Multi-Omics Approaches to ME/CFS: Advancing Understanding and Therapeutic Development

A comprehensive analysis of omics-based discoveries and their implications for diagnosis and treatment

Paper used in context:

https://www.biorxiv.org/content/10.1101/2024.06.24.600378v2

Date: March 9, 2025

Table of Contents

1. Executive Summary
2. Background on ME/CFS and Omics
3. Review of the Core Paper: BioMapAI
4. Additional Omics Studies in ME/CFS Research
5. Comparative Analysis and Integration
6. Outstanding Questions and Knowledge Gaps
7. New Hypotheses for ME/CFS Research
8. Innovative Research Approaches
9. Actionable Therapeutic Targets
10. Conclusion and Future Directions
11. References

Executive Summary: Omics-Based Discoveries in ME/CFS Research

Objective

This report synthesizes findings from the core BioMapAI study and eight additional omics studies to provide a comprehensive overview of the molecular mechanisms underlying Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). By integrating insights from transcriptomics, proteomics, and metabolomics research, we aim to advance understanding of ME/CFS pathophysiology and identify potential therapeutic targets.

Key Findings

Multi-Omics Integration Reveals Complex Disease Mechanisms

The BioMapAI study represents a significant advancement in ME/CFS research through its integration of gut metagenomics, plasma metabolomics, immune cell profiling, and clinical data using a supervised deep neural network. This approach identified both disease-specific and symptom-specific biomarkers, demonstrating that ME/CFS involves disrupted interactions between the microbiome, immune system, and metabolome.

Additional omics studies complement these findings, revealing:

1. Immune System Dysregulation:

• Monocyte abnormalities with inappropriate differentiation and migration to tissue
• Altered inflammatory responses, particularly following exercise challenge
• Dysregulated cytokine production, including elevated IL2 in extracellular vesicles
• Heightened pro-inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, γδT)

2. Energy Metabolism Disruption:

• Multiple metabolic phenotypes with elevated energy strain
• Altered utilization of fatty acids and amino acids as catabolic fuels
• Disrupted metabolic pathways during exercise response and recovery
• Potential exertion-triggered tissue hypoxia leading to systemic metabolic adaptation

3. Lipid Metabolism Abnormalities:

• Peroxisomal dysfunction affecting lipid metabolism
• Decreased levels of plasmalogens, phospholipid ethers, phosphatidylcholines, and sphingomyelins
• Dysregulation of lipid remodeling pathways
• Disrupted associations between microbial metabolism and plasma lipids

4. Microbiome-Host Interactions:

• Depleted butyrate production from pyruvate and glutarate pathways
• Altered branched-chain amino acid (BCAA) biosynthesis
• Disrupted tryptophan and benzoate metabolism
• Modified associations between microbial metabolites and immune cell function

5. Post-Exertional Malaise Mechanisms:

• Improper platelet activation following exercise challenge
• Dysregulated immune signaling pathways during recovery period
• Altered glutamate metabolism affecting recovery
• Distinct metabolic responses to first versus second exercise sessions

Biomarker Identification

Multiple studies have identified potential biomarkers for ME/CFS diagnosis and patient stratification:

• Immune Biomarkers: Increased B cells (CD19+CD3-), CCR6+ CD8 memory T cells, and CD4 naïve T cells
• Microbial Biomarkers: Dysosmobacteria welbionis, Faecalibacterium prausnitzii
• Metabolic Biomarkers: Glycodeoxycholate 3-sulfate (increased), vanillylmandelate (decreased)
• Lipid Biomarkers: Decreased plasmalogens, phosphatidylcholines, and sphingomyelins

Machine learning approaches have demonstrated promising results in distinguishing ME/CFS patients from controls, with accuracy rates of 80-90% across multiple studies.

Patient Heterogeneity

A consistent finding across studies is the heterogeneity of ME/CFS presentation, with evidence for:

• Multiple metabolic phenotypes with distinct biological signatures
• Sex-specific differences in metabolic and immune responses
• Varying symptom profiles that correlate with specific molecular changes
• Potential overlap with other conditions such as fibromyalgia

High-Level Takeaways

1. ME/CFS is a Biological Disease: The consistent identification of molecular abnormalities across multiple omics platforms provides strong evidence that ME/CFS is a biological illness with measurable physiological disruptions.
2. Systems-Level Disruption: ME/CFS involves complex interactions between multiple biological systems, including the immune system, metabolism, and microbiome, requiring integrated approaches for understanding and treatment.
3. Personalized Medicine Opportunity: The identification of patient subgroups with distinct molecular profiles suggests that personalized treatment approaches may be necessary for effective management of ME/CFS.
4. Post-Exertional Malaise Has Molecular Basis: Multiple studies provide evidence for biological mechanisms underlying post-exertional malaise, a hallmark symptom of ME/CFS, involving disrupted recovery processes after exertion.
5. Therapeutic Target Identification: The convergence of evidence on immune dysregulation, energy metabolism, and lipid abnormalities points to several promising therapeutic targets for intervention development.
6. Diagnostic Potential: Multi-omics approaches and machine learning algorithms demonstrate potential for developing reliable diagnostic tools for ME/CFS, addressing a critical unmet need.

These findings collectively advance our understanding of ME/CFS pathophysiology by providing a comprehensive molecular map of the disease, identifying potential biomarkers and therapeutic targets, and highlighting the importance of personalized approaches to diagnosis and treatment. The integration of multiple omics platforms offers unprecedented insights into the complex biology of ME/CFS and provides a foundation for future research and therapeutic development.

Background on ME/CFS and Omics

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Definition and Clinical Criteria

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multisystem illness characterized by profound fatigue that is not improved by rest, post-exertional malaise (PEM), cognitive dysfunction, unrefreshing sleep, and orthostatic intolerance. The condition affects an estimated 10 million individuals worldwide, with a disproportionate impact on women, who are affected at a rate three to four times higher than men. ME/CFS can persist for years or even decades, with fewer than 5% of patients returning to pre-illness levels of health.

Several diagnostic criteria have been developed for ME/CFS over the years, reflecting evolving understanding of the condition:

1. Fukuda Criteria (1994): Developed by the Centers for Disease Control and Prevention (CDC), these criteria require unexplained, persistent fatigue for at least six months that substantially reduces activity levels, along with at least four of eight additional symptoms (post-exertional malaise, unrefreshing sleep, impaired memory/concentration, muscle pain, joint pain, headaches, tender lymph nodes, sore throat).
2. Canadian Consensus Criteria (2003): These more specific criteria require post-exertional malaise, sleep dysfunction, pain, and neurological/cognitive manifestations, plus symptoms from at least two of the following categories: autonomic, neuroendocrine, or immune manifestations.
3. International Consensus Criteria (2011): These criteria further refined the definition, requiring post-exertional neuroimmune exhaustion, neurological impairments, immune/gastrointestinal/genitourinary impairments, and energy metabolism/transport impairments.
4. Institute of Medicine Criteria (2015): The IOM (now the National Academy of Medicine) established criteria requiring substantial reduction in ability to engage in pre-illness activities, post-exertional malaise, unrefreshing sleep, and either cognitive impairment or orthostatic intolerance.

Despite these established criteria, ME/CFS remains a diagnosis of exclusion, made after ruling out other medical conditions that could explain the symptoms. The lack of a definitive diagnostic test or biomarker represents a significant challenge in clinical practice and research.

Major Challenges in ME/CFS Research and Clinical Practice

Heterogeneous Symptom Presentation

ME/CFS presents with remarkable heterogeneity in symptom type, severity, and progression. While core symptoms like fatigue and post-exertional malaise are common to most patients, the constellation of additional symptoms varies widely. Some patients may experience predominantly neurological symptoms, while others may have more pronounced immune or gastrointestinal manifestations. This heterogeneity suggests that ME/CFS may encompass multiple disease subtypes with potentially different underlying mechanisms, complicating both research and treatment approaches.

Diagnostic Complexity

The absence of objective diagnostic biomarkers means that ME/CFS diagnosis relies heavily on subjective symptom reporting and exclusion of other conditions. This creates several challenges:

1. Delayed Diagnosis: Patients often experience significant delays in diagnosis, with many reporting years of medical consultations before receiving a definitive diagnosis.
2. Misdiagnosis: Symptoms of ME/CFS overlap with numerous other conditions, leading to potential misdiagnosis or missed comorbidities.
3. Stigma: The lack of objective diagnostic markers has historically contributed to skepticism about the biological basis of ME/CFS, leading to stigmatization of patients.
4. Research Limitations: Without clear diagnostic biomarkers, research cohorts may be heterogeneous, potentially including individuals with different underlying pathologies.

Disease Onset and Progression

ME/CFS can develop through different pathways, further complicating research efforts:

1. Post-Infectious Onset: Many patients report that their symptoms began following an acute infectious illness, with viral infections (particularly Epstein-Barr virus, enteroviruses, and more recently, SARS-CoV-2) being commonly reported triggers.
2. Gradual Onset: Some patients experience a more gradual development of symptoms without a clear triggering event.
3. Non-Linear Progression: As demonstrated in the BioMapAI study, ME/CFS often follows a non-linear progression, with symptom severity fluctuating over time rather than following a predictable trajectory.

Multisystem Involvement

ME/CFS affects multiple physiological systems, including the nervous system, immune system, cardiovascular system, and metabolic pathways. This multisystem involvement necessitates interdisciplinary research approaches and complicates the development of targeted treatments.

Limited Treatment Options

Currently, there are no FDA-approved treatments specifically for ME/CFS. Management typically focuses on symptom relief and adaptive strategies like pacing to minimize post-exertional malaise. The lack of effective treatments stems partly from incomplete understanding of the underlying pathophysiology, highlighting the critical need for research that can identify potential therapeutic targets.

The Value of Omics Approaches in ME/CFS Research

The complex, multisystem nature of ME/CFS makes it particularly well-suited for investigation using omics technologies, which provide comprehensive, system-level views of biological processes. These approaches offer several advantages for ME/CFS research:

Transcriptomics in ME/CFS Research

Transcriptomics—the study of the complete set of RNA transcripts produced by the genome—has provided valuable insights into ME/CFS pathophysiology:

1. Immune Dysregulation: Transcriptomic studies have identified altered expression of genes involved in immune function, particularly those related to inflammatory responses and T-cell activation.
2. Cellular Energy Production: Gene expression analyses have revealed dysregulation of pathways involved in mitochondrial function and energy metabolism, supporting the hypothesis that energy production deficits contribute to ME/CFS symptoms.
3. Stress Response Pathways: Transcriptomics has highlighted abnormalities in cellular stress response mechanisms, including oxidative stress pathways and circadian rhythm regulation.
4. Exercise Response: Single-cell RNA sequencing before and after exercise challenge has revealed how gene expression patterns change during post-exertional malaise, providing molecular insights into this cardinal symptom.

Proteomics in ME/CFS Research

Proteomics—the large-scale study of proteins—offers complementary insights to transcriptomics by examining the functional molecules that carry out cellular processes:

1. Biomarker Discovery: Proteomic analyses have identified potential protein biomarkers in blood and cerebrospinal fluid that may aid in diagnosis or patient stratification.
2. Immune System Proteins: Proteomics has revealed alterations in cytokines, complement proteins, and other immune mediators, supporting the role of immune dysfunction in ME/CFS.
3. Extracellular Vesicles: Analysis of proteins in extracellular vesicles has provided insights into cell-to-cell communication and potential disease mechanisms.
4. Central Nervous System Involvement: Cerebrospinal fluid proteomics has offered a window into neurological aspects of ME/CFS and its relationship to other conditions like fibromyalgia.

Metabolomics in ME/CFS Research

Metabolomics—the comprehensive study of small molecule metabolites—has been particularly valuable for understanding energy metabolism disruptions in ME/CFS:

1. Energy Metabolism: Metabolomic studies have identified abnormalities in pathways involved in cellular energy production, including glycolysis, tricarboxylic acid cycle, and fatty acid metabolism.
2. Lipid Metabolism: Analyses of lipid metabolites have revealed disruptions in membrane lipids, signaling molecules, and lipid transport, suggesting peroxisomal dysfunction.
3. Response to Exercise: Metabolomic profiling before and after exercise challenge has demonstrated abnormal metabolic responses to exertion in ME/CFS patients, providing insights into post-exertional malaise.
4. Metabolic Phenotypes: Metabolomics has helped identify distinct metabolic phenotypes within the ME/CFS population, supporting the concept of disease subtypes.

Metagenomics in ME/CFS Research

Metagenomics—the study of genetic material recovered directly from environmental samples—has been applied to investigate the gut microbiome in ME/CFS:

1. Microbial Composition: Metagenomic studies have identified alterations in the gut microbiome composition in ME/CFS patients compared to healthy controls.
2. Microbial Function: Beyond taxonomic changes, metagenomics has revealed functional differences in microbial metabolism, including pathways involved in short-chain fatty acid production and amino acid metabolism.
3. Microbiome-Host Interactions: Integration of metagenomic data with host omics data has provided insights into how microbial changes may influence host physiology in ME/CFS.

Integration of Multiple Omics Approaches

While individual omics approaches provide valuable insights, the integration of multiple omics data types offers the most comprehensive view of ME/CFS pathophysiology:

1. Complementary Perspectives: Different omics platforms capture distinct aspects of biology, from gene expression to protein function to metabolic activity, providing a more complete picture when integrated.
2. Pathway Validation: Findings from one omics platform can be validated and extended by complementary findings from other platforms, strengthening confidence in identified pathways.
3. Systems Biology: Multi-omics integration enables systems-level analyses that can capture complex interactions between different biological components and processes.
4. Patient Stratification: Combined omics data may reveal patterns that allow for more precise stratification of patients into biologically meaningful subgroups.
5. Therapeutic Target Identification: By providing a comprehensive view of disease mechanisms, integrated omics approaches can identify potential therapeutic targets that might be missed by single-platform studies.

The BioMapAI study exemplifies the power of multi-omics integration in ME/CFS research, combining gut metagenomics, plasma metabolomics, immune cell profiling, and clinical data to create a comprehensive map of disease-associated changes. This approach not only advances our understanding of ME/CFS pathophysiology but also points the way toward more targeted diagnostic and therapeutic strategies.

Conclusion

ME/CFS presents significant challenges for research and clinical practice due to its heterogeneous presentation, diagnostic complexity, multisystem involvement, and limited treatment options. Omics technologies offer powerful tools for addressing these challenges by providing comprehensive, system-level views of the biological processes underlying ME/CFS. Transcriptomics, proteomics, metabolomics, and metagenomics each contribute valuable insights, while the integration of multiple omics approaches offers the most complete picture of disease mechanisms. As demonstrated by the BioMapAI study and other recent research, these approaches are advancing our understanding of ME/CFS pathophysiology and may ultimately lead to improved diagnostic tools and targeted treatments for this debilitating condition.

Review of the Core Paper: BioMapAI

Introduction to BioMapAI

The core paper, "BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome," represents a significant advancement in ME/CFS research through its innovative application of artificial intelligence to integrate multiple omics data types. Developed by researchers at The Jackson Laboratory, University of Connecticut Health Center, and Bateman Horne Center, BioMapAI addresses a fundamental challenge in ME/CFS research: the heterogeneous nature of the disease and the difficulty in identifying consistent biomarkers across diverse patient populations.

The authors recognized that previous research approaches, which typically focused on one or two key disease indicators, were insufficient to capture the multifactorial etiology and complex symptom presentation of ME/CFS. To overcome this limitation, they developed a supervised deep neural network capable of simultaneously modeling diverse data types and predicting multiple clinical outcomes, thereby providing a more comprehensive understanding of ME/CFS pathophysiology.

Methodologies

Cohort Design and Data Collection

The study tracked 249 participants over a 3-4 year period, including:

• 153 ME/CFS patients

• 75 with "short-term" disease (symptoms < 4 years)
• 78 with "long-term" disease (symptoms > 10 years)

• 96 age- and gender-matched healthy controls

The cohort demographics reflected the epidemiological profile of ME/CFS, with 68% female and 32% male participants, consistent with the observation that women are 3-4 times more likely to develop ME/CFS. Participants ranged in age from 19 to 68 years and had body mass indexes (BMI) from 16 to 43 kg/m².

Throughout the study, the researchers collected an impressive 1,471 biological samples across 515 timepoints, including:

1. Blood Samples:

• Clinical testing at Quest Laboratory (48 features measured, 503 samples)
• Peripheral blood mononuclear cells (PBMCs) examined via flow cytometry (443 immune cells and cytokines measured, 489 samples)
• Plasma and serum analyzed by untargeted liquid chromatography with tandem mass spectrometry (LC-MS/MS) (958 metabolites identified, 414 samples)

2. Stool Samples:

• Whole genome shotgun metagenomic sequencing (479 samples)
• Average of 12,302,079 high-quality, classifiable reads per sample
• 1,293 microbial species detected
• 9,993 KEGG genes reconstructed

3. Clinical Data:

• Detailed demographic documentation
• Questionnaires covering medication use, medical history, and key ME/CFS symptoms
• Twelve essential clinical scores covering core symptoms including physical and mental health, fatigue, pain levels, cognitive efficiency, sleep disturbances, orthostatic intolerance, and gastrointestinal issues

BioMapAI Model Architecture

BioMapAI is a fully connected deep neural network with a unique architecture designed to capture both general disease patterns and symptom-specific features:

1. Input Layer (X): Takes in the omics matrices (microbiome, metabolome, immune profiles, etc.)
2. Hidden Layers:

• Two shared hidden layers (Z¹ with 64 nodes, Z² with 32 nodes) for general pattern learning
• One parallel hidden layer (Z³) with sub-layers (each with 8 nodes) tailored for each clinical outcome

3. Output Layer (Y): Produces predictions for 12 clinical symptoms

This architecture allows the model to simultaneously learn patterns common across all symptoms while also capturing features specific to individual symptoms. The model is made explainable through the incorporation of SHAP (SHapley Additive exPlanations), which quantifies the feature importance of each prediction, providing both local (symptom-level) and global (disease-level) interpretability.

The model automatically finds appropriate learning goals and loss functions for each type of outcome, accommodating both continuous and categorical clinical variables without requiring format refinement. This flexibility enhances BioMapAI's potential adaptability to broader research applications.

