Search Results (316)

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating multisystem illness characterised by post-exertional malaise, non-restorative sleep, and cognitive impairment, yet no objective diagnostic biomarkers have been established. Untargeted plasma metabolomics provides a broad view of the biochemical disturbances underlying ME/CFS; however, the high dimensionality of omics datasets and the limited interpretability of conventional classifiers nevertheless hinder translation into clinical practice. This study evaluates three ensemble classifiers—Explainable Boosting Machine (EBM), XGBoost, and LightGBM—for binary ME/CFS classification using plasma metabolomic and lipidomic profiles from 197 participants (106 ME/CFS; 91 healthy controls; 888 features). Feature dimensionality was reduced using a Pareto-Guided Recursive Neural Network (PRNN) pipeline. Model performance was assessed via 50-repeat stratified hold-out validation. EBM achieved the highest accuracy (0.909; 95% CI: 0.868–0.949) and area under the receiver operating characteristic curve (AUC: 0.940; 95% CI: 0.909–0.983), with XGBoost and LightGBM performing comparably. Interpretability analyses revealed that pairwise metabolite interaction terms—particularly proline & indole-3-lactate, tyrosine & N-acetylornithine, and maleic acid & arachidic acid—contributed the greatest discriminative signal. An ablation analysis comparing the full interaction-augmented EBM (AUC = 0.940) with a main-effects-only EBM (AUC = 0.882) confirmed that pairwise metabolite co-variation contributes additional discriminative value beyond individual metabolite levels, implicating amino acid catabolism, tryptophan–kynurenine pathway dysregulation, mitochondrial energy impairment, and lipid remodelling as central pathophysiological features. Global and instance-level explanations jointly demonstrated population-level metabolic signatures alongside individual heterogeneity, highlighting the added clinical value of explainable artificial intelligence (XAI) in metabolomics. These findings support EBM-based metabolomic profiling as an internally validated approach for ME/CFS classification, subject to external validation, calibration assessment, and prospective testing. Full article

Chronic overlapping pain conditions (COPCs) refer to a set of chronic pain disorders that frequently co-occur and may involve partially overlapping mechanisms. The U.S. National Institutes of Health currently recognizes ten COPCs: fibromyalgia, painful temporomandibular disorders, chronic low back pain, chronic migraine headache, chronic tension-type headache, irritable bowel syndrome, endometriosis, interstitial cystitis/bladder pain syndrome, vulvodynia, and myalgic encephalomyelitis/chronic fatigue syndrome. When multiple COPCs coexist, they are associated with a disproportionate multimorbidity burden, including greater pain, poorer psychological well-being, functional limitations, disability, fatigue, sleep disturbances, diminished quality of life, and increased healthcare utilization. Despite their impact, COPCs remain under-recognized, underdiagnosed, and undertreated. Combining structured literature searches and citation tracking with narrative syntheses, this review examines comorbid relationships, the burden of multimorbidity, and potentially overlapping nociplastic mechanisms. By adopting a multimorbidity-based perspective rather than a one-disease, one-treatment approach, it highlights barriers to care—including limited clinical awareness, under-recognition of additional COPCs, limited mechanistic understanding, and fragmented care—and proposes integrated strategies emphasizing prevention, systematic screening, mechanism-informed assessment, and coordinated, patient-centered multimodal management. Full article

