Tag: diagnosis

ICD-10 Diagnoses prior to ME/CFS diagnosis in children and young people suggest potential early diagnostic indicators

Abstract:

To identify ICD-10-GM codes recorded in the year preceding a Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) diagnosis, we conducted a 1:5 matched case–control study using statutory health insurance data of 6–27-year-olds with ME/CFS (ICD-10-GM: G93.3, 2020–2022). Cases (n = 6,077) were matched 1:5 to controls by birth year, sex, and postal code. ICD-10-GM codes from the preceding year were analyzed using multivariable conditional logistic regression, reporting odds ratios (OR) and 95% confidence intervals. Most cases were female and aged 18–27 years.

Forty-four ICD-10-GM code classes were associated with increased and four with decreased odds, spanning 13 diagnostic chapters. Most associations were in chapters F (mental/behavioral disorders), R (respiratory diseases), and M (musculoskeletal disorders). Frequent conditions included fatigue, depression, pain disorders, and somatoform disorders (≥ 10% in cases; ORs 1.11–2.19. Rare diagnoses (≤ 1% prevalence), such as fibromyalgia (OR 2.08, 95% CI: 1.20–3.59) and mild cognitive impairment (2.93, 1.21–7.10), were strongly associated. Four COVID-19 or vaccination-related code classes were identified, with post-COVID-19 condition showing the highest OR (3.84, 2.97–4.98). Several ICD-10-GM codes, including COVID-19 related codes, were associated with later ME/CFS diagnoses.

Prospective studies should clarify timing relative to ME/CFS onset, and distinguish between pre-existing conditions, comorbidities, early manifestations, or misdiagnoses.

Source:Wirth M, Haastert B, Linnenkamp U, Andrich S, Icks A, Pricoco R, Behrends U, De Bock F. ICD-10 Diagnoses prior to ME/CFS diagnosis in children and young people suggest potential early diagnostic indicators. Sci Rep. 2026 Feb 26. doi: 10.1038/s41598-026-40848-1. Epub ahead of print. PMID: 41741569. https://www.nature.com/articles/s41598-026-40848-1 (Full text)

Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements

Abstract:

Background: An artificial intelligence (AI) pipeline was used to differentiate patients suffering from Chronic Fatigue Syndrome (CFS) from healthy controls (HC) based on high-frequency, large-scale data obtained using beat-to-beat measurement of the autonomic nervous system (ANS) and cardiovascular function.

Methods: This prospective, case-control study included a cohort of 112 CFS patients and 61 HCs examined. Heart rate (HR), high-frequency R-to-R interval (HF RRI), diastolic blood pressure (dBP), stroke volume (SV), and SV index (SV/FFM) were measured using the Task Force Monitor. A novel sequential learning approach was applied: first, a Transformer model was trained, followed by an XGBoost classifier that learned from the errors of the Transformer. Matthews correlation coefficient (MCC), accuracy, and Area Under the Receiver Operating Characteristic Curve (ROC AUC) were assessed. Model classifications were explained globally.

Results: The applied classifier achieved a subject-level accuracy of 0.89, an MCC of 0.79, and an AUC of 1.00. Lower values of beat-to-beat difference in HR and raw HF RRI (indicating reduced cardiac vagal tone) and higher values of dBP difference (more beat-to-beat increases, indicating higher sympathetic vascular tone) were related to being more likely classified as CFS patients. Low values of SV difference and low values of SV/FFM (both indicating less effective cardiac hemodynamics) were related to being more likely classified as CFS patients.

Conclusions: The AI-driven classifier demonstrates remarkable proficiency in distinguishing between patients with CFS and HC. By leveraging this automated pipeline, beat-to-beat measurements of the ANS can significantly enhance the objective assessment of CFS diagnosis.