Validation Approach

The researchers employed a rigorous validation strategy:

• Ten-fold cross-validation for model training and initial validation
• 10% of data held out as an independent validation set to assess generalizability
• External validation using four previously published ME/CFS cohorts:

• Two microbiome cohorts (Guo, Cheng et al., 2023 and Raijmakers, Ruud et al., 2020)
• Two metabolome cohorts (Germain, Arnaud et al., 2022 and Che, Xiaoyu et al., 2022)

Key Results

Disease Classification Performance

BioMapAI demonstrated strong performance in distinguishing ME/CFS patients from healthy controls:

• 91% AUC (Area Under the Curve) in 10-fold cross-validation
• Outperformed other machine learning models (generalized linear model, support vector machine, gradient boosting) when using the full omics dataset (AUC = 91.5%)
• Maintained robust performance with unseen data in the held-out validation cohort (AUC = 82.3%)
• Showed good generalization to external cohorts despite technical and clinical differences between studies

Predictive Power of Different Omics Types

A key innovation of BioMapAI is its ability to leverage different omics data to predict individual clinical scores. The study found that different omics types had varying strengths in predicting specific symptoms:

• Immune Profiling: Consistently showed the highest ability to forecast a wide range of symptoms, including pain, fatigue, orthostatic intolerance, and general health perception
• Microbiome Profiles: Excelled at predicting gastrointestinal abnormalities, emotional well-being, and sleep disturbances
• Plasma Metabolomics: Showed strong correlations with physical health and social activity
• Blood Measurements: Demonstrated limited predictive ability except for cognitive efficiency

These findings highlight the importance of integrating multiple omics types to capture the full spectrum of ME/CFS symptoms.

Disease-Specific Biomarkers

BioMapAI identified several disease-specific biomarkers that were important across symptoms and models:

1. Immune Biomarkers:

• Increased B cells (CD19+CD3-)
• Increased CCR6+ CD8 memory T cells (mCD8+CCR6+CXCR3-)
• Increased CD4 naïve T cells (nCD4+FOXP3+)

2. Microbial Biomarkers:

• Dysosmobacteria welbionis, a gut microbe with a role in bile acid and butyrate metabolism

3. Metabolic Biomarkers:

• Increased glycodeoxycholate 3-sulfate (a bile acid)
• Decreased vanillylmandelate (VMA, a catecholamine breakdown product)

These biomarkers were consistently validated across machine learning and deep learning models, demonstrating the robustness of BioMapAI's findings.

Symptom-Specific Biomarkers

One of the most innovative aspects of BioMapAI is its ability to identify symptom-specific biomarkers. The study found distinct sets of biomarkers for each clinical symptom:

1. Pain:

• CD4 memory cells (positive association)
• CD1c+ dendritic cells (negative association)
• Faecalibacterium prausnitzii (complex biphasic relationship)

2. Gastrointestinal Issues:

• Dysosmobacteria welbionis (positive association)
• Various microbial species and metabolites

3. Sleep Disturbances:

• Dysosmobacteria welbionis (positive association)
• Microbial metabolites affecting the gut-brain axis

The study also observed different types of interactions between biomarkers and symptoms:

• Linear relationships (monotonic positive or negative associations)
• Biphasic relationships (dual effects depending on abundance levels)
• Dispersed contributions (particularly for KEGG genes)

Disrupted Microbiome-Immune-Metabolome Networks

BioMapAI constructed the first connectivity map spanning the microbiome, immune system, and plasma metabolome in health and ME/CFS, adjusted for age, gender, and additional clinical factors. This analysis revealed several disrupted networks in ME/CFS:

1. Short-Chain Fatty Acids (SCFAs):

• Depleted butyrate production from pyruvate and glutarate pathways
• Altered associations with Th17, Treg cells, and plasma lipids
• New correlations with inflammatory immune cells (γδT, CD8+ MAIT)

2. Branched-Chain Amino Acids (BCAAs):

• Disrupted biosynthesis
• Opposite correlations with immune cells and plasma lipids in patients vs. controls

3. Tryptophan Metabolism:

• Associated with gastrointestinal issues
• Lost negative association with Th22 cells
• Gained correlations with γδT cells and IFNγ/GzA secretion

4. Benzoate Metabolism:

• Increased microbial benzoate (synthesized by Clostridia sp.)
• Strong positive correlation with plasma hippurate in long-term ME/CFS
• Disrupted associations with plasma lipids, glycerophosphoethanolamine, fatty acids, bile acids
• Correlated with fatigue, emotional disturbances, sleep problems

The study also found that short-term ME/CFS patients presented a transitional profile, with some health-associated networks already dysbiotic but not yet fully stabilized, while these pathological connections became more firmly established in long-term ME/CFS.

Strengths and Limitations

Strengths

1. Comprehensive Multi-Omics Dataset: One of the most extensive multi-omics resources assembled for ME/CFS, including gut metagenomics, plasma metabolomics, immune cell profiling, and detailed clinical data.
2. Innovative AI Approach: BioMapAI's architecture allows for simultaneous modeling of multiple omics data types and prediction of multiple clinical outcomes, addressing the multifaceted nature of ME/CFS.
3. Longitudinal Design: Tracking participants over 3-4 years provides insights into disease progression and stability of biomarkers.
4. Explainable AI: The incorporation of SHAP values makes the deep learning model interpretable, allowing for identification of both disease-specific and symptom-specific biomarkers.
5. Rigorous Validation: The use of both internal cross-validation and external validation with independent cohorts strengthens confidence in the findings.
6. Systems-Level Analysis: The construction of a comprehensive connectivity map spanning multiple biological systems provides unprecedented insights into ME/CFS pathophysiology.

Limitations

1. Demographic Constraints: The study population comprised more females and older individuals, primarily Caucasian, from a single geographic location (Bateman Horne Center), potentially limiting generalizability.
2. Omics Coverage: The study did not include host PBMC RNA or ATAC sequencing, which might have provided deeper insights into regulatory changes and mitochondrial dysfunction.
3. Longitudinal Challenges: The four-year longitudinal design may be insufficient to capture stable temporal signals in a disease that typically progresses over decades.
4. Medication and Diet Effects: Long disease history increases the likelihood of exposure to various diets and medications, which could influence biomarker identification, particularly in metabolomics.
5. Model Size Limitations: BioMapAI was trained on fewer than 500 samples, which is relatively small given the complexity of the outcome matrix, potentially limiting its generalizability.
6. Symptom Weighting: The model treated all 12 studied symptoms with equal importance due to unclear symptom prioritization in ME/CFS, which may not reflect the clinical reality where some symptoms have greater impact on quality of life than others.

Significance and Implications

The BioMapAI study represents a significant advancement in ME/CFS research for several reasons:

1. Systems Biology Approach: By integrating multiple omics data types, the study provides a comprehensive view of ME/CFS as a systems-level disorder involving complex interactions between the microbiome, immune system, and metabolism.
2. Personalized Medicine Potential: The identification of symptom-specific biomarkers suggests the possibility of developing personalized treatment approaches targeted to individual symptom profiles.
3. Diagnostic Tool Development: The strong classification performance of BioMapAI suggests potential for developing diagnostic tools based on multi-omics profiles, addressing a critical unmet need in ME/CFS.
4. Therapeutic Target Identification: The disrupted networks identified in the study point to several potential therapeutic targets, including pathways involved in SCFA production, BCAA metabolism, and immune regulation.
5. Long COVID Insights: Given the recognized parallels between ME/CFS and long COVID, the findings may have broader implications for understanding post-viral syndromes in general.
6. Methodological Innovation: The AI-driven framework developed for this study may prove valuable for other complex conditions where symptom variability cannot be fully captured by a single data type.

Conclusion

The BioMapAI study represents a landmark contribution to ME/CFS research, providing unprecedented systems-level insights into the disease's pathophysiology. By integrating multiple omics data types and developing an explainable AI model capable of predicting diverse clinical symptoms, the researchers have created a powerful tool for understanding ME/CFS and identifying potential therapeutic targets.

The study's findings refine existing hypotheses about ME/CFS pathophysiology and propose new ones, particularly regarding the disrupted interactions between microbial metabolism, immune function, and host metabolism. While the findings are still preliminary for direct therapeutic application, they offer numerous hypotheses for dysbiotic microbiome-metabolome-immune connections in ME/CFS that can guide future research.

The BioMapAI approach exemplifies the power of multi-omics integration and artificial intelligence in tackling complex, heterogeneous diseases like ME/CFS, pointing the way toward more personalized, targeted approaches to diagnosis and treatment.

Additional Omics Studies in ME/CFS Research

This section provides a detailed review of eight additional omics studies that complement the core BioMapAI paper, offering diverse perspectives on ME/CFS pathophysiology through transcriptomics, proteomics, and metabolomics approaches.

Transcriptomics Studies

Study 1: Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation

Citation: Vu LT, Ahmed F, Zhu H, Iu DSH, Fogarty EA, Kwak Y, Chen W, Franconi CJ, Munn PR, Tate AE, Levine SM, Stevens J, Mao X, Shungu DC, Moore GE, Keller BA, Hanson MR, Grenier JK, Grimson A. Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation. Cell Rep Med. 2024 Jan 16;5(1):101373. doi: 10.1016/j.xcrm.2023.101373.

Objectives and Methods:

• Employed single-cell RNA sequencing (scRNA-seq) to examine immune cells in ME/CFS patients and matched controls
• Analyzed samples at baseline and 24 hours after exercise challenge (cardiopulmonary exercise test, CPET)
• Cohort: 30 ME/CFS patients and 28 matched controls
• Profiled approximately 5,000 peripheral blood mononuclear cells (PBMCs) per sample
• Samples collected before the COVID-19 pandemic, eliminating possibility of Long COVID confounding
• Two-step sequencing strategy to ensure equivalent coverage across libraries

Significant Findings:

1. Baseline Monocyte Dysregulation:

• ME/CFS patients displayed classical monocyte dysregulation
• Evidence of inappropriate differentiation and migration to tissue
• Both diseased and more normal monocytes identified within patients
• Fraction of diseased cells correlated with disease severity

2. Post-Exercise Changes:

• Patterns indicative of improper platelet activation in patients
• Minimal changes elsewhere in the immune system after exercise
• Most gene dysregulation across the ME/CFS immune system was consistent between baseline and post-exercise conditions

3. Immune Cell Alterations:

• Identified abnormalities in monocytes, natural killer (NK) cells, neutrophils, T lymphocytes, and B cells
• Elevated apoptosis in neutrophils
• Abnormal metabolism and cytokine production in T lymphocytes

Relevance to ME/CFS:

• Provides single-cell resolution of immune dysregulation in ME/CFS
• Identifies specific immune cell types with distinctive patterns of transcriptome dysregulation
• Offers insights into the molecular basis of post-exertional malaise (PEM)
• Suggests potential connections between ME/CFS and Long COVID
• Highlights the importance of examining immune cells at the single-cell level rather than as bulk samples

Study 2: Stress-Induced Transcriptomic Changes in Females with ME/CFS Reveal Disrupted Immune Signatures

Citation: Van Booven DJ, Gamer J, Joseph A, Perez M, Zarnowski O, Pandya M, Collado F, Klimas N, Oltra E, Nathanson L. Stress-Induced Transcriptomic Changes in Females with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Reveal Disrupted Immune Signatures. Int J Mol Sci. 2023 Jan 31;24(3):2698. doi: 10.3390/ijms24032698.

Objectives and Methods:

• Evaluated transcriptomic changes in female ME/CFS patients undergoing an exercise challenge
• Analyzed RNA sequencing (RNA-seq) data from peripheral blood mononuclear cells (PBMCs)
• Cohort: 20 female ME/CFS patients and 20 matched female healthy controls
• Three time points: baseline before exercise challenge (T0), maximal exertion (T1), and 4 hours after maximal exertion (T2)
• Participants matched for age and BMI
• Focus on sex-specific aspects (females only)

Significant Findings:

1. Baseline to Maximal Exertion (T0 to T1):

• Healthy Controls: 102 genes showed significant changes in expression

• 4 genes underexpressed (PHLDB3, LZTS3, SLC16A10, MAL)
• 98 genes overexpressed
• Altered functional gene networks related to signaling and integral functions of immune cells

• ME/CFS Patients: No significant changes in gene expression

• Suggests impaired ability to mount appropriate transcriptional response to exercise stress

2. Recovery Period (T1 to T2):

• ME/CFS Patients: Showed dysregulated immune signaling pathways and dysfunctional cellular responses to stress
• Specific pathways identified that may contribute to post-exertional malaise

Relevance to ME/CFS:

• Identifies specific transcriptomic changes associated with exercise challenge and PEM
• Highlights dysregulated immune signaling pathways during recovery period
• Provides insights into the molecular basis of post-exertional malaise
• Suggests potential biomarkers for diagnosis and treatment targets
• Offers sex-specific insights into ME/CFS pathophysiology

Study 3: Changes in the transcriptome of circulating immune cells of a New Zealand cohort with ME/CFS

Citation: Sweetman E, Ryan M, Edgar C, MacKay A, Vallings R, Tate W. Changes in the transcriptome of circulating immune cells of a New Zealand cohort with myalgic encephalomyelitis/chronic fatigue syndrome. Int J Immunopathol Pharmacol. 2019 Jan-Dec;33:2058738418820402. doi: 10.1177/2058738418820402.

Objectives and Methods:

• Examined the transcriptomes of peripheral blood mononuclear cells (PBMCs) by RNA-seq analysis
• Cohort: 10 ME/CFS patients and 10 age/gender-matched healthy controls from New Zealand
• ME/CFS patients diagnosed according to the Canadian Consensus Criteria (CCC)
• RNA integrity analysis by Fragment Analyzer (RIN ≥7)
• Illumina TruSeq Stranded total RNA library preparation
• Illumina HiSeq 2x125 bp paired-end sequencing
• Functional network analysis and pathway analysis of differentially expressed genes

Significant Findings:

1. Differentially Expressed Genes:

• 27 gene transcripts significantly increased 1.5- to sixfold in ME/CFS patients
• 6 gene transcripts significantly decreased three- to sixfold in ME/CFS patients
• Top enhanced gene transcripts: IL8, NFΚBIA, and TNFAIP3 (functionally related to inflammation)
• Significant changes validated for IL8 and NFΚBIA by quantitative PCR

2. Functional Network Analysis:

• Identified interactions between gene products related to:

• Inflammation
• Circadian clock function
• Metabolic dysregulation
• Cellular stress responses
• Mitochondrial function

3. Pathway Analysis:

• Ingenuity pathway analysis highlighted:

• Stress pathways
• Inflammation pathways
• Disturbances in pathways related to immune function
• Neuronal function
• Mitochondrial function
• Metabolic function

Relevance to ME/CFS:

• Provides evidence for mitochondrial dysfunction in ME/CFS
• Identifies disruption in circadian rhythm regulation
• Highlights inflammatory processes in ME/CFS pathophysiology
• Suggests metabolic dysregulation as a component of ME/CFS
• Supports the hypothesis that oxidative stress plays a role in ME/CFS

Proteomics Studies

Study 4: Proteomics and cytokine analyses distinguish ME/CFS cases from controls

Citation: Giloteaux L, Li J, Hornig M, Lipkin WI, Ruppert D, Hanson MR. Proteomics and cytokine analyses distinguish myalgic encephalomyelitis/chronic fatigue syndrome cases from controls. J Transl Med. 2023 May 13;21(1):322. doi: 10.1186/s12967-023-04179-3.

Objectives and Methods:

• Prepared extracellular vesicles (EVs) from frozen plasma samples
• Determined cytokine content of plasma-derived EVs using multiplex assay
• Performed multi-omic statistical analyses
• Cohort: 49 ME/CFS cases and 49 healthy controls from the Chronic Fatigue Initiative cohort
• ME/CFS cases met the 1994 CDC Fukuda and/or 2003 Canadian consensus criteria
• Clinical symptoms and baseline health status assessed using SF-36 and MFI scale
• Machine learning classifiers to identify proteins that discriminate between cases and controls

Significant Findings:

1. Extracellular Vesicles (EVs):

• ME/CFS cases exhibited greater size and concentration of EVs in plasma
• IL2 was significantly higher in EVs from ME/CFS cases
• Numerous correlations observed among EV cytokines, plasma cytokines, and plasma proteins

2. Clinical Correlations:

• Higher levels of pro-inflammatory cytokines (CSF2 and TNFα) correlated with greater physical and fatigue symptoms in ME/CFS cases
• Higher serine protease SERPINA5 (involved in hemostasis) correlated with higher SF-36 general health scores in ME/CFS

3. Machine Learning Classification:

• XGBoost classifier identified 20 proteins that could discriminate between cases and controls with 86.1% accuracy (AUROC = 0.947)
• Random Forest distinguished cases from controls with 79.1% accuracy (AUROC = 0.891) using only 7 proteins

Relevance to ME/CFS:

• Provides evidence for immune system dysfunction in ME/CFS
• Highlights the role of extracellular vesicles in ME/CFS pathophysiology
• Identifies specific proteins and cytokines that may serve as biomarkers
• Suggests involvement of hemostasis pathways in ME/CFS
• Demonstrates objective differences in biomolecules between ME/CFS cases and controls

Study 5: ME/CFS and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes

Citation: Schutzer SE, Liu T, Tsai CF, Petyuk VA, Schepmoes AA, Wang YT, Weitz KK, Bergquist J, Smith RD, Natelson BH. Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes. Ann Med. 2023 Sep 18;55(1):2208372. doi: 10.1080/07853890.2023.2208372.

Objectives and Methods:

• Used unbiased quantitative mass spectrometry-based proteomics to examine cerebrospinal fluid (CSF)
• Aimed to determine if ME/CFS and fibromyalgia have distinct or similar proteome profiles
• Cohort: 30 ME/CFS patients divided into two groups:

• 15 with ME/CFS only
• 15 with ME/CFS + fibromyalgia

• Immunoaffinity depletion, tandem mass tag isobaric labelling
• Two-dimensional liquid chromatography coupled to tandem mass spectrometry
• All subjects fulfilled the 1994 case criteria for CFS (with severity modifications)
• Fibromyalgia diagnosis based on 1990 case definition (four quadrant pain and ≥11/18 tender points)

Significant Findings:

1. Proteome Analysis:

• 2083 total proteins quantified
• 1789 proteins quantified in all CSF samples
• ANOVA analysis did not yield any proteins with an adjusted p-value <0.05
• No significant differences in CSF proteomes between ME/CFS patients with and without fibromyalgia

2. Clinical Implications:

• Results support the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities
• Suggests overlapping pathophysiological processes between ME/CFS and fibromyalgia
• Contrasts with previous studies that found differences between the two conditions

Relevance to ME/CFS:

• Provides evidence that ME/CFS and fibromyalgia may be part of the same illness spectrum
• Suggests shared neurological mechanisms between ME/CFS and fibromyalgia
• Highlights the neurological nature of both conditions
• Indicates that treatment approaches might be similar for both conditions
• Adds to the understanding of ME/CFS comorbidities

Metabolomics Studies

Study 6: Plasma metabolomics reveals disrupted response and recovery following maximal exercise in ME/CFS

Citation: Germain A, Giloteaux L, Moore GE, Levine SM, Chia JK, Keller BA, Stevens J, Franconi CJ, Mao X, Shungu DC, Grimson A, Hanson MR. Plasma metabolomics reveals disrupted response and recovery following maximal exercise in myalgic encephalomyelitis/chronic fatigue syndrome. JCI Insight. 2022 May 9;7(9):e157621. doi: 10.1172/jci.insight.157621.

