Tag: diagnosis

Understanding symptom clusters, diagnosis and healthcare experiences in myalgic encephalomyelitis/chronic fatigue syndrome and long COVID: a cross-sectional survey in the UK

Abstract:

Objectives: This study aims to provide an in-depth analysis of the symptoms, coexisting conditions and service utilisation among people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID. The major research questions include the clustering of symptoms, the relationship between key factors and diagnosis time, and the perceived impact of National Institute for Health and Care Excellence (NICE) guidelines on patient care.

Design: Cross-sectional survey using secondary data analysis.

Setting: Community-based primary care level across the UK, incorporating online survey participation.

Participants: A total of 10 458 individuals responded to the survey, of which 8804 confirmed that they or a close friend/family member had ME/CFS or long COVID. The majority of respondents were female (83.4%), with participants from diverse regions of the UK.

Primary and secondary outcome measures: Primary outcomes included prevalence and clustering of symptoms, time to diagnosis, and participant satisfaction with National Health Service (NHS) care, while secondary outcomes focused on symptom management strategies and the perceived effect of NICE guidelines.

Results: Fatigue (88.2%), postexertional malaise (78.2%), cognitive dysfunction (88.4%), pain (87.6%) and sleep disturbances (88.2%) were the most commonly reported symptoms among participants with ME/CFS, with similar patterns observed in long COVID. Time to diagnosis for ME/CFS ranged widely, with 22.1% diagnosed within 1-2 years of symptom onset and 12.9% taking more than 10 years. Despite updated NICE guidelines, only 10.1% of participants reported a positive impact on care, and satisfaction with NHS services remained low (6.9% for ME/CFS and 14.4% for long COVID).

Conclusions: ME/CFS and long COVID share overlapping but distinct symptom clusters, indicating common challenges in management. The findings highlight significant delays in diagnosis and low satisfaction with specialist services, suggesting a need for improved self-management resources and better-coordinated care across the NHS.

Source: Mansoubi M, Richards T, Ainsworth-Wells M, Fleming R, Leveridge P, Shepherd C, Dawes H. Understanding symptom clusters, diagnosis and healthcare experiences in myalgic encephalomyelitis/chronic fatigue syndrome and long COVID: a cross-sectional survey in the UK. BMJ Open. 2025 Apr 2;15(4):e094658. doi: 10.1136/bmjopen-2024-094658. PMID: 40180399. https://bmjopen.bmj.com/content/15/4/e094658 (Full text)

Recent research in myalgic encephalomyelitis/chronic fatigue syndrome: an evidence map

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome is a chronic condition, classified by the World Health Organization as a nervous system disease, impacting around 17 million people worldwide. Presentation involves persistent fatigue and postexertional malaise (a worsening of symptoms after minimal exertion) and a wide range of other symptoms. Case definitions have historically varied; postexertional malaise is a core diagnostic criterion in current definitions. In 2022, a James Lind Alliance Priority Setting Partnership established research priorities relating to myalgic encephalomyelitis/chronic fatigue syndrome.

Objective(s): We created a map of myalgic encephalomyelitis/chronic fatigue syndrome evidence (2018-23), showing the volume and key characteristics of recent research in this field. We considered diagnostic criteria and how current research maps against the James Lind Alliance Priority Setting Partnership research priorities.

Methods: Using a predefined protocol, we conducted a comprehensive search of Cochrane, MEDLINE, EMBASE and Cumulative Index to Nursing and Allied Health Literature. We included all English-language research studies published between January 2018 and May 2023. Two reviewers independently applied inclusion criteria with consensus involving additional reviewers. Studies including people diagnosed with myalgic encephalomyelitis/chronic fatigue syndrome using any criteria (including self-report), of any age and in any setting were eligible. Studies with < 10 myalgic encephalomyelitis/chronic fatigue syndrome participants were excluded. Data extraction, coding of topics (involving stakeholder consultation) and methodological quality assessment of systematic reviews (using A MeaSurement Tool to Assess systematic Reviews 2) was conducted independently by two reviewers, with disagreements resolved by a third reviewer. Studies were presented in an evidence map.