Source: Kujawski S, Tabisz H, Morten KJ, Modlińska A, Słomko J, Zalewski P. Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements. J Transl Med. 2025 Dec 23;23(1):1413. doi: 10.1186/s12967-025-07433-y. PMID: 41437251; PMCID: PMC12729017. https://pmc.ncbi.nlm.nih.gov/articles/PMC12729017/ (Full text)

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): diagnosis and management

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) has garnered substantial scientific and clinical interest, due to its rising global prevalence and significant pathophysiological overlap with post-acute COVID-19 syndrome (PACS). This review systematically elucidates the prevailing diagnostic criteria, summarizes recent advances in understanding the potential pathophysiological mechanisms, and evaluates pharmacological and non-pharmacological interventions, and symptom-based assessment and management strategies.

Methods: A comprehensive literature search was conducted across PubMed, Web of Science, Embase, and the Cochrane Library for articles published from inception to August 2025.

Results: Current diagnostic frameworks for ME/CFS rely primarily on clinical symptomatology and lack definitive biomarkers. Immune dysregulation, oxidative stress, mitochondrial dysfunction, and neuroinflammation are central to its pathology. Pharmacological management includes immunomodulatory treatments, antioxidant therapies, mitochondrial support, and neuroinflammation intervention. Non-pharmacological strategies such as cognitive behavioral therapy (CBT), graded exercise therapy (GET), activity pacing, and traditional Chinese medicine (TCM) complement biomedical approaches by alleviating symptom severity and promoting energy conservation.

Conclusion: Among these approaches, CBT serves as an adjunctive therapy for symptom management rather than a curative one, whereas GET is contraindicated due to its potential for harm. Comprehensive clinical assessment and management of ME/CFS requires being symptom oriented and the recognition of individual differences. Recommended directions for future research include developing biomarker-based diagnostic tools, optimizing combination therapies that target multiple pathophysiological pathways simultaneously, and integrating real-world data and digital health technologies for precise monitoring and management of ME/CFS.

Source: Fan J, Jiao J, Chang HQ, Zhong DL, Liu XB, Li J, Chen LM, Jin RJ, Wu X. Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): diagnosis and management. J Transl Med. 2025 Dec 9. doi: 10.1186/s12967-025-07506-y. Epub ahead of print. PMID: 41366804. https://link.springer.com/article/10.1186/s12967-025-07506-y

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)

Precision Medicine Study of Post-Exertional Malaise Epigenetic Changes in Myalgic Encephalomyelitis/Chronic Fatigue Patients During Exercise

Abstract:

Post-exertional malaise (PEM) is a defining symptom of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), yet its molecular underpinnings remain elusive. This study investigated the temporal-longitudinal DNA methylation changes associated with PEM using a structured two-day maximum repeated effort cardiopulmonary exercise testing (CPET) protocol involving pre- and two post-exercise blood samplings from five ME/CFS patients.

Cardiopulmonary measurements revealed complex heterogeneous profiles among the patients compared to typical healthy controls, and VO2 peak indicated all patients had poor normative fitness. The switch to anaerobic metabolism occurred at a lower workload in some patients on Day Two of the test. Reduced Representation Bisulphite Sequencing followed by analysis with Differential Methylation Analysis Package-version 2 (DMAP2) identified differentially methylated fragments (DMFs) present in the DNA genomes of all five ME/CFS patients through the exercise test compared with ‘before exercise’.

With further filtering for >10% methylation differences, there were early DMFs (0-24 h after first exercise test) and late DMFs between (24-48 h after the second exercise test), as well as DMFs that changed gradually (between 0 and 48 h). Of these, 98% were ME/CFS-specific, compared with the two healthy controls accompanying the longitudinal study. Principal component analysis illustrated the three distinct clusters at the 0 h, 24 h, and 48 h timepoints, but with heterogeneity among the patients within the clusters, highlighting dynamic methylation responses to exertion in individual patients.

There were 24 ME/CFS-specific DMFs at gene promoter fragments that revealed distinct patterns of temporal methylation across the timepoints. Functional enrichment of ME-specific DMFs revealed pathways involved in endothelial function, morphogenesis, inflammation, and immune regulation. These findings uncovered temporally dynamic epigenetic changes in stress/immune functions in ME/CFS during PEM and suggest molecular signatures with potential for diagnosis and of mechanistic significance.

