Integrative Multi-Omics Studies

The convergence of multiple omics layers on consistent pathophysiological themes strengthens confidence in ME/CFS disease mechanisms: Multi-omics integration studies have begun combining genomic, transcriptomic, proteomic, metabolomic, and microbiomic data from the same patients. Heng et al. (2025) demonstrated that multi-omics integration outperforms any single omics layer for classifying ME/CFS patients and identifying mechanistic subtypes. Key integrative findings include:

• Metabolic–immune convergence: Transcriptomic immune gene dysregulation and metabolomic hypometabolic signatures co-localize to the same patients, supporting the energy–immune vicious cycle model (Chapter Integrative Models and Multi-System Pathophysiology)
• Gut–systemic axis: Microbiomic changes in butyrate-producing bacteria correlate with metabolomic tryptophan depletion and transcriptomic immune activation, confirming the gut–immune–brain axis as a functional unit in ME/CFS
• Subgroup identification: Unsupervised clustering of multi-omics data identifies at least 2–4 molecular subtypes that may correspond to clinically distinct patient populations—a critical step toward biomarker-stratified clinical trials
• Network approaches: Systems biology network analyses integrating protein–protein interactions, metabolic pathways, and gene regulatory networks have identified hub molecules (e.g., IDO1, HIF-1\(\alpha\), NF-\(\kappa\)B) that connect apparently disparate disease features into coherent mechanistic models