Objectives and Methods:

• Monitored the evolution of 1157 plasma metabolites before and after exercise challenge
• Cohort: 60 ME/CFS patients (45 female, 15 male) and 45 matched healthy controls (30 female, 15 male)
• Two maximal cardiopulmonary exercise test (CPET) challenges separated by 24 hours to provoke post-exertional malaise (PEM)
• Four blood sampling time points:

• Before exercise on day 1 (D1PRE)
• After exercise on day 1 (D1POST)
• Before exercise on day 2 (D2PRE)
• After exercise on day 2 (D2POST)

• Metabolon Precision Metabolomics platform measuring:

• 933 identified metabolites
• 224 yet-to-be-identified metabolites
• Spanning 9 superpathways and 108 subpathways

Significant Findings:

1. Baseline Metabolic Differences:

• Several significantly different metabolites between ME/CFS and controls at baseline
• Enriched percentage of yet-to-be identified compounds in ME/CFS patients

2. Exercise Response:

• Increased metabolic disparity between cohorts after exercise
• Effects of exertion in ME/CFS predominantly highlighted:

• Lipid-related pathways
• Energy-related pathways
• Chemical structure clusters

• Different metabolic responses to first versus second exercise sessions

3. Recovery Period:

• Distinct 24-hour recovery period in ME/CFS cohort
• Over 25% of identified pathways statistically different from controls during recovery
• Numerous altered pathways dependent on glutamate metabolism
• Divergent patterns between females and males following both exercise and recovery

Relevance to ME/CFS:

• Provides metabolic evidence for post-exertional malaise (PEM), a hallmark symptom of ME/CFS
• Identifies disrupted metabolic pathways during exercise response and recovery
• Highlights the role of glutamate metabolism, crucial for homeostasis of many organs including the brain
• Suggests sex-specific metabolic responses in ME/CFS
• Identifies potential biomarkers for ME/CFS diagnosis and treatment monitoring

Study 7: Metabolomic Evidence for Peroxisomal Dysfunction in ME/CFS

Citation: Che X, Brydges CR, Yu Y, Price A, Joshi S, Roy A, Lee B, Barupal DK, Cheng A, March Palmer D, Levine S, Peterson DL, Vernon SD, Bateman L, Hornig M, Montoya JG, Komaroff AL, Fiehn O, Lipkin WI. Metabolomic Evidence for Peroxisomal Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Int J Mol Sci. 2022 Jul 18;23(14):7906. doi: 10.3390/ijms23147906.

Objectives and Methods:

• Conducted targeted and untargeted metabolomic analysis of plasma
• Cohort: 106 ME/CFS cases and 91 frequency-matched healthy controls
• Analysis of 888 metabolic analytes comprising:

• Primary metabolites (PM)
• Biogenic amines (BA)
• Complex lipids (CL)
• Oxylipins (OL)

• Regression, Bayesian, and enrichment analyses
• Machine learning algorithms for classification

Significant Findings:

1. Altered Metabolite Levels:

• Significantly decreased levels of:

• Plasmalogens and phospholipid ethers (p < 0.001)
• Phosphatidylcholines (p < 0.001)
• Sphingomyelins (p < 0.001)

• Elevated levels of:

• Dicarboxylic acids (p = 0.013)

2. Classification Performance:

• Machine learning algorithms differentiated ME/CFS or subgroups of ME/CFS from controls
• Area under the receiver operating characteristic curve (AUC) values up to 0.873

3. Metabolic Pathways:

• First metabolomic evidence of peroxisomal dysfunction in ME/CFS
• Dysregulation of lipid remodeling
• Disruption of the tricarboxylic acid cycle

4. Subgroup Analysis:

• Sex-specific differences in metabolic profiles
• Distinct metabolic signatures in patients with comorbid gastrointestinal symptoms
• Potential subtype based on presence or absence of self-reported irritable bowel syndrome (sr-IBS)

Relevance to ME/CFS:

• Provides evidence for peroxisomal dysfunction, which affects lipid metabolism
• Suggests disruption in energy metabolism via tricarboxylic acid cycle abnormalities
• Identifies potential biomarkers for diagnosis and subtyping of ME/CFS
• Highlights the role of lipid metabolism in ME/CFS pathophysiology
• Supports the concept of ME/CFS as a biological illness with measurable metabolic abnormalities

Study 8: A map of metabolic phenotypes in patients with ME/CFS

Citation: Hoel F, Hoel A, Pettersen IK, Rekeland IG, Risa K, Alme K, Sørland K, Fosså A, Lien K, Herder I, Thürmer HL, Gotaas ME, Schäfer C, Berge RK, Sommerfelt K, Marti HP, Dahl O, Mella O, Fluge Ø, Tronstad KJ. A map of metabolic phenotypes in patients with myalgic encephalomyelitis/chronic fatigue syndrome. JCI Insight. 2021 Aug 23;6(16):e149217. doi: 10.1172/jci.insight.149217.

Objectives and Methods:

• Performed global metabolomics, lipidomics, and hormone measurements
• Used exploratory data analyses
• Cohort: 83 ME/CFS patients and 35 healthy controls
• Serum samples analyzed
• Clinical data and independent blood analyses to support findings

Significant Findings:

1. Common Metabolic Changes:

• Changes compatible with elevated energy strain
• Altered utilization of fatty acids and amino acids as catabolic fuels
• Systemic metabolic adaptation and compensation

2. Heterogeneous Effects:

• Identified specific changes in 3 subsets of patients
• 2 subsets expressed characteristic contexts of deregulated energy metabolism
• Metabolic phenotypes (metabotypes) supported by clinical data and independent blood analyses

3. Energy Metabolism:

• Elevated energy strain may result from exertion-triggered tissue hypoxia
• Leads to systemic metabolic adaptation and compensation
• Metabolic dysfunction likely mediates key symptoms in ME/CFS
• Potential target for supportive intervention

Relevance to ME/CFS:

• Provides evidence for energy metabolism dysfunction in ME/CFS
• Identifies multiple metabolic phenotypes within ME/CFS patient population
• Suggests exertion-triggered tissue hypoxia as a potential mechanism
• Links metabolic dysfunction to key symptoms in ME/CFS
• Proposes metabolic pathways as targets for intervention

Summary of Additional Studies

These eight studies provide complementary perspectives on ME/CFS pathophysiology through different omics approaches:

1. Transcriptomics Studies reveal immune cell dysregulation, particularly in monocytes and T cells, abnormal responses to exercise challenge, and alterations in pathways related to inflammation, circadian rhythm, and mitochondrial function.
2. Proteomics Studies identify differences in plasma proteins and extracellular vesicle content between ME/CFS patients and controls, while suggesting potential overlap between ME/CFS and fibromyalgia at the cerebrospinal fluid proteome level.
3. Metabolomics Studies demonstrate disrupted metabolic responses to exercise, evidence for peroxisomal dysfunction affecting lipid metabolism, and multiple metabolic phenotypes within the ME/CFS population.

Together with the core BioMapAI paper, these studies provide a comprehensive view of ME/CFS as a complex, multisystem disorder with disruptions in immune function, energy metabolism, and host-microbiome interactions. The identification of potential biomarkers and therapeutic targets across multiple biological systems offers promising avenues for future research and treatment development.

Comparative Analysis and Integration of ME/CFS Omics Studies

This section integrates findings from the core BioMapAI paper and the eight additional omics studies to identify convergent biological pathways, common biomarkers, and complementary insights into ME/CFS pathophysiology.

Cross-Study Synthesis

Methodological Approaches and Study Designs

The nine studies reviewed employ diverse methodological approaches to investigate ME/CFS:

1. Multi-Omics Integration:

• BioMapAI (core paper) represents the most comprehensive approach, integrating gut metagenomics, plasma metabolomics, immune cell profiling, and clinical data using a supervised deep neural network.
• Other studies focus on single omics platforms (transcriptomics, proteomics, or metabolomics) but provide deeper analysis within their respective domains.

2. Exercise Challenge Paradigms:

• Three studies (Vu et al., Van Booven et al., and Germain et al.) employed exercise challenge protocols to investigate post-exertional malaise (PEM), a hallmark symptom of ME/CFS.
• These studies used different timepoints for sample collection:

• Vu et al.: baseline and 24 hours post-exercise
• Van Booven et al.: baseline, maximal exertion, and 4 hours post-exercise
• Germain et al.: before and after exercise on two consecutive days

3. Single-Cell vs. Bulk Analysis:

• Vu et al. employed single-cell RNA sequencing, providing cell-type-specific insights.
• Other transcriptomics studies (Van Booven et al. and Sweetman et al.) used bulk RNA sequencing of PBMCs.

4. Tissue Types Examined:

• Most studies analyzed blood components (plasma, serum, or PBMCs).
• Schutzer et al. uniquely examined cerebrospinal fluid.
• BioMapAI additionally included gut microbiome analysis.

5. Patient Populations:

• Studies varied in cohort size, from smaller (Sweetman et al., n=10 patients) to larger (BioMapAI, n=153 patients).
• Some studies focused exclusively on females (Van Booven et al.) or included sex-specific analyses (Germain et al., Che et al.).
• BioMapAI uniquely stratified patients by disease duration (short-term vs. long-term).

These methodological differences provide complementary perspectives on ME/CFS but also present challenges for direct comparison across studies. Nevertheless, several convergent findings emerge.

Similarities and Differences in Key Findings

Immune System Dysregulation

Convergent Findings:

1. Monocyte Abnormalities:

• BioMapAI identified altered monocyte function and cytokine production.
• Vu et al. found classical monocyte dysregulation with inappropriate differentiation and migration to tissue.
• The fraction of dysregulated monocytes correlated with disease severity (Vu et al.).

2. T Cell Alterations:

• BioMapAI found heightened inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, γδT) secreting IFNγ and GzA.
• Vu et al. observed abnormal metabolism and cytokine production in T lymphocytes.
• Sweetman et al. identified altered gene expression related to T cell function.

3. Inflammatory Signatures:

• Sweetman et al. found increased expression of inflammation-related genes (IL8, NFΚBIA, TNFAIP3).
• Giloteaux et al. observed higher IL2 in extracellular vesicles from ME/CFS patients.
• BioMapAI identified increased inflammatory cytokine production by specific immune cell subsets.

Divergent Findings:

1. Exercise Response:

• Van Booven et al. found that ME/CFS patients showed no significant changes in gene expression during maximal exertion, while healthy controls exhibited altered functional gene networks.
• Vu et al. observed minimal changes in immune cell transcriptomes after exercise, except for platelet activation.
• These differences may reflect different timepoints of sample collection or cell types analyzed.

2. B Cell Involvement:

• BioMapAI identified increased B cells (CD19+CD3-) as a disease-specific biomarker.
• Other studies provided limited evidence for B cell involvement, focusing more on T cells and monocytes.

Metabolic Dysregulation

Convergent Findings:

1. Energy Metabolism Disruption:

• Hoel et al. found evidence of elevated energy strain and altered utilization of fatty acids and amino acids as catabolic fuels.
• Germain et al. observed disrupted metabolic pathways during exercise response and recovery.
• Che et al. identified disruption of the tricarboxylic acid cycle.
• Sweetman et al. found altered gene expression related to mitochondrial function.
• BioMapAI highlighted energy-related pathways as differentially affected in ME/CFS.

2. Lipid Metabolism Abnormalities:

• Che et al. provided metabolomic evidence of peroxisomal dysfunction with decreased levels of plasmalogens, phospholipid ethers, phosphatidylcholines, and sphingomyelins.
• BioMapAI identified dysregulation of lipid remodeling pathways.
• Germain et al. found that lipid-related pathways were predominantly affected by exertion in ME/CFS.

3. Post-Exertional Metabolic Changes:

• Germain et al. observed that over 25% of identified metabolic pathways were statistically different from controls during recovery from exercise.
• BioMapAI found that metabolic pathways were disparately affected by first and second exercise sessions.
• These findings provide metabolic evidence for post-exertional malaise.

Divergent Findings:

1. Glutamate Metabolism:

• Germain et al. uniquely highlighted the role of glutamate metabolism, finding numerous altered pathways dependent on glutamate metabolism during recovery from exercise.
• This specific pathway was not prominently featured in other metabolomics studies.

2. Metabolic Phenotypes:

• Hoel et al. identified multiple metabolic phenotypes (metabotypes) within the ME/CFS population.
• This heterogeneity in metabolic profiles was not explicitly addressed in other metabolomics studies.

Microbiome-Host Interactions

Convergent Findings:

1. Microbial Metabolite Production:

• BioMapAI found depleted butyrate production from pyruvate and glutarate pathways.
• BioMapAI also identified altered branched-chain amino acid (BCAA) biosynthesis and disrupted tryptophan and benzoate metabolism.
• These findings were not directly addressed in the additional studies, as BioMapAI was the only study to include comprehensive gut microbiome analysis.

2. Microbiome-Immune Connections:

• BioMapAI found that microbial metabolites displayed altered associations with immune cell function in ME/CFS.
• This microbiome-immune axis was not directly examined in the additional studies.

Disease Classification and Biomarkers

Convergent Findings:

1. Machine Learning Classification:

• BioMapAI achieved 91% AUC in distinguishing ME/CFS from healthy controls.
• Giloteaux et al. used XGBoost to identify 20 proteins that could discriminate between cases and controls with 86.1% accuracy (AUROC = 0.947).
• Che et al. differentiated ME/CFS from controls with AUC values up to 0.873.
• These consistently high classification accuracies across different omics platforms suggest robust biological differences between ME/CFS patients and healthy controls.

2. Biomarker Reproducibility:

• Several studies identified similar or related biomarkers:

• Immune activation markers (BioMapAI, Vu et al., Sweetman et al.)
• Metabolic dysregulation markers (Germain et al., Che et al., Hoel et al.)
• These convergent findings strengthen the case for these biomarkers.

Divergent Findings:

1. ME/CFS vs. Fibromyalgia:

• Schutzer et al. found no significant differences in CSF proteomes between ME/CFS patients with and without fibromyalgia, suggesting these conditions may not be distinct entities.
• This contrasts with the approach of other studies, which treat ME/CFS as a distinct condition with specific biomarkers.

2. Biomarker Specificity:

• Different studies identified different sets of biomarkers, reflecting both the different omics platforms used and the heterogeneity of ME/CFS.
• This highlights the challenge of identifying universal biomarkers for ME/CFS.

Biological Pathways and Networks

Integrating findings across all studies reveals several key biological pathways and networks that appear to be consistently disrupted in ME/CFS:

1. Immune Regulation and Inflammatory Pathways

Key Components:

• Monocyte differentiation and function (Vu et al., BioMapAI)
• T cell activation and cytokine production (BioMapAI, Vu et al., Sweetman et al.)
• Inflammatory cytokine signaling (Sweetman et al., Giloteaux et al., BioMapAI)
• Extracellular vesicle content and signaling (Giloteaux et al.)

Pathway Interactions:

• Dysregulated monocytes may contribute to inappropriate inflammatory responses.
• Altered T cell function, particularly in mucosal and inflammatory subsets, may affect cytokine production.
• Changes in extracellular vesicle content may influence cell-to-cell communication and systemic inflammation.

Clinical Correlations:

• Inflammatory markers correlate with symptom severity (Giloteaux et al., BioMapAI).
• The fraction of dysregulated monocytes correlates with disease severity (Vu et al.).

2. Energy Metabolism and Mitochondrial Function

Key Components:

• Tricarboxylic acid cycle (Che et al.)
• Fatty acid and amino acid utilization (Hoel et al.)
• Mitochondrial function (Sweetman et al.)
• Exercise response and recovery metabolism (Germain et al.)

Pathway Interactions:

• Disrupted energy metabolism may result from exertion-triggered tissue hypoxia (Hoel et al.).
• Altered utilization of fatty acids and amino acids as catabolic fuels suggests metabolic adaptation (Hoel et al.).
• Mitochondrial dysfunction may contribute to fatigue and post-exertional malaise.

Clinical Correlations:

• Metabolic dysfunction likely mediates key symptoms in ME/CFS, particularly fatigue and post-exertional malaise (Hoel et al., Germain et al.).
• Different metabolic phenotypes may correspond to different symptom profiles (Hoel et al.).

3. Lipid Metabolism and Membrane Integrity

Key Components:

• Peroxisomal function (Che et al.)
• Phospholipid metabolism (Che et al., BioMapAI)
• Sphingolipid metabolism (Che et al.)
• Lipid remodeling (BioMapAI, Che et al.)

Pathway Interactions:

• Peroxisomal dysfunction affects the synthesis of plasmalogens and other membrane lipids (Che et al.).
• Altered membrane lipid composition may affect cellular function and signaling.
• Disrupted lipid metabolism may influence energy production and utilization.

Clinical Correlations:

• Lipid abnormalities may contribute to neurological symptoms through effects on membrane function and signaling.
• Disrupted lipid metabolism may affect energy availability and contribute to fatigue.

4. Microbiome-Host Interactions

Key Components:

• Short-chain fatty acid production (BioMapAI)
• Branched-chain amino acid biosynthesis (BioMapAI)
• Tryptophan metabolism (BioMapAI)
• Benzoate metabolism (BioMapAI)

Pathway Interactions:

• Altered microbial metabolism affects the production of metabolites that influence host physiology.
• Disrupted microbiome-immune interactions may contribute to immune dysregulation.
• Changes in microbial metabolites may affect the gut-brain axis.

Clinical Correlations:

• Microbial metabolite changes correlate with gastrointestinal symptoms, fatigue, emotional disturbances, and sleep problems (BioMapAI).
• Microbiome-immune interactions may influence systemic inflammation and immune function.

