Preliminary Findings from The Eve Research Project : The Autoimmune Menopause Terrain
- May 14
- 30 min read
Updated: 2 days ago
Links Between Hormonal Transition, Immune Activity, Environmental Triggers, Medication Response, and Flare Risk
This paper is part of the CYNAERA Autoimmune Library, a growing resource, built to help redefine how autoimmune conditions are diagnosed, counted and treated.
By: Cynthia Adinig, Tracey Welson-Rossman, and Jamie Nicole
Executive Summary
The Eve Research Project is a patient led, AI powered research initiative designed to examine how autoimmune symptoms change during perimenopause, menopause, and post-menopause. The project was created to address a major gap in women’s health research: many women report that flares, fatigue, pain, brain fog, sleep disruption, medication response, environmental sensitivity, and new symptoms change during midlife hormonal transition, yet these changes are often dismissed as stress, aging, anxiety, disease progression, or “just hormones.”
Preliminary intake and symptom journaling data suggest that women are not arriving with simple, single condition profiles. Early participants are showing exactly the kind of layered autoimmune, hormonal, environmental, and multi-system complexity The Eve Research Project was designed to study. Across early unique signups, 88% of participants provided location data through zip code completion, supporting the project’s environmental modeling layer. Approximately 73% of early participants identified as perimenopausal, indicating that the strongest early demand is coming from women in the active transition window, not only after menopause is complete.
This matters because perimenopause is not a static state. It is a period of hormonal fluctuation, immune recalibration, vascular change, sleep disruption, and symptom unpredictability. The medical literature increasingly recognizes that menopause and aging may influence autoimmune and rheumatic disease onset, progression, severity, and symptom burden, although the effects vary by condition and remain under-studied across many patient populations (Shah and Parks, 2020; Motta et al., 2025).
Early intake data also show that The Eve Research Project is not only reaching participants with severe conditions. Self reported autoimmune severity included mild, moderate, and severe categories. This is important because the project is designed not only to document crisis level illness, but to identify earlier instability patterns before avoidable decline occurs. Mild and moderate participants may be in the exact window where prevention, pattern recognition, medication response tracking, environmental awareness, and flare avoidance matter most.
The condition signal further supports the need for a terrain model. Participants reported autoimmune diagnoses such as Hashimoto’s thyroiditis, inflammatory bowel disease, Sjögren’s syndrome, celiac disease, rheumatoid arthritis, type 1 diabetes, psoriasis or psoriatic arthritis, and lupus. Write in responses also included IBS, MCAS, POTS, interstitial cystitis, alopecia areata, unspecified connective tissue disorder, myasthenia gravis, ankylosing spondylitis, Long COVID or unidentified autoimmunity, Graves’ disease, ME/CFS, and CREST. This suggests that autoimmune menopause terrain is rarely limited to one diagnosis. Instead, participants are bringing immune, endocrine, autonomic, mast cell, gastrointestinal, connective tissue, post-viral, and inflammatory complexity into the same tracking environment.
The Eve Research Project’s Phase 1 pilot uses a 30 day tracking window by design. This model is informed by earlier longer duration tracking data and preliminary patient feedback showing that flare relevant signals can emerge before months of tracking are required. The current Minimum Viable Dataset model suggests that 14 to 20 days of high completeness daily logging may be sufficient for moderate-confidence early flare detection when at least two symptom ramps or flares are identifiable. A 21 to 30 day window strengthens short-term model performance by adding behavioral rhythm learning and false alarm reduction.
The purpose of this work is not simply to collect symptoms. The purpose is to translate lived experience into structured, provider ready information that can support better clinical conversations, earlier intervention, research prioritization, and future flare prediction models. The Eve Research Project is designed to study the transition window where autoimmune disease, hormonal change, environmental exposure, medication response, and daily function begin moving together before the healthcare system knows how to name the pattern.
1. Background: Why The Eve Research Project Was Created
Women with autoimmune conditions often enter perimenopause and menopause without a clear clinical roadmap. Symptoms may intensify, shift, overlap, or become harder to interpret. A patient may experience worsening fatigue, insomnia, night sweats, migraine, joint pain, gastrointestinal disruption, brain fog, heat intolerance, medication sensitivity, histamine-like reactions, or new environmental triggers. These symptoms may be handled by separate specialists, or dismissed as normal midlife change.
That fragmentation is the problem The Eve Research Project was built to address.
Most care models separate autoimmune disease from menopause care. A rheumatologist may focus on immune markers and medication response. A gynecologist may focus on hot flashes, bleeding patterns, or hormone therapy. A primary care clinician may focus on metabolic labs, lipids, weight, or sleep. A neurologist may manage migraine or brain fog. An allergist may address histamine symptoms. A cardiologist may address POTS symptoms, blood pressure changes, or palpitations. The patient, however, experiences these changes together.
The Eve Research Project begins with a different assumption: autoimmune-menopause symptoms may not be isolated events. They may be part of a measurable terrain.
In this paper, “terrain” refers to the interacting biological and environmental context in which symptoms occur. This includes hormone stage, immune activity, environmental exposure, sleep stability, autonomic regulation, medication response, inflammatory threshold, daily function, and recovery capacity. The project asks whether tracking these layers together can reveal patterns that standard clinical encounters miss.
