Autonomic Nervous System Subtypes in Myalgic Encephalomyelitis/CFS: A Large-Scale Registry Reanalysis

Autonomic Nervous System Subtypes in Myalgic Encephalomyelitis/CFS: A Large-Scale Registry Reanalysis

1. Overview

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disabling condition linked to neuroinflammation, affecting an estimated 17–24 million people worldwide, with cases rising substantially since the COVID-19 pandemic. The 2024 NIH deep phenotyping study identified central nervous system dysfunction, autonomic dysregulation, and immune abnormalities as interconnected features of the disease [1]. Autonomic nervous system dysfunction (orthostatic intolerance, tachycardia, vasomotor instability) is a defining clinical feature, yet no validated system exists for subtyping patients by their pattern of autonomic involvement. One prior study attempted autonomic phenotyping in ME/CFS (Słomko et al., 2020, N=131), but was limited by small sample size and a single timepoint. The first publication from this dataset (Mudie et al., 2024) showed that joint hypermobility identifies a possible disease subgroup, but did not incorporate autonomic data.

This project is conducted in collaboration with the Brain Inflammation Collaborative (BIC), whose research mission explicitly focuses on neuroinflammation and infection-associated chronic conditions. BIC operates the unhide® + Solve Together Unified Platform, a registry of 3,000+ participants with ME/CFS and related conditions. BIC is actively seeking research partners to analyze this data to better understand the natural history and trajectory of ME/CFS, and BIC Co-founder and CEO Christy Jagdfeld is actively engaged in scoping data access, variable harmonization, and dissemination support for this project. This ResearchHub grant would directly enable an analysis that BIC wants conducted but that an independent researcher cannot fund through traditional mechanisms such as NIH grants, which require institutional affiliation and PI status. We propose to reanalyze existing, de-identified data from this registry, which collects validated autonomic (COMPASS-31), hypermobility (Beighton), symptom (DSQ-SF), anxiety (GAD-7), and functional capacity (FUNCAP-27, Karnofsky, SF-36) measures at regular intervals. This is a purely computational reanalysis: no new data collection, no additional IRB approval needed (existing Advarra IRB), and all analyses are feasible within 6 months using open-source tools. We commit to preregistering on ResearchHub, sharing all analysis code on GitHub, and publishing results as a preprint on ResearchHub regardless of outcome direction.

Estimated timeline: 6 months. Months 1-2: Data access, cleaning, harmonization, preregistration, Aim 1 cluster analysis, and Aim 2 post-COVID comparison. Month 3: Aim 3 longitudinal analysis, sensitivity analyses, and cross-aim integration. Month 4: Manuscript drafting, preprint submission on ResearchHub, and plain-language summary. Months 5-6: Active dissemination and community engagement — BIC webinar presenting findings to the patient/participant community, social media campaign, ResearchHub Updates tab posts throughout, and a dedicated episode of The DeSci Mic podcast covering the full process of working with ResearchHub from funding through publication. Data sharing: Full open-access commitment per ResearchHub requirements.

2. Introduction

Background

A growing body of evidence supports ME/CFS as a neuroimmune condition with central nervous system involvement. The 2024 NIH deep phenotyping study (Walitt et al., Nature Communications) identified dysfunction in brain effort circuits (temporoparietal junction and motor cortex), autonomic dysregulation (reduced heart rate variability, impaired baroreceptor function), and immune abnormalities (altered B-cell populations, chronic antigenic stimulation) as co-occurring features [1]. Neuroimaging studies have documented neuroinflammation in ME/CFS patients [24], and reviews of the biomarker evidence suggest possible brainstem and central autonomic network involvement [25].