Source: Sharma S, Hodges LD, Peppercorn K, Davis J, Edgar CD, Rodger EJ, Chatterjee A, Tate WP. Precision Medicine Study of Post-Exertional Malaise Epigenetic Changes in Myalgic Encephalomyelitis/Chronic Fatigue Patients During Exercise. Int J Mol Sci. 2025 Sep 3;26(17):8563. doi: 10.3390/ijms26178563. PMID: 40943482. https://www.mdpi.com/1422-0067/26/17/8563 (Full text)

Approach to nursing diagnoses of people with myalgic encephalomyelitis / chronic fatigue syndrome: a qualitative meta-synthesis

Abstract:

Objective: To identify human responses (diagnostic foci) that shape the experience of living with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and how they manifest throughout the course of the illness.

Methods: A qualitative meta-synthesis was conducted. Original studies exploring the experience of living with ME/CFS in adults with a confirmed diagnosis, published in English or Spanish between 1994 and June 2024, were included. The literature search was carried out in Medline/Medline In-Process, Embase, CINAHL, PsycINFO, SCI-EXPANDED, SSCI, SciELO, Lilacs, and Cuiden. Data analysis was based on the human responses (diagnostic foci) from the NANDA-I Nursing Diagnoses Classification, 2021–2023, with findings structured according to Fennell’s Four-Phase Model.

Results: A total of 42 articles were selected. Twenty human responses (diagnostic foci) and three classes of the NANDA-I Nursing Diagnoses Classification were identified, interwoven across the different phases of the model. Some responses were present throughout all phases, while others, such as Energy Balance and Health Self-Management, became particularly relevant from Phase 2 onwards. Phases 3 and 4 were characterised by losses and processes of subjective reconstruction, with diagnostic foci such as Sorrow, Spiritual Distress, and Personal Identity being predominant.

Conclusions: The identified human responses (diagnostic foci) highlight how the contested and chronic nature of ME/CFS profoundly shapes the lived experience of those affected. The model derived from this review provides a structured framework for targeted nursing interventions, aligned with the phase each individual is experiencing.

Source: Oter-Quintana, C., Esteban-Hernandez, J., Cuellar-Pompa, L., Gil-Carballo, C., Brito-Brito, P. R., Martín-García, A., … Alameda-Cuesta, A. (2025). Approach to nursing diagnoses of people with myalgic encephalomyelitis / chronic fatigue syndrome: a qualitative meta-synthesis. Fatigue: Biomedicine, Health & Behavior, 1–32. https://doi.org/10.1080/21641846.2025.2522028 https://www.tandfonline.com/doi/full/10.1080/21641846.2025.2522028

Biomarkers over Time: From Visual Contrast Sensitivity to Transcriptomics in Differentiating Chronic Inflammatory Response Syndrome and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Chronic inflammatory response syndrome (CIRS) and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) are debilitating multisystem illnesses that share overlapping symptoms and molecular patterns, including immune dysregulation, mitochondrial impairment, and vascular dysfunction. This review provides a chronological synthesis of biomarker development in CIRS, tracing its evolution from early functional tests such as visual contrast sensitivity (VCS) to advanced transcriptomic profiling.

Drawing on peer-reviewed studies spanning two decades, we examine the layered integration of neuroendocrine, immunologic, metabolic, and genomic markers that collectively support a multisystem model of innate immune activation specific to environmentally acquired illness. Particular focus is given to the Gene Expression: Inflammation Explained (GENIE) platform’s use of transcriptomics to classify disease stages and distinguish CIRS from other fatiguing conditions.

While ME/CFS research continues to explore overlapping pathophysiologic features, it has yet to establish a unified diagnostic model with validated biomarkers or exposure-linked mechanisms. As a result, many patients labeled with ME/CFS may, in fact, represent unrecognized CIRS cases.