5. Stress Response and Adaptation

Key Components:

• Cellular stress responses (Sweetman et al.)
• Exercise-induced stress responses (Van Booven et al., Germain et al., Vu et al.)
• Circadian rhythm regulation (Sweetman et al.)

Pathway Interactions:

• Impaired stress response mechanisms may contribute to post-exertional malaise.
• Disrupted circadian rhythm regulation may affect sleep and energy metabolism.
• Cellular stress responses may influence mitochondrial function and energy production.

Clinical Correlations:

• Dysregulated stress responses correlate with post-exertional malaise and recovery (Van Booven et al., Germain et al.).
• Circadian rhythm disruption may contribute to sleep disturbances and fatigue.

Potential Biomarkers

Integrating findings across all studies, we can compile a consolidated list of potential biomarkers for ME/CFS diagnosis, patient stratification, and treatment monitoring:

Immune Biomarkers

1. Cellular Markers:

• Increased B cells (CD19+CD3-) (BioMapAI)
• Increased CCR6+ CD8 memory T cells (BioMapAI)
• Increased CD4 naïve T cells (nCD4+FOXP3+) (BioMapAI)
• Dysregulated classical monocytes (Vu et al.)
• CD4 memory cells (positive association with pain) (BioMapAI)
• CD1c+ dendritic cells (negative association with pain) (BioMapAI)

2. Cytokines and Inflammatory Mediators:

• Increased IL2 in extracellular vesicles (Giloteaux et al.)
• Increased IL8, NFΚBIA, and TNFAIP3 gene expression (Sweetman et al.)
• Pro-inflammatory cytokines CSF2 and TNFα (correlation with symptoms) (Giloteaux et al.)
• IFNγ and GzA secretion from mucosal and inflammatory T cells (BioMapAI)

3. Extracellular Vesicles:

• Increased size and concentration of EVs in plasma (Giloteaux et al.)
• Altered cytokine content in EVs (Giloteaux et al.)

Metabolic Biomarkers

1. Energy Metabolism:

• Disrupted tricarboxylic acid cycle metabolites (Che et al.)
• Altered fatty acid and amino acid utilization patterns (Hoel et al.)
• Metabolic response to exercise challenge (Germain et al.)

2. Lipid Metabolism:

• Decreased plasmalogens and phospholipid ethers (Che et al.)
• Decreased phosphatidylcholines (Che et al.)
• Decreased sphingomyelins (Che et al.)
• Elevated dicarboxylic acids (Che et al.)

3. Other Metabolites:

• Increased glycodeoxycholate 3-sulfate (bile acid) (BioMapAI)
• Decreased vanillylmandelate (catecholamine breakdown product) (BioMapAI)
• Glutamate metabolism-related metabolites (Germain et al.)

Microbial Biomarkers

1. Microbial Species:

• Dysosmobacteria welbionis (BioMapAI)
• Faecalibacterium prausnitzii (complex relationship) (BioMapAI)
• Clostridium sp. and Alistipes communis (BioMapAI)

2. Microbial Metabolism:

• Depleted butyrate production pathways (BioMapAI)
• Altered branched-chain amino acid biosynthesis (BioMapAI)
• Disrupted tryptophan metabolism (BioMapAI)
• Altered benzoate metabolism (BioMapAI)

Protein Biomarkers

1. Plasma Proteins:

• 20-protein signature identified by Giloteaux et al. using XGBoost
• 7-protein signature identified by Giloteaux et al. using Random Forest
• Serine protease SERPINA5 (positive correlation with health scores) (Giloteaux et al.)

2. Cerebrospinal Fluid Proteins:

• No specific CSF protein biomarkers identified (Schutzer et al.)

Multi-Omics Biomarker Panels

The integration of biomarkers across multiple omics platforms offers the most promising approach for ME/CFS diagnosis and patient stratification:

1. Diagnostic Panels:

• Combining immune, metabolic, and microbial markers may provide the highest diagnostic accuracy.
• Machine learning approaches (as used in BioMapAI, Giloteaux et al., and Che et al.) can identify the most discriminative biomarker combinations.

2. Patient Stratification Panels:

• Metabolic phenotypes identified by Hoel et al. may help stratify patients for targeted interventions.
• Symptom-specific biomarkers identified by BioMapAI may help tailor treatments to individual symptom profiles.

3. Treatment Response Monitoring:

• Changes in immune and metabolic biomarkers following interventions may help assess treatment efficacy.
• Exercise challenge responses (as measured by Germain et al., Van Booven et al., and Vu et al.) may provide functional assessments of improvement.

Conclusion

This comparative analysis reveals several convergent findings across multiple omics studies of ME/CFS, despite differences in methodological approaches and patient populations. The integration of these findings provides a more comprehensive understanding of ME/CFS pathophysiology, highlighting disruptions in immune regulation, energy metabolism, lipid metabolism, microbiome-host interactions, and stress response pathways.

The identification of potential biomarkers across multiple biological systems offers promising avenues for improving ME/CFS diagnosis, patient stratification, and treatment monitoring. However, the heterogeneity observed in ME/CFS, both within and across studies, underscores the need for personalized approaches to diagnosis and treatment.

Future research should focus on validating these biomarkers in larger, more diverse cohorts, exploring their functional significance through mechanistic studies, and developing integrated multi-omics approaches that can capture the full complexity of ME/CFS pathophysiology.

Outstanding Questions and Knowledge Gaps in ME/CFS Research

Despite significant advances in our understanding of ME/CFS through omics approaches, numerous knowledge gaps remain. This section identifies key unresolved mechanistic details, technical challenges, and barriers to clinical translation that must be addressed to advance ME/CFS research and treatment.

Unresolved Mechanistic Details

1. Disease Initiation and Progression

Triggering Events:

• While many ME/CFS cases follow infectious illnesses, the precise mechanisms by which infections lead to chronic illness remain unclear.
• The BioMapAI study identified differences between short-term and long-term ME/CFS patients, but the factors driving disease progression from acute to chronic stages are poorly understood.
• The role of specific pathogens (e.g., Epstein-Barr virus, enteroviruses, SARS-CoV-2) in triggering ME/CFS requires further investigation, particularly regarding how different pathogens might lead to similar clinical presentations.

Transition to Chronicity:

• The "point of no return" at which acute post-infectious fatigue transitions to chronic ME/CFS remains undefined.
• Longitudinal studies capturing this transition are lacking, making it difficult to identify early intervention opportunities.
• The BioMapAI study suggests that pathological connections become more firmly established in long-term ME/CFS, but the mechanisms driving this stabilization are unknown.

2. Causality vs. Consequence

Primary vs. Secondary Abnormalities:

• Across all omics studies, it remains unclear which observed abnormalities are causal to ME/CFS and which are consequences or compensatory responses.
• For example, the peroxisomal dysfunction identified by Che et al. could be a primary defect or a response to other metabolic disruptions.
• The monocyte dysregulation observed by Vu et al. might be a cause of ME/CFS or a response to other immune or metabolic abnormalities.

Feedback Loops:

• The complex interactions between immune, metabolic, and microbial systems create feedback loops that obscure the initial triggering events.
• BioMapAI identified disrupted microbiome-immune-metabolome networks, but the directionality of these disruptions remains unclear.
• Understanding which system to target therapeutically requires clarification of these causal relationships.

3. Heterogeneity and Subtyping

Biological Basis of Clinical Heterogeneity:

• ME/CFS presents with remarkable clinical heterogeneity, but the biological basis of this variability is incompletely understood.
• Hoel et al. identified multiple metabolic phenotypes, but their relationship to clinical presentations and treatment responses requires further investigation.
• BioMapAI found symptom-specific biomarkers, but the mechanisms by which similar biological disruptions lead to different symptom profiles in different patients remain unclear.

Subtype Definition and Validation:

• While several studies suggest ME/CFS subtypes (e.g., based on metabolic profiles, comorbidities, or disease duration), standardized definitions and validation of these subtypes are lacking.
• The relationship between ME/CFS and other conditions like fibromyalgia remains controversial, with Schutzer et al. finding no differences in CSF proteomes between ME/CFS patients with and without fibromyalgia.
• The biological boundaries between ME/CFS, Long COVID, and other post-viral syndromes require clarification.

4. Post-Exertional Malaise Mechanisms

Cellular and Molecular Basis:

• Despite being a hallmark symptom of ME/CFS, the precise cellular and molecular mechanisms of post-exertional malaise (PEM) remain incompletely understood.
• Germain et al. found disrupted metabolic recovery following exercise, but the link between these metabolic changes and symptom exacerbation requires further elucidation.
• Van Booven et al. observed impaired transcriptional responses to exercise in ME/CFS patients, but the consequences of this impairment for cellular function and symptom generation are unclear.

Delayed Effects:

• The temporal dynamics of PEM, which often peaks 24-72 hours after exertion, are poorly explained by current mechanistic models.
• The studies examining exercise responses (Vu et al., Van Booven et al., Germain et al.) used different timepoints, making it difficult to construct a comprehensive timeline of post-exertional changes.
• The mechanisms by which acute exercise leads to prolonged symptom exacerbation remain to be fully elucidated.

5. Central Nervous System Involvement

Brain Abnormalities:

• While ME/CFS patients experience significant cognitive symptoms ("brain fog"), direct evidence of central nervous system abnormalities from omics studies is limited.
• Schutzer et al. examined cerebrospinal fluid proteomes but found no significant differences between ME/CFS patients with and without fibromyalgia.
• The relationship between peripheral immune/metabolic abnormalities and central nervous system dysfunction requires further investigation.

Neuroimmune Interactions:

• The mechanisms by which peripheral immune activation might influence central nervous system function in ME/CFS are poorly understood.
• The role of neuroinflammation in ME/CFS symptoms, particularly cognitive dysfunction and sleep disturbances, remains to be clarified.
• The contribution of the gut-brain axis to ME/CFS pathophysiology, suggested by BioMapAI's findings on microbial metabolism, requires further exploration.

6. Energy Metabolism Dysfunction

Cellular Energy Production:

• While multiple studies (Hoel et al., Che et al., Sweetman et al.) suggest energy metabolism dysfunction in ME/CFS, the specific defects in cellular energy production pathways remain incompletely characterized.
• The role of mitochondrial dysfunction, suggested by transcriptomic and metabolomic studies, requires more direct investigation.
• The mechanisms by which energy metabolism dysfunction leads to the profound fatigue experienced by ME/CFS patients need further elucidation.

Tissue-Specific Effects:

• Energy metabolism dysfunction may affect different tissues (muscle, brain, immune cells) differently, but tissue-specific analyses are limited.
• Hoel et al. proposed that exertion-triggered tissue hypoxia may contribute to ME/CFS, but direct evidence for tissue hypoxia is lacking.
• The relative contribution of different tissues to systemic energy metabolism disruption in ME/CFS remains unclear.

Technical Challenges

1. Study Design Limitations

Sample Size Constraints:

• Most ME/CFS omics studies have relatively small sample sizes, limiting statistical power and the ability to detect subtle biological signals.
• The heterogeneity of ME/CFS further compounds this issue, as subgroup analyses require even larger sample sizes.
• BioMapAI, despite being one of the largest ME/CFS studies, acknowledged that its model was trained on fewer than 500 samples, which is relatively small given the complexity of the outcome matrix.

Cross-Sectional vs. Longitudinal Approaches:

• Most studies employ cross-sectional designs, providing snapshots of ME/CFS biology rather than capturing disease dynamics over time.
• The BioMapAI study tracked participants over 3-4 years, but acknowledged that this timeframe may be insufficient to capture stable temporal signals in a disease that typically progresses over decades.
• Longitudinal studies capturing the transition from acute illness to chronic ME/CFS are particularly lacking.

Control Group Selection:

• The selection of appropriate control groups presents challenges, particularly for post-infectious ME/CFS.
• Ideally, studies would include both healthy controls and individuals who recovered from similar infections without developing ME/CFS, but the latter group is rarely included.
• The potential confounding effects of medications, comorbidities, and lifestyle adaptations in ME/CFS patients are difficult to control for.

2. Methodological Standardization

Diagnostic Criteria Variability:

• ME/CFS studies use different diagnostic criteria (Fukuda, Canadian Consensus, International Consensus, IOM), complicating cross-study comparisons.
• The studies reviewed used various criteria: BioMapAI used the IOM criteria, Giloteaux et al. used the Fukuda and/or Canadian consensus criteria, and others did not specify the criteria used.
• This variability may contribute to inconsistent findings across studies, as different criteria may select for slightly different patient populations.

Protocol Harmonization:

• Lack of standardized protocols for sample collection, processing, and analysis complicates the integration of findings across studies.
• Exercise challenge protocols vary significantly: Vu et al. and Germain et al. used cardiopulmonary exercise tests, while Van Booven et al. used a different exercise protocol.
• Analytical methods also differ substantially across studies, from the specific omics platforms used to the bioinformatic approaches employed.

Data Integration Challenges:

• Integrating data across different omics platforms and studies presents significant technical challenges.
• BioMapAI developed a sophisticated approach for multi-omics integration, but standardized methods for integrating findings across independent studies are lacking.
• Different studies measure different variables, making direct comparisons difficult.

3. Technological Limitations

Tissue Accessibility:

• Most studies rely on blood samples, which may not fully reflect the biology of other relevant tissues (muscle, brain, autonomic nervous system).
• Schutzer et al. examined cerebrospinal fluid, but tissue biopsies (e.g., muscle, gut) are rarely included in ME/CFS omics studies.
• The inaccessibility of certain tissues limits our understanding of tissue-specific abnormalities in ME/CFS.

Temporal Resolution:

• Current omics technologies provide static snapshots rather than continuous monitoring of biological processes.
• This limitation is particularly relevant for understanding dynamic phenomena like post-exertional malaise.
• Even studies with multiple timepoints (Germain et al., Van Booven et al., Vu et al.) capture only a few discrete moments in the complex temporal evolution of biological responses.

Analytical Depth:

• Despite advances in omics technologies, current methods may still miss important biological signals.
• For example, single-cell approaches (used by Vu et al.) provide greater resolution than bulk analyses but are limited by the number of cells that can be profiled.
• Metabolomics studies (Germain et al., Che et al., Hoel et al.) identify hundreds of metabolites, but thousands more remain uncharacterized.

Clinical Translation Difficulties

1. Biomarker Validation and Implementation

Reproducibility Challenges:

• Potential biomarkers identified in research settings often fail to replicate in independent cohorts or clinical validation studies.
• While multiple studies identified promising biomarkers (BioMapAI, Giloteaux et al., Che et al.), their reproducibility across diverse patient populations remains to be established.
• The heterogeneity of ME/CFS complicates biomarker validation, as markers may be relevant for some patient subgroups but not others.

Analytical Standardization:

• Translation of research-grade biomarker assays to clinical laboratory tests requires standardization and validation.
• The complex multi-omics signatures identified by BioMapAI and other studies may be difficult to implement in routine clinical settings.
• Simplification of biomarker panels for clinical use risks losing the sensitivity and specificity achieved in research settings.

Clinical Utility Demonstration:

• Beyond technical validation, biomarkers must demonstrate clinical utility by improving diagnosis, prognosis, or treatment decisions.
• The impact of ME/CFS biomarkers on clinical outcomes and patient care has not been systematically evaluated.
• Cost-effectiveness considerations may limit the implementation of complex biomarker panels in clinical practice.

2. Therapeutic Target Identification

Causal Uncertainty:

• As discussed under "Causality vs. Consequence," uncertainty about which abnormalities are causal to ME/CFS complicates therapeutic target identification.
• Targeting downstream consequences rather than root causes may provide symptomatic relief but not disease modification.
• The complex interactions between biological systems in ME/CFS suggest that multiple targets may need to be addressed simultaneously.

Target Accessibility:

• Some potential therapeutic targets identified in omics studies may be difficult to modulate with current pharmaceutical approaches.
• For example, the peroxisomal dysfunction identified by Che et al. and the microbiome abnormalities highlighted by BioMapAI present challenges for traditional drug development.
• The blood-brain barrier may limit the accessibility of central nervous system targets.

Patient Heterogeneity:

• The heterogeneity of ME/CFS suggests that different patients may require different therapeutic approaches.
• Without reliable methods for patient stratification, clinical trials may fail to demonstrate efficacy even for interventions that benefit specific subgroups.
• The metabolic phenotypes identified by Hoel et al. suggest potential for personalized treatment approaches, but methods for matching patients to treatments remain underdeveloped.

3. Clinical Trial Design

Outcome Measure Selection:

• The lack of objective, responsive outcome measures for ME/CFS clinical trials presents a significant challenge.
• While omics studies have identified potential biomarkers, their utility as surrogate endpoints for clinical trials has not been established.
• Patient-reported outcomes are important but may be subject to placebo effects and recall bias.

Patient Selection:

• The heterogeneity of ME/CFS complicates patient selection for clinical trials.
• Without validated biomarkers for patient stratification, trials may include mixed populations with different underlying pathophysiologies.
• The findings from BioMapAI and Hoel et al. regarding patient subgroups suggest the importance of targeted enrollment strategies, but these have not been widely implemented.

Study Duration and Size:

• The chronic nature of ME/CFS and the fluctuating symptom patterns necessitate longer, larger clinical trials.
• The delayed effects of interventions may be missed in short-term studies.
• The resources required for adequate ME/CFS clinical trials exceed those typically available for this historically underfunded condition.

4. Regulatory and Commercial Challenges

Regulatory Pathways:

• The lack of established regulatory pathways for ME/CFS therapeutics creates uncertainty for drug developers.
• Without validated biomarkers or surrogate endpoints, regulatory agencies may require large, long-term clinical trials with subjective outcome measures.
• The heterogeneity of ME/CFS complicates the definition of clear inclusion criteria and endpoints for pivotal trials.

Commercial Viability:

• The perceived commercial risks of ME/CFS drug development may deter investment from pharmaceutical companies.
• The heterogeneity of the condition suggests that targeted therapies may address only subsets of patients, potentially limiting market size.
• The historical stigmatization of ME/CFS has contributed to underinvestment in therapeutic development.

Healthcare System Integration:

• Even with effective biomarkers and treatments, integration into healthcare systems presents challenges.
• Many clinicians lack familiarity with ME/CFS and may be reluctant to adopt new diagnostic approaches or treatments.
• Insurance coverage for novel diagnostics and therapeutics may be limited without strong evidence of cost-effectiveness.

