Stage Zero™: When the Immune System Is Active Before It Is Named
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A Pattern Recognition Framework for Preclinical Immune and Autonomic Instability
By Cynthia Adinig
Executive Summary
Most chronic and immune-mediated illnesses are not detected when they begin. They are detected when they become measurable, classifiable, or severe enough to meet diagnostic thresholds. Before this point, many patients experience a phase of structured, repeatable system instability that is often dismissed due to inconsistent testing or fragmented clinical interpretation. This paper defines that phase as Stage Zero™. Stage Zero™ is not a diagnosis. It is a recognizable pattern of immune and autonomic instability that exists prior to formal disease classification. Symptoms cluster, fluctuate, and follow timing-dependent patterns that reflect underlying biological disruption, even when standard laboratory markers remain normal or inconclusive. By shifting focus from static thresholds to longitudinal system behavior, Stage Zero™ provides a framework for identifying disease activity earlier, improving interpretation of complex symptom patterns, and supporting more precise timing of intervention.
1. The Gap Between Symptoms and Diagnosis
Autoimmune and immune-mediated illness rarely begins with a clear diagnostic event. Instead, patients often experience a prolonged period of fluctuating symptoms affecting multiple systems, including immune, neurologic, autonomic, and inflammatory pathways. During this interval, clinical testing frequently fails to capture the underlying dysfunction. Patients are often told to monitor symptoms, reduce stress, or wait for clearer evidence. Yet the system is already behaving abnormally. This gap between lived dysfunction and formal diagnosis is well documented. Conditions such as rheumatoid arthritis, systemic lupus erythematosus, and Sjögren’s disease often develop over time, with symptoms appearing long before diagnostic thresholds are met (Aletaha et al., 2010; Aringer et al., 2019). Delayed diagnosis is common and associated with worse long-term outcomes (Feldman et al., 2013; Barnado et al., 2016). The issue is not simply that symptoms precede diagnosis. It is that structured biological activity exists during this period but is not consistently recognized. Stage Zero™ captures this interval.
2. Defining Stage Zero™
Stage Zero refers to this early, often overlooked phase of illness. Stage Zero™ is not a diagnosis. It is a pattern-based detection framework describing a preclinical state in which the system demonstrates structured instability before formal diagnostic thresholds are met. This phase is characterized not by uncertainty, but by repeatable biological behavior. Symptoms cluster across systems, follow timing rules, and respond to stressors in patterned ways. The absence of a diagnostic label does not indicate absence of disease. It reflects the limitations of detection models that rely on static thresholds rather than dynamic system behavior.
Stage Zero was first observed through autoimmune-pattern illness, where immune instability produces clear symptom clustering despite inconsistent or negative laboratory findings. However, this framework is not limited to autoimmune disease. Similar pre-diagnostic instability is observed in post-viral illness, ME/CFS, POTS, mast-cell–mediated conditions, and related syndromes in which system dysfunction becomes visible through patterns before it is captured through conventional criteria (Institute of Medicine, 2015; Komaroff and Bateman, 2021).
3. Pattern, Timing, and System Behavior
A defining feature of Stage Zero™ is timing. Symptoms often worsen hours to days after a stressor, including exertion, infection, sleep disruption, hormonal shifts, or environmental exposure. This delayed response reflects system-level strain rather than isolated symptom events. kSymptoms also cluster across systems. Mucosal dryness, inflammatory pain, neurologic sensations, and autonomic dysfunction frequently rise and fall together. When these patterns repeat, they form a coherent biological signal.
These patterns are not random. They represent trajectory-based signals that can be evaluated across time, including symptom clustering, delayed response intervals, persistence, and multi-system interaction. When assessed longitudinally rather than in isolation, structured instability becomes detectable before formal classification. This temporal and multisystem behavior is reflected in Long COVID research, where symptoms fluctuate, recur, and evolve rather than follow a linear recovery pattern (Greenhalgh et al., 2024). Longitudinal cohort studies have identified multiple symptom trajectories, including persistent, intermittent, and delayed-onset patterns, further supporting the importance of time-based evaluation (Sudre et al., 2021; Thaweethai et al., 2025).
4. Beyond Autoimmune Classification
Stage Zero™ was first articulated through autoimmune-pattern illness, where symptom clustering and instability are particularly visible. However, similar pre-diagnostic patterns appear across a broader set of conditions. Post-viral syndromes such as Long COVID demonstrate prolonged immune dysregulation and non-linear recovery. ME/CFS is characterized by post-exertional symptom exacerbation and delayed recovery. POTS and related autonomic disorders often present with years of fragmented, multisystem symptoms before diagnosis. Mast-cell–mediated conditions similarly exhibit fluctuating, trigger-dependent instability.
These conditions differ in mechanism and classification, but they share a common feature: system instability becomes visible through pattern and timing before it becomes visible through diagnostic thresholds. The COVID-19 era has made this more difficult to ignore. Large cohort studies have shown increased risk of autoimmune disease following SARS-CoV-2 infection, suggesting that immune dysregulation can precede formal diagnosis (Peng et al., 2023; Tesch et al., 2023). At the same time, longitudinal data demonstrate that symptoms may persist, recur, or newly emerge over extended periods (Thaweethai et al., 2025). Stage Zero™ provides a framework for understanding this interval.