2. The Research Gap: Autoimmune Disease and Menopause Are Usually Studied Separately
Autoimmune conditions disproportionately affects women, and many autoimmune conditions emerge or change across reproductive and endocrine transitions. Existing reviews describe menopause as a potentially important period for autoimmune and rheumatic disease because estrogen decline, aging, immune regulation, and chronic inflammation may influence disease expression and long term outcomes. The effect is not uniform. For example, rheumatoid arthritis and systemic lupus erythematosus may behave differently across menopause, and some patients may experience symptom worsening while others may experience changes in flare pattern or damage accrual (Shah and Parks, 2020; Motta et al., 2025).
The evidence base is still incomplete. Many studies focus on formally diagnosed disease activity, medication safety, or postmenopausal outcomes, while fewer examine the messy transition period when patients first notice that their symptoms, triggers, medications, and recovery patterns are changing. That transition period is often perimenopause, when hormones fluctuate rather than simply decline. Public facing women’s health education has also begun to recognize this gap. Dr. Jolene Brighten’s clinical education materials describe the overlap between menopause and autoimmune symptoms, including fatigue, brain fog, joint pain, mood changes, and flare like symptom changes that may be mistaken for menopause alone when immune dysfunction may also be involved (Brighten, 2025). This aligns with The Eve Research Project’s premise that autoimmune and menopause-related symptoms should not be evaluated in isolation.
The Eve Research Project is designed for that missing middle. It focuses on women with diagnosed or suspected autoimmune disease from perimenopause through post-menopause, especially those navigating symptom complexity that does not fit neatly into one specialty or diagnosis.
3. The Eve Research Project Model
The Eve Research Project invites diagnosed and suspected autoimmune patients from perimenopause through post-menopause to participate in a free 30 day tracking experience. Participants are not treated as passive subjects. They are collaborators helping generate the real-world evidence needed to understand symptom changes that have been overlooked, dismissed, fragmented, or treated as too complicated to measure. The participant experience is designed to be low burden and immediately useful. Participants begin with education and onboarding, complete structured symptom and context tracking, and receive a personalized, provider-ready report. The goal is to help participants bring clearer information into medical appointments rather than isolated symptom complaints.
This design reflects three core principles. First, participants should receive value quickly. Women with autoimmune disease, fatigue, pain, brain fog, caregiving responsibilities, work obligations, and medical complexity should not have to track for months before receiving useful feedback. Second, consistency matters more than excessive detail. A smaller set of consistently logged daily variables can be more useful than a large number of inconsistently tracked details. Third, the model should support prevention, not only documentation. The goal is to identify early instability, symptom ramps, environmental vulnerability, sleep disruption, medication-response shifts, and flare risk before decline becomes harder to reverse.
4. Preliminary Intake Findings: Early Participants Are Already Showing the Terrain
Early intake data show that participants are not arriving with simple, single condition profiles. They are bringing exactly the layered autoimmune, hormonal, environmental, and multi-system complexity The Eve Research Project was designed to study. Across early unique signups, 88% of participants provided location data through zip code completion. This is a meaningful early signal. Location data allows The Eve Research Project to connect symptom patterns with environmental context, including air quality, temperature shifts, humidity, barometric pressure, pollen, mold risk, wildfire smoke, and regional climate exposure. Just as importantly, it shows that participants understood why geography mattered. They were willing to share location context because the project’s environmental modeling angle made sense to them.
The age and menopause distribution also strongly supports the project’s focus. Early participants were concentrated in midlife, with the largest group between ages 41 and 50, followed by ages 51 to 60.
Age Range | Early Participant Count |
Under 30 | 1 |
30–40 | 4 |
40–50 | 1 |
41–50 | 12 |
51–60 | 7 |
Over 60 | 1 |
Menopause status was even more revealing. Approximately 73% of early participants identified as perimenopausal. This matters because perimenopause is the active transition window when hormones are fluctuating, symptoms may become less predictable, and autoimmune behavior may begin changing before patients or clinicians know how to interpret the shift.
This suggests that the strongest early demand is not only coming from women after menopause is complete. It is coming from women in the transition itself. Early autoimmune severity also supports the prevention model. This distribution suggests that The Eve Research Project is reaching women before they are all in crisis. That makes the model especially valuable for prevention, flare pattern recognition, medication response analysis, and early intervention. Mild and moderate participants may be in the exact window where environmental triggers, hormonal shifts, sleep changes, medication drift, and immune instability can be identified before further decline occurs. The condition signal further strengthens the terrain model.
Diagnosed Autoimmune Condition | Early Participant Count |
Hashimoto’s Thyroiditis | 10 |
Inflammatory Bowel Disease | 3 |
Sjögren’s Syndrome | 3 |
Celiac Disease | 2 |
Rheumatoid Arthritis | 2 |
Type 1 Diabetes | 1 |
Psoriasis / Psoriatic Arthritis | 1 |
Lupus | 1 |
The write in responses were just as important. Participants also reported IBS, MCAS, POTS, interstitial cystitis, alopecia areata, unspecified connective tissue disorder, myasthenia gravis, ankylosing spondylitis, Long COVID or unidentified autoimmunity, Graves’ disease, ME/CFS, and CREST. This is the core signal: even when the intake form is centered on autoimmune disease, participants naturally bring in IACC adjacent, dysautonomia, mast cell, gastrointestinal, connective tissue, and post-viral complexity. Autoimmune menopause terrain is not behaving like a clean single diagnosis category. It is showing up as a multi-system transition problem.
5. Menopause Stage as a Core Modeling Variable
The Eve Research Project assesses participants by menopause stage because perimenopause, menopause, and post-menopause may represent meaningfully different biological contexts for autoimmune symptom behavior. In this model, menopause stage is not treated as simple background information or a demographic descriptor. It is treated as a core analytic variable.
