Atherosclerosis and Gut Microbiota: Machine Learning Reveals Biomarkers of Disease Severity

This article introduces an open-source workflow published by Ninasb08 on GitHub (Project link: https://github.com/Ninasb08/atherosclerosis-microbiota-biomarker-pipeline) for exploratory ordinal machine learning analysis of the relationship between fecal microbiota and the severity of atherosclerosis. The project demonstrates the cross-application of bioinformatics and machine learning in medical research, with core objectives including identifying relevant microbial biomarkers, disease stratification, reproducible research, and ordinal classification (treating disease severity as ordered categories: mild < moderate < severe).

Gut Microbiota and Health

The gut microbiota consists of trillions of microbes, participating in food digestion, nutrient absorption, immune regulation, metabolic balance, and nervous system function.

Overview of Atherosclerosis

Atherosclerosis is a chronic vascular disease characterized by lipid deposits forming plaques in arterial walls, leading to vascular stenosis and hardening. It causes coronary heart disease, myocardial infarction, stroke, and peripheral arterial disease, and is one of the leading causes of death and disability globally.

Mechanisms by Which Gut Microbiota Affect Cardiovascular Health

1. Metabolites: Metabolites such as TMAO may promote atherosclerosis;
2. Inflammation Regulation: The microbiota affects systemic inflammation levels, and inflammation is a key factor in atherosclerosis;
3. Lipid Metabolism: Participates in cholesterol metabolism and influences blood lipid levels;
4. Blood Pressure Regulation: Substances produced by certain bacteria may affect blood pressure.

Core Objectives

• Biomarker Discovery: Identify microbial features associated with atherosclerosis severity;
• Disease Stratification: Classify disease severity based on microbial composition;
• Reproducible Research: Provide a complete data processing and analysis pipeline;
• Ordinal Classification: Treat disease severity as ordered categories (mild, moderate, severe).

Why Choose Ordinal Machine Learning

Traditional classification assumes no order between categories, but disease severity is naturally ordered. Ordinal machine learning leverages this order information to improve prediction performance.

Data Sources

• 16S rRNA Sequencing: Targets conserved regions of bacterial 16S rRNA, low cost, suitable for large samples, and provides genus/species-level classification;
• Metagenomic Sequencing: Sequences all DNA in samples, comprehensive information but high cost and large data volume.

Data Preprocessing

1. Quality Control: Remove low-quality reads, filter errors, and eliminate host DNA contamination;
2. Feature Extraction: Cluster sequences into OTUs/ASVs, perform species annotation, and construct feature-sample matrices;
3. Normalization: Handle sequencing depth differences (e.g., rarefaction), proportional conversion, and logarithmic transformation.

Ordinal Machine Learning Algorithms

• Traditional Modified Methods: Ordinal logistic regression, ordered extensions of support vector machines, ordinal variants of decision trees/random forests;
• Deep Learning: Neural network ordinal output layers, cumulative link models, ranking learning;
• Feature Selection: Statistical filtering, model importance selection, regularization (LASSO/Elastic Net), and bioinformatics prior guidance.

Medical Research Value

• Non-Invasive Diagnosis: Fecal sample collection is simple, and microbial biomarkers provide a convenient screening method;
• Early Warning: Microbial changes may precede clinical symptoms, offering an intervention window;
• Treatment Targets: Identify relevant microbes to guide probiotics/prebiotics/fecal microbiota transplantation;
• Personalized Medicine: Microbial composition affects drug metabolism and treatment response, guiding personalized plans.

Public Health Significance

• Risk Stratification: Identify high-risk individuals for targeted prevention;
• Health Monitoring: Regularly detect microbial changes to monitor disease progression or treatment effects;
• Lifestyle Intervention: Diet and exercise affect gut microbiota, providing actionable intervention targets.

Microbiome Data Analysis Challenges

• High Dimensionality: Many features (thousands of microbes) with few samples → Strict feature selection, dimensionality reduction (PCA/t-SNE/UMAP), regularization, and ensemble learning;
• Sparsity: Most microbes have extremely low abundance → Filter low-abundance features, sparse data statistical methods, and higher taxonomic level clustering;
• Compositional Nature: Data consists of proportional sums → Compositional data analysis (centered log-ratio transformation), algorithms considering compositional properties;
• Batch Effects: Differences between data from different sources → ComBat correction, normalization, and batch-balanced experimental design.

Ordinal Classification Challenges

• Class Imbalance: More mild patients, fewer severe ones → Sampling strategies (over/under sampling), cost-sensitive learning, and metrics suitable for imbalanced data;
• Adjacent Class Confusion: Small differences between adjacent categories → Ordinal loss functions, model structures considering adjacent relationships, and multi-task learning.

The project emphasizes reproducibility:

• Code Sharing: Full analysis code is open-source, supporting reproduction, validation, and secondary applications;
• Comprehensive Documentation: Clear README, dependency environment definitions, and usage examples;
• Data Availability: Raw data stored in public databases, with processed data and intermediate results accessible;
• Containerization: Possible use of Docker to ensure environment consistency.

Future Directions

• Multi-Omics Integration: Microbiome + metabolomics, genome + transcriptomics, clinical indicators + imaging;
• Longitudinal Studies: Track microbial changes and disease progression, evaluate intervention effects;
• Mechanism Research: Animal model validation, in vitro experiments, metabolic pathway analysis;
• Clinical Translation: Develop diagnostic kits, design clinical trials, and formulate treatment guidelines.

Conclusion

This project reflects the interdisciplinary integration of medicine, biology, computer science, and statistics, providing solutions for complex health issues. As sequencing costs decrease and AI advances, research on the relationship between the microbiome and diseases will bring more health breakthroughs.