25+ Lyme Phenotyping List
- Apr 10
- 9 min read
Updated: May 7
Clinical and Social Terrain Subtypes in Lyme
Executive Summary
Lyme disease is often treated as a singular tick-borne infection, despite extensive evidence of multi-system involvement, persistent infection, immune dysregulation, and heterogeneous symptom patterns (Steere et al., 2016; Rebman & Aucott, 2020). This oversimplification has contributed to misdiagnosis, delayed treatment, inconsistent clinical outcomes, and ongoing controversy in both research and care (Aucott et al., 2013; Marques, 2008). The CYNAERA 25+ Lyme Disease Phenotyping List organizes Lyme into structured clinical and social terrain subtypes, integrating infection burden, immune response, co-infections, neurological involvement, and environmental modifiers.
This framework aligns with CYNAERA’s broader modeling systems, including Composite Diagnostic Fingerprint™ For Lyme, IACC Twin™ modeling, and terrain-based remission frameworks, which identify patterns across infection-associated chronic conditions.
This list is designed to support:
Adaptive clinical trial design for Lyme disease subtypes
Personalized, phenotype-based treatment strategies
Improved detection of underdiagnosed and chronic Lyme populations
Core Domains of Phenotyping
Lyme disease does not present as a uniform condition, but as a system-level disruption shaped by pathogen persistence, co-infections, immune response variability, treatment timing, and environmental exposures (Rebman et al., 2017; Cameron et al., 2014). These factors interact dynamically over time, producing symptom clusters that evolve rather than remain static, which challenges traditional stage-based or infection-only models of disease classification.
CYNAERA’s core phenotype domains organize Lyme into interacting systems that reflect how symptoms cluster, shift, and respond to intervention across disease trajectories. This approach is consistent with emerging evidence that post-treatment Lyme disease and chronic symptom persistence involve complex host-pathogen and immune interactions rather than a single causal pathway (Aucott et al., 2013; Novak et al., 2019).
Formula: Phenotype = Core Axis × Trigger/Modulator + Functional Signature
10 Core Phenotype Domains of Lyme Disease
1. Infection Burden, Persistence & Co-Infection Axis
Borrelia persistence and antigenic remnants have been proposed as contributors to ongoing symptoms in a subset of patients, alongside co-infections that complicate diagnosis and treatment (Steere et al., 2016; Cameron et al., 2014).
Borrelia Persistence Dominant – ongoing infection or antigen presence
Co-Infection Cluster – Babesia, Bartonella, Ehrlichia, Anaplasma overlap
Biofilm-Protected Infection Variant – treatment resistance patterns
Treatment-Refractory Infection – limited response to antibiotics
2. Immune Response & Inflammatory Axis
Immune dysregulation in Lyme disease includes both hyperinflammatory responses and immune exhaustion states, with evidence of cytokine imbalance and autoimmune overlap in persistent cases (Novak et al., 2019; Stricker & Fesler, 2018).
Hyperinflammatory Subtype – cytokine-driven flares
Immune Exhaustion Variant – reduced pathogen clearance capacity
Autoimmune Overlap – misdirected immune targeting post-infection
Mast Cell Activation Overlap – histamine reactivity and environmental sensitivity
3. Neurocognitive & Neuroinflammatory Axis
Neurological involvement in Lyme disease ranges from direct central nervous system infection to inflammatory and cognitive dysfunction, often presenting as brain fog, psychiatric symptoms, and sensory processing disruptions (Halperin, 2015; Rebman & Aucott, 2020).
Neuroborreliosis Variant – central nervous system involvement
Brain Fog Dominant – memory, processing, executive dysfunction
Psychiatric Inflammatory Overlay – anxiety, mood disruption, cognitive shifts
Sensory Processing Dysfunction – light, sound, and motion sensitivity
4. Autonomic, Cardiovascular & Circulatory Axis
Autonomic dysfunction, including POTS-like presentations and circulatory instability, has been documented in Lyme disease and related post-infectious conditions, contributing to fatigue, tachycardia, and impaired oxygen delivery (Kanjwal et al., 2011; Novak et al., 2019).