Results: Of the 11,278 identified studies, 742 met the selection criteria, but only 639 provided sufficient data for inclusion in the evidence map. These reported data from approximately 610,000 people with myalgic encephalomyelitis/chronic fatigue syndrome. There were 81 systematic reviews, 72 experimental studies, 423 observational studies and 63 studies with other designs. Most studies (94%) were from high-income countries. Reporting of participant details was poor; 16% did not report gender, 74% did not report ethnicity and 81% did not report the severity of myalgic encephalomyelitis/chronic fatigue syndrome. Forty-four per cent of studies used multiple diagnostic criteria, 16% did not specify criteria, 24% used a single criterion not requiring postexertional malaise and 10% used a single criterion requiring postexertional malaise. Most (89%) systematic reviews had a low methodological quality. Five main topics (37 subtopics) were included in the evidence map. Of the 639 studies; 53% addressed the topic ‘what is the cause?’; 38% ‘what is the problem?’; 26% ‘what can we do about it?’; 15% ‘diagnosis and assessment’; and 13% other topics, including ‘living with myalgic encephalomyelitis/chronic fatigue syndrome’.

Discussion: Studies have been presented in an interactive evidence map according to topic, study design, diagnostic criteria and age. This evidence map should inform decisions about future myalgic encephalomyelitis/chronic fatigue syndrome research.

Limitations: An evidence map does not summarise what the evidence says. Our evidence map only includes studies published in 2018 or later and in English language. Inconsistent reporting and use of diagnostic criteria limit the interpretation of evidence. We assessed the methodological quality of systematic reviews, but not of primary studies.

Conclusions: We have produced an interactive evidence map, summarising myalgic encephalomyelitis/chronic fatigue syndrome research from 2018 to 2023. This evidence map can inform strategic plans for future research. We found some, often limited, evidence addressing every James Lind Alliance Priority Setting Partnership priority; high-quality systematic reviews should inform future studies.

Source: Todhunter-Brown A, Campbell P, Broderick C, Cowie J, Davis B, Fenton C, Markham S, Sellers C, Thomson K; NIHR Evidence Synthesis Scotland Initiative (NESSIE). Recent research in myalgic encephalomyelitis/chronic fatigue syndrome: an evidence map. Health Technol Assess. 2025 Mar 26:1-78. doi: 10.3310/BTBD8846. Epub ahead of print. PMID: 40162526. https://www.journalslibrary.nihr.ac.uk/hta/published-articles/BTBD8846 (Full text)

Pediatric Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): A Diagnostic and Communication Case Study for Health Care Providers in Training

Abstract:

Introduction: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic, complex illness. No diagnostic tests exist; illness evaluation relies on medical history, physical exam, and laboratory tests. While more is known about ME/CFS in adults, it can affect children and adolescents as a chronic condition.

Methods: We implemented an ME/CFS pediatric educational activity (diagnosis, management, and communication) with medical, physician assistant, and nursing students at one university and with medical students at a second university. Pretests, two videos and slides, and posttests were completed in approximately 40 minutes. Evaluation included quantitative and qualitative measures for knowledge, attitudes, beliefs, confidence, and clinical information about ME/CFS.

Results: The first group included 31 students who reported low familiarity and clinical exposure to ME/CFS. At posttest, 25 students (81%) recognized ME/CFS as a medical condition compared to seven (23%) at pretest. Using 0-5 scales, mean pretest-to-posttest ability to diagnose increased from 1.0 to 3.5, and confidence to communicate increased from 1.4 to 3.9. The second group, including 26 students pretest and 19 posttest, also reported low familiarity and clinical exposure The posttest showed increased self-rated ability to diagnose (pretest M: 0.6, posttest M: 3.3) and confidence to communicate (pretest M: 1.4, posttest M: 3.7). Qualitative feedback for this group showed understanding of pediatric ME/CFS symptoms, management, and communication.

Discussion: This educational activity increased knowledge of ME/CFS as self-reported ability to make a diagnosis and increased confidence to communicate about pediatric ME/CFS. Participating students showed changes in attitudes towards ME/CFS as a medical condition.

Source: Brimmer DJ, Lin JS, Selinger HA, Issa A, Fall EA, Unger ER. Pediatric Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): A Diagnostic and Communication Case Study for Health Care Providers in Training. MedEdPORTAL. 2025 Mar 14;21:11507. doi: 10.15766/mep_2374-8265.11507. PMID: 40092054; PMCID: PMC11906784. https://pmc.ncbi.nlm.nih.gov/articles/PMC11906784/ (Full text)

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)

Machine learning and multi-omics in precision medicine for ME/CFS

Abstract:

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder that defies simplistic characterisation. Traditional approaches to diagnosing and treating ME/CFS have often fallen short due to the condition’s heterogeneity and the lack of validated biomarkers. The growing field of precision medicine offers a promising approach which focuses on the genetic and molecular underpinnings of individual patients.