This review underscores the importance of structured biomarker timelines in improving differential diagnosis and guiding treatment in complex chronic illness and highlights the reproducibility of the CIRS framework in contrast to the diagnostic ambiguity surrounding ME/CFS.

Source: Dooley M. Biomarkers over Time: From Visual Contrast Sensitivity to Transcriptomics in Differentiating Chronic Inflammatory Response Syndrome and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Int J Mol Sci. 2025 Jul 28;26(15):7284. doi: 10.3390/ijms26157284. PMID: 40806417. https://www.mdpi.com/1422-0067/26/15/7284 (Full text)

Differential diagnosis between “chronic fatigue” and “chronic fatigue syndrome”

Introduction:

Fatigue is a common complaint experienced by most of subjects during lifetime, which affects approximately 30–50% of general population as point prevalence. According to the fatigue-lasting duration, it is classified as acute (<1 month), prolonged (>1 month, <6 months), and chronic fatigue (≥6 months), respectively. Acute fatigue is generally disappears after taking a rest or treating the causative diseases, while uncontrolled prolonged and chronic fatigue limit the physical and social activities. Especially, medically unexplained chronic fatigue is a debilitating status, such as idiopathic chronic fatigue (ICF) and chronic fatigue syndrome (CFS).

Source: Son CG. Differential diagnosis between “chronic fatigue” and “chronic fatigue syndrome”. Integr Med Res. 2019 Jun;8(2):89-91. doi: 10.1016/j.imr.2019.04.005. Epub 2019 Apr 12. PMID: 31193269; PMCID: PMC6522773. https://pmc.ncbi.nlm.nih.gov/articles/PMC6522773/ (Full text)

Defining a High-Quality Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cohort in UK Biobank

Abstract:

Background: Progress in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) research is being slowed by the relatively small-scale studies being performed whose results are often not replicated. Progress could be accelerated by analyses of large population-scale projects, such as UK Biobank (UKB), which provide extensive phenotype and genotype data linked to both ME/CFS cases and controls.

Methods: Here, we analysed the overlap and discordance among four UKB-defined ME/CFS cohorts, and additional questionnaire data when available.

Results: A total of 5,354 UKB individuals were linked to at least one piece of evidence of MECFS, a higher proportion (1.1%) than most prevalence estimates. Only a third (36%; n=1,922) had 2 or more pieces of evidence for MECFS, in part due to data missingness. For the same UKB participant, ME/CFS status defined by ICD-10 (International Classification of Diseases, Tenth Revision) code G93.3 (Post-viral fatigue syndrome) was most likely to be supported by another data type (72%); ME/CFS status defined by Pain Questionnaire responses is least likely to be supported (43%), in part due to data missingness.

Conclusions: We conclude that ME/CFS status in UKB, and potentially other biobanks, is best supported by multiple, and not single, lines of evidence. Finally, we raise the estimated ME/CFS prevalence in the UK to 410,000 using the most consistent evidence for ME/CFS status, and accounting for those who had no opportunity to participate in UKB due to being bed- or house-bound.

Source: Samms GL, Ponting CP. Defining a High-Quality Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cohort in UK Biobank. NIHR Open Res. 2025 Apr 28;5:39. doi: 10.3310/nihropenres.13956.1. PMID: 40443420; PMCID: PMC12120426. https://pmc.ncbi.nlm.nih.gov/articles/PMC12120426/ (Full text)

How a Clinician Makes a Diagnosis for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Abstract:

This chapter describes how a clinician with experience of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) makes a diagnosis of the condition in the absence of a routine laboratory diagnostic test.

Source: Vallings R. How a Clinician Makes a Diagnosis for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Methods Mol Biol. 2025;2920:3-11. doi: 10.1007/978-1-0716-4498-0_1. PMID: 40372674. https://link.springer.com/protocol/10.1007/978-1-0716-4498-0_1