POTS-Dominant Lyme – tachycardia, orthostatic intolerance
Circulatory Dysfunction – impaired oxygen delivery and fatigue
Temperature Dysregulation – heat and cold intolerance
Lyme Carditis Variant – conduction abnormalities and rhythm disruption
Palpitations & Arrhythmia – irregular heartbeat, autonomic overlap
Chest Pain / Pressure – inflammatory or circulatory-driven symptoms
5. Musculoskeletal & Connective Tissue Axis
Musculoskeletal involvement is one of the most recognized manifestations of Lyme disease, yet its variability in presentation, including migratory pain patterns and connective tissue involvement, reflects broader systemic disruption rather than isolated joint pathology (Steere et al., 2016; Rebman et al., 2017). In some patients, symptom patterns overlap with connective tissue disorders, suggesting downstream effects on structural integrity and inflammatory signaling.
Lyme Arthritis Subtype – joint inflammation, swelling, stiffness
Migratory Pain Pattern – shifting joint and muscle pain
Muscle Weakness & Fatigue – reduced endurance and strength
Connective Tissue Fragility Overlay – hypermobility or EDS-like features
6. Gastrointestinal, Metabolic & Nutritional Axis
Gastrointestinal and metabolic dysfunction in Lyme disease may arise from both infection-related processes and treatment effects, particularly antibiotic exposure and immune-mediated disruption of the gut microbiome (Jutras & Verma, 2020). These changes can contribute to nutrient deficiencies, altered energy metabolism, and increased sensitivity to dietary triggers, reinforcing systemic instability.
Gut Dysbiosis – microbiome disruption from infection or treatment
Food Reactivity Subtype – histamine or inflammatory food triggers
Malabsorption / Nutrient Depletion – reduced recovery capacity
Metabolic Instability – energy crashes and glucose dysregulation
7. Hormonal & Endocrine Axis
Endocrine disruption in Lyme disease reflects the interaction between chronic inflammation and hormonal regulation, particularly within the hypothalamic–pituitary–adrenal axis. These disruptions can influence stress response, thyroid function, and symptom variability over time, with some patients demonstrating hormone-sensitive fluctuations in disease severity (Chrousos, 2009).
Adrenal Dysfunction – impaired stress response regulation
Thyroid Disruption – hypo- or hyperthyroid patterns
Hormone-Sensitive Lyme – symptom shifts tied to endocrine changes
8. Sleep, Pain & Neurological Excitability Axis
Sleep disturbance and neurological excitability are common across chronic Lyme presentations, often contributing to persistent fatigue and amplified pain perception. These symptoms are increasingly understood as part of central sensitization processes, where the nervous system exhibits heightened responsiveness to internal and external stimuli (Wolfe et al., 2016).
Non-Restorative Sleep – persistent fatigue despite rest
Pain-Dominant Lyme – neuropathy, joint pain, muscle pain
Neuro-Excitability Subtype – tremors, internal vibrations, overstimulation
9. Functional Severity & Disease Progression Axis
Lyme disease trajectories vary widely, ranging from early resolution to chronic, relapsing-remitting patterns with significant functional impairment. These differences reflect the interaction between infection timing, host response, treatment access, and systemic modifiers, rather than a single linear disease course (Aucott et al., 2013; Rebman et al., 2017).
Early-Treated Recovery – partial or full recovery trajectory
Chronic Persistent Lyme – long-term multi-system involvement
Relapsing-Remitting Pattern – cycles of improvement and decline
Severe Functional Decline – housebound or bedbound states
10. Social, Structural & Access-Constrained Modifiers
Social and structural factors play a critical role in shaping Lyme disease outcomes, influencing diagnostic timing, treatment access, and long-term severity. These modifiers often determine whether biological disease processes are identified early or allowed to progress unchecked, contributing to disparities in both recognition and care (Adrion et al., 2015; Institute of Medicine, 2015). These do not define biological phenotype alone but strongly influence diagnosis, severity, and outcomes:
Delayed Diagnosis Variant – missed early infection window
Misdiagnosed Lyme – labeled as psychosomatic or alternative conditions
Geographic Exposure Risk – endemic region and tick exposure variability
Treatment Access Barrier – limited access to Lyme-literate care
Environmental Exposure Overlay – mold, toxins, and co-triggering illness
Why This Matters
Clinical Trials
Stratifying Lyme disease into clinically meaningful subtypes improves signal detection and supports targeted therapeutic development, particularly for interventions addressing infection persistence, immune dysfunction, and co-infections. This approach aligns with CYNAERA’s Composite Diagnostic Fingerprint (CDF) for Lyme, demonstrating how structured subgrouping improves research accuracy across infection-associated conditions.
