25+ ME/CFS Phenotyping List
- 7 days ago
- 5 min read
Updated: 2 days ago
Clinical and Social Terrain Subtypes in ME/CFS
Executive Summary
For decades, ME/CFS has been treated as a monolith in research, despite overwhelming evidence of heterogeneity across patients. This failure to stratify has been a primary driver of trial collapse, clinical mismanagement, and neglect (Institute of Medicine, 2015; Komaroff, 2021).
The CYNAERA 25 Core ME/CFS Phenotyping List integrates biological, environmental, and social terrain factors into a structured classification system. This list is designed to serve as:
A foundation for adaptive clinical trial design.
A roadmap for individualized care.
A tool for access aware research that includes vulnerable and overlooked populations.
Core Domains of Phenotyping
Formula: Phenotype = Core Axis × Trigger/Modulator + Functional Signature
1. Energy Dysregulation Axis
PEM-Dominant Classic – exertion → multi-day crash.
Cognitive PEM – mental effort triggers relapse.
Delayed-Onset PEM – crash 24–72h after trigger.
Remission-Relapse Oscillator – alternating stable & crash states.
Adrenergic Reserve – compensates via stress hormones, then collapse.
2. Autonomic & Circulatory Axis
POTS-Dominant – tachycardia, orthostatic intolerance.
Orthostatic Intolerance (Non-Tachycardic) – dizziness/fatigue without HR spike.
Blood Volume/Perfusion Variant – low plasma volume, poor circulation.
Baroreflex Dysregulation – unstable BP with gravity stressors.
3. Neuro-Immune & Sensory Axis
Neuroinflammatory Brain Fog – cognitive slowing, cytokine-driven.
Sensory Overload Dominant – light, sound, smell hypersensitivity.
Cognitive-Motor Coordination Decline – motor planning + thinking impaired.
Visual-Spatial Lag – delayed motion/space processing.
4. Hormonal & Endocrine Axis
Cycle-Triggered PEM – symptom worsening with menstruation.
Estrogen Withdrawal Subtype – crashes during peri-/post-menopause.
HPA Axis Dysfunction – adrenal/hypothalamic imbalance.
Postpartum Collapse – immune-hormonal breakdown post-birth.
5. Immune, Infection & Reactivation Axis
EBV/HHV-6 Reactivation Subtype – episodic flares.
Low NK Cell Immunodeficiency – recurrent infections, poor clearance.
Post-Viral Chronic Onset – mono/flu/COVID as initiating trigger.
Steroid-Triggered Relapser – immune suppression → viral rebound.
6. Sleep & Pain Axis
Non-Restorative Sleep Core – poor restorative function despite hours slept.
Sleep-Wake Reversal – flipped circadian cycles.
Migraine/Pain-Dominant – headaches, muscle spasm, neuropathic pain.
Neuro-Excitability Subtype – EEG abnormalities, seizure-like flares.
7. Social, Demographic & Access Constrained
These don’t define phenotypes alone but modify severity, access, and progression:
BIPOC Misdiagnosed Variant – dismissed as depression/anxiety.
Low-Income/Access Barrier – worsened by untreated flares, poor care.
Pediatric Misattribution – mislabeled as anxiety/behavioral.
Men Misdiagnosed as Burnout – gendered undercounting.
Mold/Housing Overlay – environmental driver of relapse.
Food Insecurity Overlay – malnutrition worsening PEM.
Why This Matters
Clinical Trials: Stratification into these phenotypes prevents dilution of signals, reduces dropouts, and enables biomarker-linked subgrouping (Fluge & Mella, 2019; Bateman et al., 2021).
Clinical Care: Recognizes terrain-specific vulnerabilities such as hormonal modulation, reactivation triggers, or environmental stressors (Hornig et al., 2015).
Accuracy: Includes structural and social barriers (late diagnosis, BIPOC misclassification, food insecurity), which are routinely excluded from research yet shape disease trajectory (Wong et al., 2023).
Integration with CYNAERA White Papers
This phenotyping framework is part of the growing CYNAERA ME/CFS Research Library, which together provides the most comprehensive adaptive model for understanding and treating ME/CFS:
Why Drug Approval for ME/CFS Was Always a Setup — A critique of failed legacy trials and a demonstration of how CYNAERA trial logic improves success rates.
Socioeconomic Burden of ME/CFS: A Hidden Catalyst of Economic Loss — Analysis of the financial and societal costs of ME/CFS, updated with Long COVID overlaps.