Background and Objectives: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex disorder characterized by persistent or relapsing fatigue lasting at least six months, not alleviated by rest and not previously present. It is accompanied by post-exertional symptom exacerbation and non-restorative sleep. Fatigue is often disabling and reduces daily activity by more than 50%. This study aimed to evaluate the long-term frequency of somatic and psychiatric disorders in women previously diagnosed with ME/CFS and to describe the long-term clinical course, laboratory findings, and fatigue-related changes during a 16-year follow-up period. Materials and Methods: Sixteen years ago, 40 women diagnosed with ME/CFS according to then-current CDC criteria were enrolled at the Clinic for Infectious Diseases and the Center for Laboratory Medicine, University Clinical Center of Vojvodina. All participants provided informed consent. After 16 years, 20 women agreed to follow-up evaluation. At both time points, participants underwent structured questionnaires, clinical examination, psychological assessment, and comprehensive laboratory testing, including hematological, biochemical, endocrinological, and virological analyses. Fatigue severity was assessed using the FibroFatigue Scale (FFS) and the Multidimensional Assessment of Fatigue (MAF) scale. Results: During follow-up, 15% of participants were diagnosed with rheumatoid arthritis, 10% with cervical or breast cancer, 5% experienced premature myocardial infarction, 5% developed bronchial asthma, and 20% were diagnosed with clinical depression. Progression of ME/CFS was observed in 15%, while 5% reported infertility. Additionally, 15% developed arterial hypertension. Only 15% of participants did not report symptom worsening or new diagnoses. Conclusions: Over the 16-year follow-up, 85% of women with ME/CFS developed significant somatic or psychiatric conditions. These findings suggest that women diagnosed with ME/CFS may experience substantial long-term somatic and psychiatric disease burden, supporting the need for continued clinical monitoring and individualized follow-up. Full article

Myalgic encephalomyelitis (ME) is characterized by profound fatigue, post-exertional malaise (PEM), and cognitive dysfunction. Despite its clinical significance, the pathophysiology of PEM and disease heterogeneity remain unclear, and no validated biomarkers are available for rapid diagnosis or monitoring. We aimed to develop a screening approach combining label-free Raman spectroscopy (RS) and machine learning modeling (ML) to detect biomolecular changes in blood plasma and differentiate patients with ME from sedentary healthy controls. Blood plasma was collected from 115 patients with ME and 45 controls at rest (T0) and 90 min after a standardized, non-invasive stress test designed to induce PEM. Plasma samples were analyzed by RS, and ML models were developed independently at each time point to differentiate patients with ME and controls. The RS-ML models identified spectral features consistent with contributions from proteins, lipids, and low-molecular-weight metabolites. At T0 and T90, the area under the receiver operating characteristic curve, accuracy, specificity and sensitivity were 0.85 and 0.83, 79% and 84%, 82% and 90%, and 73% and 69%, respectively. RS-ML provides a rapid, low-cost approach to detect ME-associated biomolecular signatures in plasma and capture biochemical alterations associated with standardized stress. Full article

Myalgic Encephalomyelitis (ME) is a chronic, multisystem disease characterized by systemic metabolic dysfunction and post-exertional malaise (PEM). In this study, we investigated the dysregulation of irisin, an exercise-induced myokine, and its potential antagonism by thrombospondin-1 (TSP-1). In a cross-sectional study (92 ME patients vs. 44 sedentary healthy controls), plasma irisin and TSP-1 levels were measured at baseline and after a 90 min mechanical stress challenge applied to induce PEM. ME patients exhibited significantly lower baseline irisin (p < 0.05) and a blunted exertional response (p < 0.05). Paradoxically, baseline irisin was an independent predictor of fatigue severity (β = 0.728, p = 0.018), with moderate-to-severe patients showing elevated levels of both irisin and TSP-1 (p < 0.05), suggesting a compensatory but ineffective response. Functional cellular dielectric spectroscopy indicated that TSP-1 inhibits irisin signaling in a concentration-dependent manner. Irisin signaling was markedly reduced by both αvβ5 blockade and HSP90α inhibition in this experimental system, consistent with a diminished ability to counteract TSP-1. Collectively, these findings support a model in which dysregulation of the irisin–TSP-1 axis contributes to metabolic dysfunction in ME. Elevated circulating TSP-1 levels are associated with symptom severity and are linked to impaired irisin signaling in an HSP90α- and αvβ5-dependent context. This interaction is consistent with defective metabolic adaptation and highlights a potential therapeutic target that warrants further validation to restore energy homeostasis. Full article