This stage based approach is supported by the existing literature, even though the research base remains limited, uneven across conditions, and still underdeveloped for many autoimmune and infection associated chronic conditions. Reviews of autoimmune and rheumatic disease in menopause suggest that hormonal transition may influence disease onset, progression, symptom burden, flare behavior, and treatment considerations, although the direction and magnitude of these effects vary by diagnosis. In rheumatoid arthritis, for example, some studies suggest symptom burden or disability may worsen after menopause, while in systemic lupus erythematosus, flare frequency may decrease even as long term damage remains a concern.
These differences reinforce the need for stage based interpretation rather than one universal model for all conditions or all patients. The broader menopause transition is also increasingly understood as more than a reproductive milestone. Perimenopause, in particular, appears to involve systemic neuroendocrine and inflammatory change. As hormones fluctuate, patients may experience changes in vascular tone, sleep architecture, thermoregulation, autonomic stability, pain sensitivity, cognition, and immune signaling. For women already living with autoimmune or multi-system chronic illness, these shifts may alter not only symptom intensity, but also the timing, clustering, and recoverability of flares.
For this reason, The Eve Research Project stratifies participants by perimenopause, menopause, and post-menopause from the outset. This allows the model to ask more precise questions: whether symptom volatility differs by stage, whether environmental sensitivity becomes more prominent at certain points in transition, whether medication response appears to drift across stages, and whether recovery from flares slows or changes in character over time. The current working interpretation is that perimenopause may be associated with greater symptom volatility, because hormones are fluctuating and cycles may become less predictable, while post-menopause may be associated with more persistent vulnerability, because hormonal buffering is reduced and environmental, inflammatory, sleep related, and autonomic stressors may have a larger relative impact. This does not mean that every perimenopausal participant will show volatile flares or that every post-menopausal participant will show slower recovery. It means that stage provides a biologically reasonable structure for analysis.
This framing is especially important for flare modeling. A perimenopausal participant may experience irregular or stacked symptom patterns shaped by hormone variability, sleep disruption, migraine threshold shifts, immune sensitivity, and cyclical or semi-cyclical changes in tolerance. A post-menopausal participant may show fewer clearly cyclic patterns, but more persistent environmental sensitivity, altered thermoregulation, longer recovery windows, or greater medication response drift. These possibilities justify stage stratified analysis even before all parameters are fully quantified. In practical terms, menopause stage allows The Eve Research Project to move beyond a one-size fits all flare model. It provides a foundation for interpreting why the same weather shift, sleep loss, inflammatory trigger, or medication regimen may affect different participants differently depending on where they are in hormonal transition.
Menopause Stage | Why It Matters for Eve Modeling |
Perimenopause | Hormones fluctuate, cycles may become irregular, symptom patterns may become more volatile, and flare stacking may be harder to interpret. |
Menopause | The transition point may show mixed patterns, including residual cyclic history, changing sleep, vasomotor symptoms, immune shifts, and medication-response changes. |
Post-menopause | Hormone cycling is reduced, but lower hormonal buffering may make environmental shifts, thermoregulation, inflammation, recovery duration, and medication tolerance more important. |
By stratifying participants in this way, The Eve Research Project can examine whether menopause stage helps explain differences in symptom volatility, flare timing, environmental reactivity, medication response, sleep disruption, and recovery patterns. This stage based structure is already built into the project because limited but meaningful research supports its biological relevance, and because the early intake data strongly suggest that the transition window itself is where many women feel the greatest instability.
6. Preliminary Findings: Symptoms, Sleep, Feeling Scores, and Environment Form a Strong Foundational Signal Layer
Across multiple deidentified patient data reviewed for this preliminary analysis, there were 4 deep unique participant tracking exports, 226 total logged participant days, 209 days with complete weather context, 226 days with sleep data, 215 days with feeling scores, 1,751 symptom observations, 63 unique symptom labels, 79 unique treatment labels, 369 applied treatment events, and 19 distinct condition labels across participant profiles. These early tracking exports support an important finding: the data does not need to be uniform to be useful. Some participants log low burden daily basics, while others log dense multi-system symptom and treatment data. This variability is not a weakness. It shows why Eve needs flexible signal extraction rather than a one-size fits all diary.
The reviewed files also show that weather context can be present even when traditional biometric fields are incomplete. For example, participant entries include symptom severity, sleep minutes, feeling scores, stress, temperature, humidity, pressure, and location linked weather fields, while HRV, blood pressure, and other vitals may be missing or inconsistently available. Some entries include rich treatment and biometric information, while others rely primarily on symptoms, sleep, feeling, stress, and environmental context. This supports The Eve Research Project’s decision to treat sleep and environmental data as core signal layers, while treating HRV, resting heart rate, blood pressure, and treatment timing as confidence boosters when available. That design is more realistic and more equitable. Many patients do not use wearables, cannot afford devices, or cannot consistently collect vitals during flares. A model that requires perfect biometric data would miss exactly the patients it is supposed to help.
7. Minimum Viable Dataset for Flare Prediction
The Eve Research Project’s early pilot work supports a practical Minimum Viable Dataset for early flare prediction. Earlier assumptions often suggested that participants would need to track for several weeks or months before useful flare pattern modeling could begin. However, preliminary case analysis and participant feedback suggest that a shorter, lower burden dataset may still provide enough signal for environmental correlation, symptom ramp detection, and prototype flare forecasting. The current Minimum Viable Dataset requires 14 to 20 days of continuous daily logging, at least 85% data completeness, daily symptom tracking across at least three domains, sleep duration, environmental context, and at least two identifiable flares or symptom ramps. This does not create a clinician-grade predictive model by itself. It creates an early-warning prototype layer with moderate confidence, sufficient for engagement, pattern visualization, environmental correlation, and preliminary risk detection.