Why ME/CFS Trials Failed — Historical review of structural flaws in study design.
SPARC Smart Study Stratification — A framework for selecting patient subgroups that preserve trial integrity.
Best Practices for a ME/CFS Study — Practical guidance for designing equitable, terrain-aware clinical trials.
CYNAERA Complex Chronic Illness Patient Stratification — Systematic classification for trial readiness across ME/CFS and related IACCs.
Stabilizing Patients Before a Trial — Protocols to reduce flare risk and improve patient safety.
Affordable FDA-Approvable Phase 1 AI Trials — CYNAERA’s model for cost-efficient, regulatory-aligned early-phase research.
CYNAERA AI ME/CFS Regimen Simulation Protocol (v1.0) — An AI-driven system for testing individualized treatment regimens in silico.
ME/CFS Treatments & Trial Mechanisms — Mapping therapeutic targets to trial designs for higher efficacy and safety.
Together, these white papers form an interconnected system — advancing trial readiness, stratification, safety, and therapeutic innovation for ME/CFS.
References
Bateman, L., Rowe, P. C., & Montoya, J. G. (2021). Post-exertional malaise in myalgic encephalomyelitis/chronic fatigue syndrome. Frontiers in Pediatrics, 9, 707819.
Fluge, Ø., & Mella, O. (2019). Clinical trials of B-cell depletion therapy for myalgic encephalomyelitis/chronic fatigue syndrome. Frontiers in Immunology, 10, 1225.
Hornig, M., Montoya, J. G., Klimas, N., Levine, S., Felsenstein, D., Bateman, L., ... & Lipkin, W. I. (2015). Distinct plasma immune signatures in ME/CFS are present early in the course of illness. Science Advances, 1(1), e1400121.
Institute of Medicine (IOM). (2015). Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. National Academies Press.
Komaroff, A. L. (2021). Advances in understanding the pathophysiology of myalgic encephalomyelitis/chronic fatigue syndrome. Nature Reviews Disease Primers, 7, 68.
Wong, T. L., Weitz, J. S., Adinig, C., et al. (2023). Findings from an online survey of people with long COVID: Characterization and impact. medRxiv.
Author’s Note:
All insights, frameworks, and recommendations in this white paper reflect the author's independent analysis and synthesis. References to researchers, clinicians, and advocacy organizations acknowledge their contributions to the field but do not imply endorsement of the specific frameworks, conclusions, or policy models proposed herein. This information is not medical guidance.
Applied Infrastructure Models Supporting This Analysis
Several standardized diagnostic and forecasting models developed through CYNAERA were utilized or referenced in the construction of this white paper. These tools support real-time surveillance, economic forecasting, and symptom stabilization planning for infection-associated chronic conditions (IACCs).
Note: These models were developed to bridge critical infrastructure gaps in early diagnosis, stabilization tracking, and economic impact modeling. Select academic and public health partnerships may access these modules under non-commercial terms to accelerate independent research and system modernization efforts.
Licensing and Customization
Enterprise, institutional, and EHR/API integrations are available through CYNAERA Market for organizations seeking to license, customize, or scale CYNAERA's predictive systems.
Learn More: https://www.cynaera.com/systems
About the Author
Cynthia Adinig is an internationally recognized systems strategist, health policy advisor, and the founder of CYNAERA, an AI-powered intelligence platform advancing diagnostic reform, clinical trial simulation, and real-world modeling for infection-associated chronic conditions (IACCs). She has developed 400+ Core AI Frameworks, 1 Billion + Dynamic AI Modules. including the IACC Progression Continuum™, US-CCUC™, and RAEMI™, which reveal hidden prevalence, map disease pathways, and close gaps in access to early diagnosis and treatment.
Her clinical trial simulator, powered by over 675 million synthesized individual profiles, offers unmatched modeling of intervention outcomes for researchers and clinicians.
Cynthia has served as a trusted advisor to the U.S. Department of Health and Human Services, collaborated with experts at Yale and Mount Sinai, and influenced multiple pieces of federal legislation related to Long COVID and chronic illness.
She has been featured in TIME, Bloomberg, USA Today, and other leading publications. Through CYNAERA, she develops modular AI platforms that operate across 32+ sectors and 180+ countries, with a local commitment to resilience in the Northern Virginia and Washington, D.C. region.
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