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a complex and debilitating disorder with limited treatment options. Camel milk (CM), known for its rich nutrients and anti-fatigue properties, may offer multi-target benefits for managing this condition. This study utilized an integrated approach combining metabolomics, network pharmacology, and animal experiments. CM metabolites were profiled and screened via ADME. Potential targets were predicted and intersected with CFS/ME-associated genes. Male BALB/c mice were subjected to chronic restraint and forced swimming to evaluate the effects of CM (1000 mg/kg) on behavioral, inflammatory, neuroendocrine, and metabolic parameters. CM administration significantly improved exhaustive swimming time and reduced immobility. It attenuated systemic inflammation (restored IL-10), normalized brain CREB and DRD2/OPRM1 mRNA, and enhanced skeletal muscle AKT/GLUT4 expression and glycogen levels. Camel milk alleviates CFS/ME symptoms through the multi-component, multi-target regulation of neuroendocrine, inflammatory, and energy metabolism pathways. These preclinical findings suggest that CM may have potential as a supportive nutritional intervention for alleviating chronic fatigue, pending validation in human studies. Full article

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID-19 syndrome (LC) show substantial clinical overlap, but direct comparative microbiome studies remain limited. Methods: In this cross-sectional study, we compared the fecal gut microbiome of patients with ME/CFS, LC, and healthy controls (HC) within a unified analytical framework using 16S rRNA profiling, differential abundance testing, and multivariate modeling. We also examined associations between microbiome variation and questionnaire-derived symptom-domain scores. Results: Alpha-diversity did not differ significantly among groups, whereas beta-diversity analyses showed small but significant disease-associated community differences with broad overlap between cohorts. Differential abundance analysis identified stronger signals in disease-versus-control contrasts than in the direct ME/CFS vs. LC contrast. Both ME/CFS and LC shared enrichment of Sutterella and depletion of Terrisporobacter and Lachnospiraceae relative to HC. Predicted functional profiling showed shared disease-versus-control changes in pathways related to anaerobic acetate/H₂ carbon flow, inositol/polyol degradation, phosphonate/C1-related metabolism, and lysine-derived fermentation. Regression analyses showed the strongest microbiome associations with fatigue-related and physiosomatic domains, while affective, cognitive, and gastrointestinal outcomes showed weaker signals. Conclusions: Overall, these findings support the presence of overlapping but non-identical gut microbiome alterations in ME/CFS and LC. The results provide a basis for future longitudinal and multi-omics studies aimed at clarifying the stability, functional relevance, and clinical utility of these microbial patterns. Full article

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long Coronavirus Disease 2019 (long COVID) are complex chronic conditions that often follow infectious triggers with overlapping clinical features but poorly defined pathophysiological relationships. This study aimed to identify disease-specific immune signatures through multiparameter immunophenotyping of monocytes, dendritic cells, and T cell subsets. A total of 207 participants were included (ME/CFS: n = 103; long COVID: n = 63; healthy controls: n = 41). Peripheral blood mononuclear cells were analyzed using multiparameter flow cytometry. Statistical analyses included non-parametric testing, age-adjusted Analysis of covariance (ANCOVA), correlation network analysis, and principal component analysis (PCA). Long COVID was characterized by increased M2-like monocyte polarization, elevated CD80 expression across monocyte subsets, expansion of dendritic cells, and reduced expression of activation markers, indicating persistent immune activation with features of immune exhaustion. In contrast, ME/CFS exhibited reduced costimulatory molecule expression, impaired C-C chemokine receptor type 7 (CCR7)-mediated immune cell trafficking, and less coordinated activation patterns, consistent with a state of immune suppression. Correlation network analysis revealed more extensive and integrated immune interactions in long COVID, while PCA identified distinct immunophenotypic components and enabled moderate discrimination between the two conditions. These findings demonstrate that ME/CFS and long COVID are characterized by distinct immune profiles, supporting the concept of divergent immunopathological mechanisms. The identified signatures may contribute to biomarker development and guide targeted therapeutic approaches. Full article

