CDF-Peds-ME/CFS™

CYNAERA’s Pediatric Composite Diagnostic Fingerprint for ME/CFS

Executive Summary

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in children and adolescents has been systematically overlooked and misdiagnosed, leaving millions of young people with debilitating post-viral illness invisible to healthcare systems. The COVID-19 pandemic amplified this crisis, with post-COVID onset now the leading trigger for pediatric ME/CFS.

Using CYNAERA’s US-CCUC™ (U.S. Chronic Condition Undercount Correction – Pediatric Edition) methodology, we estimate 1.5–3 million U.S. children and adolescents and 10–20 million globally meet ME/CFS criteria in 2025. These figures reflect a significant undercount in pre-pandemic data, compounded by systemic biases, inadequate clinical training, and educational barriers.

The CDF-Peds-ME/CFS™ introduces the first structured, child-adapted Composite Diagnostic Fingerprint designed for clinical, school, and home settings. Built on CYNAERA’s validated CDF-ME framework, it integrates multi-system biomarkers, digital phenotyping, and structural overlays into a safe, developmentally appropriate diagnostic tool. Anchored in post-exertional malaise (PEM) and autonomic dysfunction, the most reliable anchors in pediatric ME/CFS, it avoids risky protocols such as 2-day CPET and leverages wearables, parent diaries, and school logs for practical use.

1. Purpose & Definition

The CDF-Peds-ME/CFS™ was developed in response to the pediatric prevalence crisis revealed by post-COVID data. While legacy estimates suggested 70,000–350,000 children were affected, CYNAERA’s US-CCUC™ Pediatric Edition shows the reality is vastly higher: 1.5–3 million U.S. children and adolescents and 10–20 million globally.

This diagnostic framework provides:

• Non-invasive, developmentally aware protocols that reduce harm.

• Multi-system fingerprinting across immune, autonomic, neurocognitive, mitochondrial, and PEM domains.

• School-linked signals (attendance, cognitive decline, nurse visits) to flag missed cases.

By integrating these dimensions, CDF-Peds-ME/CFS™ addresses the diagnostic invisibility that has historically excluded children from recognition, care, and research.

2. Validated Biomarker Domains

Domain	Biomarkers	Notes
Immune	IL-6, IL-1β, TNF-α, CD8+ T cell exhaustion markers	Common after viral illness or EBV/HHV-6 reactivation (Mandarano et al., 2020; Ciccone et al., 2023).
Neuroinflammation	S100B, GFAP, TSPO (research use)	Linked to sensory overwhelm, brain fog, executive dysfunction (Nakatomi et al., 2014).
Autonomic/Cardio	HRV (wearables), ≥40 bpm orthostatic jump, low blood volume markers	Pediatric POTS common; HRV measurable in children under 10 (Rowe et al., 2019).
GI / Microbiome	Calprotectin, zonulin, SCFA imbalance	GI dysfunction precedes ME/CFS onset in many children (Aranow et al., 2022).
Neuroendocrine	Flattened AM cortisol, altered melatonin curve, low DHEA	Sleep dysregulation is an early sign.
Viral Persistence	EBV DNA, HHV-6, SARS-CoV-2 antigenemia (optional)	Relevant in post-COVID and post-mono onset (Proal & VanElzakker, 2021).

3. Digital Phenotyping & School-Based Inputs

Source	Signal	Tools
Wearables	HRV, PEM recovery, sleep fragmentation	Apple Watch, Garmin, Fitbit
Typing / Keystroke Lag	Cognitive fatigue, task-switching slowdown	Google Docs, school apps
School Records	Nurse visits, absences, bathroom breaks	IEP/504 logs, nurse notes
Parent Diaries	Regression, PEM timing, mood spikes	CareClinic, CYNAERA templates
Sensory Monitoring	Light/noise/temperature flare patterns	Caregiver or school logs

This layered approach enables real-world, low-burden tracking—without exposing children to flare-inducing CPET protocols (VanNess et al., 2010).