Conclusion

Despite significant advances in our understanding of ME/CFS through omics approaches, substantial knowledge gaps remain. Unresolved mechanistic details regarding disease initiation, progression, and heterogeneity limit our ability to develop targeted interventions. Technical challenges related to study design, methodological standardization, and technological limitations constrain the quality and comparability of research findings. Clinical translation difficulties, including biomarker validation, therapeutic target identification, clinical trial design, and regulatory/commercial challenges, impede the development of effective diagnostics and treatments.

Addressing these knowledge gaps will require coordinated efforts across multiple domains: larger, longitudinal studies with standardized protocols; integration of findings across omics platforms and research groups; development of more accessible and sensitive technologies; and innovative approaches to clinical translation that account for the heterogeneity and complexity of ME/CFS. The insights gained from the BioMapAI study and other omics research provide a foundation for these efforts, but much work remains to be done to fully understand and effectively treat this debilitating condition.

New Hypotheses for ME/CFS Research

Based on the integrated analysis of the core BioMapAI paper and the eight additional omics studies, I propose three novel hypotheses that could advance our understanding of ME/CFS pathophysiology and potentially lead to new diagnostic and therapeutic approaches.

Hypothesis 1: Peroxisomal Dysfunction as a Central Mediator of ME/CFS Pathophysiology

Biological Rationale

The metabolomic evidence for peroxisomal dysfunction in ME/CFS, as identified by Che et al., provides a compelling foundation for this hypothesis. Peroxisomes are essential cellular organelles involved in multiple metabolic processes, including:

1. Lipid Metabolism: Peroxisomes play a crucial role in the biosynthesis of plasmalogens and other phospholipid ethers, which were found to be significantly decreased in ME/CFS patients. These specialized membrane lipids serve as antioxidants and are particularly abundant in brain, heart, and immune cells.
2. Fatty Acid Oxidation: Peroxisomes metabolize very-long-chain fatty acids and branched-chain fatty acids that cannot be processed by mitochondria. The altered utilization of fatty acids as catabolic fuels observed by Hoel et al. may be partially explained by peroxisomal dysfunction.
3. Reactive Oxygen Species (ROS) Metabolism: Peroxisomes both generate and detoxify ROS, playing a critical role in cellular redox balance. Disruption of this function could contribute to the oxidative stress observed in ME/CFS.
4. Immune Signaling: Peroxisomes influence immune cell function and inflammatory signaling. The immune dysregulation observed across multiple studies (Vu et al., Van Booven et al., BioMapAI) could be partially mediated by peroxisomal abnormalities.

The peroxisomal dysfunction hypothesis provides a unifying framework that could explain multiple aspects of ME/CFS pathophysiology:

• Energy Metabolism Disruption: By affecting fatty acid metabolism, peroxisomal dysfunction could contribute to the energy strain observed by Hoel et al. and the metabolic abnormalities identified by Germain et al.
• Membrane Integrity: Decreased plasmalogens and other membrane lipids could affect cellular signaling, neurotransmission, and immune cell function.
• Oxidative Stress: Impaired peroxisomal ROS metabolism could contribute to oxidative stress, which has been implicated in ME/CFS pathophysiology.
• Immune Dysregulation: Peroxisomal dysfunction could influence immune cell function and inflammatory signaling, contributing to the immune abnormalities observed across studies.

Testable Predictions

1. Genetic and Epigenetic Markers: Patients with ME/CFS will show altered expression or regulation of genes involved in peroxisomal biogenesis (PEX genes) and function (e.g., AGPS, GNPAT for plasmalogen synthesis).
2. Cellular Phenotypes: Fibroblasts or immune cells from ME/CFS patients will display abnormal peroxisomal morphology, distribution, or function when examined by microscopy or functional assays.
3. Metabolic Responses: Dietary interventions that support peroxisomal function (e.g., supplementation with plasmalogens precursors like alkylglycerols) will improve metabolic profiles and potentially symptoms in ME/CFS patients.
4. Biomarker Correlations: Plasmalogen levels will correlate with disease severity and specific symptom clusters, particularly cognitive dysfunction and post-exertional malaise.
5. Exercise Challenge Effects: Post-exertional malaise will be associated with further decreases in plasmalogen levels and other markers of peroxisomal function.

Experimental Approaches

1. Comprehensive Peroxisomal Profiling: Measure a complete panel of peroxisomal metabolites, enzymes, and structural proteins in ME/CFS patients and controls, both at baseline and following exercise challenge.
2. Cell Culture Studies: Examine peroxisomal function in patient-derived cells (fibroblasts, iPSCs, immune cells) using fluorescent probes, enzyme assays, and metabolic flux analysis.
3. Animal Models: Develop animal models with peroxisomal dysfunction to determine if they recapitulate ME/CFS-like symptoms and metabolic abnormalities.
4. Clinical Trials: Test interventions targeting peroxisomal function, such as plasmalogen precursors or peroxisome proliferator-activated receptor (PPAR) agonists, in ME/CFS patients.

Hypothesis 2: Disrupted Tissue Oxygen Delivery-Utilization as the Basis for Post-Exertional Malaise

Biological Rationale

Post-exertional malaise (PEM) is a hallmark symptom of ME/CFS, yet its biological basis remains poorly understood. Integrating findings from multiple studies suggests a novel hypothesis: PEM results from a mismatch between oxygen delivery and utilization at the tissue level, leading to localized hypoxia and subsequent metabolic, immune, and neurological consequences.

This hypothesis is supported by several observations:

1. Metabolic Adaptations: Hoel et al. proposed that elevated energy strain in ME/CFS may result from exertion-triggered tissue hypoxia, leading to systemic metabolic adaptation and compensation.
2. Exercise Response Abnormalities: Germain et al. found disrupted metabolic pathways during exercise response and recovery, with over 25% of identified pathways statistically different from controls during recovery.
3. Impaired Transcriptional Response: Van Booven et al. observed that ME/CFS patients showed no significant changes in gene expression during maximal exertion, while healthy controls exhibited altered functional gene networks, suggesting an impaired ability to mount appropriate transcriptional responses to exercise stress.
4. Platelet Activation: Vu et al. discovered patterns indicative of improper platelet activation in patients after exercise challenge, which could affect microcirculation and tissue perfusion.

The oxygen delivery-utilization hypothesis proposes that ME/CFS involves:

• Microcirculatory Dysfunction: Impaired blood flow regulation at the microvascular level, potentially involving endothelial dysfunction, abnormal vasomotor control, or rheological abnormalities.
• Oxygen Extraction Impairment: Reduced ability of tissues to extract and utilize oxygen, possibly due to mitochondrial dysfunction or altered cellular respiration.
• Compensatory Mechanisms: Metabolic adaptations to chronic tissue hypoxia, including shifts to less oxygen-dependent metabolic pathways.
• Inflammatory Consequences: Tissue hypoxia triggering inflammatory responses that contribute to symptoms and further metabolic disruption.

This hypothesis could explain several features of PEM, including its delayed onset (as metabolic and inflammatory consequences develop over hours to days) and its systemic nature (affecting multiple organ systems).

Testable Predictions

1. Tissue Oxygenation Measures: ME/CFS patients will show abnormal tissue oxygen levels during and after exercise, as measured by near-infrared spectroscopy (NIRS) or other non-invasive methods.
2. Microcirculatory Parameters: Capillary blood flow, red blood cell velocity, and functional capillary density will be altered in ME/CFS patients, particularly following exercise.
3. Hypoxia-Related Biomarkers: Markers of cellular hypoxia response (e.g., HIF-1α, VEGF) will be abnormally elevated or show abnormal dynamics following exercise in ME/CFS patients.
4. Metabolic Adaptations: ME/CFS patients will show metabolic adaptations consistent with chronic hypoxia, such as increased lactate production, altered pyruvate metabolism, and shifts toward glycolytic energy production.
5. Therapeutic Response: Interventions that improve microcirculation or cellular oxygen utilization will reduce post-exertional symptom exacerbation.

Experimental Approaches

1. Dynamic Tissue Oxygenation Monitoring: Use NIRS, tissue oxygen electrodes, or novel imaging techniques to monitor tissue oxygen levels before, during, and after standardized exercise challenges.
2. Microcirculation Assessment: Employ techniques such as capillaroscopy, laser Doppler flowmetry, or contrast-enhanced ultrasound to assess microcirculatory function in ME/CFS patients.
3. Cellular Oxygen Consumption: Measure oxygen consumption rates in patient-derived cells using techniques such as high-resolution respirometry or extracellular flux analysis.
4. Metabolic Flux Analysis: Use stable isotope tracers to track metabolic pathway utilization in response to exercise challenges.
5. Therapeutic Trials: Test interventions targeting microcirculation (e.g., vasodilators, rheological agents) or cellular oxygen utilization (e.g., mitochondrial support compounds) for their effects on post-exertional symptom exacerbation.

Hypothesis 3: Microbiome-Driven Immune Priming as a Mechanism for Sustained Immune Dysregulation

Biological Rationale

The BioMapAI study revealed significant disruptions in microbiome-immune-metabolome networks in ME/CFS patients, suggesting a novel hypothesis: altered gut microbiota in ME/CFS patients continuously prime the immune system through metabolite production and direct interactions, leading to sustained immune dysregulation and systemic inflammation.

This hypothesis integrates several key findings:

1. Altered Microbial Metabolism: BioMapAI identified depleted butyrate production, altered branched-chain amino acid biosynthesis, and disrupted tryptophan and benzoate metabolism in the gut microbiome of ME/CFS patients.
2. Immune Cell Dysregulation: Multiple studies (Vu et al., Van Booven et al., BioMapAI) found evidence of immune dysregulation, particularly in monocytes and T cells.
3. Metabolite-Immune Correlations: BioMapAI found that microbial metabolites displayed altered associations with immune cell function in ME/CFS, with new correlations between microbial metabolites and inflammatory immune cells (γδT, CD8+ MAIT).
4. Inflammatory Signatures: Sweetman et al. identified increased expression of inflammation-related genes (IL8, NFΚBIA, TNFAIP3), suggesting ongoing inflammatory processes.

The microbiome-driven immune priming hypothesis proposes that:

• Dysbiotic Microbiota: Following an initial trigger (e.g., infection), the gut microbiome shifts to a dysbiotic state characterized by altered metabolite production.
• Metabolite Signaling: These altered microbial metabolites signal to the immune system through various pathways, including G-protein coupled receptors, aryl hydrocarbon receptors, and direct effects on cellular metabolism.
• Immune Training: Chronic exposure to altered microbial signals "trains" or "primes" immune cells, particularly monocytes and innate lymphoid cells, leading to sustained changes in their function and response patterns.
• Systemic Effects: These primed immune cells circulate throughout the body, affecting multiple organ systems and contributing to the diverse symptoms of ME/CFS.

This hypothesis could explain the chronic nature of ME/CFS, as the altered microbiome-immune interaction creates a self-perpetuating cycle of immune dysregulation even after the initial triggering event has resolved.

Testable Predictions

1. Immune Cell Epigenetics: Monocytes and other immune cells from ME/CFS patients will show epigenetic modifications consistent with trained immunity, such as histone modifications at promoters of inflammatory genes.
2. Metabolite Transfer Experiments: Transfer of fecal metabolites from ME/CFS patients to germ-free mice will induce immune changes similar to those observed in ME/CFS.
3. Microbial Intervention Effects: Interventions that normalize gut microbial metabolism (e.g., specific probiotics, prebiotics, or fecal microbiota transplantation) will reduce immune dysregulation and potentially improve symptoms.
4. Metabolite-Receptor Interactions: Blocking receptors for specific microbial metabolites (e.g., GPR41/43 for short-chain fatty acids, AHR for tryptophan metabolites) will alter immune cell function in ME/CFS patients.
5. Longitudinal Dynamics: Changes in microbial metabolite profiles will precede or correlate with changes in immune function and symptom severity over time.

Experimental Approaches

1. Integrated Multi-Omics: Perform longitudinal, integrated analysis of gut microbiome, metabolome, and immune cell function in ME/CFS patients to track the relationships between these systems over time.
2. Immune Cell Phenotyping: Conduct detailed phenotypic and functional analysis of immune cells from ME/CFS patients, with particular attention to markers of trained immunity and metabolic reprogramming.
3. Ex Vivo Models: Develop ex vivo models where immune cells are exposed to microbial metabolites from ME/CFS patients or controls, then assessed for functional changes and epigenetic modifications.
4. Animal Models: Use gnotobiotic animals to test the effects of ME/CFS-associated microbiota on immune function and behavior.
5. Targeted Interventions: Design clinical trials of interventions specifically targeting the microbiome-immune axis, with comprehensive assessment of microbial, metabolic, immune, and clinical outcomes.

Conclusion

These three hypotheses—peroxisomal dysfunction, disrupted tissue oxygen delivery-utilization, and microbiome-driven immune priming—offer novel frameworks for understanding ME/CFS pathophysiology. Each hypothesis integrates findings from multiple omics studies, provides a mechanistic explanation for key features of ME/CFS, and generates testable predictions that can guide future research.

Importantly, these hypotheses are not mutually exclusive and may represent different aspects or stages of ME/CFS pathophysiology. For example, peroxisomal dysfunction could contribute to microcirculatory abnormalities through effects on vascular cell membrane composition, while microbiome-driven immune priming could affect cellular metabolism and oxygen utilization through inflammatory mediators.

By pursuing these hypotheses through rigorous experimental approaches, researchers may uncover new insights into ME/CFS pathophysiology and identify novel targets for diagnostic and therapeutic development. The complex, multisystem nature of ME/CFS likely requires such integrated hypotheses that span traditional disciplinary boundaries and connect diverse biological processes.

Innovative Research Approaches for ME/CFS

To advance our understanding of ME/CFS pathophysiology and develop effective diagnostic tools and treatments, innovative research approaches are needed. This section outlines cutting-edge techniques, advanced bioinformatic tools, and cross-disciplinary collaboration strategies that could transform ME/CFS research.

Cutting-Edge Techniques

1. Single-Cell Multi-Omics

Description: Single-cell multi-omics technologies enable simultaneous profiling of multiple molecular features (genome, transcriptome, proteome, epigenome) within individual cells.

Applications in ME/CFS Research:

• Immune Cell Heterogeneity: Building on Vu et al.'s single-cell transcriptomics study, multi-omics approaches could reveal how transcriptional, epigenetic, and proteomic changes are coordinated within specific immune cell populations in ME/CFS.
• Cell State Transitions: Track how immune cells change state during post-exertional malaise, capturing dynamic responses that bulk analyses miss.
• Rare Cell Populations: Identify rare cell subsets that may play outsized roles in ME/CFS pathophysiology but are diluted in bulk analyses.

Specific Technologies:

• CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) for simultaneous measurement of cellular RNA and surface proteins
• scATAC-seq (single-cell Assay for Transposase-Accessible Chromatin using sequencing) combined with scRNA-seq to link epigenetic regulation with gene expression
• G&T-seq (Genome & Transcriptome sequencing) to correlate genetic variants with gene expression at single-cell resolution

2. Spatial Transcriptomics and Proteomics

Description: Spatial omics technologies preserve tissue architecture while measuring gene or protein expression, enabling the mapping of molecular profiles to specific anatomical contexts.

Applications in ME/CFS Research:

• Tissue Microenvironments: Examine how immune cells, neurons, glia, and other cell types interact in tissues affected by ME/CFS (e.g., muscle, brain, gut).
• Regional Heterogeneity: Map metabolic and inflammatory changes across tissue regions to identify localized disruptions.
• Cell-Cell Communication: Visualize intercellular signaling networks within intact tissues.

Specific Technologies:

• Visium Spatial Gene Expression (10x Genomics) for mapping transcriptomes to tissue sections
• Imaging Mass Cytometry (IMC) for high-dimensional protein mapping in tissues
• Spatial ATAC-seq for mapping chromatin accessibility in a tissue context
• MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization) for highly multiplexed RNA imaging

3. Organoids and Microphysiological Systems

Description: Three-dimensional tissue cultures that mimic organ structure and function, enabling the study of complex physiological processes in controlled laboratory settings.

Applications in ME/CFS Research:

• Brain Organoids: Model neuroinflammation and blood-brain barrier function to understand cognitive symptoms.
• Gut Organoids: Investigate host-microbiome interactions and intestinal barrier function, building on BioMapAI's findings on microbiome disruption.
• Muscle-on-Chip: Study exercise responses and energy metabolism in engineered muscle tissues.
• Multi-Organ Systems: Connect multiple organoids to model systemic interactions relevant to ME/CFS.

Specific Technologies:

• Patient-derived iPSCs (induced pluripotent stem cells) differentiated into relevant cell types and organoids
• Organ-on-chip platforms with integrated sensors for real-time monitoring
• Bioprinting of complex tissue architectures with multiple cell types
• Microfluidic systems for controlled delivery of metabolites, cytokines, or microbial products

4. In Vivo Molecular Imaging

Description: Non-invasive imaging techniques that visualize molecular processes in living organisms, providing dynamic information about metabolism, inflammation, and other relevant processes.

Applications in ME/CFS Research:

• Neuroinflammation: Track inflammatory processes in the brain using PET imaging with radiotracers targeting microglia or inflammatory markers.
• Metabolic Imaging: Measure energy metabolism in muscle and brain using techniques like hyperpolarized 13C MRI.
• Vascular Function: Assess microcirculation and tissue perfusion using advanced MRI techniques or contrast-enhanced ultrasound.

Specific Technologies:

• TSPO PET imaging for neuroinflammation
• Hyperpolarized 13C MRI for real-time metabolic flux analysis
• Arterial spin labeling (ASL) MRI for tissue perfusion
• Functional near-infrared spectroscopy (fNIRS) for non-invasive monitoring of tissue oxygenation

5. CRISPR-Based Functional Genomics

Description: High-throughput genetic perturbation approaches using CRISPR technology to systematically investigate gene function and regulatory networks.

Applications in ME/CFS Research:

• Pathway Validation: Systematically test the functional importance of genes in pathways identified by omics studies.
• Genetic Modifiers: Identify genes that modify cellular responses to stressors relevant to ME/CFS (e.g., oxidative stress, metabolic challenge).
• Drug Target Discovery: Screen for genes whose modulation rescues ME/CFS-related cellular phenotypes.

Specific Technologies:

• CRISPR-Cas9 screens (knockout, activation, or inhibition) in relevant cell types
• Base editing for precise modification of specific nucleotides
• Prime editing for targeted insertions or deletions
• Perturb-seq for combining CRISPR perturbations with single-cell RNA-seq readouts

6. Wearable and Remote Monitoring Technologies

Description: Continuous, real-time monitoring of physiological parameters in patients' natural environments, capturing dynamic changes and daily fluctuations relevant to ME/CFS.