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. Full article

Human endogenous retroviruses (HERVs) are potential driving forces of the pathophysiology of Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), linking post-infectious immune dysfunction to chronic inflammation and immune and neurocognitive dysfunction that are hallmark features of ME/CFS. Accumulating evidence from related autoimmune diseases and cancers has shown that reactivated HERVs can contribute to disease pathogenesis by amplifying immune activation through viral protein-mediated innate sensing, long terminal repeat (LTR)-driven transcription, and disrupting epigenetic silencing. HERV signatures are therefore promising biomarkers for diagnosis, patient stratification for drug-repurposing trials, and therapy monitoring. Accumulating evidence suggests a possible correlation between HERV expression and ME/CFS symptom severity, alterations in immune phenotypes, function and inflammatory gene networks. Importantly, locus-specific HERV profiling is a promising approach for distinguishing ME/CFS from overlapping or co-morbid conditions and healthy controls. Furthermore, HERV-targeted antibodies, immune modulators, epigenetic and antiviral interventions offer promise as concomitant therapeutic strategies for ME/CFS. Additional research incorporating viromics and other-omics validation, functional assays, and HERV-stratified clinical trials is now needed to realise this potential and to transform ME/CFS from a symptom-based syndrome into a mechanism-driven, treatable condition. Full article

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and post-COVID-19 syndrome share a symptom profile, including severe fatigue, cognitive dysfunction, exertional intolerance, sleep disturbances, hypervigilance, and the paradoxical state of being “wired but tired.” A well-established finding is sympathetic hyperactivity with reduced vagal tone, typically interpreted as autonomic nervous system dysfunction. Emerging evidence, however, suggests a broader disturbance across multiple neurotransmitter systems. This paper reviews current knowledge on neurotransmitter systems implicated in ME/CFS and Long COVID, focusing on potential mechanisms of dysregulation and their roles in disease pathology and symptom generation, as well as implications for treatment. In addition to abnormalities of the noradrenergic system, disturbances in serotonergic, GABAergic, and glutamatergic signaling have been reported. Contributing factors may include autoimmunity, neuroinflammation, gut dysbiosis, epigenetic influences, and stressors such as orthostatic intolerance, metabolic strain, and pain. A shift favoring excitatory over inhibitory neurotransmission can lead to excessive neural activation, autonomic dysfunction, sensory hypersensitivities, sleep disturbances, and cognitive impairment. Reduced GABAergic tone combined with increased glutamatergic and noradrenergic activity may elevate skeletal muscle tone, contributing to calcium overload, mitochondrial dysfunction, exertional intolerance, and post-exertional malaise. Various pharmacological treatments may partially rebalance these neurotransmitter systems, but limited efficacy highlights the need for systematic investigation and individualized strategies. Full article

Background: Post-exertional malaise (PEM), which is the cardinal feature of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), is also reported in a proportion of patients with post-COVID-19 syndrome (PCS). Our objective was to identify determinants that may be linked to the emergence of PEM in PCS patients. Methods: Patients fulfilling the World Health Organization definition for PCS who attended the post-COVID unit of the Internal Medicine Department of Angers University Hospital, France, between June 2020 and December 2023 were included retrospectively. Their medical records were reviewed to extract information on COVID-19 infection history, characteristics of post-exertional malaise (PEM), fatigue severity, and relevant epidemiological variables. Results: The study included 220 patients, grouped according to whether post-exertional malaise was present (PCS/PEM+) or absent (PCS/PEM–). PEM was observed in 26.4% of patients and was significantly linked to earlier COVID onset in 2020/2021 (OR 5.68 (95% CI: 1.66–19.45), p = 0.006), as well as higher fatigue levels (OR 2.07 (95% CI: 1.22–3.50), p = 0.007). Conclusions: Patients who contracted COVID-19 during the pre-Omicron period reported PEM more frequently than those infected in later waves. This observation could reflect differences in viral characteristics following the emergence of the Omicron variant; however, alternative explanations—such as increasing vaccination coverage, accumulating post-infectious immunity, or other unmeasured factors—cannot be ruled out. Based on the observed link between PEM and symptom severity, PCS patients should be systematically assessed for the presence of PEM. Full article