Mechanistically, Tate et al. (2022) proposed that chronic microglial activation and hypothalamic dysfunction sustain autonomic dysregulation in both ME/CFS and long COVID [30], while Arron et al. (2024) described how blood-brain barrier disruption and brainstem hypoperfusion may produce neural injury affecting autonomic regulation [31]. This places ME/CFS within the broader landscape of neural injury, dysfunction, and repair that remains incompletely understood across neurological conditions. A 2025 multi-site study from the MCAM consortium documented the prevalence and patterns of autonomic dysfunction in ME/CFS [26], and a 2026 preprint by Habermann-Horstmeier & Horstmeier identified coherent symptom clusters in 748 ME/CFS adults, including a "brain factor" cluster consistent with distinct neuroinflammatory and autonomic mechanisms [27]. Despite this converging evidence linking neuroinflammation to autonomic dysfunction, no study has tested whether patients cluster into distinct autonomic subtypes that might reflect different patterns or severities of neural involvement.

Autonomic symptoms, measured by the Composite Autonomic Symptom Score (COMPASS-31), are present in the majority of ME/CFS patients and correlate with disease severity [2,3]. The COMPASS-31 captures dysfunction across six domains (orthostatic intolerance, vasomotor, secretomotor, gastrointestinal, bladder, pupillomotor) that may reflect the downstream clinical expression of central autonomic network disruption. The clinical co-occurrence of ME/CFS with postural orthostatic tachycardia syndrome (POTS) and hypermobile Ehlers-Danlos syndrome (hEDS) has been noted by multiple research groups [4-6], but has never been systematically characterized using validated instruments in a large cohort.

Słomko et al. (2020) applied cluster analysis to autonomic measures in 131 CFS patients and identified four phenotypes (sympathetic with dysautonomia, sympathetic, parasympathetic, and sympathovagal balance) associated with different severity levels [8]. This study was limited by small sample size, single-timepoint design, and did not incorporate hypermobility, neurocognitive, or functional capacity data.

Separately, Mudie et al. (2024) published the first analysis of the You+ME Registry, finding that 15.5% of 815 ME/CFS participants had joint hypermobility (JH+), and that this group had worse physical function and more pain [9]. This finding suggests hypermobility defines a disease subgroup, but the study did not examine autonomic profiles.

An estimated half of individuals with long COVID meet diagnostic criteria for ME/CFS [10], making post-COVID onset a major contributor to new diagnoses. Whether SARS-CoV-2 produces a distinct pattern of autonomic and neurocognitive injury, or converges on the same phenotypic landscape as other triggers, remains unresolved [11,12]. Novak et al. (2026) recently reported shared autonomic features between long COVID and ME/CFS patients using standardized autonomic testing, providing preliminary support for pathophysiological convergence across these conditions [28]. However, large-cohort comparisons using patient-reported instruments to examine whether post-COVID and non-COVID ME/CFS share autonomic profiles remain scarce.

Eckey et al. (2025) demonstrated the analytical value of the You+ME Registry data in a PNAS publication examining patient-reported treatment outcomes in ME/CFS and long COVID [29], the first publication from this dataset in a top-tier journal.

The gap this project addresses: No study has combined COMPASS-31 autonomic profiling with hypermobility assessment, neurocognitive symptom measurement, and functional capacity data in a large ME/CFS cohort. Mudie et al. showed hypermobility matters. Słomko et al. showed autonomic subtypes exist. The MCAM consortium confirmed autonomic dysfunction is prevalent. Novak et al. provided preliminary evidence for autonomic convergence between long COVID and ME/CFS. This project brings those lines of evidence together at substantially larger scale, with longitudinal data to test whether subtypes are stable over time.

Research Questions and Hypotheses

Aim 1: Do ME/CFS patients cluster into distinct autonomic subtypes, and do these subtypes differ in neurocognitive impairment, functional capacity, and hypermobility status?

H1a: Participants will cluster into 2-4 distinct subtypes based on COMPASS-31 subscale profiles, replicating and extending Słomko et al. [8] at substantially larger scale.
H1b: Subtypes will differ significantly in DSQ-SF neurocognitive severity and FUNCAP-27 functional capacity.
H1c: Hypermobile participants (Beighton-positive) will be overrepresented in the orthostatic-predominant subtype, extending Mudie et al. [9] with autonomic data.

Aim 2: Do post-COVID and non-COVID ME/CFS differ in autonomic and neurocognitive profiles?