Clinical Care
Phenotype-based classification enables more precise care by identifying whether a patient’s dominant drivers are infection burden, immune dysfunction, autonomic instability, or systemic inflammation. This approach is consistent with CRISPR Remission for Lyme : A Flare-Aware Personalized Gene Editing Innovation which supports treatment strategies based on system state and timing rather than single-diagnosis models.
Accuracy and System-Level Insight
Lyme disease outcomes are shaped not only by infection, but by diagnostic timing, geographic exposure, environmental conditions, and access to appropriate care. Incorporating these factors improves prevalence estimates and long-term outcome modeling, consistent with The Diagnostic Multiplier™, which demonstrates how real-world diagnostic gaps distort disease burden.
CYNAERA Framework Papers and Core Research Libraries
This paper draws on a defined subset of CYNAERA Institute white papers that establish the methodological and analytical foundations of CYNAERA’s frameworks. These publications provide deeper context on prevalence reconstruction, remission, combination therapies and biomarker approaches. Our Long COVID Library, ME/CFS Library, Lyme Library, Autoimmune Library and CRISPR Remission Library are also in depth resources.
Author’s Note:
All insights, frameworks, and recommendations in this written material 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.
Patent-Pending Systems
Bioadaptive Systems Therapeutics™ (BST) and affiliated CYNAERA frameworks are protected under U.S. Provisional Patent Application No. 63/909,951. CYNAERA is built as modular intelligence infrastructure designed for licensing, integration, and strategic deployment across health, research, public sector, and enterprise environments.
Licensing and Integration
CYNAERA supports licensing of individual modules, bundled systems, and broader architecture layers. Current applications include research modernization, trial stabilization, diagnostic innovation, environmental forecasting, and population level modeling for complex chronic conditions. Basic licensing is available through CYNAERA Market, with additional pathways for pilot programs, institutional partnerships, and enterprise integration.
About the Author
Cynthia Adinig is the founder of CYNAERA, a modular intelligence infrastructure company that transforms fragmented real world data into predictive insight across healthcare, climate, and public sector risk environments. Her work sits at the intersection of AI infrastructure, federal policy, and complex health system modeling, with a focus on helping institutions detect hidden costs, anticipate service demand, and strengthen planning in high uncertainty environments.
Cynthia has contributed to federal health and data modernization efforts spanning HHS, NIH, CDC, FDA, AHRQ, and NASEM, and has worked with congressional offices including Senator Tim Kaine, Senator Ed Markey, Representative Don Beyer, and Representative Jack Bergman on legislative initiatives related to chronic illness surveillance, healthcare access, and data infrastructure. In 2025, she was appointed to advise the U.S. Department of Health and Human Services and has testified before Congress on healthcare data gaps and system level risk.
She is a PCORI Merit Reviewer, currently advises Selin Lab at UMass Chan, and has co-authored research with Harlan Krumholz, MD, Akiko Iwasaki, PhD, and David Putrino, PhD, including through Yale’s LISTEN Study. She also advised Amy Proal, PhD’s research group at Mount Sinai through its CoRE advisory board and has worked with Dr. Peter Rowe of Johns Hopkins on national education and outreach focused on post-viral and autonomic illness. Her CRISPR Remission™ abstract was presented at CRISPRMED26 and she has authored a Milken Institute essay on artificial intelligence and healthcare.