Data Layer | Required Inputs | Minimum Criteria | Notes |
Duration | Continuous daily logging | 14–20 days minimum | Reduced from 21 days based on preliminary case performance |
Completeness | Days with full data | At least 85% | Higher completeness helps compensate for shorter window |
Symptom Vector | Fatigue, pain, cognitive symptoms, thermoregulation, sleep quality, orthostatic or dizziness symptoms, GI/GU symptoms, global severity | Daily 0–4 scaling | At least 3 symptom domains required per day |
Vitals | Resting HR preferred, HRV if available, total sleep minutes | Optional but confidence weighted | HRV missingness acceptable if sleep data are continuous |
Environment | Temperature, humidity, pressure, optional air quality | Daily averages | Rate-of-change variables generated from deltas |
Treatments | Timestamped medications and non-pharma actions | Optional in MVD | If missing, system infers preventive baseline |
Events | Flare or ramp events | At least 2 identifiable events or ramps | May be patient- or AI-flagged |
The strongest preliminary insight is that useful flare prediction readiness may emerge earlier than traditional longitudinal research models assume. In one Eve case example, an MVD+ dataset was reached with 17 days of data, 100% completeness, three mild flares, complete environmental inputs, and no required treatment data. That does not prove 17 days is enough for every participant. It does establish a testable threshold for early signal detection.
8. Dataset Tiers and What Each Tier Can Safely Support
The Eve Research Project uses a tiered confidence model to avoid overclaiming what a dataset can support.
Tier | Duration | Completeness | Flare Events | Confidence | What It Can Support |
Exploratory | Under 14 days | Any | Fewer than 2 | Low | Engagement, visualization, baseline awareness |
MVD | 14–20 days | At least 85% | At least 2 | Moderate | Early flare detection, short pre-flare ramps, environmental correlation |
Short-Term Model | 21–30 days | At least 80% | At least 3 | Good | Behavioral rhythm learning, false-alarm reduction |
Strong Personal Model | 60–90 days | At least 80% | At least 5 | High | Lag windows, treatment effects, recovery kinetics |
Clinician-Grade with Seasonality | 90–180 days | At least 85% | At least 8 | Very High | Seasonal adjustment and counterfactual testing |
This tiering system is central to The Eve Research Project’s credibility. It does not claim that every dataset can answer every question. It defines what can be safely inferred at each stage.
The 30 day Phase 1 model is intentionally positioned above the MVD threshold. A 14 to 20 day dataset may support moderate confidence early signal detection. A 21 to 30 day dataset improves short-term pattern recognition by adding behavioral rhythm learning and false alarm reduction. This makes the 30 day Phase 1 design both practical and scientifically strategic.
9. From Symptom Journaling to Flare-Prediction Readiness
The Eve Research Project is not simply a symptom journaling project. It is designed to move participants from daily logging toward flare prediction readiness. Traditional symptom journals often collect information without translating it into timing, sequence, or context. A patient may record fatigue, headache, sleep disruption, joint pain, hot flashes, or nausea, but the record may not show whether these symptoms are clustering before a flare, responding to weather shifts, following medication changes, or appearing during hormonal transition.
The Eve Research Project instead asks whether symptom data can be organized into usable signal. This includes symptom sequencing, environmental rate of change, sleep disruption, feeling score shifts, orthostatic symptoms, GI/GU patterns, medication timing, and flare ramps.
An early personalized report illustrates this translation layer. The report described generally good day to day function with low level but recurring symptoms that appeared sensitive to environmental and physiological stressors. It noted chronic migraine history, recurrent neck discomfort, intermittent eye twitching, subtle neurologic symptoms, sleep disruption, and symptom shifts alongside weather rather than stress or activity alone.
That report also connected symptoms to environmental triggers, including rapid temperature changes, high humidity, and barometric pressure changes. It noted that on days with greater environmental variability, subtle symptoms such as neck pain, eye twitching, or migraine activity were more likely to appear. This is exactly the kind of translation The Eve Research Project is built to provide: symptoms become patterns, patterns become reportable insights, and reportable insights become more useful clinical conversations.
10. Environmental Modeling Is Not a Side Feature
Environmental modeling is one of The Eve Research Project’s strongest early differentiators.
Most clinical systems do not seriously integrate daily weather, air quality, humidity, pressure, mold risk, pollen, wildfire smoke, or temperature shifts into autoimmune and menopause care. Patients may report that they feel worse before storms, during humidity changes, after cold fronts, during wildfire smoke, or around high pollen or mold exposure, but those reports are often treated as anecdotal. The Eve Research Project treats environmental context as a measurable data layer. The preliminary intake finding that 88% of early participants provided zip code data is therefore important. It suggests that participants understand that where they live may shape how symptoms behave. It also allows The Eve Research Project to connect symptom changes with regional environmental exposures.