H2a: Post-COVID ME/CFS will show higher COMPASS-31 orthostatic intolerance scores after adjusting for illness duration, consistent with preliminary reports [13].
H2b: DSQ-SF neurocognitive impairment will be comparable between groups, supporting convergent neurological impact regardless of trigger.
H2c: Aim 1 subtypes will distribute similarly across trigger types, supporting the "final common pathway" hypothesis [11].

Aim 3 (Exploratory): Are autonomic subtypes stable over time?

H3a: Subtypes will show >70% retention on re-assessment, suggesting stable autonomic phenotypes rather than transient states.
H3b: Post-COVID patients will show more subtype transitions, consistent with evolving neural dysfunction in the early post-infectious period.

Impact Statement

This is a high-risk, high-reward study designed to generate proof of concept for autonomic subtyping in ME/CFS, and it produces useful results regardless of outcome direction. If distinct autonomic subtypes exist, they could support precision approaches to a disease with no approved treatments. Concretely, validated subtypes could generate hypotheses about differential treatment response (e.g., whether orthostatic-predominant patients respond differently to autonomic-targeted therapies than patients with other autonomic profiles), guide comorbidity screening protocols, and provide stratification variables for clinical trials. Characterizing these subtypes also lays the groundwork for future neuroimaging or biomarker studies that could link autonomic symptom patterns to specific neuroimmune mechanisms. If subtypes do not emerge, it challenges assumptions about autonomic heterogeneity in ME/CFS and reorients the field toward continuous severity models. If post-COVID ME/CFS produces a distinct autonomic signature, it identifies a targetable phenotype with implications for understanding post-infectious autonomic injury. If it does not, it strengthens the case that ME/CFS converges on a common autonomic pathway regardless of trigger.

Path to follow-up: Results from this study will directly support applications to NINDS (neuroinflammation mechanisms) and the ME/CFS Collaborative Research Centers. The longitudinal stability data would inform clinical trial design and patient selection. Beyond ME/CFS, the autonomic subtypes identified here could be tested across broader dysautonomia conditions (POTS, hEDS-associated dysautonomia, post-infectious autonomic neuropathy), and could inform future neuroimaging studies investigating whether distinct autonomic profiles correspond to distinct patterns of brain inflammation or central autonomic network dysfunction. All findings will be published as a preprint on ResearchHub and submitted to an open-access journal, with active dissemination through BIC's patient/researcher community and DeSci channels.

3. Methods

Participants and Sample

Source: De-identified data from the unhide® + Solve Together Unified Platform, operated by the Brain Inflammation Collaborative (BIC), incorporating data from the former Solve ME/CFS Initiative You+ME Registry. A collaboration with BIC is being finalized; BIC Co-founder and CEO Christy Jagdfeld is actively engaged in defining data access scope and harmonization procedures. The platform integrates two existing datasets: ~3,000 current unhide® participants and ~5,000 legacy You+ME Registry participants (1,000+ overlapping, providing additional years of longitudinal data). This project reanalyzes and integrates these existing datasets.

Inclusion: Adults (18+) with self-reported ME/CFS diagnosis who have completed COMPASS-31 and DSQ-SF at minimum.

Exclusion: Pediatric participants; participants missing both core instruments.

Sample size justification: For cluster analysis with 6 variables, methodological guidance recommends N=300-500 for stable solutions [14,15]. For between-group comparisons (Aim 2) with small-to-medium effect size (d=0.3), alpha=0.05, power=0.80: N=176 per group required. Published registry completion rates for core instruments range from 50-80% [16]. Conservative estimate of 30% completion across ~8,000 total records (~7,000 unique participants after accounting for 2,100, well above all thresholds. Post-COVID subgroup size will be confirmed with registry descriptive statistics prior to analysis.

