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Distinct Symptom Clusters Reflect Pathophysiological Mechanisms in ME/CFS
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Abstract
Introduction
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe multisystemic disease with a broad spectrum of symptoms. A previous study showed evidence that certain symptoms often occur together in ME/CFS patients. Therefore, literature-based, hypothesis-driven ME/CFS symptom groups have now been formed. This study aimed to empirically test and validate these ME/CFS symptom clusters using statistical methods.
Methods
Symptom responses from 748 adult ME/CFS patients (≥ 20 years; 608 female, 137 male, 3 non-binary) in the APAV-ME/CFS study were analyzed. Participants were recruited by self-activation and snowball sampling. Reported symptoms were assigned to predefined groups aligned with known pathophysiological hypotheses. Exploratory and Confirmatory Factor Analyses, followed by Structural Equation Modeling (SEM), assessed the coherence and distinctiveness of each cluster. To assess the robustness of the findings, the same analyses were repeated on a stratified, randomized training dataset.
Results
Brain
subgroup symptoms (brain fog, sensory hypersensitivity, visual disturbances, sleep disturbances, headaches) formed a single coherent factor with high loadings and excellent fit (RMSEA = 0.021; CFI = 0.996). Gastrointestinal (
Gut
) symptoms demonstrated stronger internal consistency than immunological (
Immune
) symptoms. Model comparisons favored a two-factor Gut versus Immune structure over a unidimensional model. All analyses consistently identified internally coherent, distinct symptom groups with robust fit indices. SEM incorporating a common latent factor also yielded excellent fit for the vegetative symptom complex (
Vegetative
).
Conclusions
Findings reinforce ME/CFS as a complex neuro-immunological multisystem disease and show that symptoms can be attributed to functional body systems. Symptom-based subgrouping may support pathophysiology-guided diagnosis and inform the development of individualized therapeutic approaches.
Title: Distinct Symptom Clusters Reflect Pathophysiological Mechanisms in ME/CFS
Description:
Abstract
Introduction
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe multisystemic disease with a broad spectrum of symptoms.
A previous study showed evidence that certain symptoms often occur together in ME/CFS patients.
Therefore, literature-based, hypothesis-driven ME/CFS symptom groups have now been formed.
This study aimed to empirically test and validate these ME/CFS symptom clusters using statistical methods.
Methods
Symptom responses from 748 adult ME/CFS patients (≥ 20 years; 608 female, 137 male, 3 non-binary) in the APAV-ME/CFS study were analyzed.
Participants were recruited by self-activation and snowball sampling.
Reported symptoms were assigned to predefined groups aligned with known pathophysiological hypotheses.
Exploratory and Confirmatory Factor Analyses, followed by Structural Equation Modeling (SEM), assessed the coherence and distinctiveness of each cluster.
To assess the robustness of the findings, the same analyses were repeated on a stratified, randomized training dataset.
Results
Brain
subgroup symptoms (brain fog, sensory hypersensitivity, visual disturbances, sleep disturbances, headaches) formed a single coherent factor with high loadings and excellent fit (RMSEA = 0.
021; CFI = 0.
996).
Gastrointestinal (
Gut
) symptoms demonstrated stronger internal consistency than immunological (
Immune
) symptoms.
Model comparisons favored a two-factor Gut versus Immune structure over a unidimensional model.
All analyses consistently identified internally coherent, distinct symptom groups with robust fit indices.
SEM incorporating a common latent factor also yielded excellent fit for the vegetative symptom complex (
Vegetative
).
Conclusions
Findings reinforce ME/CFS as a complex neuro-immunological multisystem disease and show that symptoms can be attributed to functional body systems.
Symptom-based subgrouping may support pathophysiology-guided diagnosis and inform the development of individualized therapeutic approaches.
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