The model prioritizes rate of change variables rather than static weather conditions alone. In other words, the question is not simply whether a day was hot, humid, or cold. The question is what changed. A pressure drop, humidity jump, temperature swing, wildfire smoke shift, or rapid environmental transition may matter more than the absolute value on a single day. Migraine literature supports the broader concept that some patients may be sensitive to weather variables, including barometric pressure, humidity, temperature, and wind, although studies remain inconsistent and individual sensitivity varies (Denney et al., 2024). Clinical resources also recognize that some people with migraine report weather-related triggers such as extreme heat or cold, high humidity, dry air, windy or stormy weather, and barometric pressure changes. The Eve Research Project extends this logic beyond migraine alone. It asks whether environmental changes may lower flare thresholds in autoimmune, IACC, dysautonomia, mast cell, and menopause-transition contexts.
11. Sleep as a Core Signal Layer
Sleep is one of the most practical and consistently available data layers in early tracking.
In the reviewed deidentified patient data, sleep data were more consistently available than HRV, blood pressure, or other biometric variables. This supports The Eve Research Project’s decision to treat sleep as a required or strongly preferred core signal and to treat HRV and resting heart rate as optional confidence boosters. Sleep matters because it is linked to nervous system regulation, migraine threshold, immune resilience, pain sensitivity, mood stability, and recovery capacity. In the personalized report example, sleep duration varied across the tracking period, and sleep disruption often coincided with environmental stress or symptom fluctuation. The report highlighted sleep as closely tied to nervous system regulation, migraine threshold, and overall symptom resilience.
For patients without wearables, sleep duration and sleep quality may serve as accessible proxies for resilience. For patients with wearables, HRV, resting heart rate, and activity data can add confidence. This flexible design keeps the model accessible while preserving a path toward more advanced digital twin development.
12. Autonomic Regulation and Orthostatic Clues
The Eve Research Project’s early reports and data structure suggest that autonomic regulation may be a key missing layer in autoimmune-menopause terrain. Symptoms such as dizziness, lightheadedness, shakiness, temperature intolerance, heart racing, fatigue after standing, sleep disruption, migraine, cognitive changes, and post-exertional worsening may be labeled as anxiety, menopause, deconditioning, stress, or nonspecific fatigue. Yet in many complex chronic illness populations, these symptoms may reflect autonomic instability or dysautonomia.
A personalized report noted that heart rate and blood pressure data were not available, limiting direct assessment of orthostatic or autonomic changes. However, based on symptoms and a recent emergency room experience, the report recommended capturing simple heart rate changes with position, such as lying down versus standing. It also framed the symptom pattern as potentially consistent with autonomic nervous system dysregulation, where the body has difficulty adjusting to stressors like position changes, temperature, or illness.
This is not the same as diagnosing POTS or dysautonomia through a report. It is a triage intelligence function. The report helps identify when a patient may benefit from asking a clinician about orthostatic vitals, lying to standing heart rate and blood pressure checks, or autonomic evaluation. That is the bridge The Eve Research Project is designed to build.
13. Multi-System Complexity and Diagnostic Suppression
The early intake data show that participants are bringing more than autoimmune diagnoses into the project. They are bringing multi-system complexity that includes mast cell, autonomic, gastrointestinal, connective tissue, post-viral, and infection associated chronic condition patterns. This matters because many symptom logging platforms and clinical intake forms do not adequately capture these overlaps. Earlier internal analysis of complex longitudinal data identified the importance of flagging suspected MCAS and EDS patterns when formal diagnostic labels are missing, especially when patients show combinations of POTS, GI distress, fatigue, brain fog, eye burning, headaches, joint pain, antihistamine use, and environmental reactivity.
The clinical literature also supports the need for caution and nuance around POTS and mast cell activation overlap. A 2021 study examined the frequency of symptoms and laboratory findings suggesting mast cell activation disorder among patients diagnosed with POTS, reflecting growing recognition of this overlap in dysautonomia populations. Reviews note that the relationship between POTS and MCAS remains complex, incompletely understood, and in need of more controlled study. The Eve Research Project’s contribution is not to assume that every participant has these conditions. The contribution is to build a model that does not erase these patterns when formal diagnosis is missing.
14. Medication Response During Menopause Transition
One of the most important research questions emerging from The Eve Research Project is whether menopause transition changes medication response in autoimmune and IACC patients. Patients often report that medications feel less reliable, less tolerable, or less complete during midlife transition. A medication that previously helped may feel weaker. A dose that was previously tolerable may cause more insomnia, agitation, blood pressure change, GI symptoms, fatigue, or rebound effects. A treatment that controlled one symptom cluster may no longer control the whole flare pattern.
The key question is:
Did the medication fail, or did the patient’s terrain change?
This question should guide future Eve analyses. Menopause transition may affect immune activity, sleep, metabolism, vascular tone, bone risk, histamine sensitivity, mood, cognition, and recovery time. Those shifts may alter how patients experience biologics, steroids, NSAIDs, immunosuppressants, hormone therapy, antihistamines, SSRIs/SNRIs, supplements, metabolic medications, and low dose repurposed agents.
Current research already recognizes that menopause and hormone therapy questions are complex in autoimmune disease. For example, the effects of hormone therapy in systemic lupus erythematosus have been studied but remain limited, condition specific, and dependent on patient risk profile (Khafagy et al., 2015). A 2025 study reported associations between menopausal hormone therapy and systemic lupus erythematosus risk in some age windows, underscoring the need for careful, individualized, condition aware research rather than blanket claims about hormone therapy safety or benefit. The Eve Research Project does not frame medication response as a one-size fits all issue. It frames it as a terrain specific research question.
15. Therapeutic Hypotheses Emerging from the Terrain Model
The preliminary therapeutic landscape suggests that existing medications and adjuncts may have underexplored relevance in autoimmune menopause terrain. These agents should not be broadly promoted as universal treatments. They should be studied by terrain subtype. The purpose of this section is to identify research hypotheses, not prescribe care.