Materials and Procedures

All data come from validated instruments collected through the unhide® platform at established intervals:

Instrument

Measure

Collection Frequency

COMPASS-31 [7]	Autonomic dysfunction (6 weighted subscales; total score 0-100)	Semi-annual
Beighton Questionnaire [17]	Joint hypermobility (5-item self-report)	Semi-annual
DSQ-SF [18]	ME/CFS symptom frequency and severity (14 items)	Quarterly
GAD-7 [19]	Anxiety (7 items, 0-21)	Quarterly
FUNCAP-27 [32]	Functional capacity (27 items)	Quarterly
Karnofsky Performance Scale [33]	Global functional status (0-100)	Quarterly
SF-36 [20]	Health-related quality of life (8 domains)	Semi-annual

Additional data: Illness History (onset, trigger, duration), Medical Conditions (comorbidities), Family Health History.

Planned Analyses

Aim 1: Autonomic subtyping

Score COMPASS-31 per Sletten et al. [7]: 6 weighted subscale scores.
Multi-method clustering on the 6 subscales using complementary approaches including k-means, agglomerative hierarchical clustering, and latent profile analysis (LPA). Solutions will be compared across methods to identify robust subtypes.
Optimal number of clusters determined by fit indices including silhouette width, gap statistic, Bayesian Information Criterion (for LPA), and bootstrapped likelihood ratio test [15].
Cluster stability assessed via bootstrap resampling (≥500 iterations, Jaccard similarity) [21].
Characterize subtypes by: DSQ-SF domains, FUNCAP-27, Karnofsky, SF-36, Beighton, GAD-7 (item-level), Medical Conditions, Family Health History. Between-subtype comparisons via appropriate parametric or nonparametric tests with correction for multiple comparisons.
Test H1c (hypermobility × subtype association) via chi-square / logistic regression.

Contingency decisions:

IF silhouette < 0.25 for all solutions → apply PCA to COMPASS-31 subscales before clustering
IF optimal k = 1 → report as negative result; proceed with continuous COMPASS-31 analysis for Aim 2
IF Beighton completion < 50% → drop H1c; report as limitation

Aim 2: Post-COVID vs. non-COVID comparison

Classify onset trigger from structured Illness History data.
Primary analysis: covariate-adjusted linear regression (covariates: illness duration, age, sex) comparing COMPASS-31 subscales, DSQ-SF domains, FUNCAP-27 between groups.
Sensitivity analysis: inverse probability of treatment weighting (IPTW) [22].
Test H2c: appropriate regression model (e.g., multinomial logistic) predicting Aim 1 cluster membership from trigger type.
Compare comorbidity profiles (Medical Conditions) by trigger type.

Contingency decisions:

IF structured trigger data unavailable → extract onset trigger from free-text illness narrative fields via manual coding (PI codes all entries; a random subset re-coded after an interval to assess intra-rater reliability via Cohen's kappa)
IF post-COVID N < 100 → use IPTW only (preserves full sample); report as limited power
IF illness duration distributions do not overlap between groups → restrict comparison to matched duration window

Aim 3 (Exploratory): Longitudinal stability

For participants with 2+ COMPASS-31 assessments, assign subtype at each timepoint.
Calculate transition probability matrix.
Test whether retention rates differ by trigger type (chi-square).
IF N > 200 with repeat data → model predictors of transition via appropriate regression model (e.g., multinomial logistic).

Contingency decisions:

IF < 200 participants have repeat COMPASS-31 → descriptive transition rates only
IF < 50 → report as underpowered; flag for future study

Missingness handling: Multiple imputation (mice package) for variables with <30% missingness. Complete-case sensitivity analysis to confirm robustness. Variables with >50% missingness excluded from primary clustering but reported descriptively.

Multiple comparisons: Bonferroni correction for between-subtype comparisons. False discovery rate (FDR) correction for exploratory item-level analyses.

Software: Analyses will be conducted using open-source statistical software (e.g., R or Python) with established packages for clustering, causal inference, and multiple imputation, with all analysis code shared on GitHub for full reproducibility.

Limitations acknowledged: ME/CFS diagnosis is self-reported; misclassification is possible but mitigated by the DSQ-SF, which captures core ME/CFS symptom criteria. Registry participants are self-selected, likely skewing toward more engaged and potentially more severe patients; generalizability to the full ME/CFS population will be discussed in the manuscript. These are inherent to registry-based research and do not compromise the primary aim of identifying within-sample subtypes.