Applications in ME/CFS Research:

• Activity-Symptom Relationships: Track physical activity, sleep, heart rate variability, and symptom reports to understand triggers and patterns of post-exertional malaise.
• Physiological Biomarkers: Identify objective, measurable correlates of subjective symptoms.
• Treatment Response: Monitor real-time changes in physiological parameters during therapeutic interventions.

Specific Technologies:

• Advanced fitness trackers with heart rate variability, sleep tracking, and activity monitoring
• Continuous glucose monitoring systems
• Smart garments with integrated biosensors
• Smartphone-based ecological momentary assessment for real-time symptom reporting
• Remote neuropsychological testing platforms for cognitive assessment

Advanced Bioinformatic Tools

1. Multi-Omics Integration Frameworks

Description: Computational approaches for integrating diverse omics data types to construct comprehensive models of biological systems.

Applications in ME/CFS Research:

• Extend BioMapAI: Build on the BioMapAI framework to incorporate additional omics layers (e.g., epigenomics, lipidomics) and clinical variables.
• Network Analysis: Construct multi-layer networks representing interactions between genes, proteins, metabolites, and microbes in ME/CFS.
• Causal Modeling: Infer causal relationships between molecular changes and clinical features using directed graphical models.

Specific Tools:

• MOFA+ (Multi-Omics Factor Analysis) for unsupervised integration of multiple omics datasets
• mixOmics R package for multi-omics data integration and feature selection
• DIABLO (Data Integration Analysis for Biomarker discovery using Latent cOmponents) for identifying multi-omics biomarker signatures
• Causal inference frameworks like Bayesian networks or structural equation modeling

2. Machine Learning for Patient Stratification

Description: Advanced machine learning approaches for identifying patient subgroups with distinct biological profiles and clinical trajectories.

Applications in ME/CFS Research:

• Unsupervised Clustering: Identify natural subgroups within ME/CFS based on molecular and clinical data.
• Disease Trajectory Modeling: Predict individual disease courses and treatment responses.
• Transfer Learning: Leverage insights from related conditions (e.g., Long COVID, fibromyalgia) to improve ME/CFS models.

Specific Tools:

• Deep learning architectures for complex pattern recognition
• Topological data analysis for identifying clusters in high-dimensional data
• Semi-supervised learning approaches for integrating labeled and unlabeled data
• Reinforcement learning for optimizing treatment strategies
• Federated learning for collaborative model building across institutions while preserving data privacy

3. Dynamic and Temporal Modeling

Description: Computational approaches for analyzing time-series data and modeling dynamic biological processes.

Applications in ME/CFS Research:

• PEM Dynamics: Model the temporal evolution of molecular and physiological changes during post-exertional malaise.
• Disease Progression: Track changes in biomarkers and symptoms over the course of ME/CFS.
• Treatment Response: Predict and monitor responses to therapeutic interventions over time.

Specific Tools:

• Differential equation models of biological pathways
• Hidden Markov Models for inferring latent disease states
• Gaussian process models for flexible time-series analysis
• Recurrent neural networks for sequence modeling
• Dynamic Bayesian networks for causal inference in time-series data

4. Knowledge Graph and Literature Mining

Description: Computational approaches for extracting and organizing knowledge from scientific literature and databases to guide research and generate hypotheses.

Applications in ME/CFS Research:

• Hypothesis Generation: Identify novel connections between ME/CFS and other biological processes or diseases.
• Drug Repurposing: Discover existing drugs that might address ME/CFS mechanisms.
• Pathway Enrichment: Contextualize omics findings within the broader biomedical knowledge landscape.

Specific Tools:

• Biomedical knowledge graphs integrating multiple databases
• Natural language processing for automated literature mining
• Network-based drug repurposing algorithms
• Semantic web technologies for knowledge representation
• Explainable AI approaches for transparent reasoning

5. Digital Twin Modeling

Description: Computational models that simulate individual patients' physiology, enabling personalized predictions and treatment optimization.

Applications in ME/CFS Research:

• Personalized Simulations: Create virtual patient models that capture individual variations in ME/CFS pathophysiology.
• Treatment Optimization: Simulate responses to potential interventions before actual implementation.
• Mechanism Exploration: Test hypotheses about disease mechanisms in silico.

Specific Tools:

• Multi-scale physiological modeling frameworks
• Agent-based models of immune system dynamics
• Metabolic flux balance analysis
• Pharmacokinetic/pharmacodynamic (PK/PD) modeling
• Reinforcement learning for treatment policy optimization

Cross-Disciplinary Collaboration

1. Integrated Research Centers

Description: Dedicated research centers that bring together experts from multiple disciplines to focus on ME/CFS.

Key Features:

• Co-located Facilities: Physical proximity to facilitate spontaneous collaboration and shared resources.
• Shared Biorepositories: Centralized collection and storage of biological samples with standardized protocols.
• Patient Involvement: Integration of patient perspectives in research design and prioritization.
• Longitudinal Cohorts: Sustained follow-up of well-characterized patient populations.

Example Implementation:

• Establish ME/CFS Centers of Excellence with core facilities for multi-omics, clinical assessment, and computational analysis.
• Include clinicians, basic scientists, computational biologists, and patient advocates in leadership structures.
• Implement standardized protocols for sample collection, processing, and data generation.

2. Collaborative Research Networks

Description: Distributed networks of researchers and institutions working together on coordinated ME/CFS research projects.

Key Features:

• Standardized Protocols: Common methods for patient assessment, sample collection, and data generation.
• Data Sharing Platforms: Infrastructure for secure, ethical sharing of data across institutions.
• Distributed Expertise: Leveraging specialized capabilities at different institutions.
• Multi-site Clinical Studies: Coordinated recruitment and assessment across geographic regions.

Example Implementation:

• Establish an International ME/CFS Research Consortium with shared funding and governance.
• Develop common data elements and standardized protocols for multi-site studies.
• Implement a federated data platform that enables analysis across distributed datasets while addressing privacy concerns.

3. Interdisciplinary Training Programs

Description: Educational initiatives designed to train researchers with expertise spanning multiple disciplines relevant to ME/CFS.

Key Features:

• Cross-training: Exposure to multiple methodologies and conceptual frameworks.
• Team Science Skills: Training in collaborative research approaches.
• Patient Engagement: Direct interaction with ME/CFS patients to understand lived experience.
• Translational Focus: Emphasis on bridging basic science and clinical application.

Example Implementation:

• Develop graduate and postdoctoral training programs specifically focused on ME/CFS.
• Create summer institutes or boot camps for established researchers to gain new skills.
• Establish mentorship networks connecting trainees with experts across disciplines.

4. Industry-Academic Partnerships

Description: Collaborative relationships between academic researchers and industry partners to accelerate translation of discoveries into diagnostics and treatments.

Key Features:

• Complementary Expertise: Combining academic innovation with industry development capabilities.
• Resource Sharing: Access to proprietary technologies, compounds, or datasets.
• Translational Pipeline: Clear pathways from discovery to development and commercialization.
• Sustainable Funding: Diverse funding sources to support long-term research programs.

Example Implementation:

• Establish pre-competitive consortia focused on ME/CFS biomarker validation.
• Develop public-private partnerships for drug repurposing or target validation.
• Create innovation incubators focused on ME/CFS diagnostics and therapeutics.

5. Patient-Researcher Partnerships

Description: Collaborative relationships between researchers and patients/advocates to ensure research addresses patient priorities and incorporates lived experience.

Key Features:

• Research Co-design: Patient involvement in study design and outcome selection.
• Bidirectional Knowledge Exchange: Mechanisms for sharing expertise between patients and researchers.
• Inclusive Governance: Patient representation in research oversight and priority-setting.
• Accessible Dissemination: Communication of research findings in formats accessible to patients.

Example Implementation:

• Establish patient advisory boards for research programs and centers.
• Develop training for patients in research methods and for researchers in patient engagement.
• Implement participatory research approaches where patients are active collaborators.
• Create platforms for patient-reported research priorities and outcomes.

Conclusion

Advancing ME/CFS research requires innovative approaches that match the complexity and heterogeneity of the condition. Cutting-edge techniques like single-cell multi-omics, spatial transcriptomics, and organoid models can provide unprecedented insights into disease mechanisms. Advanced bioinformatic tools enable the integration and interpretation of complex, multi-dimensional data. Cross-disciplinary collaboration frameworks ensure that diverse expertise is brought to bear on the challenges of ME/CFS research.

By adopting these innovative approaches, the research community can overcome the limitations of traditional methods and accelerate progress toward understanding ME/CFS pathophysiology, developing reliable biomarkers, and discovering effective treatments. The complexity of ME/CFS demands nothing less than the most sophisticated and integrated research strategies available to modern biomedical science.

Actionable Therapeutic Targets in ME/CFS

Based on the integrated analysis of the core BioMapAI paper and the eight additional omics studies, this section identifies promising therapeutic targets for ME/CFS and discusses approaches for drug development, target prioritization, and clinical trial design.

Target Identification

Immune System Targets

1. Monocyte Activation and Differentiation Pathways

Target Rationale:

Vu et al. identified classical monocyte dysregulation with inappropriate differentiation and migration to tissue in ME/CFS patients. The fraction of dysregulated monocytes correlated with disease severity, suggesting a causal role in symptomatology.

Specific Targets:

• CD14/CD16 Signaling: Modulate monocyte subset differentiation and activation
• CCR2/CCL2 Axis: Target monocyte recruitment and tissue migration
• MERTK (Mer Tyrosine Kinase): Regulate monocyte inflammatory responses and efferocytosis
• NLRP3 Inflammasome: Control monocyte-derived inflammatory cytokine production

Potential Interventions:

• CCR2 antagonists (e.g., cenicriviroc) to reduce monocyte recruitment
• NLRP3 inhibitors (e.g., MCC950, dapansutrile) to decrease inflammatory cytokine production
• Specialized pro-resolving mediators (SPMs) to promote resolution of inflammation
• Targeted nanoparticle delivery of anti-inflammatory compounds to monocytes

2. T Cell Inflammatory Pathways

Target Rationale:

BioMapAI identified heightened pro-inflammatory responses mediated by γδT cells and CD8 MAIT cells, with increased production of IFNγ and GzA. These cells were associated with subjective health perception and social functioning.

Specific Targets:

• JAK-STAT Signaling: Modulate cytokine signaling in T cells
• MAIT Cell Activation: Target MR1-dependent activation of MAIT cells
• γδT Cell Receptors: Modulate γδT cell activation and function
• IFNγ Signaling: Reduce downstream effects of IFNγ production

Potential Interventions:

• JAK inhibitors (e.g., tofacitinib, baricitinib) to modulate cytokine signaling
• Anti-IFNγ antibodies to neutralize excessive IFNγ
• Vitamin A derivatives to modulate MAIT cell function
• Selective immunomodulators targeting specific T cell subsets

3. B Cell and Antibody-Mediated Processes

Target Rationale:

BioMapAI identified increased B cells (CD19+CD3-) as a disease-specific biomarker. While not a focus of all studies, B cell abnormalities suggest potential autoimmune or inflammatory components.

Specific Targets:

• CD20: Target B cell depletion or modulation
• BAFF/APRIL: Modulate B cell survival and maturation
• BTK (Bruton's Tyrosine Kinase): Regulate B cell receptor signaling
• Plasma Cell Differentiation: Target antibody-producing cells

Potential Interventions:

• B cell depleting therapies (e.g., rituximab) for selected patients
• BTK inhibitors (e.g., ibrutinib, acalabrutinib) to modulate B cell activation
• BAFF antagonists (e.g., belimumab) to reduce B cell survival signals
• Proteasome inhibitors to target plasma cells

Metabolic Targets

1. Peroxisomal Function

Target Rationale:

Che et al. provided the first metabolomic evidence of peroxisomal dysfunction in ME/CFS, with decreased levels of plasmalogens, phospholipid ethers, phosphatidylcholines, and sphingomyelins.

Specific Targets:

• PPAR (Peroxisome Proliferator-Activated Receptors): Enhance peroxisomal biogenesis and function
• Plasmalogen Synthesis Pathway: Support production of plasmalogens
• PEX Genes: Promote peroxisomal protein import and assembly
• ACOX (Acyl-CoA Oxidase): Enhance peroxisomal fatty acid oxidation

Potential Interventions:

• PPAR agonists (e.g., fibrates, thiazolidinediones) to enhance peroxisomal function
• Plasmalogen precursors (e.g., alkylglycerols) for replacement therapy
• Choline and ethanolamine supplementation to support phospholipid synthesis
• Antioxidants targeting peroxisomal ROS metabolism

2. Mitochondrial Energy Production

Target Rationale:

Multiple studies (Hoel et al., Sweetman et al.) identified energy metabolism dysfunction in ME/CFS. Hoel et al. found elevated energy strain and altered utilization of fatty acids and amino acids as catabolic fuels.

Specific Targets:

• Electron Transport Chain Complexes: Enhance mitochondrial ATP production
• Mitochondrial Biogenesis: Increase mitochondrial mass and function
• Fatty Acid Transport: Improve substrate delivery to mitochondria
• Mitochondrial Dynamics: Optimize fusion/fission balance

Potential Interventions:

• CoQ10 and other electron transport chain cofactors
• PGC-1α activators (e.g., AICAR, resveratrol) to enhance mitochondrial biogenesis
• L-carnitine to support fatty acid transport
• NAD+ precursors (e.g., nicotinamide riboside) to support mitochondrial function
• Mitochondrial-targeted antioxidants (e.g., MitoQ, SS-31)

3. Cellular Stress Response Pathways

Target Rationale:

Sweetman et al. identified altered gene expression related to cellular stress responses. Van Booven et al. found impaired transcriptional responses to exercise stress in ME/CFS patients.

Specific Targets:

• Nrf2 Pathway: Enhance cellular antioxidant defenses
• Heat Shock Proteins: Support protein folding and cellular stress adaptation
• Integrated Stress Response: Modulate cellular responses to various stressors
• Autophagy: Enhance cellular quality control and organelle turnover

Potential Interventions:

• Nrf2 activators (e.g., sulforaphane, bardoxolone methyl)
• Heat shock protein inducers (e.g., geranylgeranylacetone)
• Integrated stress response modulators (e.g., ISRIB)
• Autophagy enhancers (e.g., rapamycin analogs, trehalose)

Microbiome-Host Interaction Targets

1. Short-Chain Fatty Acid Production

Target Rationale:

BioMapAI identified depleted butyrate production from pyruvate and glutarate pathways in ME/CFS patients. SCFAs play important roles in gut barrier function, immune regulation, and metabolism.

Specific Targets:

• Butyrate-Producing Bacteria: Enhance colonization and function
• SCFA Receptors (GPR41/43): Modulate signaling from microbial metabolites
• Dietary Fiber Fermentation: Provide substrates for SCFA production
• Butyrate Transport: Enhance delivery to colonocytes and circulation

Potential Interventions:

• Prebiotics targeting butyrate-producing bacteria (e.g., resistant starch, inulin)
• Probiotics containing butyrate-producing species (e.g., Faecalibacterium prausnitzii)
• Butyrate or butyrate prodrugs for direct supplementation
• GPR41/43 agonists to mimic beneficial SCFA signaling

2. Tryptophan Metabolism

Target Rationale:

BioMapAI found disrupted tryptophan metabolism in ME/CFS, with altered associations with gastrointestinal issues, Th22 cells, and inflammatory immune cells.

Specific Targets:

• IDO (Indoleamine 2,3-dioxygenase): Modulate kynurenine pathway activation
• AhR (Aryl Hydrocarbon Receptor): Regulate responses to tryptophan metabolites
• Serotonin Synthesis and Signaling: Support serotonergic function
• Microbial Tryptophanases: Influence bacterial tryptophan metabolism

Potential Interventions:

• IDO inhibitors to reduce kynurenine pathway activation
• AhR modulators to regulate immune responses to tryptophan metabolites
• Tryptophan or 5-HTP supplementation to support serotonin synthesis
• Probiotics that favorably modulate tryptophan metabolism

3. Bile Acid Metabolism

Target Rationale:

BioMapAI identified increased glycodeoxycholate 3-sulfate (a bile acid) as a metabolic biomarker. Bile acids function as signaling molecules affecting metabolism and inflammation.

Specific Targets:

• FXR (Farnesoid X Receptor): Modulate bile acid signaling
• TGR5 (G Protein-Coupled Bile Acid Receptor): Regulate energy expenditure and inflammation
• Bile Acid Synthesis Enzymes: Influence bile acid composition
• Bile Acid Transport: Affect enterohepatic circulation and systemic levels

Potential Interventions:

• FXR agonists (e.g., obeticholic acid) to modulate bile acid signaling
• TGR5 agonists to enhance energy expenditure
• Bile acid sequestrants to reduce systemic bile acid levels
• Probiotics that modify bile acid metabolism (e.g., those with bile salt hydrolase activity)

Neurological Targets

1. Neuroinflammation

Target Rationale:

While direct evidence from the reviewed studies is limited, cognitive symptoms ("brain fog") in ME/CFS suggest neuroinflammatory processes that may be targetable.

Specific Targets:

• Microglial Activation: Modulate neuroinflammatory responses
• Blood-Brain Barrier Integrity: Reduce neuroinflammatory triggers
• Astrocyte Function: Regulate neuronal support and inflammatory signaling
• Neuronal Cytokine Receptors: Protect neurons from inflammatory damage

Potential Interventions:

• Microglial modulators (e.g., minocycline, palmitoylethanolamide)
• Blood-brain barrier stabilizers (e.g., angiotensin receptor blockers)
• Glial cell modulators (e.g., propentofylline)
• Neuroprotective compounds (e.g., N-acetylcysteine)

2. Autonomic Nervous System Regulation

Target Rationale:

Orthostatic intolerance is a common symptom in ME/CFS, suggesting autonomic nervous system dysfunction that may be amenable to therapeutic intervention.

Specific Targets:

• Adrenergic Receptors: Modulate sympathetic nervous system activity
• Acetylcholine Signaling: Regulate parasympathetic function
• Baroreceptor Sensitivity: Improve blood pressure regulation
• Vasoactive Neuropeptides: Influence vascular tone and permeability

Potential Interventions:

• Beta-adrenergic blockers (e.g., propranolol) for sympathetic overactivation
• Alpha-1 adrenergic agonists (e.g., midodrine) for orthostatic hypotension
• Acetylcholinesterase inhibitors to enhance parasympathetic function
• Vasoactive intestinal peptide (VIP) for autonomic and immune regulation

Drug Development Opportunities

1. Drug Repurposing

Approach: Identify existing approved drugs that target pathways implicated in ME/CFS pathophysiology.

