{"paper_id":"099f6558-8375-4193-a18b-7bd0127d175f","body_text":"Distinct Symptom Clusters Reflect Pathophysiological Mechanisms in ME/CFS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinct Symptom Clusters Reflect Pathophysiological Mechanisms in ME/CFS Lotte Habermann-Horstmeier, Lukas M. Horstmeier This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8319139/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) symptom clusters Exploratory Factor Analysis (EFA) Confirmatory Factor Analysis (CFA) Structural Equation Modeling (SEM) Figures Figure 1 Figure 2 Introduction Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS; ICD-10-GM-2025: G93.3) is a severe multisystemic disease with a broad spectrum of symptoms [1]. It is usually triggered by a viral infection and often leads to severe disability and a significant reduction in quality of life [2]. ME/CFS is characterized by disorders of the organism's control organs (nervous, endocrine and immune systems) and cellular energy supply as well as chronic mild inflammation [3], so that homeostasis in the body's open systems can no longer be maintained [1, 4]. The central symptom is post-exertional malaise (PEM). PEM is a pronounced and persistent intensification of symptoms after minor physical or mental exertion, which usually occurs after a time delay and generally lasts for more than 24 hours [5]. It is accompanied by a high degree of physical and mental loss of strength and energy as well as a rapid deterioration of the general condition (fatigue). In addition, there are a variety of symptoms that fluctuate in type and intensity as well as over the course of the disease, including neurocognitive and immunological symptoms, sleep disorders and numerous neurovegetative regulatory disorders [6, 7]. Since little is known about the development of various symptoms over the course of the disease, the authors have already conducted an exploratory study investigating possible changes in ME/CFS symptoms over the course of the disease (Study 1) [8]. The results found there suggested that, in a second phase, symptom clusters should be identified whose symptoms occur significantly more frequently together in ME/CFS patients (= Study 2, the results of which are presented here). These will then be examined in more detail in a third to fifth phase (Studies 3 to 5), with possible influences in terms of sex and duration of illness being investigated (see Figure 1). The aim of this study (Study 2) is to empirically test and validate the ME/CFS subgroups suggested by the results of Study 1 (such as a statistical correlation between gastrointestinal complaints and (food) intolerances, and between brain fog and sleep disorders) using statistical methods as Exploratory and Confirmatory Factor Analyses. It is expected that symptom groups can be identified that can be related to known pathophysiological processes, especially in the central and autonomic nervous systems. Knowledge of such potential associations could be important for ME/CFS diagnosis, but also for the development of preventive or therapeutic approaches. It could also provide researchers with indications as to where underlying pathophysiological relationships should be investigated in more detail. Methods This study is based on data from the APAV-ME/CFS study. The predominantly qualitative APAV-ME/CFS study focused primarily on the doctor-patient relationship in ME/CFS from the perspective of patients and their relatives. In addition, a range of basic data on gender/sex, age, etc., as well as information on whether the participant has a medical diagnosis of ME/CFS was also collected (see below). Sampling A total of 1,238 people (ME/CFS patients and their close relatives) took part in the APAV-ME/CFS study. They were recruited through self-activation and according to the snowball principle. To this end, contact was made in summer 2022 with six ME/CFS patient organizations in Germany as well as the Fatigue Centre at Charité in Berlin and the Chronic Fatigue Centre for Young People at the Technical University of Munich. The organizations forwarded the call for study participation to ME/CFS patients and their relatives. Through them, the information reached other affected persons. Long-term or post-COVID patients who did not meet the Canadian or International Consensus Criteria for ME/CFS were not included in the study. Study participants The present study (Study 2) includes the data of the 748 adult participants in the APAV-ME/CFS study who had already been medically diagnosed with ME/CFS (> 20 years; 608 ♀, 137 ♂, 3 non-binary). The data of the participating children and adolescents (up to 20 years of age), the relatives of ME/CFS patients and the diseased persons who had not yet been medically diagnosed with ME/CFS were excluded as well as questionnaires that lacked information on the relevant basic questions like gender/sex, age, etc., and information on whether the participant was medically diagnoses with ME/CFS (Table 1, Figure 2). Table 1 Description of the adult subjects (> 20 years) with medically diagnosed ME/CFS, differentiated by gender and age group. Age groups 21-30 yrs. 31-40 yrs. 41-50 yrs. 51-60 yrs. ≥ 60 yrs. Total [n] % [n] % [n] % [n] % [n] % [n] % Women 51 8.4 115 18.9 183 30.1 197 32.4 62 10.2 608 100 Men 14 10.2 28 20.4 30 21.9 49 35.8 16 11.7 137 100 Non-binar 3 100.0 0 0.0 0 0.0 0 0.0 0 0.0 3 100 Total 68 9.1 143 19.1 213 28.5 247 33.0 78 10.4 748 100 Abbreviations: n = number of individuals, % = percentage, yrs. = years Prior to participation, all respondents were informed about the study’s objectives, structure, and content. Participation was voluntary and anonymous, with no possibility of linking responses to individual identities. Informed consent was obtained through completion of the online questionnaire. To ensure appropriate inclusion, participants provided basic demographic and diagnostic information or symptom-related self-reports. Study implementation and data processing Almost all respondents completed the questionnaire we developed, which consisted of a short quantitative and a more extensive qualitative section, online (n = 1,118). In addition, the questionnaire was also available for download, so 120 participants sent us the completed questionnaire by email, fax or post. The quantitative part of the questionnaire mainly collected basic information in the form of closed questions, e.g. about the age and gender of the respondents. Questions were also asked about the duration of the illness (self-assessment), the existence of a medical diagnosis, the date of diagnosis, the specialty and number of doctors consulted, the number of hospital and rehabilitation stays, the predominant ME/CFS symptoms, and the improvement/worsening of symptoms as a result of these hospital stays. In the subsequent statistical analysis of the symptoms regularly occurring in the test subjects - except PEM and fatigue, as these are the basic requirements for an ME/CFS diagnosis and therefore must be present in all patients -, tests for bivariate associations were first carried out, including a check of internal consistency (cross-tabulations, Cramér's V and tetrachoric correlations). The Exploratory (EFA) and Confirmatory Factor Analysis (CFA) then showed whether the previously defined, hypothesis-driven and pathophysiologically based subgroups were actually statistically detectable clusters. All analyses were based on the sample of 748 patients. This provides a sufficient number of cases for exploratory and confirmatory factor analyses. Although it is methodologically desirable to perform EFA and CFA on separate subsamples, we opted for a combined analysis because the sample size is too small to be halved. To assess the robustness of the findings, the same analyses were repeated on a stratified, randomized training dataset derived from the full sample [1] . The dataset was stratified by sex and illness duration to ensure that both variables were proportionally represented across all subsamples. This approach enhances the representativeness of the data and allows for meaningful comparisons between groups. Randomization within each stratum was applied to minimize potential biases and to prevent systematic distortions in the results. This procedure is particularly relevant in ME/CFS research, as both sex and illness duration are known to influence symptom profiles and underlying pathophysiological mechanisms. By employing a stratified, randomized split, we established a methodologically sound basis for robust statistical analyses (e.g., EFA, CFA) and enabled a valid cross-validation of findings derived from the full dataset. Results In the following, hypothesis-led subgroups will be compared with the empirical-statistical symptom groups. In other words, it should be shown, which theoretically defined clusters are statistically confirmed, where there may be deviations or overlaps and how new hypotheses or subtypes can be derived from this. Table 2 shows the four hypothesized subgroups ( Brain , Vegetative , Gut and Immune ) including the most important pathophysiological mechanisms discussed in the literature. Table 2 Classification of the ME/CFS symptoms surveyed into functional clusters based on pathophysiological concepts. Neurocognitive-sensory symptom group ( Brain ) For the statistical validation of the theoretically defined clusters, cross-tabulation analyses and calculations of Cramér's V and tetrachoric correlations were first carried out in the area of the potential neurocognitive-sensory symptom group ( Brain ). An exploratory factor analysis was then carried out, followed by a Confirmatory Factor Analysis, which was used to test the fit of the Brain model to the data collected. - Bivariate associations and internal consistency First, a neurocognitive-sensory symptom group ( Brain ) was defined, in which pathophysiological changes in the brain are assumed to be the central triggers for early-onset ME/CFS symptoms: (1) cognitive impairment (brain fog), (2) sensory hypersensitivity, (3) sleep disturbances, (4) visual disturbances and (5) headaches. Cross-tabulations and χ²-tests revealed significant associations, especially between cognitive impairment and sleep disturbances or sensory hypersensitivity as well as between visual disturbances and sensory hypersensitivity. The tetrachoric correlations (r) were consistently above the values of Cramér's V and more adequately reflect the underlying continuous relationships. Almost all symptom pairs showed medium to high correlations (r ≥ 0.34), indicating substantial latent coupling. The consistency of the results is confirmed by the consistency of the ranking of the association strengths in both metrics (Table 3). - Explorative factor analysis Box 1: Model specification and eigenvalue criterion Method Principal factors on five binary symptoms: - Brain fog - Sensory hypersensitivity - Headache - Visual disturbances - Sleep disorders Retention according to Kaiser criterion (eigenvalue > 1): One factor The exploratory factor analysis (EFA) as a multivariate method for data reduction and structure identification serves to uncover latent factors behind the observed variables without a priori model specification (for details on the model specification and the eigenvalue criterion, see Box 1). The first eigenvalue was 1.20, all other eigenvalues were below 1. The likelihood ratio test against the independence model showed a significant deviation [χ²(10) = 378.20, p < 0.0001], which indicates substantial correlations between the items. The factor loadings on the first factor ranged from 0.43 to 0.55 and were thus classified as moderate. However, the uniqueness values of 0.70-0.82 showed that only 18-30% of the variance of the symptoms could be explained by the common factor. Since only one factor was extracted, the Varimax rotation did not lead to any change, as expected. Box 2: Model specification One-factor model with latent variable Brain Five binary indicators: - Brain fog - Sensory hypersensitivity - Headache - Visual disturbances - Sleep disorders Estimation with maximum likelihood (by default [ML]) Factor loading of Brain fog fixed to 1 to set the scale of the factor - Confirmatory factor analysis A Confirmatory Factor Analysis (CFA) within the Structural Equation Modeling (SEM) was used to estimate the strength of the relationships between the latent factor Brain and the observed variables (Box 2). The model was fitted using maximum likelihood estimation. Table 4 shows that all factor loadings were high and statistically significant (p < 0.001). The residual variances of 0.06 to 0.17 prove that the factor explains a large part of the variance in the respective indicators. The latent variance of the Brain factor was estimated at 0.0301 (SE = 0.0048). The 95 % confidence interval was completely above zero, which indicates a significant variance of the factor. Table 3 Statistical correlations in the neurocognitive-sensory symptom group ( Brain ). Cross-tabulations, Cramér's V and tetrachoric correlations Pair of variables N (total) χ² (df) p-value Cramér's V Interpretation V according to Cohen Tetrachoric r Interpretation 1 according to Cohen Brain fog ⬌ Headache 748 38.27 < 0.0001 0.2262 moderate correlation (above weak effect) 0.4404 medium effect Brain fog ⬌ Visual disturbances 748 57.30 < 0.0001 0.2768 moderate to strong correlation 0.5418 large effect Brain fog ⬌ Sleep disorders 748 70.00 < 0.0001 0.3059 moderate correlation 0.5622 large effect Brain fog ⬌ Sensory hypersensitivity 748 72.36 < 0.0001 0.3110 moderate correlation 0.5659 large effect Visual disturbances ⬌ Headache 748 37.56 < 0.0001 0.2241 moderate correlation 0.3589 medium effect Visual disturbances ⬌ Sleep disorders 748 32.84 < 0.0001 0.2095 moderate correlation 0.3888 medium effect Visual disturbances ⬌ Sensory hypersensitivity 748 69.24 < 0.0001 0.3042 moderate correlation 0.5283 large effect Headache ⬌ Sleep disorders 748 25.14 < 0.0001 0.1833 weak to moderate correlation 0.3418 medium effect Headache ⬌ Sensory hypersensitivity 748 60.33 < 0.0001 0.2840 moderate correlation 0.4906 large effect Sleep disorders ⬌ Sensory hypersensitivity 748 47.43 < 0.0001 0.2518 moderate correlation 0.4635 large effect Abbreviations: N = number; χ² (df) = Chi 2 test (degrees of freedom); p-value = probability; Cramer's V = variable V, which quantifies the statistical relationship between two nominally scaled variables; tetrachoric r = tetrachoric correlation coefficient Table 4 Confirmatory Factor Analysis (CFA) for the ‘neurocognitive-sensory symptom group model’ ( Brain ). Step 1: Charges, residual variances and intercepts Indicator Factor loading λ SE z-value p-value 95 % CI for λ Residual variance Brain fog 1 (fixiert) - - - - 0.0654 Sensory hypersensitivity 1.2522 0.1417 8.84 < 0.001 [0.975, 1.530] 0.0911 Sleep disorders 0.9263 0.1091 8.45 < 0.001 [0.712, 1.140] 0.0958 Visual disturbances 1.3846 0.1634 8.47 < 0.001 [1.064, 1.705] 0.1741 Headache 1.1718 0.1512 7.75 < 0.001 [0.875, 1.468] 0.1727 Abbreviations: λ = lambda; SE = standard error, p-value = probability; z-value measures how many standard deviations a data point is away from the mean; CI = confidence interval Step 2: Latent variance Value Variance of \"Brain\" 0.0301 SE 0.0048 95 % CI [0.0230, 0.0411] ⟶ Factorized variance significant > 0 Step 3: Global fit indices Fit index Value Recommended limit value Interpretation χ² (df=5) 6.658 p > 0.05 Not significant ⟶ good fit RMSEA 0.021 ≤ 0.05 Excellent fit 90 % CI RMSEA [0.000, 0.058] - Tight interval pclose 0.886 > 0.05 RMSEA ≤ 0.05 is plausible CFI 0.996 > 0.95 Excellent fit TLI 0.991 > 0.95 Excellent fit SRMR 0.016 ≤ 0.08 Excellent CD (R 2 equivalent) 0.645 - Explains 64.5 % of the total variance Abbreviations: χ² (df) = Chi 2 test (degrees of freedom); p-value = probability; RMSEA = Root Mean Square Error of Approximation; CI = confidence interval; pclose = Probability RMSEA; CFI = Comperative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual; CD = Coefficient of Determination; R 2 = statistical measure of how closely the data fit the fitted regression line In summary, the high and significant loadings (Table 4) confirm that the symptoms brain fog, sensory hypersensitivity, visual disturbances, sleep disturbances and headaches represent a coherent construct Brain . The low residual variances indicate that the latent factor explains a high proportion of the symptom variance. The global fit indices (non-significant χ², RMSEA = 0.021, CFI = 0.996, SRMR = 0.016) indicate a very good model fit. Symptom group Vegetative dysregulation (Vegetative) - Bivariate associations and internal consistency In order to check the correlation between the Vegetative symptoms, cross-tabulations, Cramér's V, tetrachoric correlations and Cronbach's alpha were first determined (Table 5). Cardiovascular problems correlated significantly with breathing problems and temperature intolerance. The corresponding tetrachoric correlations were r=0.605 (SE=0.049) and r=0.620 (SE=0.048). Cronbach's alpha for the two pairings was α=0.53 and α=0.55, respectively, demonstrating moderate internal consistency - with some heterogeneity. Table 5 Bivariate associations and reliability of vegetative symptoms V ariable pair N (total) χ² (df=1) p -value Cramér’s V Tetrachoric ϱ (SE) Crohnbach’s α Interitem covariance Cardiovascular problems ⬌ Breathing problems 748 101.25 < 0.001 0.368 0.605 (0.049) 0.530 0.067 Cardiovascular problems ⬌ Temperature intolerance 748 110.75 < 0.001 0.385 0.620 (0.048) 0.5551 0,067 Abbreviations: N = number; χ² (df) = Chi 2 test (degrees of freedom); p-value = probability; Cramer's V = variable V, which quantifies the statistical relationship between two nominally scaled variables; tetrachoric ϱ (SE) = tetrachoric rho (standard error) - Explorative Factor Analysis A one-factor solution on four Vegetative indicators (cardiovascular, respiratory problems, temperature intolerance, gastrointestinal complaints) resulted in a single eigenvalue > 1 (eigenvalue=1.153), which explains 28.8 % of the total variance. The unweighted factor loadings ranged from 0.477 to 0.585, uniqueness values were between 0.658 and 0.773. The factor analysis thus supports a unidimensional vegetative construct ( Vegetative ; Table 6). Table 6 Principal factors of the four vegetative indicators. Variable Load weight λ to factor 1 Uniqueness Eigenvalue factor 1 Explained variance (%) Cardiovascular problems 0.585 0.658 1,153 28,8% Breathing problems 0.553 0.716 Temperature incompatibility 0.547 0.701 Gastrointestinal complaints 0.477 0.773 LR test: χ²(6)=370.2, p<.001 (against independence assumption) - Logistic regression In a logistic regression model for the prediction of cardiovascular problems (0/1) [3] by breathing problems, temperature intolerance and gastrointestinal complaints, the values summarized in Table 7 were found. Respiratory problems and the intolerance of hot and cold (outside) temperatures multiply the odds for cardiovascular problems by a factor of 4 to 4.5, gastrointestinal complaints by a factor of 2. The pseudo-R² according to McFadden is 0.233, indicating a reasonably good model fit compared to a null model. Table 7 Predictors for cardiovascular problems (0/1) - Logistic regression Predictor β (SE) z p 95 % CI (β) OR [95 % CI] Breathing problems 1.466 (0.226) 6.49 < 0.001 [1.024, 1.909] 4.33 [2.79, 7.10] Temperature incompatibility 1.521 (0.223) 6.82 < 0.001 [1.084, 1.958] 4.58 [2.96, 8.03] Gastrointestinal complaints 0.703 (0.226] 3.11 0.002 [0.259, 1.146] 2.02 [1.30, 3.14] Constant -0.606 (0.193) -3.14 0.002 [-0.983, -0.228] - Pseudo R 2 0.233 - Confirmatory Factor Analysis Structural Equation Modeling (SEM) with a common latent factor Vegetative for all four vegetative indicators provided excellent fit indices. The factor loadings were high and significant (Table 8). The residual variances ranged from θ=0.0879 to θ=0.1600, and the latent variance of the factors was φ=0.0617 (SE=0.0085). Proposed covariances between residuals of cardiovascular and gastrointestinal problems and between respiratory problems and temperature intolerance could not be confirmed either substantively or statistically. Table 8 SEM model with a latent-variable factor for vegetative symptoms and fit indices for a common factor Vegetative . Indicator Load weight λ SE Residual variance (SE) Latent variance (SE) Cardiovascular problems 1 (fixed) - 0.0879 (0.0073) 0.0617 (0.0085) Breathing problems 1.056 0.109 0.1536 (0.0105) Temperature incompatibility 1.048 0.106 0.1352 (0.0096) Gastrointestinal complaints 0.883 0.103 0.1600 (0.0099) χ ²(2) p RMSEA [90 % CI] pclose CFI TLI SRMR AIC BIC 5.38 0.068 0.047 [0.000 – 0.098] 0.445 0.991 0.972 0.015 3237.65 3293.06 Abbreviations: λ = Lambda; SE = Standard error, χ² (df) = Chi 2 test (degrees of freedom); p-value = Probability; RMSEA = Root Mean Square Error of Approximation; CI = Convidence Interval; pclose = Probability RMSEA; CFI = Comperative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual; AIC = Akaike information criterion, BIC = Bayes information criterion Gastrointestinal symptom group (Gut) and immunological symptom group (Immune) To test the dimensional structure of gastrointestinal and immunological symptoms, three structural equation models (SEM) were estimated: a one-factor model for gastrointestinal and immune-related symptoms, respectively, and a two-factor model with correlated latent variables. The most important parameters and fit indices are summarized in Table 9. Table 9 Comparison of three structural equation model variants. Models A and B are separately estimated one-factor models, model C is a jointly estimated two-factor model with correlated latent variables. Both Cramér's V and tetrachoric correlations were calculated to assess the association within the Gut (gastrointestinal complaints, (food) intolerances) and Immune (susceptibility to infections, flu-like symptoms) symptom groups. With a Cramér's V of 0.405 and a tetrachoric correlation of r = 0.614 (SE = 0.044), the Gut group showed a significantly stronger association between the symptoms than the Immune group (V = 0.294; r = 0.505, SE = 0.053). These results suggest a higher symptomatic coherence within the Gut symptom group (Table 10a). While Cramér's V as a dimensionless measure describes the strength of the association between two categorical variables, the tetrachoric correlation allows an estimation of the relationship between the underlying latent constructs under the assumption of a bivariate normal distribution. The parallel consideration of both measures thus provides complementary evidence for the structural coherence of the respective symptom groups. In addition, internal consistency was determined as a measure of the reliability of the groups using Cronbach's alpha. A higher value of α = 0.575 was found for the Gut group than for Immune (α = 0.450). Although both values are below the threshold value of .70 often used as a guideline in psychometric studies, lower reliabilities are often to be expected with clinical symptom data due to great heterogeneity and are nevertheless meaningful. The combination of effect sizes and consistency scores thus supports the assumption that the Gut symptom group represents a more homogeneous sub-construct within the ME/CFS symptom spectrum than the Immune symptom group (Table 10b). The separately estimated models converged without any problems and yielded plausible factor loadings. In particular, the model for immune-related symptoms (susceptibility to infection, flu-like symptoms) showed a high factor loading of λ = 0.74 with simultaneously low residual variance, which indicates a strong link between the indicators and the underlying factor. In contrast, the joint one-factor model showed a significantly poorer model fit (χ²(2) = 23.78, p < .001), indicating that the joint variance of all four symptoms cannot be adequately explained by a single latent factor. The results therefore clearly support a differentiated modeling, i.e. the symptoms should be recorded in two separate groups - a gastrointestinal group ( Gut ) consisting of gastrointestinal complaints and (food) intolerances and an immunological group ( Immune ) consisting of susceptibility to infections and flu-like symptoms. At the same time, the cross-tabulation of GutScore and ImmuneScore reveals a significant association between gastrointestinal and immune-related symptoms (χ²(4) = 89.24, p < 0.001). This indicates that many participants exhibit symptoms in both domains simultaneously. For example, among those with high GutScore (= both Gut symptoms are present), more than half (54.4%) also show high ImmuneScore (= both Immune symptoms are present), suggesting a frequent co-occurrence of these symptom clusters. Table 10 a. Statistical correlations in the two symptom groups Gut and Immune . Cross-tabulations, Cramér's V and tetrachoric correlations. Variable pair N (total) χ² (df) p-value Cramér’s V Interpretation V according to Cohen Tetrachoric r Interpretation r according to Cohen Cronbach’s Alpha Interitem covariance Gut : Gastrointestinal problems ⬌ (Food) intolerances 748 122.54 1 < 0.001 0.405 Moderate to strong (clear) correlation 0.614 (SE = 0.044) strong effect 0.575 0.090 Immune : Flu-like symptoms ⬌ Susceptibility to infections 748 64.720 1 < 0.001 0.294 almost moderate correlation 0.505 (SE = 0.053) strong effect 0.450 0.063 b. SEM analysis Model Indicator Factor loading λ (SE) Residual variance (SE) Latent variance (SE) χ²(df), p RMSEA (90 % CI), pclose CFI TLI SRMR Gut (1- factor) Gastrointestinal complaints 1 (fixed) ≈ 0* 0.2391 (0.0233) – (df = 0) 1.000 1.000 (Food) intolerances 0.4338 (0.0179) 0.19995 (0.01083) Immune (1-factor) Flu-like symptoms 1 (fixed) ≈ 0* 0.2491 (0.0323) – (df = 0) 1.000 1.000 Susceptibility to infection 0.3448 (0.0183) 0.22758 (0.01204) Gut & Immune (2-factors) Susceptibility to infection 1 (fixed) 0.19934 (0.01269) 0.04342 23.78(2), ≤ 0.000 0.121 [0.080, 0.166], 0.003 0.923 0.769 0.038 Flu-like symptoms 0.7330 (0.1063) 0.15459 (0.00912) Gastrointestinal complaints 1.2327 (0.1722) 0.13250 (0.11057) (Food) intolerances 1.3526 (0.1878) 0.14803 (0.01298) Covariance Gut – Immune 0.03792 (0.00675) * Fixed charge leads to arithmetically vanishing residual variance. Abbreviations: N = number; χ² (df) = Chi 2 test (degrees of freedom); p-value = probability; Cramér's V = variable V, which quantifies the statistical relationship between two nominally scaled variables; Tetrachoric r = Tetrachoric correlation coefficient, RMSEA = Root Mean Square Error of Approximation; CI = Convidence Interval; pclose = Probability RMSEA; CFI = Comperative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual Special features of the symptoms ‘gastrointestinal complaints’ and ‘breathing difficulties’ Two of the symptoms ticked by the participants (gastrointestinal complaints and breathing difficulties) could each be assigned to two symptom groups, so they play a special role. - Gastrointestinal complaints Gastrointestinal complaints belong to the Vegetative subgroup, but together with (food) intolerances they define the Gut subgroup. The results of the factor analysis in the Vegetative subgroup (Table 8) show that gastrointestinal complaints only load moderately on the general vegetative factor (λ = 0.477; Uniqueness = 0.773). At the same time, the two-factorial Structural Equation Modeling (SEM; Gut vs. Immune, Table 10b) resulted in a factor loading of λ = 1.00 for gastrointestinal complaints in the pure Gut model, while (food) intolerances were associated with λ = 0.434. The bivariate associations also support these findings. Thus, gastrointestinal complaints correlated significantly with other vegetative indicators (e.g. Cramér's V: 0.37-0.39; tetrachoric r ≈ 0.62). On the one hand, they reflect the generalized vegetative dysregulation; on the other hand, the Gut construct forms an independent, relatively homogeneous dimension of gastrointestinal phenomena (Cronbach's α = 0.575; interitem covariance = 0.090), which will be discussed further in the discussion and in the planned third part of our five-stage study series. - Breathing problems The statistical analyses (cross-tabulations, association and reliability measures), which are not explained in detail here, show a moderate association between muscle and breathing problems (Cramér's V = 0.292; ρ = 0.530), which indicates a clear, but not very strong, co-occurrence of these symptoms. At the same time, there is a low internal consistency (Cronbach's α = 0.433), which is below established threshold values for scale homogeneity (α ≥ 0.70). This means that muscle and breathing problems form a partially overlapping but not uniform dimension. At the same time, the symptom \"breathing difficulties\" is part of the vegetative subgroup, which shows in Table 8 that breathing difficulties load moderately on the general vegetative factor (λ = 0.553; uniqueness = 0.716). Possible pathophysiological correlations underlying this will be discussed in a later publication. Brief summary of the statistical results Highly significant correlations were found within the previously pathophysiologically defined symptom subgroups. The statistical calculations confirmed symptom clusters in the following areas: - neurocognitive-sensory - vegetative - gastrointestinal - immunological The fact that the neurocognitive-sensory symptoms in particular represent a coherent Brain construct was shown above all by the high and significant loadings in the factor analysis. At the same time, low residual variances indicated that the latent factor explains a high proportion of the symptom variance. In addition, the global fit indices demonstrated a very good model fit. Structural Equation Modeling (SEM) with a common latent factor also provided very good fit indices for the Vegetative symptom complex. A comparison of the two one-factor models for the gastrointestinal symptoms ( Gut ) and the symptoms assigned to the immune group ( Immun e) with a joint two-factor model with correlated latent variables showed a significantly poorer model fit for the two-factor model. This supports the assumption of two clearly distinguishable but related latent constructs. Comparison of hypothesis-driven symptom groups and empirically-statistically found symptom clusters The aim of this study was to empirically test and validate hypothesized ME/CFS symptom groups using statistical methods. Table 11 summarizes the extent to which the four theoretically defined ME/CFS subgroups were confirmed by the statistical analyses performed. It also indicates the overlaps identified (\"bridging phenomena\"). Bridging phenomena are defined here as those cases in which symptoms could be assigned to more than one functional subsystem, as they mediate both statistically and pathophysiologically between two subgroups. They are therefore not pure markers of a single subgroup, but connect two dimensions and show that the subgroups are not strictly separated from each other, but are functionally networked. Table 11 Extent to which the four theoretically defined ME/CFS subgroups were confirmed by the statistical analyses performed Discussion The present analysis is the second part of a five-stage series of studies on the symptoms of ME/CFS. In the first, exploratory part of the study series, there was no correlation between the duration of the disease and the frequency of the individual symptoms for most symptoms. However, there were indications of differences between the sexes and a parallel occurrence of individual symptoms [8]. We therefore wondered whether it would be possible to identify such symptom groups and then relate them to known pathophysiological correlations in order to develop gender-appropriate preventive or therapeutic approaches at a later stage. In this study, we were able to identify at least four stable symptom groups ( Brain , Immune , Vegetative , Gut ) that can be plausibly assigned to pathophysiological mechanisms. For this purpose, we used data from 748 medically diagnosed ME/CFS patients, which we analyzed statistically primarily with the help of factor analyses and structural equation modeling. To validate the robustness of our findings, all analyses were repeated on a stratified, randomized training dataset derived from the full sample. Results obtained from the stratified, randomized training dataset closely mirrored those from the full dataset, with only very minor deviations. Assignment of the results to the pathophysiology of ME/CFS Our results emphasize that neurocognitive symptoms ( Brain ) are associated with proven changes in the brainstem, thalamus and prefrontal cortex. These include, for example, structural changes [9-11] and significantly reduced blood flow to the brainstem [12], extensive structural changes in the white and gray matter [13] as well as numerous pathophysiological processes [14, 15]. For example, the prefrontal cortex, which is altered in ME/CFS, may be involved in the development of sleep disorders and sensory hypersensitivity. Pathological changes in the thalamus can both impair sensory stimulus processing and disrupt sleep. Bechny et al [16] also recently found pathologically altered sleep patterns in people with ME/CFS. Restricted cerebral blood flow can contribute to the development of cognitive impairments such as brain fog, word-finding difficulties and memory problems [17]. Cerebral hypoperfusion is also a plausible cause for the development of headaches and visual disturbances. In the immunological subgroup ( Immune ), the symptoms 'increased susceptibility to infection' and 'flu-like symptoms' can be explained by the proven NK/T-cell dysfunction and immunosuppression as well as the chronic activation of proinflammatory cytokines (→ Sickness Behavior [18]) [19, 20]). In addition, there is the reactivation of latent viruses in the body (e.g. EBV, HHV-6; [21]), which contributes to immune stress and the phenomenon of neuroinflammation [22]. The neuroinflammatory effects in turn impair the blood-brain barrier [23]. Vegetative symptoms ( Vegetative : cardiovascular, respiratory and temperature disorders) reflect a sympathovagal imbalance, which can manifest itself in ME/CFS patients in reduced heart rate variability (HRV), tachycardia, reduced stroke volume and circulatory instability when standing up (→ orthostatic intolerance & POTS) [24]. Sympathetic overactivity leads to inadequate vasoconstriction or dilation and blood pooling in the legs, while the brain is undersupplied [25]. Vagus dysfunction can be associated with delayed gastric emptying, nausea and dyspepsia [26], while dysregulation of autonomic pain modulation in the gut increases the sensation of abdominal pain and bloating [27]. Overactivity of the sympathetic nervous system, exacerbated by dysregulation of the autonomic respiratory center (chemoreceptors, baroreceptors), can affect respiratory rate and CO₂ partial pressure and lead to dyspnea and dizziness via reduced cerebral perfusion [28]. Vegetative dysregulation of the skin blood vessels and sweat glands can contribute to heat or cold intolerance [29]. However, gastrointestinal symptoms in ME/CFS do not only occur as a result of autonomic dysregulation, but also as part of an independent Gut subtype characterized by pathological gut mechanisms. The latter is associated with specific enteric pathomechanisms, such as reduced intestinal microbiome diversity or dysbiosis [30, 31], often in combination with increased intestinal permeability (\"leaky gut\"), which can result in bacterial translocation and immune activation [32-34]. Plausible explanations for the existence of such enteric-neuro-immunological pathways include the dysregulation of neurotransmitters, SCFAs and cytokines [31]. In particular, the classification of gastrointestinal complaints into two symptom groups showed that there are ‘bridging phenomena’ in ME/CFS symptoms that make it clear that the individual subgroups are distinguishable but functionally interconnected. Our results also show how important the choice of words is when recording symptoms in the context of ME/CFS diagnosis (for example, it is still typical to use the term 'fatigue' in the sense of tiredness/sleepiness instead of asking about 'severe physical lack of strength and energy'). This lack of linguistic precision on the part of the treating physicians (unclear terminology) was repeatedly mentioned by the test subjects in the qualitative part of the APAV-ME/CFS study [35] and has a considerable impact on the course of diagnosis and subsequent treatment. Comparison with previous studies on cluster and factor analysis in ME/CFS Earlier cluster and factor analyses (e.g. [6, 36-40]) arrive at similar summarizing subgroups. They mainly contain symptoms from our Brain , Vegetative and Immune subgroups. Differences relate in particular to the classification of various gastrointestinal and neuroendocrine symptoms. Overall, however, there is remarkable consistency across different methods and samples. Comparison with CCC-based diagnostic tools A comparison with CCC-based diagnostic questionnaires frequently used in Germany (Charité, MBSQ; [41]) makes it clear that our clusters capture additional ‘bridging phenomena’ (e.g. gastrointestinal complaints or respiratory problems) that can be assigned to several subgroups and are missing in established instruments. In addition, relevant symptoms such as muscle weakness or visual disturbances are missing. This argues for the further development of diagnostic questionnaires with more differentiated subscales. Implications for research, diagnostics and therapy The structured comparison thus shows that our results are not only statistically robust, but also allow direct conclusions to be drawn about pathophysiology and clinical practice. The results also support the model of ME/CFS as a neuro-immunological multisystem disease. Further research could replicate the clusters found in representative, independent, larger cohorts and possibly also include longitudinal analyses. In practice, validated diagnostic tools with differentiated subscales for bridging symptoms should be developed to enable personalized treatment approaches. Important symptoms that are currently missing (e.g. muscle weakness, visual disturbances) should also be added. It is also important to review the already planned measurement invariance across age and gender groups as well as the duration of the disease, as a preliminary study already showed indications of large differences in the frequency of (food) intolerances in the first five years of the disease in male and female ME/CFS patients [42]. More evidence from a structural analysis perspective for ME/CFS as a neuro-immunological disease The clearly definable clusters with high factor loadings and good model fits show that the ME/CFS symptoms occur at the level of functional body systems. Psychosocial or affective variables do not play a central role in the models presented here and also have no overarching explanatory power as in the classical psychosomatic interpretation. This is consistent with the finding of a large number of replicated, diverse blood biomarkers in ME/CFS patients with PEM in a recently published study, which has massively weakened the assumption that ME/CFS is caused by deconditioning and stress intolerance [3]. Our statistical models show not only a high structural coherence within the functional subsystems, but also a systematic separation of these clusters that is consistent with known pathophysiological mechanisms. In addition, the participants repeatedly emphasized in the qualitative part of the study that pathobiological factors played a central role in the development of the disease (viral infection as a trigger, no pre-existing trauma or chronic stress) and symptom persistence. Almost all affected test subjects with psychological/psychosomatic diagnoses had good arguments for these being misdiagnoses [42, 43]. The present results thus also support the growing body of evidence that ME/CFS is not primarily a psychosomatic syndrome, but a complex neuro-immunological disease. Exploratory and Confirmatory Factor Analyses as well as structural modeling methods (SEM) were able to clearly differentiate the hypothesized subgroups statistically and confirm their internal coherence. The high factor loadings indicate independent, functionally coherent subsystems. These can be reconciled with known pathophysiological mechanisms. Limitations Participants were recruited via ME/CFS self-help groups and two German treatment centers in Berlin and Munich. As recruitment was based on self-selection and snowballing, selection and homogeneity effects may have occurred, but with a sample size of n = 748 these can be considered moderate. A comparison with the actual gender- and age-related prevalence/incidence in Germany was not possible due to a lack of reliable data. Women are likely slightly overrepresented (611 women vs. 138 men), but prevalence and incidence peaks correspond to international findings (incidence peak: 30–39 years; prevalence peak: 40–59 years) [44, 45]. Data are based on self-report and may be affected by recall bias. Pathophysiological classifications discussed are derived from literature, not biological measurements. Due to the limited sample size, Exploratory and Confirmatory Factor Analyses (EFA and CFA) could not be conducted on independent subsamples; instead, the full dataset was used. To assess robustness, additional analyses were conducted on a randomized, stratified training dataset (by sex and disease duration). While this improves representativeness, it does not replace independent samples. Dichotomization of symptoms (yes/no) reduces variability and power in Structural Equation Modeling (SEM), masking subtle effects. Potential effects of sex, age, or disease duration on factors were not examined; these and missing measurement invariance tests will be addressed in later study phases. In addition, we were unable to elegantly map local dependencies of certain symptoms (e.g. heart ↔ gut) using the Stata 12 software available to us. As a result, parameters could be distorted or fit indices misleading. Multiple model comparisons risked alpha inflation; thus, Bonferroni correction was applied. Confounding factors such as psychiatric comorbidities, medication, or socioeconomic status could not be controlled due to missing data, which may have biased symptom clusters. Moreover, recorded psychiatric diagnoses may themselves represent misdiagnoses [42]. Conclusion Exploratory and Confirmatory Factor Analyses as well as Structural Equation Modeling (SEM) consistently indicate differentiable symptom clusters in ME/CFS that are internally coherent, show excellent model fit indices, and align with known pathophysiological hypotheses. These results complement biomedical research and support the understanding of ME/CFS as a complex neuro-immunological multisystem disease. The statistically confirmed clusters largely correspond to previous findings and provide concrete starting points for differentiated diagnostics and individualized therapy. Our results also suggest that ME/CFS diagnostic questionnaires should more clearly define technical terms used in medical history and better reflect pathophysiological processes relevant to the disease. Diagnostic forms should include separate subscales—e.g., for gastrointestinal and immune symptoms—to capture disease heterogeneity and target therapeutic approaches to the dominant subsystem. They should also be expanded to include important, previously unassessed symptoms such as muscle and eye problems, again as distinct subscales. Future research should replicate the symptom groups identified here in larger, independent, and ideally longitudinal cohorts, while also investigating the pathophysiological basis of underexplored ME/CFS symptoms. Integrating biological markers such as immune profiles, microbiome composition, and imaging results could further refine the understanding of disease mechanisms. The development of validated diagnostic tools with differentiated subscales is essential for implementing personalized therapeutic strategies. Despite the excellent fit indices of our SEM models, methodological limitations must be noted: the dichotomization of all symptoms on yes/no scales reduces variance and the ability to detect subtle effects. Moreover, measurement invariance tests (e.g., multi-group CFA by gender, age, or disease duration) have not yet been performed. Future studies should therefore use symptom-oriented polytomous scales and integrate measurement invariance tests to enhance robustness and transferability. Our next step will be to examine individual ME/CFS symptoms and clusters for differences by sex, age, and disease duration to develop gender-appropriate preventive and therapeutic approaches. Declarations 1. Funding The study on which this publication is based was supported in part by the Ministry of Social Affairs, Health and Integration from state funds approved by the Baden-Württemberg state parliament (AZ: M57-5434-9/2) 2. Conflicts of interest The corresponding author declares on behalf of all authors that there is no conflict of interest. 3. Ethic approval This study was approved by the Ethics Committee of Furtwangen University, Germany, in 2022 (application number: 22-057). 4. Consent to participate & 5. Written Consent for publication “Consent to participate” and “Written Consent for publication” were part of the following written information provided to all study participants: “Information and declaration of consent: Participation in the study is voluntary. You can withdraw from it at any time without giving reasons and without suffering any disadvantages. Your data will be collected anonymously. It will not be possible to link the data to your person. The data will be evaluated and used exclusively within the framework of this research project (including the publications that result from it). All data will be processed without your name or any other direct means of identification. If the results of the study are published, it will not be possible to link the data to your person. The study results will be summarized in a scientific publication. At the same time, an easy-to-understand summary will be prepared for people affected by ME/CFS, patient and self-help organizations, etc. In addition, the participants will be quoted (anonymously) in detail in a planned reference book on the subject, based on quotes from this study. Due to the anonymous nature of the data collection, it is not possible to delete your data retrospectively upon request. The data collected from you consists exclusively of the questions you answer in the questionnaire. No other information that could enable identification (e.g., IP address) will be stored. If you have any questions about the study, please feel free to contact the study leader, [name and email address]. I have read and understood all of the information regarding participation in the study and agree to participate in the study. ☐ Yes ☐ No“ 6, Availability of data and material All data supporting the results of this study are included in the article or can be requested from the corresponding author. 7. Code availability Not applicable. 8. Author’s Contributions LHH contributed to the development and conception of the research project, the preparation, collection, procurement, and provision of the data, the analysis and interpretation of the data, the review of the sources, the conclusions drawn from these findings, and the writing of the manuscript. LMH contributed to the preparation, provision, and interpretation of the data. Additional Notes The translation from German was supported by www.DeepL.com/Translator (free version). Furthermore, we would like to thank the AI tool ChatGPT (OpenAI) for language editing and wording optimization in individual sections under human guidance. 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Footnotes Literature sources see discussion The notation \"(0/1)\" indicates that the target variable is dichotomous—i.e., it has only two possible values—and that the logistic regression aims to model the probability of the value 1 (occurrence of problems). The results of these investigations can be viewed upon request from the corresponding author. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8319139\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":561836076,\"identity\":\"fe07074a-c49f-46db-905e-190764d482a0\",\"order_by\":0,\"name\":\"Lotte Habermann-Horstmeier\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Villingen Institute of Public Health (VIPH)\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Lotte\",\"middleName\":\"\",\"lastName\":\"Habermann-Horstmeier\",\"suffix\":\"\"},{\"id\":561836077,\"identity\":\"44e66148-9ef7-4fd1-8f89-2dd29abb9523\",\"order_by\":1,\"name\":\"Lukas M. 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09:51:48\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":96017,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eChronological placement of Study 2 within the sequence of studies.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8319139/v1/e375a6cf53a51b685ccbb34c.jpg\"},{\"id\":98779774,\"identity\":\"1116c1df-1d34-4319-a126-520b9cae0b60\",\"added_by\":\"auto\",\"created_at\":\"2025-12-22 12:30:44\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":102234,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComposition of the study sample.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8319139/v1/9742af5c1349782ae62f8812.jpg\"},{\"id\":98783834,\"identity\":\"4418261c-c149-4095-8cb8-de3aa7ae4bb6\",\"added_by\":\"auto\",\"created_at\":\"2025-12-22 12:42:42\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2008142,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8319139/v1/ce89bd73-d6a7-434b-9d44-493a5da15f7c.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Distinct Symptom Clusters Reflect Pathophysiological Mechanisms in ME/CFS\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eMyalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS; ICD-10-GM-2025: G93.3) is a severe multisystemic disease with a broad spectrum of symptoms [1]. It is usually triggered by a viral infection and often leads to severe disability and a significant reduction in quality of life [2]. ME/CFS is characterized by disorders of the organism\\u0026apos;s control organs (nervous, endocrine and immune systems) and cellular energy supply as well as chronic mild inflammation [3], so that homeostasis in the body\\u0026apos;s open systems can no longer be maintained [1, 4]. The central symptom is post-exertional malaise (PEM). PEM is a pronounced and persistent intensification of symptoms after minor physical or mental exertion, which usually occurs after a time delay and generally lasts for more than 24 hours [5]. It is accompanied by a high degree of physical and mental loss of strength and energy as well as a rapid deterioration of the general condition (fatigue). In addition, there are a variety of symptoms that fluctuate in type and intensity as well as over the course of the disease, including neurocognitive and immunological symptoms, sleep disorders and numerous neurovegetative regulatory disorders [6, 7]. Since little is known about the development of various symptoms over the course of the disease, the authors have already conducted an exploratory study investigating possible changes in ME/CFS symptoms over the course of the disease (Study 1) [8]. The results found there suggested that, in a second phase, symptom clusters should be identified whose symptoms occur significantly more frequently together in ME/CFS patients (= Study 2, the results of which are presented here). These will then be examined in more detail in a third to fifth phase (Studies 3 to 5), with possible influences in terms of sex and duration of illness being investigated (see Figure 1).\\u003c/p\\u003e\\n\\u003cp\\u003eThe aim of this study (Study 2) is to empirically test and validate the ME/CFS subgroups suggested by the results of Study 1 (such as a statistical correlation between gastrointestinal complaints and (food) intolerances, and between brain fog and sleep disorders) using statistical methods as Exploratory and Confirmatory Factor Analyses. It is expected that symptom groups can be identified that can be related to known pathophysiological processes, especially in the central and autonomic nervous systems. Knowledge of such potential associations could be important for ME/CFS diagnosis, but also for the development of preventive or therapeutic approaches. It could also provide researchers with indications as to where underlying pathophysiological relationships should be investigated in more detail.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eThis study is based on data from the APAV-ME/CFS study. The predominantly qualitative APAV-ME/CFS study focused primarily on the doctor-patient relationship in ME/CFS from the perspective of patients and their relatives. In addition, a range of basic data on gender/sex, age, etc., as well as information on whether the participant has a medical diagnosis of ME/CFS was also collected (see below).\\u003c/p\\u003e\\n\\u003ch2\\u003eSampling\\u003c/h2\\u003e\\n\\u003cp\\u003eA total of 1,238 people (ME/CFS patients and their close relatives) took part in the APAV-ME/CFS study. They were recruited through self-activation and according to the snowball principle. To this end, contact was made in summer 2022 with six ME/CFS patient organizations in Germany as well as the Fatigue Centre at Charit\\u0026eacute; in Berlin and the Chronic Fatigue Centre for Young People at the Technical University of Munich. The organizations forwarded the call for study participation to ME/CFS patients and their relatives. Through them, the information reached other affected persons. Long-term or post-COVID patients who did not meet the Canadian or International Consensus Criteria for ME/CFS were not included in the study.\\u003c/p\\u003e\\n\\u003ch2\\u003eStudy participants\\u003c/h2\\u003e\\n\\u003cp\\u003eThe present study (Study 2) includes the data of the 748 adult participants in the APAV-ME/CFS study who had already been medically diagnosed with ME/CFS (\\u0026gt; 20 years; 608 ♀, 137 ♂, 3 non-binary). The data of the participating children and adolescents (up to 20 years of age), the relatives of ME/CFS patients and the diseased persons who had not yet been medically diagnosed with ME/CFS were excluded as well as questionnaires that lacked information on the relevant basic questions like gender/sex, age, etc., and information on whether the participant was medically diagnoses with ME/CFS \\u0026nbsp;(Table 1, Figure 2).\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1\\u0026nbsp;\\u003c/strong\\u003eDescription of the adult subjects (\\u0026gt; 20 years) with medically diagnosed ME/CFS, differentiated by gender and age group.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAge groups\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e21-30 yrs.\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e31-40 yrs.\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e41-50 yrs.\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e51-60 yrs.\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026ge; 60 yrs.\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTotal\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e[n]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e[n]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e[n]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e[n]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e[n]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e[n]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eWomen\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e8.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e115\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e18.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e183\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e30.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e197\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e32.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e10.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e608\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMen\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e10.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e20.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e21.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e35.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e11.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e137\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNon-binar\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e100.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e0.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e0.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e0.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e0.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTotal\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e68\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e9.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e143\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e19.1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e213\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e28.5\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e247\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e33.0\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e78\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e10.4\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e748\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 38px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e100\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eAbbreviations: n = number of individuals, % = percentage, yrs. = years\\u003c/p\\u003e\\n\\u003cp\\u003ePrior to participation, all respondents were informed about the study\\u0026rsquo;s objectives, structure, and content. Participation was voluntary and anonymous, with no possibility of linking responses to individual identities. Informed consent was obtained through completion of the online questionnaire. To ensure appropriate inclusion, participants provided basic demographic and diagnostic information or symptom-related self-reports.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch2\\u003eStudy implementation and data processing\\u0026nbsp;\\u003c/h2\\u003e\\n\\u003cp\\u003eAlmost all respondents completed the questionnaire we developed, which consisted of a short quantitative and a more extensive qualitative section, online (n = 1,118). In addition, the questionnaire was also available for download, so 120 participants sent us the completed questionnaire by email, fax or post. The quantitative part of the questionnaire mainly collected basic information in the form of closed questions, e.g. about the age and gender of the respondents. Questions were also asked about the duration of the illness (self-assessment), the existence of a medical diagnosis, the date of diagnosis, the specialty and number of doctors consulted, the number of hospital and rehabilitation stays, the predominant ME/CFS symptoms, and the improvement/worsening of symptoms as a result of these hospital stays.\\u003c/p\\u003e\\n\\u003cp\\u003eIn the subsequent statistical analysis of the symptoms regularly occurring in the test subjects - except PEM and fatigue, as these are the basic requirements for an ME/CFS diagnosis and therefore must be present in all patients -, tests for bivariate associations were first carried out, including a check of internal consistency (cross-tabulations, Cram\\u0026eacute;r\\u0026apos;s V and tetrachoric correlations). The Exploratory (EFA) and Confirmatory Factor Analysis (CFA) then showed whether the previously defined, hypothesis-driven and pathophysiologically based subgroups were actually statistically detectable clusters. All analyses were based on the sample of 748 patients. This provides a sufficient number of cases for exploratory and confirmatory factor analyses. Although it is methodologically desirable to perform EFA and CFA on separate subsamples, we opted for a combined analysis because the sample size is too small to be halved. To assess the robustness of the findings, the same analyses were repeated on a stratified, randomized training dataset derived from the full sample\\u003csup\\u003e[1]\\u003c/sup\\u003e. The dataset was stratified by sex and illness duration to ensure that both variables were proportionally represented across all subsamples. This approach enhances the representativeness of the data and allows for meaningful comparisons between groups. Randomization within each stratum was applied to minimize potential biases and to prevent systematic distortions in the results. This procedure is particularly relevant in ME/CFS research, as both sex and illness duration are known to influence symptom profiles and underlying pathophysiological mechanisms. By employing a stratified, randomized split, we established a methodologically sound basis for robust statistical analyses (e.g., EFA, CFA) and enabled a valid cross-validation of findings derived from the full dataset.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eIn the following, hypothesis-led subgroups will be compared with the empirical-statistical symptom groups.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn other words, it should be shown,\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003ewhich theoretically defined clusters are statistically confirmed,\\u003c/li\\u003e\\n \\u003cli\\u003ewhere there may be deviations or overlaps and\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003ehow new hypotheses or subtypes can be derived from this.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003eTable 2 shows the four hypothesized subgroups (\\u003cem\\u003eBrain\\u003c/em\\u003e, \\u003cem\\u003eVegetative\\u003c/em\\u003e, \\u003cem\\u003eGut\\u0026nbsp;\\u003c/em\\u003eand\\u003cem\\u003e\\u0026nbsp;Immune\\u003c/em\\u003e) including the most important pathophysiological mechanisms discussed in the literature.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2\\u0026nbsp;\\u003c/strong\\u003eClassification of the ME/CFS symptoms surveyed into functional clusters based on pathophysiological concepts.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg 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\\\" width=\\\"609\\\" height=\\\"834\\\"\\u003e\\u003c/p\\u003e\\n\\u003ch2\\u003eNeurocognitive-sensory symptom group (\\u003cem\\u003eBrain\\u003c/em\\u003e)\\u003c/h2\\u003e\\n\\u003cp\\u003eFor the statistical validation of the theoretically defined clusters, cross-tabulation analyses and calculations of Cram\\u0026eacute;r\\u0026apos;s V and tetrachoric correlations were first carried out in the area of the potential neurocognitive-sensory symptom group (\\u003cem\\u003eBrain\\u003c/em\\u003e). An exploratory factor analysis was then carried out, followed by a Confirmatory Factor Analysis, which was used to test the fit of the \\u003cem\\u003eBrain\\u003c/em\\u003e model to the data collected.\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e- Bivariate associations and internal consistency\\u0026nbsp;\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eFirst, a neurocognitive-sensory symptom group (\\u003cem\\u003eBrain\\u003c/em\\u003e) was defined, in which pathophysiological changes in the brain are assumed to be the central triggers for early-onset ME/CFS symptoms: (1) cognitive impairment (brain fog), (2) sensory hypersensitivity, (3) sleep disturbances, (4) visual disturbances and (5) headaches. Cross-tabulations and \\u0026chi;\\u0026sup2;-tests revealed significant associations, especially between cognitive impairment and sleep disturbances or sensory hypersensitivity as well as between visual disturbances and sensory hypersensitivity. The tetrachoric correlations (r) were consistently above the values of Cram\\u0026eacute;r\\u0026apos;s V and more adequately reflect the underlying continuous relationships. Almost all symptom pairs showed medium to high correlations (r \\u0026ge; 0.34), indicating substantial latent coupling. The consistency of the results is confirmed by the consistency of the ranking of the association strengths in both metrics (Table 3).\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e- Explorative factor analysis\\u0026nbsp;\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003ctable cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBox 1:\\u0026nbsp;\\u003c/strong\\u003eModel specification and eigenvalue criterion\\u003c/p\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003e\\u003cem\\u003eMethod\\u003c/em\\u003e\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003cp\\u003ePrincipal factors on five binary symptoms:\\u003c/p\\u003e\\n \\u003cp\\u003e- Brain fog\\u003c/p\\u003e\\n \\u003cp\\u003e- Sensory hypersensitivity\\u003c/p\\u003e\\n \\u003cp\\u003e- Headache\\u003c/p\\u003e\\n \\u003cp\\u003e- Visual disturbances\\u003c/p\\u003e\\n \\u003cp\\u003e- Sleep disorders\\u003c/p\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eRetention according to Kaiser criterion (eigenvalue \\u0026gt; 1): One factor\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eThe exploratory factor analysis (EFA) as a multivariate method for data reduction and structure identification serves to uncover latent factors behind the observed variables without a priori model specification (for details on the model specification and the eigenvalue criterion, see Box 1). The first eigenvalue was 1.20, all other eigenvalues were below 1.\\u003c/p\\u003e\\n\\u003cp\\u003eThe likelihood ratio test against the independence model showed a significant deviation [\\u0026chi;\\u0026sup2;(10) = 378.20, p \\u0026lt; 0.0001], which indicates substantial correlations between the items. The factor loadings on the first factor ranged from 0.43 to 0.55 and were thus classified as moderate. However, the uniqueness values of 0.70-0.82 showed that only 18-30% of the variance of the symptoms could be explained by the common factor. Since only one factor was extracted, the Varimax rotation did not lead to any change, as expected.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch3\\u003e\\n \\u003ctable cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBox 2:\\u0026nbsp;\\u003c/strong\\u003eModel specification\\u003c/p\\u003e\\n \\u003cp\\u003eOne-factor model with latent variable \\u003cem\\u003eBrain\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eFive binary indicators:\\u003c/li\\u003e\\n \\u003cli\\u003e- Brain fog\\u003c/li\\u003e\\n \\u003cli\\u003e- Sensory hypersensitivity\\u003c/li\\u003e\\n \\u003cli\\u003e- Headache\\u003c/li\\u003e\\n \\u003cli\\u003e- Visual disturbances\\u003c/li\\u003e\\n \\u003cli\\u003e- Sleep disorders\\u003c/li\\u003e\\n \\u003cli\\u003eEstimation with maximum likelihood (by default [ML])\\u003c/li\\u003e\\n \\u003cli\\u003eFactor loading of Brain fog fixed to 1 to set the scale of the factor\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\u0026nbsp;\\u003cem\\u003e- Confirmatory factor analysis\\u003c/em\\u003e\\n\\u003c/h3\\u003e\\n\\u003cp\\u003eA Confirmatory Factor Analysis (CFA) within the Structural Equation Modeling (SEM) was used to estimate the strength of the relationships between the latent factor \\u003cem\\u003eBrain\\u0026nbsp;\\u003c/em\\u003eand the observed variables (Box 2). The model was fitted using maximum likelihood estimation. Table 4 shows that all factor loadings were high and statistically significant (p \\u0026lt; 0.001). The residual variances of 0.06 to 0.17 prove that the factor explains a large part of the variance in the respective indicators. The latent variance of the \\u003cem\\u003eBrain\\u0026nbsp;\\u003c/em\\u003efactor was estimated at 0.0301 (SE = 0.0048). The 95 % confidence interval was completely above zero, which indicates a significant variance of the factor.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3\\u0026nbsp;\\u003c/strong\\u003eStatistical correlations in the neurocognitive-sensory symptom group (\\u003cem\\u003eBrain\\u003c/em\\u003e). Cross-tabulations, Cram\\u0026eacute;r\\u0026apos;s V and tetrachoric correlations\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePair of variables\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN (total)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026chi;\\u0026sup2; (df)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCram\\u0026eacute;r\\u0026apos;s V\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInterpretation V according to Cohen\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTetrachoric r\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInterpretation 1 according to Cohen\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eBrain fog ⬌ Headache\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e38.27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.2262\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emoderate correlation (above weak effect)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.4404\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emedium effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eBrain fog ⬌\\u003c/p\\u003e\\n \\u003cp\\u003eVisual disturbances\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e57.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.2768\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emoderate to strong correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.5418\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003elarge effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eBrain fog ⬌\\u003c/p\\u003e\\n \\u003cp\\u003eSleep disorders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e70.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.3059\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emoderate correlation\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.5622\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003elarge effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eBrain fog ⬌ Sensory hypersensitivity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e72.36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.3110\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emoderate correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.5659\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003elarge effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eVisual disturbances ⬌ Headache\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e37.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.2241\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emoderate correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.3589\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emedium effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eVisual disturbances ⬌ Sleep disorders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e32.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.2095\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emoderate correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.3888\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emedium effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eVisual disturbances ⬌ Sensory hypersensitivity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e69.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.3042\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emoderate correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.5283\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003elarge effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eHeadache ⬌ Sleep disorders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e25.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.1833\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eweak to moderate correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.3418\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emedium effect\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eHeadache ⬌ Sensory hypersensitivity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e60.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.2840\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emoderate correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.4906\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003elarge effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 150px;\\\"\\u003e\\n \\u003cp\\u003eSleep disorders ⬌ Sensory hypersensitivity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e47.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.2518\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003emoderate correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e0.4635\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003elarge effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Abbreviations: N = number; \\u0026chi;\\u0026sup2; (df) = Chi\\u003csup\\u003e2\\u003c/sup\\u003e test (degrees of freedom); p-value = probability; Cramer\\u0026apos;s V = variable V, which quantifies the statistical relationship between two nominally scaled variables; tetrachoric r = tetrachoric correlation coefficient\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 4\\u0026nbsp;\\u003c/strong\\u003eConfirmatory Factor Analysis (CFA) for the \\u0026lsquo;neurocognitive-sensory symptom group model\\u0026rsquo; (\\u003cem\\u003eBrain\\u003c/em\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eStep 1: Charges, residual variances and intercepts\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 120px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eIndicator\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFactor loading \\u0026lambda;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSE\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ez-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e95 % CI for \\u0026lambda;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eResidual variance\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 120px;\\\"\\u003e\\n \\u003cp\\u003eBrain fog\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003e1 (fixiert)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e0.0654\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 120px;\\\"\\u003e\\n \\u003cp\\u003eSensory hypersensitivity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003e1.2522\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e0.1417\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e8.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e[0.975, 1.530]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e0.0911\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 120px;\\\"\\u003e\\n \\u003cp\\u003eSleep disorders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003e0.9263\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e0.1091\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e8.45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e[0.712, 1.140]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e0.0958\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 120px;\\\"\\u003e\\n \\u003cp\\u003eVisual disturbances\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003e1.3846\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e0.1634\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e8.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e[1.064, 1.705]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e0.1741\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 120px;\\\"\\u003e\\n \\u003cp\\u003eHeadache\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003e1.1718\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e0.1512\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e7.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e[0.875, 1.468]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e0.1727\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eAbbreviations: \\u0026lambda; = lambda; SE = standard error, p-value = probability; z-value measures how many standard deviations a data point is away from the mean; CI = confidence interval\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eStep 2: Latent variance\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 290px;\\\"\\u003e\\u0026nbsp;\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 290px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eValue\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 290px;\\\"\\u003e\\n \\u003cp\\u003eVariance of \\u0026quot;Brain\\u0026quot;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 290px;\\\"\\u003e\\n \\u003cp\\u003e0.0301\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 290px;\\\"\\u003e\\n \\u003cp\\u003eSE\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 290px;\\\"\\u003e\\n \\u003cp\\u003e0.0048\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 290px;\\\"\\u003e\\n \\u003cp\\u003e95 % CI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 290px;\\\"\\u003e\\n \\u003cp\\u003e[0.0230, 0.0411]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 580px;\\\"\\u003e\\n \\u003cp\\u003e⟶ Factorized variance significant \\u0026gt; 0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eStep 3: Global fit indices\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFit index\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eValue\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRecommended limit value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInterpretation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026chi;\\u0026sup2; (df=5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e6.658\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003ep \\u0026gt; 0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eNot significant ⟶ good fit\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eRMSEA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e0.021\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026le; 0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eExcellent fit\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e90 % CI RMSEA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e[0.000, 0.058]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eTight interval\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003epclose\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e0.886\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt; 0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eRMSEA \\u0026le; 0.05 is plausible\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eCFI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e0.996\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt; 0.95\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eExcellent fit\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eTLI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e0.991\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt; 0.95\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eExcellent fit\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eSRMR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e0.016\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026le; 0.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eExcellent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eCD (R\\u003csup\\u003e2\\u003c/sup\\u003e equivalent)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e0.645\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003eExplains 64.5 % of the total variance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eAbbreviations: \\u0026chi;\\u0026sup2; (df) = Chi\\u003csup\\u003e2\\u003c/sup\\u003e test (degrees of freedom); p-value = probability; RMSEA = Root Mean Square Error of Approximation; CI = confidence interval; pclose = Probability RMSEA; CFI = Comperative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual; CD = Coefficient of Determination; R\\u003csup\\u003e2\\u003c/sup\\u003e = statistical measure of how closely the data fit the fitted regression line\\u003c/p\\u003e\\n\\u003cp\\u003eIn summary, the high and significant loadings (Table 4) confirm that the symptoms brain fog, sensory hypersensitivity, visual disturbances, sleep disturbances and headaches represent a coherent construct \\u003cem\\u003eBrain\\u003c/em\\u003e. The low residual variances indicate that the latent factor explains a high proportion of the symptom variance. The global fit indices (non-significant \\u0026chi;\\u0026sup2;, RMSEA = 0.021, CFI = 0.996, SRMR = 0.016) indicate a very good model fit.\\u003c/p\\u003e\\n\\u003cp\\u003eSymptom group Vegetative dysregulation (Vegetative)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e- Bivariate associations and internal consistency\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn order to check the correlation between the \\u003cem\\u003eVegetative\\u003c/em\\u003e symptoms, cross-tabulations, Cram\\u0026eacute;r\\u0026apos;s V, tetrachoric correlations and Cronbach\\u0026apos;s alpha were first determined (Table 5). Cardiovascular problems correlated significantly with breathing problems and temperature intolerance. The corresponding tetrachoric correlations were r=0.605 (SE=0.049) and r=0.620 (SE=0.048). Cronbach\\u0026apos;s alpha for the two pairings was \\u0026alpha;=0.53 and \\u0026alpha;=0.55, respectively, demonstrating moderate internal consistency - with some heterogeneity.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 5\\u0026nbsp;\\u003c/strong\\u003eBivariate associations and reliability of vegetative symptoms\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eV\\u003cstrong\\u003eariable pair\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN (total)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026chi;\\u0026sup2; (df=1)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep -value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCram\\u0026eacute;r\\u0026rsquo;s V\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTetrachoric ϱ (SE)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 88px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCrohnbach\\u0026rsquo;s \\u0026alpha;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 77px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInteritem covariance\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003eCardiovascular problems ⬌ Breathing problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e101.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e0.368\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.605 (0.049)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 88px;\\\"\\u003e\\n \\u003cp\\u003e0.530\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 77px;\\\"\\u003e\\n \\u003cp\\u003e0.067\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003eCardiovascular problems ⬌ Temperature intolerance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e110.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e0.385\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.620 (0.048)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 88px;\\\"\\u003e\\n \\u003cp\\u003e0.5551\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 77px;\\\"\\u003e\\n \\u003cp\\u003e0,067\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: N = number; \\u0026chi;\\u0026sup2; (df) = Chi\\u003csup\\u003e2\\u003c/sup\\u003e test (degrees of freedom); p-value = probability; Cramer\\u0026apos;s V = variable V, which quantifies the statistical relationship between two nominally scaled variables; tetrachoric ϱ (SE) = tetrachoric rho (standard error)\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e- Explorative Factor Analysis\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA one-factor solution on four \\u003cem\\u003eVegetative\\u003c/em\\u003e indicators (cardiovascular, respiratory problems, temperature intolerance, gastrointestinal complaints) resulted in a single eigenvalue \\u0026gt; 1 (eigenvalue=1.153), which explains 28.8 % of the total variance. The unweighted factor loadings ranged from 0.477 to 0.585, uniqueness values were between 0.658 and 0.773. The factor analysis thus supports a unidimensional vegetative construct (\\u003cem\\u003eVegetative\\u003c/em\\u003e; Table 6).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 6\\u0026nbsp;\\u003c/strong\\u003ePrincipal factors of the four vegetative indicators.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 168px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLoad weight\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026lambda;\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;to factor 1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eUniqueness\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 115px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEigenvalue factor 1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eExplained variance (%)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 168px;\\\"\\u003e\\n \\u003cp\\u003eCardiovascular problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003e0.585\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e0.658\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" style=\\\"width: 115px;\\\"\\u003e\\n \\u003cp\\u003e1,153\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e28,8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 168px;\\\"\\u003e\\n \\u003cp\\u003eBreathing problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003e0.553\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e0.716\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 168px;\\\"\\u003e\\n \\u003cp\\u003eTemperature incompatibility\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003e0.547\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e0.701\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 168px;\\\"\\u003e\\n \\u003cp\\u003eGastrointestinal complaints\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003e0.477\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e0.773\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eLR test: \\u0026chi;\\u0026sup2;(6)=370.2, p\\u0026lt;.001 (against independence assumption)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e- Logistic regression\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn a logistic regression model for the prediction of cardiovascular problems (0/1)\\u003csup\\u003e[3]\\u003c/sup\\u003e by breathing problems, temperature intolerance and gastrointestinal complaints, the values summarized in Table 7 were found. Respiratory problems and the intolerance of hot and cold (outside) temperatures multiply the odds for cardiovascular problems by a factor of 4 to 4.5, gastrointestinal complaints by a factor of 2. The pseudo-R\\u0026sup2; according to McFadden is 0.233, indicating a reasonably good model fit compared to a null model.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 7\\u0026nbsp;\\u003c/strong\\u003ePredictors for cardiovascular problems (0/1) - Logistic regression\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003ePredictor\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026beta; (SE)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003ez\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003ep\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e95 % CI (\\u0026beta;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003eOR [95 % CI]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003eBreathing problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.466 (0.226)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e6.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e[1.024, 1.909]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e4.33 [2.79, 7.10]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003eTemperature incompatibility\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.521 (0.223)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e6.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e[1.084, 1.958]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e4.58 [2.96, 8.03]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003eGastrointestinal complaints\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.703 (0.226]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e3.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e[0.259, 1.146]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e2.02 [1.30, 3.14]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eConstant\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e-0.606 (0.193)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e-3.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e[-0.983, -0.228]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\" valign=\\\"top\\\" style=\\\"width: 604px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePseudo R\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"5\\\" style=\\\"width: 467px;\\\"\\u003e\\n \\u003cp\\u003e0.233\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e- Confirmatory Factor Analysis\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eStructural Equation Modeling (SEM) with a common latent factor \\u003cem\\u003eVegetative\\u0026nbsp;\\u003c/em\\u003efor all four vegetative indicators provided excellent fit indices. The factor loadings were high and significant (Table 8). The residual variances ranged from \\u0026theta;=0.0879 to \\u0026theta;=0.1600, and the latent variance of the factors was \\u0026phi;=0.0617 (SE=0.0085). Proposed covariances between residuals of cardiovascular and gastrointestinal problems and between respiratory problems and temperature intolerance could not be confirmed either substantively or statistically.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 8\\u0026nbsp;\\u003c/strong\\u003eSEM model with a latent-variable factor for vegetative symptoms and fit indices for a common factor \\u003cem\\u003eVegetative\\u003c/em\\u003e.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"604\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eIndicator\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 99px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLoad weight \\u0026lambda;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSE\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 159px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eResidual variance (SE)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 159px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLatent variance (SE)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003eCardiovascular problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 99px;\\\"\\u003e\\n \\u003cp\\u003e1 (fixed)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 159px;\\\"\\u003e\\n \\u003cp\\u003e0.0879 (0.0073)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" rowspan=\\\"4\\\" style=\\\"width: 159px;\\\"\\u003e\\n \\u003cp\\u003e0.0617 (0.0085)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003eBreathing problems\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 99px;\\\"\\u003e\\n \\u003cp\\u003e1.056\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e0.109\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 159px;\\\"\\u003e\\n \\u003cp\\u003e0.1536 (0.0105)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003eTemperature incompatibility\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 99px;\\\"\\u003e\\n \\u003cp\\u003e1.048\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e0.106\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 159px;\\\"\\u003e\\n \\u003cp\\u003e0.1352 (0.0096)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 137px;\\\"\\u003e\\n \\u003cp\\u003eGastrointestinal complaints\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 99px;\\\"\\u003e\\n \\u003cp\\u003e0.883\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e0.103\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 159px;\\\"\\u003e\\n \\u003cp\\u003e0.1600 (0.0099)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"13\\\" style=\\\"width: 604px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026chi;\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026sup2;(2)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRMSEA [90 % CI]\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003epclose\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCFI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTLI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSRMR\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAIC\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBIC\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003e5.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003e0.068\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003e0.047 [0.000 \\u0026ndash; 0.098]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e0.445\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e0.991\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e0.972\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e0.015\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e3237.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e3293.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eAbbreviations: \\u0026nbsp; \\u0026lambda; = Lambda; SE = Standard error, \\u0026chi;\\u0026sup2; (df) = Chi\\u003csup\\u003e2\\u003c/sup\\u003e test (degrees of freedom); p-value = Probability; RMSEA = Root Mean Square Error of Approximation; CI = Convidence Interval; pclose = Probability RMSEA; CFI = Comperative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual; AIC = Akaike information criterion, BIC = Bayes information criterion\\u003c/p\\u003e\\n\\u003cp\\u003eGastrointestinal symptom group (Gut) and immunological symptom group (Immune)\\u003c/p\\u003e\\n\\u003cp\\u003eTo test the dimensional structure of gastrointestinal and immunological symptoms, three structural equation models (SEM) were estimated: a one-factor model for gastrointestinal and immune-related symptoms, respectively, and a two-factor model with correlated latent variables. The most important parameters and fit indices are summarized in Table 9.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 9\\u003c/strong\\u003e Comparison of three structural equation model variants. Models A and B are separately estimated one-factor models, model C is a jointly estimated two-factor model with correlated latent variables.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg 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\\\" width=\\\"609\\\" height=\\\"322\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBoth Cram\\u0026eacute;r\\u0026apos;s V and tetrachoric correlations were calculated to assess the association within the \\u003cem\\u003eGut\\u003c/em\\u003e (gastrointestinal complaints, (food) intolerances) and \\u003cem\\u003eImmune\\u0026nbsp;\\u003c/em\\u003e(susceptibility to infections, flu-like symptoms) symptom groups. With a Cram\\u0026eacute;r\\u0026apos;s V of 0.405 and a tetrachoric correlation of r = 0.614 (SE = 0.044), the \\u003cem\\u003eGut\\u0026nbsp;\\u003c/em\\u003egroup showed a significantly stronger association between the symptoms than the \\u003cem\\u003eImmune\\u003c/em\\u003e group (V = 0.294; r = 0.505, SE = 0.053). These results suggest a higher symptomatic coherence within the \\u003cem\\u003eGut\\u003c/em\\u003e symptom group (Table 10a). While Cram\\u0026eacute;r\\u0026apos;s V as a dimensionless measure describes the strength of the association between two categorical variables, the tetrachoric correlation allows an estimation of the relationship between the underlying latent constructs under the assumption of a bivariate normal distribution. The parallel consideration of both measures thus provides complementary evidence for the structural coherence of the respective symptom groups.\\u003c/p\\u003e\\n\\u003cp\\u003eIn addition, internal consistency was determined as a measure of the reliability of the groups using Cronbach\\u0026apos;s alpha. A higher value of \\u0026alpha; = 0.575 was found for the \\u003cem\\u003eGut\\u0026nbsp;\\u003c/em\\u003egroup than for \\u003cem\\u003eImmune\\u0026nbsp;\\u003c/em\\u003e(\\u0026alpha; = 0.450). Although both values are below the threshold value of .70 often used as a guideline in psychometric studies, lower reliabilities are often to be expected with clinical symptom data due to great heterogeneity and are nevertheless meaningful. The combination of effect sizes and consistency scores thus supports the assumption that the \\u003cem\\u003eGut\\u003c/em\\u003e symptom group represents a more homogeneous sub-construct within the ME/CFS symptom spectrum than the \\u003cem\\u003eImmune\\u003c/em\\u003e symptom group (Table 10b).\\u003c/p\\u003e\\n\\u003cp\\u003eThe separately estimated models converged without any problems and yielded plausible factor loadings. In particular, the model for immune-related symptoms (susceptibility to infection, flu-like symptoms) showed a high factor loading of \\u0026lambda; = 0.74 with simultaneously low residual variance, which indicates a strong link between the indicators and the underlying factor.\\u003c/p\\u003e\\n\\u003cp\\u003eIn contrast, the joint one-factor model showed a significantly poorer model fit (\\u0026chi;\\u0026sup2;(2) = 23.78, p \\u0026lt; .001), indicating that the joint variance of all four symptoms cannot be adequately explained by a single latent factor. The results therefore clearly support a differentiated modeling, i.e. the symptoms should be recorded in two separate groups - a gastrointestinal group (\\u003cem\\u003eGut\\u003c/em\\u003e) consisting of gastrointestinal complaints and (food) intolerances and an immunological group (\\u003cem\\u003eImmune\\u003c/em\\u003e) consisting of susceptibility to infections and flu-like symptoms.\\u003c/p\\u003e\\n\\u003cp\\u003eAt the same time, the cross-tabulation of GutScore and ImmuneScore reveals a significant association between gastrointestinal and immune-related symptoms (\\u0026chi;\\u0026sup2;(4) = 89.24, p \\u0026lt; 0.001). This indicates that many participants exhibit symptoms in both domains simultaneously. For example, among those with high GutScore (= both \\u003cem\\u003eGut\\u003c/em\\u003e symptoms are present), more than half (54.4%) also show high ImmuneScore (= both Immune symptoms are present), suggesting a frequent co-occurrence of these symptom clusters.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 10\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ea.\\u003c/strong\\u003e Statistical correlations in the two symptom groups \\u003cem\\u003eGut\\u003c/em\\u003e and \\u003cem\\u003eImmune\\u003c/em\\u003e. Cross-tabulations, Cram\\u0026eacute;r\\u0026apos;s V and tetrachoric correlations.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable pair\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 68px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN (total)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 65px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026chi;\\u0026sup2; (df)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCram\\u0026eacute;r\\u0026rsquo;s V\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInterpretation V according to Cohen\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTetrachoric r\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInterpretation r according to Cohen\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCronbach\\u0026rsquo;s Alpha\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 121px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eInteritem covariance\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eGut\\u003c/em\\u003e: Gastrointestinal problems ⬌ (Food) intolerances\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 68px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 65px;\\\"\\u003e\\n \\u003cp\\u003e122.54\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e0.405\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003eModerate to strong (clear) correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.614 (SE = 0.044)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003estrong effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.575\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 121px;\\\"\\u003e\\n \\u003cp\\u003e0.090\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 151px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eImmune\\u003c/em\\u003e: Flu-like symptoms ⬌ Susceptibility to infections\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 68px;\\\"\\u003e\\n \\u003cp\\u003e748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 65px;\\\"\\u003e\\n \\u003cp\\u003e64.720\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e0.294\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003ealmost moderate correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.505 (SE = 0.053)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003estrong effect\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.450\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 121px;\\\"\\u003e\\n \\u003cp\\u003e0.063\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eb.\\u003c/strong\\u003e SEM analysis\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" align=\\\"left\\\" width=\\\"954\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eIndicator\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFactor loading \\u0026nbsp;\\u0026lambda; (SE) \\u0026nbsp;\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eResidual variance (SE)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLatent variance (SE) \\u0026nbsp; \\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026chi;\\u0026sup2;(df), p \\u0026nbsp; \\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRMSEA\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(90 % CI), pclose \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCFI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTLI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSRMR\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eGut\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e(1- factor)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eGastrointestinal complaints\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1 (fixed)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026asymp; 0*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e0.2391 (0.0233)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash; (df = 0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003e(Food) intolerances\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e0.4338 (0.0179)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.19995 (0.01083)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eImmune\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e(1-factor)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eFlu-like symptoms\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1 (fixed)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026asymp; 0*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e0.2491 (0.0323)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash; (df = 0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eSusceptibility to infection\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e0.3448 (0.0183)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.22758 (0.01204)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"4\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eGut\\u003c/em\\u003e \\u0026amp; \\u003cem\\u003eImmune\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e(2-factors)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eSusceptibility to infection\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1 (fixed)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.19934 (0.01269)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e0.04342\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e23.78(2),\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026le; 0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e0.121 [0.080, 0.166], 0.003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.923\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.769\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.038\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eFlu-like symptoms\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e0.7330 (0.1063)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.15459 (0.00912)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eGastrointestinal complaints\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.2327 (0.1722)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.13250 (0.11057)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003e(Food) intolerances\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.3526 (0.1878)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.14803 (0.01298)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eCovariance \\u003cem\\u003eGut\\u003c/em\\u003e \\u0026ndash; \\u003cem\\u003eImmune\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"9\\\" style=\\\"width: 879px;\\\"\\u003e\\n \\u003cp\\u003e0.03792 (0.00675)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e* Fixed charge leads to arithmetically vanishing residual variance.\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: N = number; \\u0026chi;\\u0026sup2; (df) = Chi\\u003csup\\u003e2\\u003c/sup\\u003e test (degrees of freedom); p-value = probability; Cram\\u0026eacute;r\\u0026apos;s V = variable V, which quantifies the statistical relationship between two nominally scaled variables; Tetrachoric r = Tetrachoric correlation coefficient, RMSEA = Root Mean Square Error of Approximation; CI = Convidence Interval; pclose = Probability RMSEA; CFI = Comperative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual\\u003c/p\\u003e\\n\\u003ch2\\u003eSpecial features of the symptoms \\u0026lsquo;gastrointestinal complaints\\u0026rsquo; and \\u0026lsquo;breathing difficulties\\u0026rsquo;\\u003c/h2\\u003e\\n\\u003cp\\u003eTwo of the symptoms ticked by the participants (gastrointestinal complaints and breathing difficulties) could each be assigned to two symptom groups, so they play a special role.\\u003c/p\\u003e\\n\\u003ch3\\u003e- \\u003cem\\u003eGastrointestinal complaints\\u003c/em\\u003e\\u0026nbsp;\\u003c/h3\\u003e\\n\\u003cp\\u003eGastrointestinal complaints belong to the \\u003cem\\u003eVegetative\\u003c/em\\u003e subgroup, but together with (food) intolerances they define the \\u003cem\\u003eGut\\u0026nbsp;\\u003c/em\\u003esubgroup. The results of the factor analysis in the \\u003cem\\u003eVegetative\\u003c/em\\u003e subgroup (Table 8) show that gastrointestinal complaints only load moderately on the general vegetative factor (\\u0026lambda; = 0.477; Uniqueness = 0.773). At the same time, the two-factorial Structural Equation Modeling (SEM; Gut vs. Immune, Table 10b) resulted in a factor loading of \\u0026lambda; = 1.00 for gastrointestinal complaints in the pure \\u003cem\\u003eGut\\u003c/em\\u003e model, while (food) intolerances were associated with \\u0026lambda; = 0.434. The bivariate associations also support these findings. Thus, gastrointestinal complaints correlated significantly with other vegetative indicators (e.g. Cram\\u0026eacute;r\\u0026apos;s V: 0.37-0.39; tetrachoric r \\u0026asymp; 0.62). On the one hand, they reflect the generalized vegetative dysregulation; on the other hand, the Gut construct forms an independent, relatively homogeneous dimension of gastrointestinal phenomena (Cronbach\\u0026apos;s \\u0026alpha; = 0.575; interitem covariance = 0.090), which will be discussed further in the discussion and in the planned third part of our five-stage study series.\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e- Breathing problems\\u0026nbsp;\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eThe statistical analyses (cross-tabulations, association and reliability measures), which are not explained in detail here, show a moderate association between muscle and breathing problems (Cram\\u0026eacute;r\\u0026apos;s V = 0.292; \\u0026rho; = 0.530), which indicates a clear, but not very strong, co-occurrence of these symptoms. At the same time, there is a low internal consistency (Cronbach\\u0026apos;s \\u0026alpha; = 0.433), which is below established threshold values for scale homogeneity (\\u0026alpha; \\u0026ge; 0.70). This means that muscle and breathing problems form a partially overlapping but not uniform dimension. At the same time, the symptom \\u0026quot;breathing difficulties\\u0026quot; is part of the vegetative subgroup, which shows in Table 8 that breathing difficulties load moderately on the general vegetative factor (\\u0026lambda; = 0.553; uniqueness = 0.716). Possible pathophysiological correlations underlying this will be discussed in a later publication.\\u003c/p\\u003e\\n\\u003cp\\u003eBrief summary of the statistical results\\u003c/p\\u003e\\n\\u003cp\\u003eHighly significant correlations were found within the previously pathophysiologically defined symptom subgroups. The statistical calculations confirmed symptom clusters in the following areas:\\u003c/p\\u003e\\n\\u003cp\\u003e- neurocognitive-sensory\\u003c/p\\u003e\\n\\u003cp\\u003e- vegetative\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e- gastrointestinal\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e- immunological\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe fact that the neurocognitive-sensory symptoms in particular represent a coherent \\u003cem\\u003eBrain\\u0026nbsp;\\u003c/em\\u003econstruct was shown above all by the high and significant loadings in the factor analysis. At the same time, low residual variances indicated that the latent factor explains a high proportion of the symptom variance. In addition, the global fit indices demonstrated a very good model fit. Structural Equation Modeling (SEM) with a common latent factor also provided very good fit indices for the \\u003cem\\u003eVegetative\\u003c/em\\u003e symptom complex. A comparison of the two one-factor models for the gastrointestinal symptoms (\\u003cem\\u003eGut\\u003c/em\\u003e) and the symptoms assigned to the immune group (\\u003cem\\u003eImmun\\u003c/em\\u003ee) with a joint two-factor model with correlated latent variables showed a significantly poorer model fit for the two-factor model. This supports the assumption of two clearly distinguishable but related latent constructs.\\u003c/p\\u003e\\n\\u003ch2\\u003eComparison of hypothesis-driven symptom groups and empirically-statistically found symptom clusters\\u0026nbsp;\\u003c/h2\\u003e\\n\\u003cp\\u003eThe aim of this study was to empirically test and validate hypothesized ME/CFS symptom groups using statistical methods. Table 11 summarizes the extent to which the four theoretically defined ME/CFS subgroups were confirmed by the statistical analyses performed. It also indicates the overlaps identified (\\u0026quot;bridging phenomena\\u0026quot;). Bridging phenomena are defined here as those cases in which symptoms could be assigned to more than one functional subsystem, as they mediate both statistically and pathophysiologically between two subgroups. They are therefore not pure markers of a single subgroup, but connect two dimensions and show that the subgroups are not strictly separated from each other, but are functionally networked.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 11\\u003c/strong\\u003e Extent to which the four theoretically defined ME/CFS subgroups were confirmed by the statistical analyses performed\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg 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\\\" width=\\\"609\\\" height=\\\"813\\\"\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe present analysis is the second part of a five-stage series of studies on the symptoms of ME/CFS. In the first, exploratory part of the study series, there was no correlation between the duration of the disease and the frequency of the individual symptoms for most symptoms. However, there were indications of differences between the sexes and a parallel occurrence of individual symptoms [8]. We therefore wondered whether it would be possible to identify such symptom groups and then relate them to known pathophysiological correlations in order to develop gender-appropriate preventive or therapeutic approaches at a later stage. In this study, we were able to identify at least four stable symptom groups (\\u003cem\\u003eBrain\\u003c/em\\u003e, \\u003cem\\u003eImmune\\u003c/em\\u003e, \\u003cem\\u003eVegetative\\u003c/em\\u003e, \\u003cem\\u003eGut\\u003c/em\\u003e) that can be plausibly assigned to pathophysiological mechanisms. For this purpose, we used data from 748 medically diagnosed ME/CFS patients, which we analyzed statistically primarily with the help of factor analyses and structural equation modeling. To validate the robustness of our findings, all analyses were repeated on a stratified, randomized training dataset derived from the full sample. Results obtained from the stratified, randomized training dataset closely mirrored those from the full dataset, with only very minor deviations.\\u003c/p\\u003e\\n\\u003ch2\\u003eAssignment of the results to the pathophysiology of ME/CFS\\u003c/h2\\u003e\\n\\u003cp\\u003eOur results emphasize that neurocognitive symptoms (\\u003cem\\u003eBrain\\u003c/em\\u003e) are associated with proven changes in the brainstem, thalamus and prefrontal cortex. These include, for example, structural changes [9-11] and significantly reduced blood flow to the brainstem [12], extensive structural changes in the white and gray matter [13] as well as numerous pathophysiological processes [14, 15]. For example, the prefrontal cortex, which is altered in ME/CFS, may be involved in the development of sleep disorders and sensory hypersensitivity. Pathological changes in the thalamus can both impair sensory stimulus processing and disrupt sleep. Bechny et al [16] also recently found pathologically altered sleep patterns in people with ME/CFS. Restricted cerebral blood flow can contribute to the development of cognitive impairments such as brain fog, word-finding difficulties and memory problems [17]. Cerebral hypoperfusion is also a plausible cause for the development of headaches and visual disturbances.\\u003c/p\\u003e\\n\\u003cp\\u003eIn the immunological subgroup (\\u003cem\\u003eImmune\\u003c/em\\u003e), the symptoms 'increased susceptibility to infection' and 'flu-like symptoms' can be explained by the proven NK/T-cell dysfunction and immunosuppression as well as the chronic activation of proinflammatory cytokines (→ Sickness Behavior [18]) [19, 20]). In addition, there is the reactivation of latent viruses in the body (e.g. EBV, HHV-6; [21]), which contributes to immune stress and the phenomenon of neuroinflammation [22]. The neuroinflammatory effects in turn impair the blood-brain barrier [23].\\u003c/p\\u003e\\n\\u003cp\\u003eVegetative symptoms (\\u003cem\\u003eVegetative\\u003c/em\\u003e: cardiovascular, respiratory and temperature disorders) reflect a sympathovagal imbalance, which can manifest itself in ME/CFS patients in reduced heart rate variability (HRV), tachycardia, reduced stroke volume and circulatory instability when standing up (→ orthostatic intolerance \\u0026amp; POTS) [24]. Sympathetic overactivity leads to inadequate vasoconstriction or dilation and blood pooling in the legs, while the brain is undersupplied [25]. Vagus dysfunction can be associated with delayed gastric emptying, nausea and dyspepsia [26], while dysregulation of autonomic pain modulation in the gut increases the sensation of abdominal pain and bloating [27]. Overactivity of the sympathetic nervous system, exacerbated by dysregulation of the autonomic respiratory center (chemoreceptors, baroreceptors), can affect respiratory rate and CO₂ partial pressure and lead to dyspnea and dizziness via reduced cerebral perfusion [28]. Vegetative dysregulation of the skin blood vessels and sweat glands can contribute to heat or cold intolerance [29]. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHowever, gastrointestinal symptoms in ME/CFS do not only occur as a result of autonomic dysregulation, but also as part of an independent\\u003cem\\u003e\\u0026nbsp;Gut\\u003c/em\\u003e subtype characterized by pathological gut mechanisms. The latter is associated with specific enteric pathomechanisms, such as reduced intestinal microbiome diversity or dysbiosis [30, 31], often in combination with increased intestinal permeability (\\\"leaky gut\\\"), which can result in bacterial translocation and immune activation [32-34]. Plausible explanations for the existence of such enteric-neuro-immunological pathways include the dysregulation of neurotransmitters, SCFAs and cytokines [31].\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn particular, the classification of gastrointestinal complaints into two symptom groups showed that there are ‘bridging phenomena’ in ME/CFS symptoms that make it clear that the individual subgroups are distinguishable but functionally interconnected. Our results also show how important the choice of words is when recording symptoms in the context of ME/CFS diagnosis (for example, it is still typical to use the term 'fatigue' in the sense of tiredness/sleepiness instead of asking about 'severe physical lack of strength and energy'). This lack of linguistic precision on the part of the treating physicians (unclear terminology) was repeatedly mentioned by the test subjects in the qualitative part of the APAV-ME/CFS study [35] and has a considerable impact on the course of diagnosis and subsequent treatment.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch2\\u003eComparison with previous studies on cluster and factor analysis in ME/CFS\\u003c/h2\\u003e\\n\\u003cp\\u003eEarlier cluster and factor analyses (e.g. [6, 36-40]) arrive at similar summarizing subgroups. They mainly contain symptoms from our \\u003cem\\u003eBrain\\u003c/em\\u003e, \\u003cem\\u003eVegetative\\u003c/em\\u003e and \\u003cem\\u003eImmune\\u003c/em\\u003e subgroups. Differences relate in particular to the classification of various gastrointestinal and neuroendocrine symptoms. Overall, however, there is remarkable consistency across different methods and samples.\\u003c/p\\u003e\\n\\u003ch2\\u003eComparison with CCC-based diagnostic tools\\u003c/h2\\u003e\\n\\u003cp\\u003eA comparison with CCC-based diagnostic questionnaires frequently used in Germany (Charité, MBSQ; [41]) makes it clear that our clusters capture additional ‘bridging phenomena’ (e.g. gastrointestinal complaints or respiratory problems) that can be assigned to several subgroups and are missing in established instruments. In addition, relevant symptoms such as muscle weakness or visual disturbances are missing. This argues for the further development of diagnostic questionnaires with more differentiated subscales.\\u003c/p\\u003e\\n\\u003ch2\\u003eImplications for research, diagnostics and therapy\\u003c/h2\\u003e\\n\\u003cp\\u003eThe structured comparison thus shows that our results are not only statistically robust, but also allow direct conclusions to be drawn about pathophysiology and clinical practice. The results also support the model of ME/CFS as a neuro-immunological multisystem disease. Further research could replicate the clusters found in representative, independent, larger cohorts and possibly also include longitudinal analyses. In practice, validated diagnostic tools with differentiated subscales for bridging symptoms should be developed to enable personalized treatment approaches. Important symptoms that are currently missing (e.g. muscle weakness, visual disturbances) should also be added. It is also important to review the already planned measurement invariance across age and gender groups as well as the duration of the disease, as a preliminary study already showed indications of large differences in the frequency of (food) intolerances in the first five years of the disease in male and female ME/CFS patients [42].\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch2\\u003eMore evidence from a structural analysis perspective for ME/CFS as a neuro-immunological disease\\u003c/h2\\u003e\\n\\u003cp\\u003eThe clearly definable clusters with high factor loadings and good model fits show that the ME/CFS symptoms occur at the level of functional body systems. Psychosocial or affective variables do not play a central role in the models presented here and also have no overarching explanatory power as in the classical psychosomatic interpretation. This is consistent with the finding of a large number of replicated, diverse blood biomarkers in ME/CFS patients with PEM in a recently published study, which has massively weakened the assumption that ME/CFS is caused by deconditioning and stress intolerance [3]. Our statistical models show not only a high structural coherence within the functional subsystems, but also a systematic separation of these clusters that is consistent with known pathophysiological mechanisms. In addition, the participants repeatedly emphasized in the qualitative part of the study that pathobiological factors played a central role in the development of the disease (viral infection as a trigger, no pre-existing trauma or chronic stress) and symptom persistence. Almost all affected test subjects with psychological/psychosomatic diagnoses had good arguments for these being misdiagnoses [42, 43]. The present results thus also support the growing body of evidence that ME/CFS is not primarily a psychosomatic syndrome, but a complex neuro-immunological disease. Exploratory and Confirmatory Factor Analyses as well as structural modeling methods (SEM) were able to clearly differentiate the hypothesized subgroups statistically and confirm their internal coherence. The high factor loadings indicate independent, functionally coherent subsystems. These can be reconciled with known pathophysiological mechanisms.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch1 id=\\\"_Toc109804418\\\"\\u003eLimitations\\u003c/h1\\u003e\\n\\u003cp\\u003eParticipants were recruited via ME/CFS self-help groups and two German treatment centers in Berlin and Munich. As recruitment was based on self-selection and snowballing, selection and homogeneity effects may have occurred, but with a sample size of n = 748 these can be considered moderate. A comparison with the actual gender- and age-related prevalence/incidence in Germany was not possible due to a lack of reliable data. Women are likely slightly overrepresented (611 women vs. 138 men), but prevalence and incidence peaks correspond to international findings (incidence peak: 30–39 years; prevalence peak: 40–59 years) [44, 45].\\u003c/p\\u003e\\n\\u003cp\\u003eData are based on self-report and may be affected by recall bias. Pathophysiological classifications discussed are derived from literature, not biological measurements. Due to the limited sample size, Exploratory and Confirmatory Factor Analyses (EFA and CFA) could not be conducted on independent subsamples; instead, the full dataset was used. To assess robustness, additional analyses were conducted on a randomized, stratified training dataset (by sex and disease duration). While this improves representativeness, it does not replace independent samples. Dichotomization of symptoms (yes/no) reduces variability and power in Structural Equation Modeling (SEM), masking subtle effects. Potential effects of sex, age, or disease duration on factors were not examined; these and missing measurement invariance tests will be addressed in later study phases.\\u003c/p\\u003e\\n\\u003cp\\u003eIn addition, we were unable to elegantly map local dependencies of certain symptoms (e.g. heart ↔ gut) using the Stata 12 software available to us. As a result, parameters could be distorted or fit indices misleading. Multiple model comparisons risked alpha inflation; thus, Bonferroni correction was applied. Confounding factors such as psychiatric comorbidities, medication, or socioeconomic status could not be controlled due to missing data, which may have biased symptom clusters. Moreover, recorded psychiatric diagnoses may themselves represent misdiagnoses [42].\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eExploratory and Confirmatory Factor Analyses as well as Structural Equation Modeling (SEM) consistently indicate differentiable symptom clusters in ME/CFS that are internally coherent, show excellent model fit indices, and align with known pathophysiological hypotheses. These results complement biomedical research and support the understanding of ME/CFS as a complex neuro-immunological multisystem disease. The statistically confirmed clusters largely correspond to previous findings and provide concrete starting points for differentiated diagnostics and individualized therapy.\\u003c/p\\u003e\\n\\u003cp\\u003eOur results also suggest that ME/CFS diagnostic questionnaires should more clearly define technical terms used in medical history and better reflect pathophysiological processes relevant to the disease. Diagnostic forms should include separate subscales—e.g., for gastrointestinal and immune symptoms—to capture disease heterogeneity and target therapeutic approaches to the dominant subsystem. They should also be expanded to include important, previously unassessed symptoms such as muscle and eye problems, again as distinct subscales.\\u003c/p\\u003e\\n\\u003cp\\u003eFuture research should replicate the symptom groups identified here in larger, independent, and ideally longitudinal cohorts, while also investigating the pathophysiological basis of underexplored ME/CFS symptoms. Integrating biological markers such as immune profiles, microbiome composition, and imaging results could further refine the understanding of disease mechanisms. The development of validated diagnostic tools with differentiated subscales is essential for implementing personalized therapeutic strategies.\\u003c/p\\u003e\\n\\u003cp\\u003eDespite the excellent fit indices of our SEM models, methodological limitations must be noted: the dichotomization of all symptoms on yes/no scales reduces variance and the ability to detect subtle effects. Moreover, measurement invariance tests (e.g., multi-group CFA by gender, age, or disease duration) have not yet been performed. Future studies should therefore use symptom-oriented polytomous scales and integrate measurement invariance tests to enhance robustness and transferability. Our next step will be to examine individual ME/CFS symptoms and clusters for differences by sex, age, and disease duration to develop gender-appropriate preventive and therapeutic approaches.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003e1. Funding\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study on which this publication is based was supported in part by the Ministry of Social Affairs, Health and Integration from state funds approved by the Baden-W\\u0026uuml;rttemberg state parliament (AZ: M57-5434-9/2)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e2. Conflicts of interest\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe corresponding author declares on behalf of all authors that there is no conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e3. Ethic approval\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was approved by the Ethics Committee of Furtwangen University, Germany, in 2022 (application number: 22-057).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e4. Consent to participate \\u0026amp; 5. Written Consent for publication\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026ldquo;Consent to participate\\u0026rdquo; and \\u0026ldquo;Written Consent for publication\\u0026rdquo; were part of the following written information provided to all study participants:\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026ldquo;Information and declaration of consent: Participation in the study is voluntary. You can withdraw from it at any time without giving reasons and without suffering any disadvantages. Your data will be collected anonymously. It will not be possible to link the data to your person. The data will be evaluated and used exclusively within the framework of this research project (including the publications that result from it). All data will be processed without your name or any other direct means of identification. If the results of the study are published, it will not be possible to link the data to your person.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe study results will be summarized in a scientific publication. At the same time, an easy-to-understand summary will be prepared for people affected by ME/CFS, patient and self-help organizations, etc. In addition, the participants will be quoted (anonymously) in detail in a planned reference book on the subject, based on quotes from this study. Due to the anonymous nature of the data collection, it is not possible to delete your data retrospectively upon request. The data collected from you consists exclusively of the questions you answer in the questionnaire. No other information that could enable identification (e.g., IP address) will be stored. If you have any questions about the study, please feel free to contact the study leader, [name and email address].\\u003c/p\\u003e\\n\\u003cp\\u003eI have read and understood all of the information regarding participation in the study and agree to participate in the study.\\u0026nbsp;☐\\u0026nbsp; Yes\\u0026nbsp;☐\\u0026nbsp; No\\u0026ldquo;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e6, Availability of data and material\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data supporting the results of this study are included in the article or can be requested from the corresponding author.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e7. Code availability\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e8. Author\\u0026rsquo;s Contributions\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003col\\u003e\\n \\u003cli\\u003eLHH contributed to the development and conception of the research project, the preparation, collection, procurement, and provision of the data, the analysis and interpretation of the data, the review of the sources, the conclusions drawn from these findings, and the writing of the manuscript.\\u003c/li\\u003e\\n \\u003cli\\u003eLMH contributed to the preparation, provision, and interpretation of the data.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eAdditional Notes\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe translation from German was supported by www.DeepL.com/Translator (free version).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFurthermore, we would like to thank the AI tool ChatGPT (OpenAI) for language editing and wording optimization in individual sections under human guidance. All ideas, data and interpretations originate from the authors.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eWalitt B, Singh K, LaMunion SR, et al. Deep phenotyping of post-infectious myalgic encephalomyelitis/chronic fatigue syndrome. Nat Commun. 2024;15:907. https://doi.org/10.1038/s41467-024-45107-3.\\u003c/li\\u003e\\n\\u003cli\\u003eVyas J, Muirhead N, Singh R, et al. Impact of myalgic encephalomyelitis/chronic fatigue syndrome on the quality of life of people with ME/CFS and their partners and family members: an online cross-sectional survey. BMJ Open. 2022;12:e058128. https://doi.org/10.1136/bmjopen-2021-058128.\\u003c/li\\u003e\\n\\u003cli\\u003eBeentjes SV, Miralles M\\u0026eacute;haron A et al. Replicated blood-based biomarkers for myalgic encephalomyelitis not explicable by inactivity. EMBO Mol Med. 2025;17(7):1868-1891. https://doi.org/10.1038/s44321-025-00258-8. \\u003c/li\\u003e\\n\\u003cli\\u003eTate W, Walker M, Sweetman E, et al. Molecular mechanisms of neuroinflammation in ME/CFS and Long COVID to sustain disease and promote relapses. Front Neurol. 2022;13:877772. https://doi.org/10.3389/fneur.2022.877772.\\u003c/li\\u003e\\n\\u003cli\\u003eStussman B, Williams A, Snow J, et al. Characterization of post-exertional malaise in patients with myalgic encephalomyelitis/chronic fatigue syndrome. Front Neurol. 2020;11:1025. https://doi.org/10.3389/fneur.2020.01025.\\u003c/li\\u003e\\n\\u003cli\\u003eHuber KA, Sunnquist M, Jason LA. Latent class analysis of a heterogeneous international sample of patients with myalgic encephalomyelitis/chronic fatigue syndrome. Fatigue. 2018;6(3):163\\u0026ndash;178. https://doi.org/10.1080/21641846.2018.1494530.\\u003c/li\\u003e\\n\\u003cli\\u003eKrumina A, Vecvagare K, Svirskis S, et al. Clinical profile and aspects of differential diagnosis in patients with ME/CFS from Latvia. Medicina. 2021;57(9):958. https://doi.org/10.3390/medicina57090958.\\u003c/li\\u003e\\n\\u003cli\\u003eHabermann-Horstmeier L, Horstmeier LM. ME/CFS-Symptome im Krankheitsverlauf \\u0026ndash; eine Public-Health-Studie auf Basis von Patientenberichten. Pr\\u0026auml;v Gesundheitsf. 2025. Received: August 16, 2025; Accepted: October 27, 2025. https://doi.org/10.1007/s11553-025-01280-x. \\u003c/li\\u003e\\n\\u003cli\\u003eFinkelmeyer A, He J, Maclachlan L, et al. Grey and white matter differences in chronic fatigue syndrome: a voxel-based morphometry study. Neuroimage Clin. 2018;17:24\\u0026ndash;30. https://doi.org/10.1016/j.nicl.2017.09.024.\\u003c/li\\u003e\\n\\u003cli\\u003eBarnden LR, Kwiatek R, Crouch B, Burnet R, Del Fante P. Vegetative correlations with MRI are abnormal in the brainstem vasomotor centre in Chronic Fatigue Syndrome. Neuroimage Clin. 2016;11:530\\u0026ndash;537. https://doi.org/10.1016/j.nicl.2016.03.017 \\u003c/li\\u003e\\n\\u003cli\\u003eBarnden LR, Shan ZY, Staines DR, et al. Hyperintense sensorimotor T1 spin echo MRI is associated with brainstem abnormality in chronic fatigue syndrome. Neuroimage Clin. 2018;20:102\\u0026ndash;109. https://doi.org/10.1016/j.nicl.2018.07.011 \\u003c/li\\u003e\\n\\u003cli\\u003eKuratsune H, Yamaguti K, Lindh G, et al. Brain regions involved in fatigue sensation: reduced acetylcarnitine uptake into the brain. Neuroimage. 2002;17:1256\\u0026ndash;1265. https://doi.org/10.1006/nimg.2002.1260 \\u003c/li\\u003e\\n\\u003cli\\u003eRenz-Polster H, Tremblay ME, Bienzle D, Fischer JE. The pathobiology of myalgic encephalomyelitis/chronic fatigue syndrome: the case for neuroglial failure. Front Cell Neurosci. 2022;16:888232. https://doi.org/10.3389/fncel.2022.888232.\\u003c/li\\u003e\\n\\u003cli\\u003eLee JS, Sato W, Son CG. Brain-regional characteristics and neuroinflammation in ME/CFS patients from neuroimaging: a systematic review and meta-analysis. Autoimmun Rev. 2024;23(2):103484. https://doi.org/10.1016/j.autrev.2023.103484.\\u003c/li\\u003e\\n\\u003cli\\u003eKasimir F, Toomey D, Liu Z, et al. Tissue-specific signature of HHV-6 infection in ME/CFS. Front Mol Biosci. 2022;9:1044964. https://doi.org/10.3389/fmolb.2022.1044964.\\u003c/li\\u003e\\n\\u003cli\\u003eBechny M, Scutari M, van der Meer J, et al. Unveiling sleep dysregulation in chronic fatigue syndrome with and without fibromyalgia through Bayesian networks. arXiv. 2025. Available from: https://arxiv.org/abs/2503.07876.\\u003c/li\\u003e\\n\\u003cli\\u003eChristopoulos EM, Tantanis D, Huang K, et al. Mapping cerebral blood flow in myalgic encephalomyelitis/chronic fatigue syndrome and orthostatic intolerance: insights from a systematic review. J Transl Med. 2025;23(1):963. https://doi.org/10.1186/s12967-025-06954-w.\\u003c/li\\u003e\\n\\u003cli\\u003eMorris G, Anderson G, Galecki P, Berk M, Maes M. A narrative review on the similarities and dissimilarities between myalgic encephalomyelitis/chronic fatigue syndrome and sickness behavior. BMC Med. 2013;11:64. https://doi.org/10.1186/1741-7015-11-64.\\u003c/li\\u003e\\n\\u003cli\\u003eBrenu EW, van Driel ML, Staines DR, et al. Longitudinal investigation of natural killer cells and cytokines in chronic fatigue syndrome/myalgic encephalomyelitis. J Transl Med. 2012;10:88. https://doi.org/10.1186/1479-5876-10-88.\\u003c/li\\u003e\\n\\u003cli\\u003eDuan L, Yang J, Zhao J, et al. Evaluating the causal role of genetically inferred immune cells and inflammatory cytokines on myalgic encephalomyelitis/chronic fatigue syndrome. Biomedicines. 2025;13(5):1200. https://doi.org/10.3390/biomedicines13051200.\\u003c/li\\u003e\\n\\u003cli\\u003eRasa S, Nora-Krukle Z, Henning N, et al. Chronic viral infections in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). J Transl Med. 2018;16:268. doi:10.1186/s12967-018-1644-y.\\u003c/li\\u003e\\n\\u003cli\\u003eJonsj\\u0026ouml; MA, Olsson GL, Wicksell RK, et al. The role of low-grade inflammation in ME/CFS: associations with symptoms. Psychoneuroendocrinology. 2020;113:104578. https://doi.org/10.1016/j.psyneuen.2019.104578.\\u003c/li\\u003e\\n\\u003cli\\u003eTate WP, Walker MOM, Peppercorn K, Blair ALH, Edgar CD. Towards a better understanding of the complexities of myalgic encephalomyelitis/chronic fatigue syndrome and Long COVID. Int J Mol Sci. 2023;24(6):5124. https://doi.org/10.3390/ijms24065124.\\u003c/li\\u003e\\n\\u003cli\\u003eMiwa K. Orthostatic intolerance and chronotropic incompetence in patients with ME/CFS. Circ Rep. 2023;5(2):55\\u0026ndash;61. https://doi.org/10.1253/circrep.CR-22-0114.\\u003c/li\\u003e\\n\\u003cli\\u003eNunes JM, Kell DB, Pretorius E. Cardiovascular and haematological pathology in myalgic encephalomyelitis/chronic fatigue syndrome: a role for viruses. Blood Rev. 2023;60:101075. https://doi.org/10.1016/j.blre.2023.101075.\\u003c/li\\u003e\\n\\u003cli\\u003eSteinsvik EK, Hausken T, Fluge \\u0026Oslash;, Mella O, Gilja OH. Gastric dysmotility and gastrointestinal symptoms in myalgic encephalomyelitis/chronic fatigue syndrome. Scand J Gastroenterol. 2023;58(7):718\\u0026ndash;725. https://doi.org/10.1080/00365521.2023.2173533.\\u003c/li\\u003e\\n\\u003cli\\u003eLakhan SE, Kirchgessner A. Gut inflammation in chronic fatigue syndrome. Nutr Metab (Lond). 2010;7:79. https://doi.org/10.1186/1743-7075-7-79.\\u003c/li\\u003e\\n\\u003cli\\u003eWirth KJ, Scheibenbogen C, Paul F. An attempt to explain the neurological symptoms of myalgic encephalomyelitis/chronic fatigue syndrome. J Transl Med. 2021;19(1):471. doi:10.1186/s12967-021-03143-3. Erratum in: J Transl Med. 2022;20(1):25. https://doi.org/10.1186/s12967-021-03216-3.\\u003c/li\\u003e\\n\\u003cli\\u003eCambras T, Zer\\u0026oacute;n-Rugerio MF, D\\u0026iacute;ez-Noguera A, et al. Skin temperature circadian rhythms and vegetative in myalgic encephalomyelitis/chronic fatigue syndrome: the role of endothelin-1 in the vascular tone dysregulation. Int J Mol Sci. 2023;24(5):4835. https://doi.org/10.3390/ijms24054835.\\u003c/li\\u003e\\n\\u003cli\\u003eHsu CY, Ahmad I, Maya RW, et al. The potential therapeutic approaches targeting gut health in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): a narrative review. J Transl Med. 2025;23(1):530. https://doi.org/10.1186/s12967-025-06527-x.\\u003c/li\\u003e\\n\\u003cli\\u003eStallmach A, Quickert S, Puta C, Reuken PA. The gastrointestinal microbiota in the development of ME/CFS: a critical view and potential perspectives. Front Immunol. 2024;15:1352744. https://doi.org/10.3389/fimmu.2024.1352744.\\u003c/li\\u003e\\n\\u003cli\\u003eMart\\u0026iacute;n F, Blanco-Suarez M, Zambrano P, et al. Increased gut permeability and bacterial translocation are associated with fibromyalgia and ME/CFS: implications for disease-related biomarker discovery. Front Immunol. 2023;14:1253121. https://doi.org/10.3389/fimmu.2023.1253121.\\u003c/li\\u003e\\n\\u003cli\\u003eME Research UK. Leaky gut and the immune system in ME/CFS. 2023 Sep 6. Available from: https://www.meresearch.org.uk/leaky-gut-and-the-immune-system-in-me-cfs/.\\u003c/li\\u003e\\n\\u003cli\\u003eBorrego-Ruiz A, Borrego JJ. Microbial involvement in myalgic encephalomyelitis/ chronic fatigue syndrome pathophysiology. Microbes Immunity. 2025;2(1):17\\u0026ndash;26. https://doi.org/10.36922/mi.4783.\\u003c/li\\u003e\\n\\u003cli\\u003eHabermann-Horstmeier L, Horstmeier LM. Auswirkungen der Qualit\\u0026auml;t der Arzt-Patient-Beziehung auf die Gesundheit von ME/CFS-Erkrankten. MMW Fortschr Med. 2023;165(Suppl 5):16\\u0026ndash;27. https://doi.org/10.1007/s15006-023-2894-z.\\u003c/li\\u003e\\n\\u003cli\\u003eJason LA, Corradi K, Torres-Harding S. Toward an empirical case definition of CFS. J Soc Serv Res. 2007;34(2):43\\u0026ndash;54. https://doi.org/10.1300/J079v34n02_04.\\u003c/li\\u003e\\n\\u003cli\\u003eJason LA, Sunnquist M, Brown A, et al. Factor analysis of the DePaul Symptom Questionnaire: identifying core domains. J Neurol Neurobiol. 2015;1(4). https://doi.org/10.16966/2379-7150.114.\\u003c/li\\u003e\\n\\u003cli\\u003eFriedberg F, Dechene L, McKenzie MJ, Fontanetta R. Symptom patterns in long-duration chronic fatigue syndrome. J Psychosom Res. 2000;48(1):59\\u0026ndash;68. https://doi.org/10.1016/S0022-3999(99)00077-X.\\u003c/li\\u003e\\n\\u003cli\\u003eNisenbaum R, Reyes M, Unger ER, Reeves WC. Factor analysis of symptoms among subjects with unexplained chronic fatigue: what can we learn about chronic fatigue syndrome? J Psychosom Res. 2004;56(2):171\\u0026ndash;178. https://doi.org/10.1016/S0022-3999(03)00039-4 \\u003c/li\\u003e\\n\\u003cli\\u003eJonsj\\u0026ouml; MA, Wicksell RK, Holmstr\\u0026ouml;m L, Andreasson A, Bileviciute-Ljungar I, Olsson GL. Identifying symptom subgroups in patients with ME/CFS \\u0026ndash; relationships to functioning and quality of life. Fatigue: Biomedicine, Health \\u0026amp; Behavior. 2017;5(1):33\\u0026ndash;42. https://doi.org/10.1080/21641846.2017.1287546.\\u003c/li\\u003e\\n\\u003cli\\u003eCarruthers BM, Jain AK, De Meirleir KL, et al. Myalgic encephalomyelitis/chronic fatigue syndrome: clinical working case definition, diagnostic and treatment protocols. J Chronic Fatigue Syndr. 2003;11(1):7\\u0026ndash;115. https://doi.org/10.1300/J092v11n01_02.\\u003c/li\\u003e\\n\\u003cli\\u003eHabermann-Horstmeier L, Horstmeier LM. Systemisches Denken, subjektive Befunde und das \\u0026auml;rztliche Schubladendenken bei ME/CFS \\u0026ndash; eine qualitative Public-Health-Studie aus Patientensicht. Dtsch Med Wochenschr. 2023; Published online. https://doi.org/10.1055/a-2197-6479.\\u003c/li\\u003e\\n\\u003cli\\u003eHabermann-Horstmeier L, Horstmeier LM. Wahrnehmung von Genderaspekten in der Beziehung zwischen \\u0026Auml;rzt:innen und Patient:innen bei ME/CFS. Pr\\u0026auml;v Gesundheitsf. 2024; Published online Jan 26. https://doi.org/10.1007/s11553-023-01098-5.\\u003c/li\\u003e\\n\\u003cli\\u003eBakken IJ, Tveito K, Gunnes N, et al. Two age peaks in the incidence of chronic fatigue syndrome/myalgic encephalomyelitis: a population-based registry study from Norway 2008\\u0026ndash;2012. BMC Med. 2014;12:167. https://doi.org/10.1186/s12916-014-0167-5.\\u003c/li\\u003e\\n\\u003cli\\u003eLim EJ, Ahn YC, Jang ES, et al. Systematic review and meta-analysis of the prevalence of chronic fatigue syndrome/myalgic encephalomyelitis. J Transl Med. 2020;18(1):100. https://doi.org/10.1186/s12967-020-02269-0.\\u003c/li\\u003e\\n\\n\\u003c/ol\\u003e\"},{\"header\":\"Footnotes\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003e Literature sources see discussion\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003e The notation \\\"(0/1)\\\" indicates that the target variable is dichotomous\\u0026mdash;i.e., it has only two possible values\\u0026mdash;and that the logistic regression aims to model the probability of the value 1 (occurrence of problems).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003e The results of these investigations can be viewed upon request from the corresponding author.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), symptom clusters, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Structural Equation Modeling (SEM)\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8319139/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8319139/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e \\u003cem\\u003eIntroduction\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eMyalgic 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.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eMethods\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eSymptom responses from 748 adult ME/CFS patients (\\u0026ge;\\u0026thinsp;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.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eResults\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eBrain\\u003c/em\\u003e subgroup symptoms (brain fog, sensory hypersensitivity, visual disturbances, sleep disturbances, headaches) formed a single coherent factor with high loadings and excellent fit (RMSEA\\u0026thinsp;=\\u0026thinsp;0.021; CFI\\u0026thinsp;=\\u0026thinsp;0.996). Gastrointestinal (\\u003cem\\u003eGut\\u003c/em\\u003e) symptoms demonstrated stronger internal consistency than immunological (\\u003cem\\u003eImmune\\u003c/em\\u003e) 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 (\\u003cem\\u003eVegetative\\u003c/em\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eConclusions\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eFindings 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.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Distinct Symptom Clusters Reflect Pathophysiological Mechanisms in ME/CFS\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-22 09:51:43\",\"doi\":\"10.21203/rs.3.rs-8319139/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"0560a47c-35fd-4481-bcbc-017cd3b81afd\",\"owner\":[],\"postedDate\":\"December 22nd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-12-22T09:51:45+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-22 09:51:43\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8319139\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8319139\",\"identity\":\"rs-8319139\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}