Category: 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)

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)

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)

Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

People living with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) experience heterogeneous and debilitating symptoms that lack sufficient biological explanation, compounded by the absence of accurate, noninvasive diagnostic tools. To address these challenges, we explored circulating cell-free RNA (cfRNA) as a blood-borne bioanalyte to monitor ME/CFS. cfRNA is released into the bloodstream during cellular turnover and reflects dynamic changes in gene expression, cellular signaling, and tissue-specific processes.

We profiled cfRNA in plasma by RNA sequencing for 93 ME/CFS cases and 75 healthy sedentary controls, then applied machine learning to develop diagnostic models and advance our understanding of ME/CFS pathobiology. A generalized linear model with least absolute shrinkage selector operator regression trained on condition-specific signatures achieved a test-set AUC of 0.81 and an accuracy of 77%.

Immune cfRNA deconvolution revealed differences in platelet-derived cfRNA between cases and controls, as well as elevated levels of plasmacytoid dendritic, monocyte, and T cell-derived cfRNA in ME/CFS. Biological network analysis further implicated immune dysfunction in ME/CFS, with signatures of cytokine signaling and T cell exhaustion. These findings demonstrate the utility of RNA liquid biopsy as a minimally invasive tool for unraveling the complex biology behind chronic illnesses.

Source: Gardella AE, Eweis-LaBolle D, Loy CJ, Belcher ED, Lenz JS, Franconi CJ, Scofield SY, Grimson A, Hanson MR, De Vlaminck I. Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome. Proc Natl Acad Sci U S A. 2025 Aug 19;122(33):e2507345122. doi: 10.1073/pnas.2507345122. Epub 2025 Aug 11. PMID: 40789036. https://pubmed.ncbi.nlm.nih.gov/40789036/

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)

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

Possible Racial Disparities in the Diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Abstract:

Myalgic encephalomyelitis (ME/CFS) a chronic, disabling illness with no established etiopathology. It has been indicated in some population-based studies that Black and ethnic minority populations are underdiagnosed with ME/CFS. The aims of the present study were to (1) identify the agreement between receiving an ME/CFS diagnosis and meeting diagnostic criteria, (2) identify the demographic characteristics associated with receiving a diagnosis, and (3) explore patient satisfaction with healthcare.
Self-reported medical history and symptoms were collected via online survey from respondents with and without fatigue. The agreement between self-reporting an ME/CFS diagnosis and meeting the Center for Disease Control’s (CDC) ME/CFS criteria or Institute of Medicine (IOM) criteria was assessed with Cohen’s kappa. Patient characteristics predicting a physician diagnosis were analyzed with logistic regression. Associations between diagnosis, demographics, and healthcare satisfaction were assessed with chi-square tests of independence. There were 1110 responses. The agreement between meeting ME/CFS criteria and reporting an ME/CFS diagnosis was fair (CDC: κ = 0.29; SE = 0.02; IOM: κ = 0.28, SE = 0.03).
White respondents had 2.94 greater odds of being diagnosed with ME/CFS than non-White respondents. Having an ME/CFS diagnosis was associated with dissatisfaction with healthcare (χ2 (3, N = 1063) = 14.17, p = 0.003). The findings suggest racial disparities in the diagnostic processes for ME/CFS.
Source: Jones CL, Younger J. Possible Racial Disparities in the Diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). International Journal of Environmental Research and Public Health. 2025; 22(2):280. https://doi.org/10.3390/ijerph22020280 https://www.mdpi.com/1660-4601/22/2/280 (Full text)

Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank

Abstract:

Background: Diagnosing complex illnesses like Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is complicated due to the diverse symptomology and presence of comorbid conditions. ME/CFS patients often present with multiple health issues, therefore, incorporating comorbidities into research can provide a more accurate understanding of the condition’s symptomatology and severity, to better reflect real-life patient experiences.

Methods: We performed association studies and machine learning on 1194 ME/CFS individuals with blood plasma nuclear magnetic resonance (NMR) metabolomics profiles, and seven exclusive comorbid cohorts: hypertension (n = 13,559), depression (n = 2522), asthma (n = 6406), irritable bowel syndrome (n = 859), hay fever (n = 3025), hypothyroidism (n = 1226), migraine (n = 1551) and a non-diseased control group (n = 53,009).

Results: We present a lipoprotein perspective on ME/CFS pathophysiology, highlighting gender-specific differences and identifying overlapping associations with comorbid conditions, specifically surface lipids, and ketone bodies from 168 significant individual biomarker associations. Additionally, we searched for, trained, and optimised a machine learning algorithm, resulting in a predictive model using 19 baseline characteristics and nine NMR biomarkers which could identify ME/CFS with an AUC of 0.83 and recall of 0.70. A multi-variable score was subsequently derived from the same 28 features, which exhibited ~2.5 times greater association than the top individual biomarker.

Conclusions: This study provides an end-to-end analytical workflow that explores the potential clinical utility that association scores may have for ME/CFS and other difficult to diagnose conditions.

Source: Huang K, G C de Sá A, Thomas N, Phair RD, Gooley PR, Ascher DB, Armstrong CW. Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank. Commun Med (Lond). 2024 Nov 26;4(1):248. doi: 10.1038/s43856-024-00669-7. PMID: 39592839; PMCID: PMC11599898.  https://pmc.ncbi.nlm.nih.gov/articles/PMC11599898/ (Full text)