Advantages:

• Established safety profiles
• Reduced development time and cost
• Known pharmacokinetics and drug interactions
• Immediate availability for off-label use or clinical trials

Promising Repurposing Candidates:

• Low-dose Naltrexone: Modulates glial activation and immune function
• Metformin: Enhances mitochondrial function and AMPK activation
• Fluvoxamine: Beyond serotonergic effects, activates sigma-1 receptors with anti-inflammatory properties
• JAK Inhibitors: Target cytokine signaling implicated in immune dysregulation
• PPAR Agonists: Address peroxisomal dysfunction and metabolic abnormalities
• GLP-1 Receptor Agonists: Emerging evidence for neuroprotective and anti-inflammatory effects

Implementation Strategy:

• Computational drug repurposing using ME/CFS omics signatures
• In vitro screening of drug libraries using cellular models of ME/CFS
• Small proof-of-concept clinical trials with careful patient selection
• N-of-1 trials for personalized repurposing approaches

2. Biologics Development

Approach: Develop monoclonal antibodies or other biologics targeting specific immune pathways dysregulated in ME/CFS.

Advantages:

• High specificity for molecular targets
• Potential for precise intervention in immune pathways
• Longer duration of action
• Reduced off-target effects

Promising Biologic Approaches:

• Anti-cytokine Antibodies: Target specific inflammatory mediators (e.g., IFNγ, IL-6)
• Cell-depleting Antibodies: Selectively target dysregulated immune cell populations
• Receptor Antagonists: Block activation of specific immune receptors
• Immune Checkpoint Modulators: Regulate T cell activation thresholds
• Engineered Regulatory Proteins: Novel biologics designed to resolve inflammation

Implementation Strategy:

• Target validation in patient samples and animal models
• Humanized antibody development and optimization
• Careful patient selection based on immune profiles
• Biomarker-guided dosing and response monitoring

3. Microbiome-Based Therapeutics

Approach: Develop interventions that target the gut microbiome to address the microbiome-immune-metabolome disruptions identified in ME/CFS.

Advantages:

• Addresses a root cause of systemic disruption
• Potential for sustained effects through ecosystem modification
• Generally favorable safety profiles
• Multiple intervention modalities available

Promising Microbiome Approaches:

• Precision Probiotics: Strains selected to address specific deficiencies (e.g., butyrate producers)
• Targeted Prebiotics: Compounds designed to selectively feed beneficial bacteria
• Postbiotics: Beneficial microbial metabolites or components
• Phage Therapy: Bacteriophages targeting harmful bacteria
• Fecal Microbiota Transplantation: Ecosystem-level intervention for selected patients
• Small Molecule Microbiome Modulators: Compounds that shape microbial communities

Implementation Strategy:

• Microbiome profiling to identify patient-specific targets
• In vitro fermentation models to test intervention effects
• Careful monitoring of both microbiome and host responses
• Combination approaches targeting multiple aspects of dysbiosis

4. Metabolic Support Strategies

Approach: Develop interventions that support cellular energy production and metabolic function.

Advantages:

• Addresses fundamental bioenergetic disruptions
• Potential to improve multiple symptoms simultaneously
• Often amenable to nutritional and supplement-based approaches
• Can complement other therapeutic strategies

Promising Metabolic Approaches:

• Mitochondrial Cocktails: Combinations of cofactors, antioxidants, and substrates
• Lipid Replacement Therapy: Phospholipids and other membrane components
• Metabolic Modulators: Compounds that shift metabolic pathway utilization
• NAD+ Enhancers: Support cellular energy production and signaling
• Ketogenic Approaches: Alternative fuel sources for compromised metabolism

Implementation Strategy:

• Metabolomic profiling to identify patient-specific deficiencies
• Biomarker monitoring to assess metabolic responses
• Personalized formulations based on individual metabolic phenotypes
• Pulsed or cyclic administration to prevent adaptation

5. Neuromodulation Approaches

Approach: Develop non-pharmacological interventions that modulate neural activity to address symptoms and potentially underlying pathophysiology.

Advantages:

• Targeted effects on specific neural circuits
• Reduced systemic side effects
• Potential for home-based, patient-controlled administration
• Complementary to pharmacological approaches

Promising Neuromodulation Approaches:

• Vagus Nerve Stimulation: Non-invasive approaches to modulate autonomic function and inflammation
• Transcranial Magnetic Stimulation: Target neuroinflammation and cognitive symptoms
• Transcranial Direct Current Stimulation: Modulate cortical excitability and cognitive function
• Heart Rate Variability Biofeedback: Enhance autonomic regulation
• Peripheral Nerve Stimulation: Target specific symptom pathways

Implementation Strategy:

• Neurophysiological profiling to identify patient-specific targets
• Development of home-usable devices for sustained treatment
• Combination with cognitive or behavioral interventions
• Personalized stimulation parameters based on individual responses

Criteria for Prioritization

1. Evidence Strength

Assessment Criteria:

• Consistency Across Studies: Targets supported by multiple independent studies
• Effect Size: Magnitude of abnormalities in target pathways
• Correlation with Symptoms: Association between target engagement and clinical features
• Mechanistic Plausibility: Clear biological rationale linking target to disease processes

Implementation:

• Develop an evidence grading system specific to ME/CFS targets
• Require convergent evidence from multiple omics platforms
• Prioritize targets with established links to core symptoms
• Consider both statistical significance and biological significance

2. Therapeutic Feasibility

Assessment Criteria:

• Druggability: Existence of compounds or modalities that can effectively engage the target
• Delivery Accessibility: Ability to reach the target in relevant tissues
• Safety Profile: Potential for adverse effects, especially with chronic administration
• Development Timeline: Time required to advance to clinical testing

Implementation:

• Prioritize targets with existing drug candidates
• Consider blood-brain barrier penetration for neurological targets
• Favor targets with established safety data in related conditions
• Balance near-term opportunities with longer-term novel approaches

3. Patient Stratification Potential

Assessment Criteria:

• Subgroup Specificity: Relevance to identifiable patient subgroups
• Biomarker Availability: Existence of measures to identify suitable patients
• Response Prediction: Ability to forecast treatment response
• Personalization Potential: Opportunity for individualized dosing or combination approaches

Implementation:

• Develop companion diagnostics alongside therapeutic candidates
• Identify biomarkers that predict target engagement
• Create decision algorithms for treatment selection
• Design adaptive trials that incorporate patient heterogeneity

4. Therapeutic Impact

Assessment Criteria:

• Symptom Coverage: Range of symptoms potentially addressed
• Functional Improvement: Likelihood of enhancing quality of life and function
• Disease Modification: Potential to alter underlying disease processes rather than just symptoms
• Combination Potential: Synergy with other therapeutic approaches

Implementation:

• Prioritize targets linked to multiple symptom domains
• Focus on core symptoms with greatest functional impact
• Consider both symptomatic relief and disease-modifying potential
• Develop rational combination strategies based on pathway analysis

Clinical Trial Design Considerations

1. Patient Selection

Key Considerations:

• Diagnostic Criteria: Standardized, validated criteria for case definition
• Disease Duration: Stratification by short-term vs. long-term illness
• Symptom Profile: Selection based on predominant symptom clusters
• Biomarker Status: Enrichment based on relevant biomarkers
• Prior Treatment Response: Consideration of previous therapeutic experiences

Innovative Approaches:

• Biomarker-Guided Enrollment: Select patients based on specific molecular profiles
• Adaptive Enrichment: Modify enrollment criteria based on interim analyses
• N-of-1 Series: Multiple crossovers within individual patients to address heterogeneity
• Basket Trials: Include related conditions (e.g., Long COVID, fibromyalgia) with similar biological signatures

2. Outcome Measures

Key Considerations:

• Symptom Assessments: Validated, ME/CFS-specific symptom measures
• Functional Outcomes: Objective measures of physical and cognitive function
• Biomarker Endpoints: Molecular measures of target engagement and disease activity
• Patient-Reported Outcomes: Capture of subjective experience and quality of life
• Long-term Monitoring: Assessment of durability and delayed benefits

Innovative Approaches:

• Wearable Device Data: Continuous monitoring of activity, sleep, and physiological parameters
• Ecological Momentary Assessment: Real-time symptom reporting in natural environments
• Provocation Testing: Standardized exercise or cognitive challenges to assess post-exertional responses
• Digital Phenotyping: Smartphone-based passive monitoring of behavior and function
• Patient-Centered Outcome Development: Collaborative creation of relevant measures

3. Trial Designs

Key Considerations:

• Study Duration: Sufficient length to capture delayed responses and durability
• Placebo Effects: Strategies to minimize placebo responses in subjective outcomes
• Disease Fluctuations: Accounting for natural symptom variability
• Dropout Prevention: Approaches to maintain participation despite illness burden
• Statistical Power: Adequate sample sizes for heterogeneous populations

Innovative Approaches:

• Adaptive Trial Designs: Modification of treatment arms based on interim results
• Sequential Multiple Assignment Randomized Trials (SMART): Testing treatment sequences
• Platform Trials: Evaluation of multiple interventions against a shared control group
• Crossover Designs: Within-subject comparisons to reduce variability
• Enrichment Designs: Focus on responsive subgroups identified during the trial

4. Combination Approaches

Key Considerations:

• Rational Combinations: Targeting complementary pathways
• Sequencing vs. Simultaneous: Timing of multiple interventions
• Interaction Assessment: Evaluation of synergistic or antagonistic effects
• Personalized Combinations: Tailoring regimens to individual patient profiles
• Polypharmacy Management: Minimizing adverse effects from multiple agents

Innovative Approaches:

• Factorial Designs: Systematic evaluation of combination effects
• Bayesian Adaptive Designs: Efficient exploration of combination space
• Biomarker-Guided Combinations: Selection of components based on individual profiles
• Pulsed or Cyclic Regimens: Temporal separation to minimize interactions
• Multimodal Approaches: Combining pharmacological with non-pharmacological interventions

5. Special Considerations for ME/CFS Trials

Key Considerations:

• Post-Exertional Malaise: Minimizing trial participation burden
• Cognitive Limitations: Simplifying assessment procedures
• Sensitivity to Medications: Starting with lower doses and gradual titration
• Comorbidity Management: Accounting for common comorbid conditions
• Heterogeneity: Strategies for identifying responder subgroups

Innovative Approaches:

• Home-Based Assessments: Reducing travel burden for participants
• Remote Monitoring: Telemedicine and digital tools for data collection
• Flexible Scheduling: Accommodating unpredictable symptom fluctuations
• Caregiver Reports: Supplementary observations from family members
• Patient Partnership: Meaningful involvement in trial design and interpretation

Conclusion

The integration of findings from multiple omics studies has identified numerous promising therapeutic targets for ME/CFS, spanning immune, metabolic, microbiome, and neurological domains. Drug development opportunities include repurposing existing medications, developing novel biologics, targeting the microbiome, supporting metabolic function, and exploring neuromodulation approaches.

Prioritization of these targets should consider evidence strength, therapeutic feasibility, patient stratification potential, and anticipated therapeutic impact. Clinical trials in ME/CFS require careful attention to patient selection, outcome measures, trial design, combination approaches, and special considerations related to the unique challenges of this condition.

Given the complexity and heterogeneity of ME/CFS, the most successful therapeutic strategies will likely involve personalized, multimodal approaches targeting multiple aspects of disease pathophysiology. The continued integration of omics data with clinical observations will be essential for refining therapeutic targets and developing effective treatments for this debilitating condition.

Conclusion and Future Directions

Summary of Significance

The integration of findings from the core BioMapAI paper and eight additional omics studies represents a significant advancement in our understanding of ME/CFS pathophysiology. This comprehensive multi-omics approach has transformed our view of ME/CFS from a poorly understood, often stigmatized condition to a complex biological illness with measurable molecular abnormalities across multiple systems.

Establishing ME/CFS as a Biological Illness

The consistent identification of molecular abnormalities across transcriptomics, proteomics, metabolomics, and metagenomics studies provides irrefutable evidence that ME/CFS is a biological illness with objective, measurable disruptions in multiple physiological systems. This evidence directly counters historical misconceptions of ME/CFS as primarily a psychological or psychosomatic condition, helping to reduce stigma and validate patients' experiences.

The BioMapAI study, in particular, demonstrated that ME/CFS can be distinguished from healthy controls with 91% accuracy using multi-omics data, while other studies achieved similar classification performance using single omics platforms. These findings establish that ME/CFS has a distinct biological signature, even if that signature is complex and heterogeneous.

Revealing Systems-Level Disruption

Perhaps the most significant contribution of these omics studies is the revelation that ME/CFS involves coordinated disruptions across multiple biological systems, including:

1. Immune System: Dysregulation of monocytes, T cells, and inflammatory pathways, with evidence for sustained immune activation and altered responses to stimuli.
2. Energy Metabolism: Disruptions in mitochondrial function, peroxisomal metabolism, and cellular energy production, with evidence for metabolic adaptations to chronic energy strain.
3. Microbiome-Host Interactions: Alterations in gut microbial composition and metabolism, with disrupted connections between microbial metabolites and host immune and metabolic functions.
4. Lipid Metabolism: Abnormalities in membrane lipids, signaling molecules, and lipid transport, with evidence for peroxisomal dysfunction affecting multiple cellular processes.
5. Stress Response Systems: Impaired cellular responses to various stressors, including oxidative stress, metabolic challenge, and exercise.

The BioMapAI study's construction of a comprehensive connectivity map spanning the microbiome, immune system, and plasma metabolome represents a landmark achievement in understanding these systems-level disruptions. This integrated view helps explain why ME/CFS presents with such diverse symptoms affecting multiple organ systems.

Explaining Post-Exertional Malaise

The studies examining responses to exercise challenge (Vu et al., Van Booven et al., Germain et al.) have provided crucial insights into the biological basis of post-exertional malaise (PEM), a hallmark symptom of ME/CFS. These studies revealed:

1. Impaired transcriptional responses to exercise in ME/CFS patients
2. Disrupted metabolic recovery following exertion
3. Abnormal immune cell activation patterns after exercise

These findings help explain why ME/CFS patients experience delayed symptom exacerbation following physical, cognitive, or emotional exertion, providing a biological basis for this previously enigmatic symptom and validating the need for activity management strategies like pacing.

Identifying Patient Subgroups

The omics studies have consistently revealed heterogeneity within the ME/CFS population, suggesting the existence of distinct patient subgroups with different underlying pathophysiologies. Hoel et al. identified multiple metabolic phenotypes, while BioMapAI found differences between short-term and long-term ME/CFS patients. This recognition of heterogeneity has profound implications for diagnosis, treatment, and research, suggesting that personalized approaches may be necessary for effective management of ME/CFS.

Providing Biomarker Candidates

Across the studies, numerous potential biomarkers have been identified, spanning immune, metabolic, and microbial domains. While no single universal biomarker has emerged, the convergence of evidence on certain pathways and molecules suggests that panels of biomarkers may eventually provide reliable diagnostic tools and treatment response indicators. The machine learning approaches employed by BioMapAI, Giloteaux et al., and Che et al. demonstrate the feasibility of developing such multi-marker diagnostic panels.

Identifying Therapeutic Targets

Perhaps most importantly for patients, these omics studies have identified numerous potential therapeutic targets, offering hope for the development of effective treatments. From immune modulators targeting specific cell populations to metabolic support strategies addressing energy production deficits, these findings provide a rational basis for therapeutic development that has been largely absent in ME/CFS research to date.

Action Items for Stakeholders

Based on the integrated findings from these omics studies, we propose the following action items for researchers, clinicians, funding agencies, and other stakeholders in the ME/CFS field.