In this review, we explore how machine learning and multi-omics (genomics, transcriptomics, proteomics, and metabolomics) can transform precision medicine in ME/CFS research and healthcare. We provide an overview on machine learning concepts for analysing large-scale biological data, highlight key advancements in multi-omics biomarker discovery, data quality and integration strategies, while reflecting on ME/CFS case study examples. We also highlight several priorities, including the critical need for applying robust computational tools and collaborative data-sharing initiatives in the endeavour to unravel the biological intricacies of ME/CFS.

Source: Huang K, Lidbury BA, Thomas N, Gooley PR, Armstrong CW. Machine learning and multi-omics in precision medicine for ME/CFS. J Transl Med. 2025 Jan 14;23(1):68. doi: 10.1186/s12967-024-05915-z. PMID: 39810236. Huang K, Lidbury BA, Thomas N, Gooley PR, Armstrong CW. Machine learning and multi-omics in precision medicine for ME/CFS. J Transl Med. 2025 Jan 14;23(1):68. doi: 10.1186/s12967-024-05915-z. PMID: 39810236. https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-024-05915-z (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)

Patient-Generated Data as Interventions in Doctor-Patient Relationships? Negotiating (Un)Invited Participation in Medical Consultations

Abstract:

Health data generated by apps and devices are increasingly popular and expected to affect various aspects of doctor-patient relationships. No longer confined to medically authorised and certified health technologies, a range of biomedical data-from heart rate to blood pressure or oxygen saturation-are captured and processed by consumer health devices. This article outlines different responses of physicians to patients collecting data with popular consumer devices and considers how the data may challenge or reify medical authority.

Based on semi-structured interviews with doctors and chronically ill patients in Germany from 2021 to 2023, we compare cases from diabetes, sleep disorders, cardiovascular conditions, obesity and ME/CFS and explore when, how and for what reasons different medical specialists consider patient-generated data (PGD) from consumer devices in outpatient settings.

Their response registers vary: whereas some physicians reject PGD that seem to compete with their diagnostic activities, others tolerate the data (collection), whereas still others more readily include them into their diagnostic practices. This suggests nuanced strategies for navigating the demarcation between accepting or rejecting ‘uninvited’ participation through PGD from consumer apps and devices.

Source: Augst AK, Lämmerhirt D, Schubert C. Patient-Generated Data as Interventions in Doctor-Patient Relationships? Negotiating (Un)Invited Participation in Medical Consultations. Sociol Health Illn. 2024 Nov 14. doi: 10.1111/1467-9566.13864. Epub ahead of print. PMID: 39540662. https://onlinelibrary.wiley.com/doi/10.1111/1467-9566.13864 (Full text)

What can wage development before and after a G93.3 diagnosis tell us about prognoses for myalgic encephalomyelitis?

Highlights:

•The article used public register data to assess the prognosis of G93.3 patients.
•Patient wages started declining around 3 years before the G93.3 diagnosis.
•Dependency on public transfers had started to increase 7 years before diagnosis.
•Less than 6% maintained an income of at least median wages after diagnosis.
•Very few moved from no or very low wage incomes to median wages.

Abstract:

Prognoses for persons affected by myalgic encephalomyelitis (ME) are rarely studied systematically. Existing studies are often based on smaller samples with unclear inclusion and subjective outcome criteria, and few look at wages as indicators of illness trajectories. This article considers how ME affects the wages and dependency on public transfers of people affected over time, especially in the period when the welfare authorities investigate eligibility for disability pension.
We matched Norwegian population register data on 8485 working-age individuals diagnosed with G93.3 (postviral fatigue syndrome) from 2009 to 2018 with wage and transfer data and compared male and female cases to control groups. The G93.3 population’s wages fell sharply from around 3 years before diagnosis to 1 year after and stabilized at a low level. Public transfers started increasing several years before diagnosis and stabilized at a high level after.
Few of those making no or very low income around the time of the diagnosis resumed earning moderate wages, and only exceptional cases returned to wages corresponding to median wages.
Source: Anne Kielland, Jing Liu. What can wage development before and after a G93.3 diagnosis tell us about prognoses for myalgic encephalomyelitis? Social Sciences & Humanities Open. Volume 11, 2025, 101206. https://www.sciencedirect.com/science/article/pii/S2590291124004030 (Full text)

Replicated blood-based biomarkers for Myalgic Encephalomyelitis not explicable by inactivity

Abstract:

Myalgic Encephalomyelitis (ME; sometimes referred to as chronic fatigue syndrome) is a relatively common and female-biased disease of unknown pathogenesis that profoundly decreases patients’ health-related quality-of-life. ME diagnosis is hindered by the absence of robustly-defined and specific biomarkers that are easily measured from available sources such as blood, and unaffected by ME patients’ low level of physical activity.

