and PCo2) with their respective percentage variances

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  Fig. 3. Gut microbiota composition differs across CAD and healthy individuals. (A) Relative abundance of the healthy controls and CAD patients at the phylum level. (B) Stacked bar plots of relative abundances in the gut microbiome of CAD patients (left panel) and healthy controls (right panel) at the genus level. The CAD group exhibits greater microbial heterogeneity and instability, while the control group is more stable and balanced with predominant microbiota

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  Fig. 4. Differential abundances of genera in gut microbiota of the control group vs. CAD patients. A volcano plot showing taxa significantly associated with health status. The x-axis represents the log2 fold change (|log2FC|> 1), and the y-axis represents the -log10 p-value. Taxa with higher representation in CADs and controls are red dots on the left and right sides of the x-axis, respectively. Dashed lines indicate the thresholds for significance, corresponding to a p-value of < 0.05 (horizontal line) and a log2 fold change of ± 1 (vertical lines)

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  Fig. 5. Network visualization of microbial co-occurrence patterns in the gut microbiomes of (A) healthy controls and (B) CAD patients. Nodes represent microbial genera, with hub nodes identified based on degree centrality (degree ≥ 50), indicating highly connected taxa within the network. Keystone genera are highlighted in red. Nodes colored green correspond to genera with positive associations predominantly observed in control samples, while blue nodes represent genera with positive associations primarily found in CAD patients. Edges denote significant correlations between genera, illustrating the community structure and interaction patterns in each group. The network was constructed and visualized using Cytoscape

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  Fig. 6. Stochastic simulation of microbial abundance dynamics in CAD and control groups using the Euler–Maruyama method. The curves represent average taxa abundance over 1000 time-steps, incorporating logistic growth and stochastic perturbations. The CAD group exhibits a consistently higher abundance trajectory, reflecting altered microbial growth patterns compared to controls

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  Fig. 7. (A) Receiver Operating Characteristic curves comparing the diagnostic performance of three machine learning models- Random Forest (RF), SVM, and XGBoost-trained on OTU data to classify CAD patients and controls. RF demonstrates a higher sensitivity and specificity with an ROC curve (top-left corner of the plot). This suggests that the RF model has the best balance between true positive rate (sensitivity) and false positive rate (specificity) compared to SVM and XGBoost for distinguishing CAD patients from healthy controls. (B) Top 10 microbial genera identified as important features for distinguishing CAD patients from healthy individuals using the RF model. The importance score, derived from the mean decrease in accuracy, highlights Holdemanella as the most predictive genus, followed by Acinetobacter, and Fusicatenibacter. These Genera are ranked in descending order of their contribution to the model's performance