For Researchers

1. Establish Multi-Omics Research Consortia

• Create collaborative networks with standardized protocols for patient assessment, sample collection, and data generation
• Implement common data elements to facilitate cross-study comparisons
• Develop shared biorepositories with well-characterized samples
• Coordinate multi-site studies to increase sample sizes and diversity

2. Validate and Extend Key Findings

• Replicate the most promising biomarkers in independent cohorts
• Extend single-omics studies to include multiple omics platforms
• Conduct longitudinal studies to track disease progression and biomarker stability
• Develop and validate standardized assays for research and clinical use

3. Develop Integrated Computational Frameworks

• Extend the BioMapAI approach to incorporate additional omics layers
• Create open-source tools for multi-omics data integration
• Establish standardized bioinformatic pipelines for ME/CFS research
• Apply advanced machine learning approaches to patient stratification

4. Investigate Mechanistic Hypotheses

• Test the peroxisomal dysfunction hypothesis through targeted studies
• Examine tissue oxygen delivery-utilization in relation to post-exertional malaise
• Investigate microbiome-driven immune priming as a mechanism for sustained immune dysregulation
• Develop cellular and animal models that recapitulate key aspects of ME/CFS

5. Translate Findings to Therapeutic Development

• Conduct preclinical studies of promising therapeutic targets
• Develop biomarker-guided approaches to patient selection
• Establish academic-industry partnerships for drug development
• Design innovative clinical trials addressing ME/CFS-specific challenges

For Clinicians

1. Incorporate Emerging Biomarkers into Practice

• Stay informed about validated biomarkers with clinical utility
• Consider biomarker testing when available and appropriate
• Use biomarker results to guide management strategies
• Participate in biomarker validation studies when possible

2. Recognize Biological Subgroups

• Assess patients for features suggesting specific phenotypes (e.g., metabolic, immune, autonomic)
• Consider tailoring management approaches based on predominant mechanisms
• Document response patterns to different interventions
• Contribute to patient registries that capture phenotypic data

3. Implement Evidence-Based Management Strategies

• Apply pacing and energy conservation based on PEM biology
• Consider targeted supportive therapies based on individual patient profiles
• Monitor for comorbidities highlighted in omics studies
• Engage in shared decision-making informed by emerging research

4. Participate in Clinical Research

• Refer patients to research studies when appropriate
• Collaborate with researchers on clinically relevant questions
• Contribute to the development of clinically meaningful outcome measures
• Provide feedback on the feasibility of research protocols in clinical settings

5. Educate Colleagues and Patients

• Disseminate information about the biological basis of ME/CFS
• Explain emerging research findings in accessible language
• Counter stigma with evidence from omics studies
• Set realistic expectations about the translation of research to clinical practice

For Funding Agencies

1. Establish Dedicated ME/CFS Research Programs

• Create specific funding mechanisms for ME/CFS research
• Ensure adequate representation of ME/CFS expertise on review panels
• Develop strategic research priorities based on omics findings
• Support both investigator-initiated and targeted research initiatives

2. Fund Multi-Omics Research Infrastructure

• Support the development of centralized biorepositories
• Invest in computational infrastructure for data integration and analysis
• Fund technology development specific to ME/CFS research needs
• Support training programs in multi-omics approaches to ME/CFS

3. Prioritize Translational Research

• Bridge the gap between basic science findings and clinical applications
• Support biomarker validation and standardization
• Fund early-stage therapeutic development
• Support innovative clinical trial designs for ME/CFS

4. Encourage Cross-Disciplinary Collaboration

• Create funding mechanisms that require multi-disciplinary teams
• Support conferences and workshops bringing together diverse expertise
• Fund training programs that cross traditional disciplinary boundaries
• Incentivize collaboration between ME/CFS researchers and experts in related fields

5. Engage Patient Communities

• Include patient input in research priority setting
• Fund patient-centered outcome measure development
• Support community-based participatory research approaches
• Ensure research findings are communicated to patient communities

For Industry Partners

1. Invest in ME/CFS Therapeutic Development

• Recognize the significant unmet medical need and market opportunity
• Partner with academic researchers on target validation
• Apply drug repurposing strategies to accelerate development
• Develop companion diagnostics alongside therapeutic candidates

2. Develop ME/CFS-Specific Technologies

• Create specialized diagnostic tools based on omics signatures
• Develop remote monitoring technologies for ME/CFS symptoms
• Innovate in drug delivery for ME/CFS-specific challenges
• Design adaptive clinical trial platforms for ME/CFS

3. Participate in Pre-competitive Collaborations

• Join consortia focused on biomarker validation
• Share data and resources to accelerate progress
• Contribute to the development of standards and best practices
• Engage with patient communities to understand needs and priorities

For Patient Advocacy Organizations

1. Promote Research Funding

• Advocate for increased government funding for ME/CFS research
• Support pilot studies through direct funding
• Raise awareness about the economic impact of ME/CFS
• Highlight success stories and progress in ME/CFS research

2. Facilitate Patient Participation in Research

• Educate patients about research opportunities
• Support patient-researcher partnerships
• Advocate for accessible research protocols
• Provide input on patient-centered outcome measures

3. Disseminate Research Findings

• Translate complex scientific findings for patient audiences
• Highlight the biological basis of ME/CFS revealed by omics studies
• Provide balanced information about research progress
• Counter misinformation with evidence-based resources

4. Engage in Research Priority Setting

• Represent patient perspectives in research planning
• Advocate for studies addressing patient-identified priorities
• Participate in the design and review of research protocols
• Provide feedback on the relevance and feasibility of proposed studies

Vision for Therapeutic Development

The integration of omics findings provides a foundation for a new era in ME/CFS therapeutic development. We envision a pathway from current research to effective treatments through the following stages:

Near-Term (1-3 Years)

1. Biomarker Validation and Standardization

• Validation of key biomarkers in larger, diverse cohorts
• Development of standardized, clinically applicable assays
• Creation of biomarker panels for diagnosis and patient stratification
• Regulatory qualification of biomarkers for use in clinical trials

2. Drug Repurposing Initiatives

• Systematic screening of approved drugs against ME/CFS omics signatures
• Small proof-of-concept trials of promising candidates
• Development of N-of-1 trial platforms for personalized repurposing
• Identification of combination approaches based on mechanistic insights

3. Research Infrastructure Development

• Establishment of ME/CFS Centers of Excellence
• Creation of standardized biorepositories and data sharing platforms
• Development of cellular and animal models based on omics findings
• Formation of academic-industry-patient partnerships

Medium-Term (3-5 Years)

1. Targeted Therapeutic Development

• Advancement of novel compounds targeting validated pathways
• Development of biologics addressing specific immune abnormalities
• Creation of microbiome-based therapeutics for ME/CFS
• Refinement of metabolic support strategies based on phenotyping

2. Precision Medicine Approaches

• Implementation of biomarker-guided treatment selection
• Development of algorithms predicting individual treatment responses
• Creation of adaptive treatment strategies based on biomarker changes
• Validation of digital biomarkers for real-time monitoring

3. Innovative Clinical Trials

• Conduct of platform trials testing multiple interventions
• Implementation of adaptive trial designs with biomarker-based enrichment
• Development of novel outcome measures capturing ME/CFS complexity
• Execution of trials specifically designed for ME/CFS heterogeneity

Long-Term (5-10 Years)

1. Disease-Modifying Therapies

• Development of interventions targeting root causes rather than symptoms
• Creation of preventive strategies for high-risk individuals
• Implementation of early intervention approaches
• Development of combination therapies addressing multiple pathways

2. Comprehensive Care Models

• Integration of pharmacological and non-pharmacological approaches
• Development of personalized, multi-modal treatment regimens
• Implementation of technology-enabled remote monitoring and management
• Creation of specialized care centers with integrated research capabilities

3. Transformative Understanding

• Comprehensive mapping of ME/CFS pathophysiology across tissues and over time
• Elucidation of causal relationships between molecular abnormalities and symptoms
• Understanding of factors determining individual susceptibility and resilience
• Integration of ME/CFS insights with broader understanding of post-viral syndromes

Final Thoughts

The application of multi-omics approaches to ME/CFS research has transformed our understanding of this debilitating condition, providing objective evidence of biological abnormalities, identifying potential biomarkers, and revealing promising therapeutic targets. The BioMapAI study and complementary omics research have laid a foundation for a new era in ME/CFS research and treatment.

The path forward requires sustained commitment from researchers, clinicians, funding agencies, industry partners, and patient communities. By building on the insights gained from these omics studies and implementing the action items outlined above, we can accelerate progress toward effective diagnostics and treatments for ME/CFS.

The millions of individuals living with ME/CFS worldwide deserve nothing less than our full dedication to translating these scientific advances into tangible improvements in diagnosis, treatment, and ultimately, quality of life. The omics revolution in ME/CFS research offers hope that this goal is within reach, provided we maintain focus, collaboration, and adequate resources to complete the journey from molecular insights to clinical solutions.

References

Core Paper

1. Xiong R, Aiken E, Caldwell R, Vernon SD, Kozhaya L, Gunter C, Bateman L, Unutmaz D, Oh J. BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. bioRxiv preprint. 2025 Feb 13. doi: 10.1101/2024.06.24.600378.

Transcriptomics Studies

2. Vu LT, Ahmed F, Zhu H, Iu DSH, Fogarty EA, Kwak Y, Chen W, Franconi CJ, Munn PR, Tate AE, Levine SM, Stevens J, Mao X, Shungu DC, Moore GE, Keller BA, Hanson MR, Grenier JK, Grimson A. Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation. Cell Rep Med. 2024 Jan 16;5(1):101373. doi: 10.1016/j.xcrm.2023.101373. PMID: 38232699; PMCID: PMC10829790.
3. Van Booven DJ, Gamer J, Joseph A, Perez M, Zarnowski O, Pandya M, Collado F, Klimas N, Oltra E, Nathanson L. Stress-Induced Transcriptomic Changes in Females with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Reveal Disrupted Immune Signatures. Int J Mol Sci. 2023 Jan 31;24(3):2698. doi: 10.3390/ijms24032698. PMID: 36769022; PMCID: PMC9916639.
4. Sweetman E, Ryan M, Edgar C, MacKay A, Vallings R, Tate W. Changes in the transcriptome of circulating immune cells of a New Zealand cohort with myalgic encephalomyelitis/chronic fatigue syndrome. Int J Immunopathol Pharmacol. 2019 Jan-Dec;33:2058738418820402. doi: 10.1177/2058738418820402. PMID: 30791746; PMCID: PMC6350121.

Proteomics Studies

5. Giloteaux L, Li J, Hornig M, Lipkin WI, Ruppert D, Hanson MR. Proteomics and cytokine analyses distinguish myalgic encephalomyelitis/chronic fatigue syndrome cases from controls. J Transl Med. 2023 May 13;21(1):322. doi: 10.1186/s12967-023-04179-3. PMID: 37171865; PMCID: PMC10179651.
6. Schutzer SE, Liu T, Tsai CF, Petyuk VA, Schepmoes AA, Wang YT, Weitz KK, Bergquist J, Smith RD, Natelson BH. Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes. Ann Med. 2023 Sep 18;55(1):2208372. doi: 10.1080/07853890.2023.2208372. PMID: 37722890; PMCID: PMC10512920.

Metabolomics Studies

7. Germain A, Giloteaux L, Moore GE, Levine SM, Chia JK, Keller BA, Stevens J, Franconi CJ, Mao X, Shungu DC, Grimson A, Hanson MR. Plasma metabolomics reveals disrupted response and recovery following maximal exercise in myalgic encephalomyelitis/chronic fatigue syndrome. JCI Insight. 2022 May 9;7(9):e157621. doi: 10.1172/jci.insight.157621. PMID: 35358096; PMCID: PMC9090259.
8. Che X, Brydges CR, Yu Y, Price A, Joshi S, Roy A, Lee B, Barupal DK, Cheng A, March Palmer D, Levine S, Peterson DL, Vernon SD, Bateman L, Hornig M, Montoya JG, Komaroff AL, Fiehn O, Lipkin WI. Metabolomic Evidence for Peroxisomal Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Int J Mol Sci. 2022 Jul 18;23(14):7906. doi: 10.3390/ijms23147906. PMID: 35887233; PMCID: PMC9319718.
9. Hoel F, Hoel A, Pettersen IK, Rekeland IG, Risa K, Alme K, Sørland K, Fosså A, Lien K, Herder I, Thürmer HL, Gotaas ME, Schäfer C, Berge RK, Sommerfelt K, Marti HP, Dahl O, Mella O, Fluge Ø, Tronstad KJ. A map of metabolic phenotypes in patients with myalgic encephalomyelitis/chronic fatigue syndrome. JCI Insight. 2021 Aug 23;6(16):e149217. doi: 10.1172/jci.insight.149217. PMID: 34423789; PMCID: PMC8409979.

Additional References

10. Institute of Medicine. Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. Washington, DC: The National Academies Press; 2015. doi: 10.17226/19012.
11. Fukuda K, Straus SE, Hickie I, Sharpe MC, Dobbins JG, Komaroff A. The chronic fatigue syndrome: a comprehensive approach to its definition and study. International Chronic Fatigue Syndrome Study Group. Ann Intern Med. 1994 Dec 15;121(12):953-9. doi: 10.7326/0003-4819-121-12-199412150-00009. PMID: 7978722.
12. Carruthers BM, Jain AK, De Meirleir KL, Peterson DL, Klimas NG, Lerner AM, Bested AC, Flor-Henry P, Joshi P, Powles ACP, Sherkey JA, van de Sande MI. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Clinical Working Case Definition, Diagnostic and Treatment Protocols. Journal of Chronic Fatigue Syndrome. 2003;11(1):7-115. doi: 10.1300/J092v11n01_02.
13. Carruthers BM, van de Sande MI, De Meirleir KL, Klimas NG, Broderick G, Mitchell T, Staines D, Powles AC, Speight N, Vallings R, Bateman L, Baumgarten-Austrheim B, Bell DS, Carlo-Stella N, Chia J, Darragh A, Jo D, Lewis D, Light AR, Marshall-Gradisnik S, Mena I, Mikovits JA, Miwa K, Murovska M, Pall ML, Stevens S. Myalgic encephalomyelitis: International Consensus Criteria. J Intern Med. 2011 Oct;270(4):327-38. doi: 10.1111/j.1365-2796.2011.02428.x. PMID: 21777306; PMCID: PMC3427890.
14. Naviaux RK, Naviaux JC, Li K, Bright AT, Alaynick WA, Wang L, Baxter A, Nathan N, Anderson W, Gordon E. Metabolic features of chronic fatigue syndrome. Proc Natl Acad Sci U S A. 2016 Sep 13;113(37):E5472-80. doi: 10.1073/pnas.1607571113. PMID: 27573827; PMCID: PMC5027464.
15. Montoya JG, Holmes TH, Anderson JN, Maecker HT, Rosenberg-Hasson Y, Valencia IJ, Chu L, Younger JW, Tato CM, Davis MM. Cytokine signature associated with disease severity in chronic fatigue syndrome patients. Proc Natl Acad Sci U S A. 2017 Aug 22;114(34):E7150-E7158. doi: 10.1073/pnas.1710519114. PMID: 28760971; PMCID: PMC5576836.
16. Nagy-Szakal D, Williams BL, Mishra N, Che X, Lee B, Bateman L, Klimas NG, Komaroff AL, Levine S, Montoya JG, Peterson DL, Ramanan D, Jain K, Eddy ML, Hornig M, Lipkin WI. Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome. Microbiome. 2017 Apr 26;5(1):44. doi: 10.1186/s40168-017-0261-y. PMID: 28441964; PMCID: PMC5405467.
17. Sotzny F, Blanco J, Capelli E, Castro-Marrero J, Steiner S, Murovska M, Scheibenbogen C; European Network on ME/CFS (EUROMENE). Myalgic Encephalomyelitis/Chronic Fatigue Syndrome - Evidence for an autoimmune disease. Autoimmun Rev. 2018 Jun;17(6):601-609. doi: 10.1016/j.autrev.2018.01.009. PMID: 29635081.
18. Komaroff AL, Lipkin WI. Insights from myalgic encephalomyelitis/chronic fatigue syndrome may help unravel the pathogenesis of postacute COVID-19 syndrome. Trends Mol Med. 2021 Sep;27(9):895-906. doi: 10.1016/j.molmed.2021.06.002. PMID: 34175230; PMCID: PMC8233978.
19. Tomas C, Brown A, Strassheim V, Elson JL, Newton J, Manning P. Cellular bioenergetics is impaired in patients with chronic fatigue syndrome. PLoS One. 2017 Oct 24;12(10):e0186802. doi: 10.1371/journal.pone.0186802. PMID: 29065167; PMCID: PMC5641727.
20. Cliff JM, King EC, Lee JS, Sepúlveda N, Wolf AS, Kingdon C, Bowman E, Dockrell HM, Nacul L, Lacerda E, Riley EM. Cellular Immune Function in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Front Immunol. 2019 Apr 16;10:796. doi: 10.3389/fimmu.2019.00796. PMID: 31040847; PMCID: PMC6479204.
21. Missailidis D, Annesley SJ, Fisher PR. Pathological Mechanisms Underlying Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Diagnostics (Basel). 2019 Jul 20;9(3):80. doi: 10.3390/diagnostics9030080. PMID: 31330791; PMCID: PMC6787594.
22. Blomberg J, Gottfries CG, Elfaitouri A, Rizwan M, Rosén A. Infection Elicited Autoimmunity and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: An Explanatory Model. Front Immunol. 2018 Feb 15;9:229. doi: 10.3389/fimmu.2018.00229. PMID: 29497420; PMCID: PMC5816784.
23. Vink M, Vink-Niese A. Could Cognitive Behavioural Therapy Be an Effective Treatment for Long COVID and Post COVID-19 Fatigue Syndrome? Lessons from the Qure Study for Q-Fever Fatigue Syndrome. Healthcare (Basel). 2020 Dec 11;8(4):552. doi: 10.3390/healthcare8040552. PMID: 33322316; PMCID: PMC7766045.
24. Proal A, Marshall T. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in the Era of the Human Microbiome: Persistent Pathogens Drive Chronic Symptoms by Interfering With Host Metabolism, Gene Expression, and Immunity. Front Pediatr. 2018 Dec 4;6:373. doi: 10.3389/fped.2018.00373. PMID: 30564562; PMCID: PMC6290757.
25. Lacerda EM, Mudie K, Kingdon CC, Butterworth JD, O'Boyle S, Nacul L. The UK ME/CFS Biobank: A Disease-Specific Biobank for Advancing Clinical Research Into Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Front Neurol. 2018 Dec 4;9:1026. doi: 10.3389/fneur.2018.01026. PMID: 30564186; PMCID: PMC6288194.
26. Bateman L, Bested AC, Bonilla HF, Chheda BV, Chu L, Curtin JM, Dempsey TT, Dimmock ME, Dowell TG, Felsenstein D, Kaufman DL, Klimas NG, Komaroff AL, Lapp CW, Levine SM, Montoya JG, Natelson BH, Peterson DL, Podell RN, Rey IR, Ruhoy IS, Vera-Nunez MA, Yellman BP. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Essentials of Diagnosis and Management. Mayo Clin Proc. 2021 Nov;96(11):2861-2878. doi: 10.1016/j.mayocp.2021.07.004. PMID: 34454716; PMCID: PMC8541507.
27. Komaroff AL. Advances in Understanding the Pathophysiology of Chronic Fatigue Syndrome. JAMA. 2019 Aug 13;322(6):499-500. doi: 10.1001/jama.2019.8312. PMID: 31276153.
28. Lim EJ, Ahn YC, Jang ES, Lee SW, Lee SH, Son CG. Systematic review and meta-analysis of the prevalence of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). J Transl Med. 2020 Mar 2;18(1):100. doi: 10.1186/s12967-020-02269-0. PMID: 32122370; PMCID: PMC7052751.
29. Fluge Ø, Mella O, Bruland O, Risa K, Dyrstad SE, Alme K, Rekeland IG, Sapkota D, Røsland GV, Fosså A, Ktoridou-Valen I, Lunde S, Sørland K, Lien K, Herder I, Thürmer H, Gotaas ME, Baranowska KA, Bohnen LM, Schäfer C, McCann A, Sommerfelt K, Helgeland L, Ueland PM, Dahl O, Tronstad KJ. Metabolic profiling indicates impaired pyruvate dehydrogenase function in myalgic encephalopathy/chronic fatigue syndrome. JCI Insight. 2016 Dec 22;1(21):e89376. doi: 10.1172/jci.insight.89376. PMID: 28018972; PMCID: PMC5161229.
30. Mandarano AH, Maya J, Giloteaux L, Peterson DL, Maynard M, Gottschalk CG, Hanson MR. Myalgic encephalomyelitis/chronic fatigue syndrome patients exhibit altered T cell metabolism and cytokine associations. J Clin Invest. 2020 Mar 2;130(3):1491-1505. doi: 10.1172/JCI132185. PMID: 31830003; PMCID: PMC7269566