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disorder, characterized by symptoms such as post-exertional malaise (PEM) and cognitive impairments. This study assessed reaction time (RT) metrics in three-dimensional (3D) visual tasks with the aim of objectively quantifying the cognitive impairments in ME/CFS patients compared to controls. Methods: A total of 120 participants (60 ME/CFS patients and 60 controls) were recruited at the Department of Ophthalmology, Universität of Erlangen-Nürnberg. RT was assessed using a virtual reality–oculomotor test system, presenting 3D stimuli at three disparity levels (275″, 550″, and 1100″) within three gaming repetitions (R1, R2, and R3). Mixed-effects models were used to evaluate group differences, with age and gender as covariates. Pairwise contrasts were calculated to assess changes across repetitions. Fatigue self-assessments were recorded by validated questionnaires, (FACIT Fatigue Scale, Chalder Fatigue Scale, Bell Score and Health Assessment Questionnaire), and their correlation with RT metrics was portrayed using a Spearman correlation matrix. Results: Estimated means (EM-means) for RT were significantly prolonged in ME/CFS patients compared to controls at disparity 275″ (1969 ms vs. 1384 ms; p = 0.0001), 550″ (1409 vs. 1071 ms; p = 0.0012) and 1100″ (1126 ms vs. 891 ms; p = 0.00223). Age was a significant covariate (p < 0.001), while gender showed no effect. Both groups demonstrated improvements in RT over repetitions; however, ME/CFS patients showed a significantly lower improvement compared to controls, reaching significance in R3 (p = 0.0042). RT metrics did not correlate with patients’ self-assessment scores. Conclusions: ME/CFS patients showed consistently slower RTs compared to controls, particularly in later, easier gaming repetitions, potentially reflecting the impact of fatigue. Full article

Despite the increasing prevalence and median severity of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), medical education on the disease is scant, leading to a diagnostic crisis in which the majority of people with ME/CFS are undiagnosed. We created a care process algorithm in AskMayoExpert accessible to all Mayo Clinic medical providers as a source for information on diagnosis and management of ME/CFS. To evaluate whether the algorithm was associated with improved diagnostic accuracy, we compared concordance before versus after the algorithm was introduced, where concordance was defined as agreement between an appropriately coded referral to Mayo Clinic’s Chronic Fatigue Specialty Clinic and the specialty clinic with an expert diagnosis of ME/CFS. Referrals to the Chronic Fatigue Specialty Clinic increased overall and were more likely to show concordance between specialist diagnosis and referral after the introduction of the ME/CFS AskMayoExpert algorithm. Particularly in diseases that are prevalent and poorly understood, a point-of-care clinical tool may offer just-in-time opportunities to improve diagnosis and management. Full article

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a high-burden, under-researched condition characterized by heterogeneous and fluctuating symptoms, including cognitive dysfunction commonly described by patients as “brain fog”. Despite growing interest in digital health technologies for symptom monitoring and personalized care, their application to the assessment and management of cognitive dysfunction in ME/CFS remains unclear. This descriptive review aimed to examine the current scientific evidence on digital approaches related to brain fog in ME/CFS. A structured literature search following PRISMA guidance was conducted to identify relevant studies. The available literature remains limited in scope and methodological maturity. During synthesizing across studies, three main functional domains of digital application become apparent: (1) digital tools for cognitive assessment, which have the strongest evidence base; (2) digital platforms for longitudinal monitoring; and (3) digitally mediated interventions or rehabilitation approaches, both of which are less well studied. Simultaneously, the findings suggest that patient-reported brain fog may represent a visible component of the broader ME/CFS disease spectrum and could serve as an early clinical indicator guiding diagnosis and management. Interpreting these symptoms within a biopsychosocial framework may facilitate understanding of the complex nature of the disease and optimize the use of digital technologies for monitoring cognitive dysfunction and supporting patient-centered care in ME/CFS. Full article