Hormone-Immune Terrain
Potential candidates include estradiol, progesterone, DHEA, and selected hormone supportive adjuncts. These may be relevant where symptoms appear linked to perimenopausal fluctuation, sleep disruption, vasomotor symptoms, cognition, pain sensitivity, or immune threshold changes. However, hormone therapy must be studied and used with attention to diagnosis, cancer history, clot risk, uterine status, age, timing, route, dose, and individual risk profile.
Neuroimmune Fatigue and Brain Fog Terrain
Potential candidates include low dose naltrexone, ibudilast, low dose modafinil, microdose aripiprazole, oxytocin, and selected SSRIs or SNRIs. These agents may be relevant to fatigue, brain fog, neuroinflammation, sleep-wake regulation, mood instability, and functional collapse patterns. The research need is especially strong in patients whose symptoms are dismissed as psychiatric when the pattern may be immune, autonomic, hormonal, or post-viral.
Inflammatory and Metabolic Terrain
Potential candidates include metformin, GLP-1 receptor agonists, statins, and carefully monitored low-dose or pulsed steroid strategies. These may be relevant where insulin resistance, weight change, systemic inflammation, vascular risk, fatigue, brain fog, or inflammatory load changes during menopause transition. This category should be evaluated carefully because metabolic and immune benefits may depend heavily on patient subtype and safety profile.
Mast Cell and Sensitivity Terrain
Potential candidates include ketotifen, H1/H2 blockers, montelukast, and related mast cell or leukotriene-targeting strategies. These may be relevant when patients report new food sensitivity, smell sensitivity, flushing, itching, reflux, migraine, insomnia, asthma-like symptoms, environmental sensitivity, or histamine-like symptom clusters during hormonal transition.
Oxidative Stress and Recovery Terrain
Potential candidates include NAC, glutathione, CoQ10, and other mitochondrial or antioxidant-supportive approaches. These may be relevant where patients experience fatigue, brain fog, slow recovery, post-viral symptoms, or inflammatory stress. These should be studied structurally rather than left only to informal self-experimentation.
Herbal Adjuncts Requiring Structured Study
Ashwagandha, rhodiola, black cohosh, berberine, and other adjuncts may be relevant for some patients, but they require careful study around dosing, safety, drug interactions, endocrine effects, autoimmune risk, and patient subtype. These should not be treated as automatically safe because they are “natural.” The therapeutic implication is simple: The Eve Research Project can help identify which terrain a patient appears to be in before assuming which intervention category deserves study.
16. Preliminary Findings Summary
Early intake, report, and tracking data from The Eve Research Project support several preliminary findings.
Preliminary Finding 1: The active transition window is the strongest early demand signal. Approximately 73% of early participants identified as perimenopausal, suggesting that the highest-need window may be the period before menopause is complete.
Preliminary Finding 2: Participants are willing to provide geographic context. Approximately 88% of early participants provided zip code data, supporting environmental overlay modeling for air quality, weather, humidity, pressure, pollen, mold, wildfire smoke, and regional exposures.
Preliminary Finding 3: Early participants represent a prevention relevant severity range. Mild, moderate, and severe autoimmune severity groups are all represented. This suggests that The Eve Research Project may be useful not only for severe illness documentation, but for early instability detection and decline prevention.
Preliminary Finding 4: Participants bring multi-system complexity into an autoimmune-centered project. The condition data include autoimmune diagnoses, IACC-adjacent conditions, dysautonomia, mast cell concerns, gastrointestinal conditions, connective tissue disorders, post-viral symptoms, and unclear autoimmune presentations.
Preliminary Finding 5: Useful flare prediction signals may emerge with lower data burden than expected. The current MVD model suggests that 14 to 20 days of high-completeness daily logging may support moderate-confidence early flare detection when at least two symptom ramps are identifiable.
Preliminary Finding 6: A 30 day Phase 1 model is strategically justified. A 21 to 30 day window improves short-term pattern recognition, behavioral rhythm learning, and false-alarm reduction without requiring months of participant tracking before value is returned.
Preliminary Finding 7: Symptoms, sleep, feeling scores, and environment form a practical foundational signal layer. The reviewed JSON exports show that these variables can be present even when HRV, blood pressure, or treatment timing are incomplete.
Preliminary Finding 8: Provider ready reports are a critical translation tool. Early report outputs show how symptom tracking can be translated into practical clinical discussion points, such as weather sensitivity, orthostatic questions, sleep patterns, and pre-flare preparation.
Preliminary Finding 9: Medication response during menopause transition deserves focused research. The project identifies medication-response drift as a major under-studied question in autoimmune-menopause terrain.
Preliminary Finding 10: Repurposed and low dose therapies should be studied by terrain subtype. The therapeutic opportunity is not a universal treatment protocol. It is a structured research agenda.
17. Clinical and Research Implications
The Eve Research Project suggests that autoimmune menopause transition should be studied as an integrated biological and environmental transition, not as a set of isolated specialty complaints.
For patients, this model may support better self advocacy, clearer provider conversations, earlier flare recognition, and more confidence in describing symptom patterns. For clinicians, the model may offer structured patient-generated data that helps identify overlooked relationships among symptoms, sleep, environment, medications, autonomic symptoms, and hormone transition.
For researchers, The Eve Research Project creates a pathway for studying under-measured variables, including environmental exposure, medication response, symptom sequencing, hormone stage, autonomic symptoms, sleep disruption, and multi-system condition overlap.