5. Why Stage Zero™ Is Missed
Stage Zero™ is frequently overlooked not because it lacks structure, but because it does not conform to the dominant logic of disease detection, which prioritizes stability, measurability, and specificity over early sensitivity. Most diagnostic frameworks are designed to confirm disease once it has reached a level of consistency that can be captured through laboratory thresholds, imaging, or well-defined clinical criteria, rather than to identify dynamic and evolving system behavior in its earlier phases. In autoimmune disease, classification systems such as those for rheumatoid arthritis and systemic lupus erythematosus require accumulated evidence across clinical and serologic domains, a design choice that improves specificity for research and treatment decisions but inherently delays recognition of early disease states (Aletaha et al., 2010; Aringer et al., 2019).
This structural delay is reflected in real-world outcomes, where patients frequently experience symptoms for years before diagnosis, with delayed identification associated with increased disease severity, organ involvement, and reduced response to treatment (Feldman et al., 2013; Barnado et al., 2016). Serologic testing further contributes to under-recognition of Stage Zero™, particularly in conditions where antibody expression is inconsistent, delayed, or absent. While biomarkers such as ANA, anti-SSA/Ro, and anti-SSB/La provide important diagnostic signals, they do not capture the full spectrum of immune-mediated disease, especially in early or seronegative presentations.
Studies in Sjögren’s syndrome and related autoimmune conditions demonstrate that patients may exhibit characteristic symptoms, glandular dysfunction, and systemic involvement despite negative or fluctuating antibody profiles, highlighting the limitations of relying on static biomarker thresholds to define disease presence (Veenbergen et al., 2021; Lan et al., 2024). This mismatch between biological activity and laboratory visibility reinforces that early immune dysfunction is often present but incompletely measured, rather than absent.
The limitations of snapshot-based detection are particularly evident in post-viral and complex chronic conditions, where symptom expression is inherently time-dependent and multi-system. In Long COVID, patients frequently experience fluctuating symptom burden, delayed exacerbation following stressors, and migration of symptoms across physiologic domains, patterns that are poorly captured by single time-point assessments or narrow specialty frameworks (Greenhalgh et al., 2024). Longitudinal analyses from the NIH RECOVER Initiative further demonstrate that symptom trajectories are heterogeneous and dynamic, with distinct patterns including persistent high burden, intermittent symptom recurrence, and delayed onset of worsening months after initial infection, reinforcing that disease expression cannot be fully understood through static evaluation alone (Thaweethai et al., 2025). These findings align with earlier observations in ME/CFS, where post-exertional malaise and delayed symptom exacerbation reflect underlying metabolic and immunologic dysfunction that becomes visible only when assessed across time (Institute of Medicine, 2015; Komaroff and Bateman, 2021).
Fragmentation across clinical specialties compounds these detection challenges by dividing multisystem presentations into isolated domains that are evaluated independently rather than as part of an integrated system. Patients presenting with autonomic dysfunction, inflammatory symptoms, neurologic complaints, and mucosal or immune-related changes may be seen by multiple specialists, each assessing a subset of the presentation without capturing the full pattern of interaction. This fragmentation contributes to diagnostic delay, misclassification, and under-recognition of conditions that do not fit neatly within a single specialty framework, particularly in populations already facing disparities in access to care and diagnostic resources (Bove et al., 2014; CDC, 2024). As a result, Stage Zero™ is not missed because it is subtle or ambiguous, but because current detection models are not designed to interpret longitudinal, multi-system, and pattern-based signals that define early disease behavior.
Stage Zero Detection: Threshold Testing vs Trajectory Tracking
Why time-based pattern recognition identifies disease activity earlier than snapshot-based evaluation
Domain | Threshold Testing | Trajectory Tracking (TT) |
Detection Model | Single timepoint evaluation and fixed cutoffs | Longitudinal pattern recognition across time |
Timing Sensitivity | Limited detection of delayed responses | Captures lagged symptom worsening |
Symptom Interpretation | Isolated symptom evaluation | Pattern-based cluster recognition |
Lab Dependence | Heavy reliance on abnormal biomarkers | Incorporates trajectory despite normal labs |
System Integration | Fragmented across specialties | Multi-system integration over time |
Early Detection | Identifies disease after progression | Detects instability before classification |
Outcome | Waits for disease visibility | Identifies pre-diagnostic instability |
6. Implications for Research and Care
Recognizing Stage Zero™ shifts the foundation of clinical and research practice from threshold-based classification toward trajectory-based understanding, allowing earlier identification of structured instability before disease becomes fully established. In clinical care, this reframing provides a framework for interpreting symptoms that are often dismissed due to normal or inconclusive testing, particularly in patients presenting with recurrent, multisystem complaints that follow recognizable timing patterns. Rather than categorizing these presentations as unexplained or non-specific, Stage Zero™ enables clinicians to identify underlying system instability, which may support earlier monitoring, targeted evaluation, and more informed clinical decision-making. This approach has direct implications for improving diagnostic timelines, as delayed recognition of autoimmune and immune-mediated disease is consistently associated with worse outcomes, including increased disease activity, organ damage, and reduced treatment responsiveness (Feldman et al., 2013; Barnado et al., 2016).
