Cardiometabolic Multi-Omics: Biomarkers, Metabolic Pathways & Precision Therapeutics

Aim

Workshop Objectives

Workshop Structure

📅 Day 1: Cardiometabolic Genomics, Transcriptomics & Molecular Signatures

Focus: Identify disease-associated genes and expression signatures involved in cardiometabolic disorders.

Topics Covered

• Molecular basis of obesity, diabetes, cardiovascular disease, metabolic syndrome, and related disorders
• Introduction to cardiometabolic genomics and transcriptomics
• Gene-expression datasets and disease-associated genes
• Data quality control, normalization, and preprocessing
• Differential gene-expression analysis
• Identification of disease-associated molecular signatures
• Gene Ontology and pathway enrichment
• Key pathways involved in insulin resistance, lipid metabolism, inflammation, and cardiovascular signalling
• Heatmaps, volcano plots, and molecular-data visualization

🛠️ Hands-on

Analyse a curated cardiometabolic gene-expression dataset, identify differentially expressed genes, and interpret disease-associated pathways.

Workflow:
Omics Data → QC → Differential Analysis → Molecular Signatures → Pathways → Disease Interpretation

🧰 Tools Covered

R, Python, pandas, Bioconductor, GEO, DESeq2 concepts, g:Profiler/Enrichr, ggplot2, Google Colab

📅 Day 2: Metabolomics, Biomarkers & Metabolic Pathway Analysis

Focus: Identify altered metabolites and evaluate their potential as cardiometabolic biomarkers.

Topics Covered

• Introduction to cardiometabolic metabolomics
• Targeted and untargeted metabolomics concepts
• Metabolite data preprocessing, normalization, and quality assessment
• Differential metabolite analysis
• PCA and multivariate metabolic profiling
• Identification of metabolic signatures
• Lipid metabolism, glucose metabolism, amino-acid metabolism, and energy pathways
• Metabolic pathway and metabolite-set enrichment
• Biomarker discovery and prioritisation
• ROC-AUC, sensitivity, specificity, and diagnostic performance
• Metabolite–gene and metabolite–pathway relationships

🛠️ Hands-on

Process a curated metabolomics dataset, identify altered metabolites, analyse metabolic pathways, and evaluate candidate biomarkers.

Workflow:
Metabolomics Data → Preprocessing → Differential Metabolites → Pathways → Biomarkers → Diagnostic Evaluation

🧰 Tools Covered

MetaboAnalyst, Python, R, pandas, scikit-learn, matplotlib, Plotly, Google Colab

📅 Day 3: Multi-Omics Integration & Precision Therapeutics

Focus: Integrate molecular layers to discover disease mechanisms, stratify patients, and identify therapeutic opportunities.

Topics Covered

• Principles of cardiometabolic multi-omics integration
• Integration of genomics, transcriptomics, and metabolomics
• Gene–metabolite correlation analysis
• Molecular and metabolic network construction
• Identification of cross-omics molecular signatures
• Biomarker-panel development and prioritisation
• Machine-learning approaches for disease classification
• Patient/molecular stratification concepts
• Target and pathway prioritisation for therapeutic research
• Precision therapeutics and treatment-response concepts
• Explainable AI and interpretation of molecular predictors
• Translational considerations and validation of multi-omics biomarkers

🛠️ Hands-on

Integrate multi-omics datasets, identify correlated molecular features, build a predictive model, and develop a prioritised cardiometabolic biomarker/therapeutic signature.

Workflow:
Multi-Omics Data → Integration → Molecular Networks → ML Prediction → Biomarker Panel → Patient Stratification → Therapeutic Insights

🧰 Tools Covered

Python, R, pandas, scikit-learn, XGBoost, SHAP, Cytoscape, Plotly, Google Colab

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure