Category: Methodology

Novel activity and participation scales for children, adolescents, and young adults with postacute infection and vaccination syndromes and/or ME/CFS

Abstract:

Children, adolescents, and young adults (CYP) with postacute infection and vaccination syndromes (PAIVS), and/or myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), experience profound loss in activity and participation. We introduce and psychometrically validate two new brief, age-adapted, and domain-specific questionnaires for clinical use assessing activity and participation in this vulnerable patient group.

For this, 91 patients (aged 10-25 years) were assessed at the Munich Chronic Fatigue Center (MCFC) from 12/2022 to 11/2024. We designed the MCFC Activity Scale and MCFC Participation Scale and assessed construct validity using confirmatory factor analysis for both questionnaires. Reliability was evaluated via Cronbach’s α. Factor-based MCFC Activity and Participation Scores (0-100) were derived and correlated with Bell Score, FSS, DSQ-PEM, and SF-12 Component Summary Scales (PCS and MCS). Discrimination for ME/CFS was evaluated using ROC analyses. Participants (mean age 15.6 ± 2.4 years) were predominantly female (64%). 65% were diagnosed with ME/CFS.

The MCFC Activity Scale showed excellent one-factor fit (comparative fit index, CFI = 1.00) and good internal consistency (α = 0.82). The MCFC Participation Scale showed good internal consistency (α = 0.85) and acceptable one-factor fit (CFI = 0.817). Factor-based activity and participation were strongly correlated yet distinct (r = 0.73). Derived MCFC Activity and Participation Scores differed significantly by ME/CFS diagnosis (p ≤ 0.009). Scores correlated with Bell Score, FSS, DSQ-PEM, and SF-12 PCS (all p ≤ .002). For ME/CFS discrimination, the Activity Score achieved an AUC = 0.78 and the Participation Score an AUC = 0.72.

Conclusion: The Activity Scale demonstrated strong construct validity. The Participation Scale showed good internal consistency. Both scores demonstrated good convergent validity with established patient-reported outcome measures, supporting clinical utility. They may serve as pragmatic screening tools for this vulnerable patient group.

Source: Weidmann C, Grabbe A, Eberhartinger M, Kircher A, Leone A, Warlitz C, Stojanov S, Behrends U, Mihatsch LL. Novel activity and participation scales for children, adolescents, and young adults with postacute infection and vaccination syndromes and/or ME/CFS. Eur J Pediatr. 2026 Jun 5;185(7):471. doi: 10.1007/s00431-026-07125-9. PMID: 42249231. https://link.springer.com/article/10.1007/s00431-026-07125-9 (Full text)

Toward a Molecular Reclassification of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Integrating Multi-Omics, Machine Learning, and Precision Medicine

Abstract:

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multi-system disease characterized by a multitude of symptoms across various organ systems. Diagnosis has relied heavily on heterogeneous clinical symptom presentation and evolving case definitions, with treatment focused on addressing presenting symptoms due to the paucity of validated biomarkers. Meanwhile, advances have been made in understanding the underlying pathophysiology through strong epidemiologic, clinical, and basic science studies. This narrative review synthesizes recent advances that are likely to drive a shift in understanding from symptom-based classification toward a molecularly defined understanding of the disease.

This shift in understanding will likely provide the foundation for future research efforts focused on targeting diagnosis and treatment more effectively. Specifically, we reference the identification of rare genetic risk variants through the HEAL2 deep learning framework, the large-scale DecodeME genome-wide association study, and dynamic epigenetic markers of disease state.

In addition, the findings revealed the downstream consequences of this genetic and epigenetic priming: chronic innate immune activation, CD8+ T cell exhaustion characterized by upregulation of the exhaustion-driving transcription factors Thymocyte Selection-Associated HMG Box (TOX) and Eomesodermin (EOMES), and a cellular energy crisis centered on mitochondrial dysfunction. Furthermore, results of recent studies have revealed sex-specific transcriptomic and proteomic signatures of maladaptive recovery.

We also highlight the role of machine learning and artificial intelligence integrations in translating high-dimensional multi-omics data into actionable biological insights, including the identification of monocyte subsets via Positive Unlabeled Learning, circulating cell-free RNA diagnostic signatures, and integrated multi-modal disease models such as BioMapAI.

The combination of these findings, which highlight multiple identifiable mechanisms of molecular activity, support the feasibility of molecular subtyping, precision diagnostics, and targeted therapeutic strategies for ME/CFS.

Source: Frank J, Nesterovitch N, Movva C, Klimas NG, Nathanson L. Toward a Molecular Reclassification of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Integrating Multi-Omics, Machine Learning, and Precision Medicine. Int J Mol Sci. 2026 May 15;27(10):4436. doi: 10.3390/ijms27104436. PMID: 42196410; PMCID: PMC13207433. https://pmc.ncbi.nlm.nih.gov/articles/PMC13207433/ (Full text)

Designing studies for post-treatment Lyme disease and other infection-associated chronic illnesses

Abstract:

Infection-associated chronic illnesses (IACIs) encompass a spectrum of poorly understood syndromes often marked by significant neurologic and multisystem symptoms following an infectious event. This review focuses on several diseases representative of the IACI spectrum. These are post-treatment Lyme disease syndrome (PTLDS), long COVID, myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and multiple sclerosis (MS). Their clinical and biological complexity, combined with a lack of clear diagnostic criteria and objective available laboratory biomarkers, makes them difficult to distinguish from conditions with overlapping features.

This presents challenges for research studies, as well as diagnosis and clinical management. This diagnostic ambiguity, coupled with heterogeneous patient presentations, has led to challenges in research, including misclassification of study participants and inconsistent or irreproducible findings. Some PTLDS research exemplifies these issues, which also extend to other IACIs.

To advance the field, we highlight key methodological refinements and approaches for studying IACIs, including rigorous participant selection, standardized sample collection protocols, and the use of appropriate control groups, including those with microbiologic proof of the initial infection when known and technologically feasible. We also address broader influences on research quality, such as stigma, historical neglect, and the urgency to find treatments, which have contributed to the proliferation of poorly controlled studies and questionable practices. Drawing lessons from past challenges, we propose a path forward grounded in fit-for-purpose methodological rigour to improve scientific understanding and support evidence-based therapeutic development for IACIs.

Source: Arnaboldi PM, Becker J, Nath A, Coyle PK, Handel A, Sellati TJ, Gomes-Solecki M, Garcet S, Henderson MK, Mullins P, Cowan E, McCombie WR, Wellins AM, Allegretta M, Bergquist J, Schutzer SE. Designing studies for post-treatment Lyme disease and other infection-associated chronic illnesses. Brain. 2026 May 18:awag016. doi: 10.1093/brain/awag016. Epub ahead of print. PMID: 42148664. https://academic.oup.com/brain/advance-article/doi/10.1093/brain/awag016/8586348 (Full text)

Evidence of White Matter Neuroinflammation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Diffusion-Based Neuroinflammation Imaging Study

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disorder with suspected neuroinflammatory pathophysiology. However, previous diffusion tensor imaging (DTI) studies have reported inconsistent white matter abnormalities in ME/CFS, and specific white matter inflammatory changes remain poorly characterised. This study employed an advanced diffusion-based neuroinflammation imaging (NII) model to investigate white matter neuroinflammation in ME/CFS.

Diffusion MRI data from 67 ME/CFS patients (median age, 38; and 54 women) and 67 rigorously matched healthy controls (HCs) (median age 38; and 52 women) were analysed. Seven NII-derived metrics were computed: hindered water ratio (NII-HR), restricted fraction (NII-RF), fibre fraction (NII-FF), axial diffusivity (NII-AD), radial diffusivity (NII-RD), mean diffusivity (NII-MD) and fractional anisotropy (NII-FA). Conventional DTI metrics were also calculated. Tract-based spatial statistics were used to perform voxel-wise group comparisons, and multiple regression analysis was conducted to examine the relationship between NII/DTI metrics and clinical measures of mental health, physical health, sleep quality, disability, disease severity and disease duration.

Compared to HCs, ME/CFS patients exhibited widespread white matter abnormalities, including significantly lower NII-HR and NII-RF, and significantly higher NII-FF, NII-AD, NII-MD and NII-FA across association, commissural and projection fibres. Additionally, some regions showed decreased NII-AD and NII-MD in ME/CFS. Lower NII-RF, NII-AD and NII-MD in ME/CFS were significantly associated with worse mental health, while lower NII-RF was also associated with a higher level of disability. Among ME/CFS patients, higher NII-FF was associated with lower disease severity. Conventional DTI showed minimal group differences and no significant clinical associations.

This study provides in vivo evidence of white matter neuroinflammation in ME/CFS, characterised by cerebral edema (reduced NII-HR), cellular infiltration (reduced NII-RF) and axonal reorganisation (increased NII-FF). This suggests NII-derived indices may serve as sensitive biomarkers for neuroinflammation in ME/CFS.

Source: Yu, Q., K.Kothe, R. A.Kwiatek, et al. 2026. “Evidence of White Matter Neuroinflammation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Diffusion-Based Neuroinflammation Imaging Study.” Human Brain Mapping47, no. 4: e70505. https://doi.org/10.1002/hbm.70505. https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.70505 (Full text)

Assessment of symptoms in myalgic encephalomyelitis/chronic fatigue syndrome: a comparative study of existing scales

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a multifaceted disorder characterized by persistent fatigue, post-exertional malaise (PEM), cognitive dysfunction, sleep disturbance, pain, psychological distress, orthostatic intolerance, and impaired multidimensional health status and functioning. In the absence of reliable biomarkers, standardized symptom assessment is essential for accurate diagnosis and comparability across studies.

This narrative literature review synthesized studies identified through PubMed and Web of Science up to June 2024, covering assessment instruments across major ME/CFS symptom domains. Tools were evaluated for their psychometric validity, clinical applicability, and key limitations.

Overall, existing scales demonstrate acceptable reliability but vary in sensitivity and disease specificity. Harmonized, multidimensional, and digitally or objectively validated measures are needed to improve diagnostic precision, longitudinal monitoring, and clinical translation in ME/CFS.

Source: Lu J, Sun W, Li S, Qu Y, Liu T, Guo S, Feng C, Yang T. Assessment of symptoms in myalgic encephalomyelitis/chronic fatigue syndrome: a comparative study of existing scales. Front Neurol. 2025 Nov 18;16:1618272. doi: 10.3389/fneur.2025.1618272. PMID: 41341517; PMCID: PMC12668935. https://pmc.ncbi.nlm.nih.gov/articles/PMC12668935/ (Full text)

Leveraging Explainable Automated Machine Learning (AutoML) and Metabolomics for Robust Diagnosis and Pathophysiological Insights in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Abstract:

Background/Objectives: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating complex disease with an elusive etiology, lacking objective diagnostic biomarkers. This study leverages advanced Automated Machine Learning (AutoML) to analyze plasma metabolomic and lipidomic profiles for the purpose of ME/CFS detection.

Methods: We utilized a publicly available dataset comprising 888 metabolic features from 106 ME/CFS patients and 91 matched controls. Three AutoML frameworks-TPOT, Auto-Sklearn, and H2O AutoML-were benchmarked under identical time constraints. Univariate ROC and PLS-DA analyses with cross-validation, permutation testing, and VIP-based feature selection were applied to standardized, log-transformed omics data to identify significant discriminatory metabolites/lipids and assess their intercorrelations.

Results: TPOT significantly outperformed its counterparts, achieving an area under the curve (AUC) of 92.1%, accuracy of 87.3%, sensitivity of 85.8%, and specificity of 89.0%. The PLS-DA model revealed a moderate but statistically significant discrimination between ME/CFS and controls. Explainable artificial intelligence (XAI) via SHAP analysis of the optimal TPOT model identified key metabolites implicating dysregulated pathways in mitochondrial energy metabolism (succinic acid, pyruvic acid, leucine), chronic inflammation (prostaglandin D2, 11,12-EET), gut-brain axis communication (glycocholic acid), and cell membrane integrity (pc(35:2)a).

Conclusions: Our results demonstrate that TPOT-derived models not only provide a highly accurate and robust diagnostic tool but also yield biologically interpretable insights into the pathophysiology of ME/CFS, highlighting its potential for clinical decision support and elucidating novel therapeutic targets.

Source: Yagin FH, Colak C, Al-Hashem F, Alzakari SA, Alhussan AA, Aghaei M. Leveraging Explainable Automated Machine Learning (AutoML) and Metabolomics for Robust Diagnosis and Pathophysiological Insights in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Diagnostics (Basel). 2025 Oct 30;15(21):2755. doi: 10.3390/diagnostics15212755. PMID: 41226047. https://www.mdpi.com/2075-4418/15/21/2755 (Full text)

Psychometric evaluation of the PROMIS® physical function short form 12a for use by adults with myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating, long-term illness that significantly impairs physical functioning. Despite its impact, the use of modern generic instruments to assess physical function in this population remains underexplored. This study aims to assess the psychometric properties of the Patient-Reported Outcome Measurement Information System® (PROMIS) Physical Function Short Form (PF-SF) 12a for use in adults with ME/CFS.

Methods: This study included 334 participants (173 with ME/CFS and 161 healthy controls) who took part in a Cognitive and Exercise sub-study of the Multi-Site Clinical Assessment of ME/CFS study from six clinics across the US. Data was used to examine the ceiling/floor effects, internal consistency reliability, known-groups validity, and convergent validity of the PROMIS PF-SF.

Results: The mean T-score of the PROMIS PF-SF was 40.5 for participants with ME/CFS, about one standard deviation below the national norm (T-score = 50). The PROMIS PF-SF showed no substantial floor/ceiling effects and high internal consistency (standardized Cronbach’s α = 0.88 and ω = 0.92). In addition, this instrument showed good known-groups validity with medium-to-large effect sizes (η2 = 0.08-0.35). A significant, monotonic increase of the physical function score was found across ME/CFS participant groups with low, medium, and high functional impairment as defined by four different measures. Participants with ME/CFS had significantly worse physical function scores than healthy controls (η2 = 0.70). The PROMIS PF-SF also demonstrated good convergent validity with high correlations (magnitude of r = 0.47-0.55) with other relevant measures.

Conclusions: The PROMIS PF-SF 12a demonstrated satisfactory reliability and validity for use in ME/CFS research and clinical practice.

Source: Yang M, Keller S, Rafiee P, Lin JS. Psychometric evaluation of the PROMIS® physical function short form 12a for use by adults with myalgic encephalomyelitis/chronic fatigue syndrome. Health Qual Life Outcomes. 2025 Oct 6;23(1):95. doi: 10.1186/s12955-025-02431-6. PMID: 41053836; PMCID: PMC12502361. https://pmc.ncbi.nlm.nih.gov/articles/PMC12502361/ (Full text)

Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence

Abstract:

We studied a generalized question: chronic diseases like ME/CFS and long COVID exhibit high heterogeneity with multifactorial etiology and progression, complicating diagnosis and treatment. To address this, we developed BioMapAI, an explainable Deep Learning framework using the richest longitudinal multi-omics dataset for ME/CFS to date.

This dataset includes gut metagenomics, plasma metabolome, immune profiling, blood labs, and clinical symptoms. By connecting multi-omics to a symptom matrix, BioMapAI identified both disease- and symptom-specific biomarkers, reconstructed symptoms, and achieved state-of-the-art precision in disease classification.

We also created the first connectivity map of these omics in both healthy and disease states and revealed how microbiome-immune-metabolome crosstalk shifted from healthy to ME/CFS.

Source: Xiong R. Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence. ArXiv [Preprint]. 2025 Jun 18:arXiv:2506.15761v1. PMID: 40980765; PMCID: PMC12447721. https://pmc.ncbi.nlm.nih.gov/articles/PMC12447721/ (Full text available as PDF file)

AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic illness with a multifactorial etiology and heterogeneous symptomatology, posing major challenges for diagnosis and treatment. Here we present BioMapAI, a supervised deep neural network trained on a 4-year, longitudinal, multi-omics dataset from 249 participants, which integrates gut metagenomics, plasma metabolomics, immune cell profiling, blood laboratory data and detailed clinical symptoms.

By simultaneously modeling these diverse data types to predict clinical severity, BioMapAI identifies disease- and symptom-specific biomarkers and classifies ME/CFS in both held-out and independent external cohorts. Using an explainable AI approach, we construct a unique connectivity map spanning the microbiome, immune system and plasma metabolome in health and ME/CFS adjusted for age, gender and additional clinical factors.

This map uncovers altered associations between microbial metabolism (for example, short-chain fatty acids, branched-chain amino acids, tryptophan, benzoate), plasma lipids and bile acids, and heightened inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, γδT) secreting IFN-γ and GzA.

Overall, BioMapAI provides unprecedented systems-level insights into ME/CFS, refining existing hypotheses and hypothesizing unique mechanisms—specifically, how multi-omics dynamics are associated to the disease’s heterogeneous symptoms.

Source: Xiong, R., Aiken, E., Caldwell, R. et al. AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome. Nat Med (2025). https://doi.org/10.1038/s41591-025-03788-3  https://www.nature.com/articles/s41591-025-03788-3

Using the Ratio of Phosphorylated to Non-phosphorylated Forms of Stress Kinase PKR as a Potential Diagnostic Test for ME/CFS

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex illness characterized by a set of mainly neurological symptoms lasting for over 6 months. Currently, there is no definitive laboratory diagnostic test readily accessible to all clinicians and patients, and so clinical diagnosis occurs only after an exhaustive process of exclusion of all other possible causes of the varied symptoms experienced by the patient.

Here we present the development of a method that uses specific antibodies able to identify a changed ratio of phosphorylated and active protein kinase R in the peripheral blood monocyte cells (PBMCs) and neutrophil cells from a small group of ME/CFS sufferers, compared to age and sex-matched controls.

Protein kinase R (PKR) is an RNA-activated immune protein and stress kinase that has been observed to be present in its cleaved, auto-phosphorylated, and active form in past ME/CFS studies. After further validation, the activation status of PKR detected via specific antibodies in an ELISA format has potential for a simple readily accessible diagnostic tool for the early acute stage of ME/CFS illness, or as a long-term measure to evaluate the disease status.

Source: Sweetman E, Tate WP. Using the Ratio of Phosphorylated to Non-phosphorylated Forms of Stress Kinase PKR as a Potential Diagnostic Test for ME/CFS. Methods Mol Biol. 2025;2920:13-28. doi: 10.1007/978-1-0716-4498-0_2. PMID: 40372675. https://link.springer.com/protocol/10.1007/978-1-0716-4498-0_2