For sponsors and partners, the project offers a low burden, high signal model with immediate participant value and scalable research infrastructure.
18. Limitations
These findings are preliminary. Early intake numbers are small and should not be interpreted as population prevalence. Participants are self-selected and likely represent women already motivated to understand autoimmune and menopause-related symptom changes. Self reported conditions and severity require future clinical validation. Early tracking exports vary in completeness, symptom density, treatment detail, and biometric availability. The Minimum Viable Dataset model should be understood as a flare-prediction readiness framework, not a validated diagnostic tool. A 14 to 20 day dataset can support early pattern detection and environmental correlation, but clinician grade modeling requires longer follow up, more flare events, stronger completeness, seasonal adjustment, and ideally integration with clinical data or objective measures.
Environmental correlations in lesser studied conditions also require caution. Weather, humidity, pressure, and air quality may contribute to some conditions, but not all overlapping comorbidities. The purpose of the early model is to identify patterns worth investigating and acting on cautiously, not to replace clinical judgment.
19. Next Steps for Phase 1
The next phase of The Eve Research Project should focus on five priorities. First, expand enrollment while preserving participant trust, low burden, privacy, and provider ready value.
Second, validate the MVD model across additional participants by testing whether 14 to 20 days of high completeness tracking can reliably identify early symptom ramps, environmental correlations, and short pre-flare signatures.
Third, compare 14 to 20 day MVD outputs with 21 to 30 day short-term model outputs to determine how much additional signal is gained by completing the full Phase 1 window.
Fourth, refine terrain subtypes, including hormone-immune, neuroimmune fatigue, mast cell sensitivity, metabolic-inflammatory, dysautonomia, gastrointestinal, connective tissue, and post-viral overlap patterns.
Fifth, develop partner ready outputs, including participant reports, clinician summaries, sponsor dashboards, de-identified cohort insights, and digital twin readiness scoring.
To expand the next phase of The Eve Research Project, CYNAERA and collaborating partners will also host The Invisible Trigger: Why Menopause Resets the Rules for Autoimmune Health, a live educational panel scheduled for June 10th 2026, bringing together Cynthia Adinig, Jamie Nicole, Tracey Welson-Rossman, and Dr. Jolene Brighten to discuss the growing intersection between menopause transition, autoimmune illness, IACCs, flare instability, environmental load, and women’s health. The event is designed to translate emerging findings into practical, patient centered conversation while increasing visibility around the unmet needs facing women navigating complex chronic illness during hormonal transition.
As interest in The Eve Research Project continues to grow, additional sponsorship and institutional support are needed to expand cohort size, strengthen longitudinal tracking capacity, improve environmental overlay modeling, support patient participation accessibility, and accelerate development of provider ready reporting tools and future research infrastructure. Organizations interested in supporting patient-generated data research, women’s health innovation, autoimmune and IACC visibility, digital health infrastructure, or longitudinal chronic illness modeling are encouraged to connect regarding sponsorship and collaboration opportunities.
Preliminary Activation of CYNAERA Intelligence Engines
The Eve Research Project preliminary cohort also provided early operational support for several core CYNAERA intelligence engines designed to interpret flare behavior, environmental instability, symptom sequencing, multi-system illness overlap, and patient generated health data.
Among the strongest signals was support for SymCas™, CYNAERA’s symptom cascade flare prediction engine. Participant reporting demonstrated recurring symptom sequencing patterns involving fatigue, sleep disruption, migraine activity, autonomic symptoms, cognitive dysfunction, thermoregulation shifts, and environmental sensitivity, reinforcing the importance of longitudinal flare-pattern analysis rather than isolated symptom interpretation.
The cohort also strongly supported VitalGuard™, CYNAERA’s environmental flare modeling engine. Approximately 88% of early participants provided zip code data, allowing symptom behavior to be interpreted alongside weather variability, humidity, air quality, mold exposure risk, wildfire smoke, pressure changes, and broader environmental instability patterns. Hormone-linked symptom instability within the cohort supported activation of HORMOD™ and Hormonic™, CYNAERA frameworks focused on hormone-= immune interaction, menopause stage stratification, and flare variability across hormonal transition. The strong concentration of perimenopausal participants reinforced the importance of treating hormonal stage as a core systems-level modeling variable rather than a secondary demographic characteristic.
The pilot also supported NeuroVerse™, Pathos™, and the Composite Diagnostic Fingerprint (CDF™) architecture through the presence of overlapping neuroimmune, autonomic, inflammatory, mast cell, post-viral, and autoimmune symptom patterns that frequently crossed traditional diagnostic boundaries. These findings reinforce terrain based approaches to chronic illness interpretation rather than rigid single condition classification systems. Longitudinal symptom tracking within the Eve pilot further supported CYNAERA’s broader Digital Twin and IACC Twin Architecture frameworks, demonstrating the feasibility of transforming patient generated symptom logs into structured flare pattern intelligence, provider ready summaries, and future individualized modeling systems.
Preliminary cohort behavior also aligned with, TrialSim™ and the STAIR Stable Method™, supporting the hypothesis that immune volatility, environmental instability, symptom sequencing, and terrain fragility may significantly influence treatment response, flare risk, participant retention, and clinical trial interpretation in autoimmune and infection associated chronic conditions. Additional operational examples, deployment environments, and expanded module applications are available through CYNAERA case studies and infrastructure documentation.