In research, Stage Zero™ highlights a critical limitation in current study design, where inclusion criteria often require fully established disease, thereby excluding individuals in earlier phases of instability who may be most informative for understanding disease onset and progression. This restriction contributes to cohort heterogeneity and limits the ability to study early intervention strategies, particularly in conditions characterized by fluctuating and non-linear trajectories. Longitudinal studies of post-viral illness have emphasized the importance of tracking symptom evolution over time, demonstrating that disease burden may persist, recur, or newly emerge well beyond initial infection, and that these trajectories vary significantly across individuals (Sudre et al., 2021; Thaweethai et al., 2025). Incorporating Stage Zero™ populations into research frameworks would allow for more precise stratification, improved understanding of early disease mechanisms, and more effective identification of intervention windows before irreversible damage occurs.
From a population health perspective, Stage Zero™ provides a lens for understanding why current prevalence estimates may significantly underestimate the true burden of disease. Epidemiologic models are typically based on diagnosed cases, which reflect individuals who have successfully navigated complex healthcare systems and met formal criteria at a specific point in time. However, these models fail to capture individuals whose symptoms fluctuate, overlap diagnostic categories, or fall below thresholds during clinical evaluation, resulting in systematic undercounting of disease prevalence. This underestimation disproportionately affects women and marginalized populations, who are more likely to experience delays in diagnosis and barriers to specialty care, further amplifying disparities in health outcomes (Bove et al., 2014; CDC, 2024).
The COVID-19 pandemic has further highlighted the importance of recognizing pre-diagnostic instability, as large cohort studies have demonstrated increased risk of autoimmune disease following SARS-CoV-2 infection, suggesting that immune dysregulation may begin well before formal diagnosis is established (Peng et al., 2023; Tesch et al., 2023). At the same time, longitudinal data from RECOVER and other studies show that symptom patterns are often prolonged, fluctuating, and non-linear, reinforcing the need for models that capture disease behavior across time rather than relying solely on static classification (Sudre et al., 2021; Thaweethai et al., 2025). These findings support a broader shift toward recognizing early phases of disease as structured and measurable, rather than ambiguous or undefined.
Taken together, these implications suggest that Stage Zero™ is not simply a conceptual addition to existing frameworks, but a necessary evolution in how disease is detected, studied, and managed. By identifying structured instability earlier, it becomes possible to improve patient outcomes, refine research design, and develop more accurate models of disease burden that reflect the full continuum of illness rather than only its most visible stages.
Conclusion
Stage Zero™ reframes early illness as a measurable phase of system behavior rather than a period of uncertainty or incomplete information. Across autoimmune disease, post-viral conditions, autonomic dysfunction, and related syndromes, the evidence is consistent: biological instability often precedes formal diagnosis, yet current detection models are not designed to capture it. Symptoms cluster, follow timing-dependent patterns, and respond to stressors in repeatable ways, but remain unrecognized when evaluated through static thresholds and isolated clinical encounters. This gap is not a reflection of ambiguity in the disease process, but a limitation in how that process is measured.
The integration of longitudinal research further strengthens this framework, as studies in Long COVID, ME/CFS, and autoimmune disease consistently demonstrate that symptom burden evolves over time, often with delayed onset, recurrence, or fluctuation that cannot be fully understood through snapshot-based evaluation (Sudre et al., 2021; Thaweethai et al., 2025). Similarly, the documented delays in autoimmune diagnosis and the limitations of serologic testing reinforce that early disease activity may be present but incompletely captured by conventional models (Aletaha et al., 2010; Veenbergen et al., 2021). Taken together, these findings support a shift toward recognizing structured pre-diagnostic instability as a meaningful and observable phase of illness.
Stage Zero™ provides a framework for identifying that phase by focusing on pattern, timing, and system interaction rather than waiting for disease to become fully classifiable. This approach does not replace diagnostic criteria, but complements them by addressing the interval in which biological change is already underway but not yet formally recognized. In doing so, it creates opportunities for earlier monitoring, improved interpretation of complex symptom presentations, and more precise timing of intervention in conditions where progression is often non-linear and delayed in recognition.
Ultimately, disease does not begin at the moment it is diagnosed. It begins when the system starts to change. Stage Zero™ defines that transition point, offering a structured way to observe and understand early instability before it progresses into established dysfunction. Recognizing this phase is not an expansion of uncertainty, but a refinement of detection, allowing medicine to more accurately reflect the realities of how complex, multi-system illness develops over time.
CYNAERA Framework Papers
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, ME/CFS, Lyme and CRISPR Remission Libraries 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.
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