4. Scoring Thresholds

Composite Score Formula: CDF-Peds-ME/CFS Score = Σ (Domain × Weight)

Domain Weights:

• PEM (verified) → 25%

• Autonomic/Cardio → 20%

• Immune/Inflammatory → 15%

• Neurocognitive/School → 15%

• GI/Microbiome → 10%

• Sleep/Endocrine → 10%

• Parent-Reported → 5%

Thresholds:

• ≥ 0.75 = High-Confidence Pediatric ME/CFS

• 0.50–0.74 = Probable; monitor or refer

• < 0.50 = Low probability; reassess in 4–6 weeks

5. Clinical & Ethical Safeguards

• No 2-day CPET in children → risk of prolonged PEM.

• Favor wearables, journaling, and logs to confirm PEM.

• Pediatric assent + parental consent required.

6. Adaptations for Access

• Translated PEM checklists (Spanish, Vietnamese, Tagalog, Haitian Creole, ASL).

• Medicaid-friendly billing adaptation guides.

• Advocacy packets for IEP/504 integration.

7. Use Cases

• Children with post-COVID regression lacking alternative diagnoses.

• Pediatric Long COVID clinics where ME/CFS is suspected.

• School nurses managing repeated absences or “school refusal” tied to PEM.

8. Next Steps

CYNAERA invites pediatricians, ME/CFS researchers, school psychologists, and Long COVID clinic leads to pilot and refine CDF-Peds-ME/CFS™. Beta testing and feedback loops are open through Fall 2025.

Conclusion

Pediatric ME/CFS is not rare. It is systematically invisible. With up to 3 million U.S. youth and 20 million globally affected, the absence of pediatric-specific diagnostic tools has delayed recognition, misclassified symptoms, and harmed families.

The CDF-Peds-ME/CFS™ represents a practical, terrain-sensitive solution — replacing invasive, adult-centric protocols with child-appropriate diagnostics validated against the scale of the crisis. By embedding PEM and autonomic dysfunction at its core, and incorporating digital phenotyping with school-based data, this framework ensures that children are no longer erased from care.

References

1. Aranow, C., et al. (2022). Intestinal permeability in chronic fatigue syndrome. Journal of Autoimmunity, 127, 102874.

2. Bell, D. S., et al. (2001). Pediatric chronic fatigue syndrome: case definitions and diagnostic challenges. J Chronic Fatigue Syndrome, 8(3), 5–33.

3. Carruthers, B. M., et al. (2011). International Consensus Criteria for ME/CFS. J Intern Med, 270(4), 327–338.

4. Ciccone, E. J., et al. (2023). T cell exhaustion and viral persistence in post-viral syndromes. Cell Reports Medicine, 4(1), 100870.

5. Jason, L. A., et al. (2020). Prevalence of pediatric ME/CFS in community samples. Child & Youth Care Forum, 49(2), 181–197.

6. Komaroff, A. L., & Lipkin, W. I. (2021). Insights from ME/CFS into post-acute COVID-19. Trends Mol Med, 27(9), 895–906.

7. Mandarano, A. H., et al. (2020). Immunological profiling reveals T cell exhaustion in ME/CFS. Front Immunol, 11, 82.

8. Nakatomi, Y., et al. (2014). Neuroinflammation in ME/CFS: PK11195 PET study. J Nucl Med, 55(6), 945–950.

9. Newton, J. L., et al. (2013). Autonomic dysfunction in ME/CFS. QJM, 106(6), 495–503.

10. Proal, A. D., & VanElzakker, M. B. (2021). Long COVID or PASC: Persistent viral reservoirs. Front Microbiol, 12, 698169.

11. Rowe, P. C., et al. (2019). Orthostatic intolerance in pediatric ME/CFS. Front Pediatr, 7, 131.

12. VanNess, J. M., et al. (2010). Post-exertional malaise in women with ME/CFS after exercise testing. J Women’s Health, 19(2), 239–244.

13. CDC (2023). ME/CFS prevalence estimates, United States. Centers for Disease Control and Prevention.

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.