Confirmatory vs. exploratory: Aims 1-2 are confirmatory (preregistered hypotheses). Aim 3 and item-level GAD-7 analyses within Aim 1 are exploratory (clearly labeled).

Ethics and Data Management

Data collected under existing Advarra IRB approval (unhide® platform)
All data received as fully de-identified; no additional IRB required
Study protocol preregistered on ResearchHub prior to analysis
Analysis code shared on GitHub (public repository)
Aggregated results and derived datasets shared via OSF
Raw de-identified data sharing per Brain Inflammation Collaborative governance policies
Preprint published on ResearchHub regardless of outcome direction
Plain-language summary prepared for the ME/CFS patient community
Results disseminated through BIC webinar to patient/participant community
Dedicated episode of The DeSci Mic podcast covering the full process of working with ResearchHub from funding through publication
Progress updates shared via the ResearchHub "Updates" tab throughout the project
Social media dissemination campaign targeting ME/CFS, dysautonomia, and long COVID patient communities

4. Pilot Data

Published evidence, registry characteristics, and investigator experience directly support the feasibility of the proposed analyses:

Słomko et al. (2020) [8]: Identified 4 autonomic phenotypes in N=131 CFS patients using COMPASS-31 and physiological monitoring. Our study replicates this at substantially larger scale with additional instruments.
Mudie et al. (2024) [9]: Characterized joint hypermobility in N=815 ME/CFS participants from the same registry, finding JH+ patients have worse outcomes. Our study extends this by adding autonomic profiling.
Registry statistics: 3,000+ current participants, 5,000+ legacy [23]. Quarterly and semi-annual instrument collection provides longitudinal data for Aim 3. The registry also includes linked wearable data (48% of participants) and electronic health records (25%), available for future extensions of this work.
van Campen et al. (2020) [2]: Cerebral blood flow reductions during orthostatic stress in ME/CFS patients demonstrate the hemodynamic impact of autonomic dysfunction. Rekeland et al. (2022) [3]: COMPASS-31 scores correlate significantly with ME/CFS severity (p=0.037), validating the instrument for this population.
MCAM consortium (2025) [26]: Multi-site documentation of autonomic dysfunction prevalence and patterns in ME/CFS confirms the construct targeted by this study.

Investigator qualifications: The PI has direct research training in autonomic physiology, including POTS tilt table testing and systemic vascular resistance measurement at the Children's Heart Institute under Dr. Hasan Abdallah through Dysautonomia International, as well as stress biomarker research (salivary cortisol, alpha-amylase), bioinformatics (HHMI SEA-PHAGES program), and statistical analysis (SPSS). As a patient-researcher with lived experience of dysautonomia, eight years as a Patient Advocate Lead for Dysautonomia International (2012-2020) provides deep domain knowledge of the dysautonomia and ME/CFS patient populations, clinical landscape, and unmet needs that academic researchers often lack. The PI also has experience in the open science and decentralized science ecosystem, including community-driven research governance and open-access publishing. As an independent researcher not embedded in a traditional academic lab, this project is the kind of researcher-driven investigation that this funding mechanism was designed to support.

5. Budget Category Amount Justification

Personnel (PI effort, part-time across 6 months)	$7,000	Data cleaning, statistical analysis, manuscript preparation, and community dissemination
Compute / statistical software	$500	Cloud compute for bootstrap stability analyses
Data management and harmonization	$1,000	Variable harmonization between legacy and current datasets
Open-access publication fees	$1,500	ResearchHub publication or partial journal APC
Total	$10,000

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Rekeland IG, et al. Activity monitoring and patient-reported outcome measures in myalgic encephalomyelitis/chronic fatigue syndrome patients. PLoS ONE 17(9), e0274472 (2022).
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Autonomic Nervous System Subtypes in Myalgic Encephalomyelitis/CFS: A Large-Scale Registry Reanalysis

Autonomic Nervous System Subtypes in Myalgic Encephalomyelitis/CFS: A Large-Scale Registry Reanalysis

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