Previous studies of blood biomarkers have not yielded replicated results, perhaps due to low study sample sizes (n<100). Here, we use UK Biobank (UKB) data for up to 1,455 ME cases and 131,303 population controls to discover hundreds of molecular and cellular blood traits that differ significantly between cases and controls. Importantly, 116 of these traits are replicated, as they are significant for both female and male cohorts.

Our analysis used semi-parametric efficient estimators, an initial Super Learner fit followed by a one-step correction, three types of mediators, and natural direct and indirect estimands, to decompose the average effect of ME status on molecular and cellular traits. Strikingly, these trait differences cannot be explained by ME cases’ restricted activity.

Of 3,237 traits considered, ME status had a significant effect on only one, via the “Duration of walk” (UKB field 874) mediator. By contrast, ME status had a significant direct effect on 290 traits (9%). As expected, these effects became more significant with increased stringency of case and control definition.

Significant female and male traits were indicative of chronic inflammation, insulin resistance and liver disease. Individually, significant effects on blood traits, however, were not sufficient to cleanly distinguish cases from controls. Nevertheless, their large number, lack of sex-bias, and strong significance, despite the ‘healthy volunteer’ selection bias of UKB participants, keep alive the future ambition of a blood-based biomarker panel for accurate ME diagnosis.

Source: Sjoerd V Beentjes, Julia Kaczmarczyk, Amanda Cassar, Gemma Louise Samms, Nima S Hejazi, Ava Khamseh, Chris P Ponting. Replicated blood-based biomarkers for Myalgic Encephalomyelitis not explicable by inactivity. medRxiv 2024.08.26.24312606; doi: https://doi.org/10.1101/2024.08.26.24312606 https://www.medrxiv.org/content/10.1101/2024.08.26.24312606v1 (Full text available as PDF file)

Systematic review of fatigue severity in ME/CFS patients: insights from randomized controlled trials

Abstract:

Background: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating illness medically unexplained, affecting approximately 1% of the global population. Due to the subjective complaint, assessing the exact severity of fatigue is a clinical challenge, thus, this study aimed to produce comprehensive features of fatigue severity in ME/CFS patients.

Methods: We systematically extracted the data for fatigue levels of participants in randomized controlled trials (RCTs) targeting ME/CFS from PubMed, Cochrane Library, Web of Science, and CINAHL throughout January 31, 2024. We normalized each different measurement to a maximum 100-point scale and performed a meta-analysis to assess fatigue severity by subgroups of age, fatigue domain, intervention, case definition, and assessment tool, respectively.

Results: Among the total of 497 relevant studies, 60 RCTs finally met our eligibility criteria, which included a total of 7088 ME/CFS patients (males 1815, females 4532, and no information 741). The fatigue severity of the whole 7,088 patients was 77.9 (95% CI 74.7-81.0), showing 77.7 (95% CI 74.3-81.0) from 54 RCTs in 6,706 adults and 79.6 (95% CI 69.8-89.3) from 6 RCTs in 382 adolescents. Regarding the domain of fatigue, ‘cognitive’ (74.2, 95% CI 65.4-83.0) and ‘physical’ fatigue (74.3, 95% CI 68.3-80.3) were a little higher than ‘mental’ fatigue (70.1, 95% CI 64.4-75.8). The ME/CFS participants for non-pharmacological intervention (79.1, 95% CI 75.2-83.0) showed a higher fatigue level than those for pharmacological intervention (75.5, 95% CI 70.0-81.0). The fatigue levels of ME/CFS patients varied according to diagnostic criteria and assessment tools adapted in RCTs, likely from 54.2 by ICC (International Consensus Criteria) to 83.6 by Canadian criteria and 54.2 by MFS (Mental Fatigue Scale) to 88.6 by CIS (Checklist Individual Strength), respectively.

Conclusions: This systematic review firstly produced comprehensive features of fatigue severity in patients with ME/CFS. Our data will provide insights for clinicians in diagnosis, therapeutic assessment, and patient management, as well as for researchers in fatigue-related investigations.

Source: Park JW, Park BJ, Lee JS, Lee EJ, Ahn YC, Son CG. Systematic review of fatigue severity in ME/CFS patients: insights from randomized controlled trials. J Transl Med. 2024 Jun 3;22(1):529. doi: 10.1186/s12967-024-05349-7. PMID: 38831460; PMCID: PMC11145935. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11145935/ (Full text)