Cynthia has been covered by outlets including TIME, Bloomberg, Fortune, and USA Today for her policy, advocacy, and public health work. Her perspective on complex chronic conditions is also informed by lived experience, which sharpened her commitment to reforming how chronic illness is understood, studied, and treated. She also advocates for domestic violence prevention and patient safety, bringing a trauma informed lens to her research, systems design, and policy work. Based in Northern Virginia, she brings more than a decade of experience in strategy, narrative design, and systems thinking to the development of cross sector intelligence infrastructure designed to reduce uncertainty, improve resilience, and support institutional decision making at scale.
References
Adrion, E.R., Aucott, J., Lemke, K.W. and Weiner, J.P. (2015). Health care costs, utilization and patterns of care following Lyme disease. PLoS ONE, 10(2), e0116767.
Aucott, J.N., Rebman, A.W., Crowder, L.A. and Kortte, K.B. (2013). Post-treatment Lyme disease syndrome symptomatology and the impact on life functioning: is there something here? Quality of Life Research, 22(1), pp.75–84.
Cameron, D.J., Johnson, L.B. and Maloney, E.L. (2014). Evidence assessments and guideline recommendations in Lyme disease: the clinical management of known tick bites, erythema migrans rashes and persistent disease. Expert Review of Anti-infective Therapy, 12(9), pp.1103–1135.
Chrousos, G.P. (2009). Stress and disorders of the stress system. Nature Reviews Endocrinology, 5(7), pp.374–381.
Ford, I. and Norrie, J. (2009). Pragmatic trials. New England Journal of Medicine, 375(5), pp.454–463.
Halperin, J.J. (2015). Nervous system Lyme disease: diagnosis and treatment. Clinical Infectious Diseases, 61(3), pp.371–378.
Institute of Medicine (2003). Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: National Academies Press.
Institute of Medicine (2015). Critical Needs and Gaps in Understanding Prevention, Amelioration, and Resolution of Lyme and Other Tick-Borne Diseases. Washington, DC: National Academies Press.
Jutras, B.L. and Verma, A. (2020). Lyme disease: current state of diagnostics and emerging technologies. Future Microbiology, 15(7), pp.543–557.
Kanjwal, K., Karabin, B. and Kanjwal, Y. (2011). Postural orthostatic tachycardia syndrome following Lyme disease. Cardiology Journal, 18(1), pp.63–66.
Marques, A. (2008). Chronic Lyme disease: a review. Infectious Disease Clinics of North America, 22(2), pp.341–360.
Novak, P., Mukerji, S.S., Alabsi, H.S., Systrom, D., Nilles, E.J., Elias, S.P. and Sundel, R.P. (2019). Multisystem involvement in Lyme disease: autonomic, small fiber neuropathy, and immune-mediated mechanisms. Frontiers in Neurology, 10, 79.
Rebman, A.W. and Aucott, J.N. (2020). Post-treatment Lyme disease as a model for persistent symptoms in Lyme disease. Frontiers in Medicine, 7, 57.
Rebman, A.W., Bechtold, K.T., Yang, T., Mohapatra, S. and Aucott, J.N. (2017). The clinical, symptom, and quality-of-life characterization of a well-defined group of patients with post-treatment Lyme disease syndrome. Frontiers in Medicine, 4, 224.
Steere, A.C., Strle, F., Wormser, G.P., Hu, L.T., Branda, J.A., Hovius, J.W.R., Li, X. and Mead, P.S. (2016). Lyme borreliosis. Nature Reviews Disease Primers, 2, 16090.
Stricker, R.B. and Fesler, M.C. (2018). Chronic Lyme disease: a working case definition. American Journal of Infectious Diseases, 14(1), pp.1–44.
Wolfe, F., Clauw, D.J., Fitzcharles, M.A., Goldenberg, D.L., Häuser, W., Katz, R.L., Mease, P., Russell, A.S., Russell, I.J. and Walitt, B. (2016). 2016 revisions to the fibromyalgia diagnostic criteria. Seminars in Arthritis and Rheumatism, 46(3), pp.319–329.
Comments