20. Conclusion
The Eve Research Project was created because too many women are left to navigate autoimmune symptoms, hormonal transition, medication changes, environmental sensitivity, and multi-system instability without a clear map. Preliminary intake and tracking data suggest that this need is real, reachable, and concentrated in the active transition window of perimenopause.
Early participants are not simply seeking menopause education or autoimmune education in isolation. They are seeking a way to understand why multiple systems appear to shift at once. They are providing location data, reporting layered conditions, identifying mild to severe autoimmune severity, and entering the project with exactly the kind of complexity The Eve Research Project was designed to measure.
The strongest early signal is not just that women signed up. It is that they signed up with the precise terrain complexity the model was built to study. The Eve Research Project’s early data also suggest that flare prediction readiness may be achievable with a lower-burden dataset than traditional longitudinal research models assume. A 14 to 20 day minimum viable dataset can support moderate confidence early signal detection, while a 30 day Phase 1 model improves short term pattern recognition without requiring months of participant tracking before value is returned.
This is the core promise of The Eve Research Project: to translate dismissed symptoms into structured patterns, support provider ready conversations, identify earlier flare risk, and build the research infrastructure such as our Autoimmune Research Library and DAWN, needed to understand autoimmune menopause terrain before women are pushed into preventable decline.
Licensing and Partnership Opportunities
Eve Research Sponsorship License
For founding sponsors and brand partners supporting participant onboarding, 30 day tracking, provider-ready reports, community support, and early research infrastructure.
Eve Intake Intelligence License
For partners interested in identifying high need autoimmune menopause populations based on age range, menopause stage, condition overlap, geography, and severity distribution.
Eve Environmental Overlay License
For platforms, clinics, or research partners seeking to connect zip code, weather, pressure, humidity, AQI, pollen, mold, wildfire smoke, and environmental volatility with symptom tracking.
Eve MVD Flare-Prediction Readiness License
For platforms, clinics, researchers, and sponsors that want to determine when a participant has logged enough data to support early flare-pattern detection.
Eve Short-Term Model License
For 21 to 30 day patient tracking programs that need low-burden pattern detection, behavioral rhythm learning, and provider-ready reporting.
Eve Digital Twin Readiness License
For partners interested in moving participants from MVD into personalized flare forecasting, recovery kinetics, treatment timing analysis, prevention planning, and longitudinal digital twin development.
Eve Provider-Ready Report License
For care teams, coaching programs, advocacy organizations, and clinical partners that want patient-friendly reports translating symptom tracking into practical clinical discussion points.
How to Cite This Paper
Adinig, C., Welson-Rossman, T., and Nicole, J. (2026). The Autoimmune-Menopause Terrain: Preliminary Findings from The Eve Research Project. CYNAERA. Available at: https://www.cynaera.com/post/eve-research-project-preliminary-findings
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.
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 Authors
Cynthia Adinig is a healthcare advocate, researcher, federal policy advisor, systems architect, and founder of CYNAERA, a modular intelligence company focused on chronic illness, health equity, predictive modeling, and real world data infrastructure. Her work centers on infection associated chronic conditions, Long COVID, ME/CFS, dysautonomia, MCAS, autoimmune illness, flare prediction, environmental load, and the structural gaps that cause patients to be missed, delayed, or dismissed.
Adinig has contributed to national Long COVID policy and research efforts, including collaborations with leading researchers, federal agencies, patient-led initiatives, and legislative teams focused on chronic illness surveillance, care access, and data modernization. Through CYNAERA, she develops frameworks that translate lived experience, symptom patterns, environmental exposure, and system-level inequities into actionable tools for research, care design, and policy. Her work on The Eve Research Project reflects her broader mission to build patient centered intelligence systems that recognize complex illness before it becomes invisible inside traditional healthcare models.
Jamie Nicole is the Founder and CEO of The AIP BIPOC Network, an autoimmune health strategist, chronic illness speaker, functional movement coach, and health equity advocate. Living with multiple chronic illnesses, including autoimmune conditions and narcolepsy, she brings both lived experience and professional expertise to her work supporting individuals and communities navigating complex chronic illness.
Through The AIP BIPOC Network and The Natural HEALing Coach, Jamie develops culturally responsive wellness programs, community education, autoimmune coaching, functional movement support, and patient centered advocacy initiatives. Her work includes chronic illness coaching, AIP informed strategies, community based programming, podcast hosting, workshop facilitation, and leadership in initiatives focused on BIPOC communities, autoimmune illness, disability, sleep health, and equitable access to care. Her contribution to The Eve Research Project strengthens the bridge between lived experience, community wellness, autoimmune education, and practical strategies women can use in real life.
Tracey Welson-Rossman is the Founder and CEO of Journal My Health, Chief Marketing Officer of Chariot Solutions, founder of TechGirlz, and a longtime technology entrepreneur, marketing executive, and community builder. Her work spans enterprise technology, digital health, patient-generated data, women in technology, startup leadership, and the translation of market trends into scalable business opportunities.
At Journal My Health, she leads development of a SaaS platform designed to help healthcare companies collect patient generated real world data from populations living with chronic and episodic conditions. Her career also includes more than two decades of leadership at Chariot Solutions, where she oversees branding, marketing, strategic initiatives, partner development, and sales team management. She founded TechGirlz to expand girls’ access to technology education, ultimately helping inspire more than 25,000 girls before the organization was acquired. Her contribution to The Eve Research Project brings essential expertise in patient generated data, digital health infrastructure, technology adoption, marketing strategy, and scalable data collection for chronic illness research.
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