Tag: diagnosis

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)

Epidemiology of Myalgic Encephalomyelitis among individuals with self-reported Chronic Fatigue Syndrome in British Columbia, Canada, and their health-related quality of life

Abstract:

Background: There is no accurate data on the epidemiology of Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) in Canada. The aims of the study were to describe the epidemiology of confirmed ME/CFS cases and their health-related quality of life (HRQoL).

Methods: This is a cross-sectional study with British Columbia Generations Project (BCGP) participants who self-reported having CFS and population-based controls with no fatiguing illness. Participants completed the Symptoms Assessment Questionnaire, RAND 36-item Health Survey, and Phenotyping Questionnaire Short-form. These assessments enabled the identification and characterization of confirmed cases of ME/CFS. Those with self-reported diagnoses who did not meet study diagnosis of ME/CFS were subcategorized as non-ME/CFS cases.

Results: We included 187 participants, 45.5% (n=85) self-reported cases and 54.5% (n=102) controls; 34% (n=29) of those who self-reported ME/CFS fulfilled diagnostic criteria for ME/CFS. The population prevalence rates were 1.1% and 0.4% for self-reported and confirmed ME/CFS cases respectively. Participants displayed significantly lower scores in all eight SF-36 domains compared to the other groups. Mental component scores were similar between ME/CFS and non-ME/CFS groups. The main risk factor for low HRQoL scores was fatigue severity (β = -0.6, p<0.001 for physical health; β = -0.7, p<0.001 for mental health).

Conclusions: The majority of self-reported cases do not meet diagnostic criteria for ME/CFS, suggesting that self-reported CFS may not be a reliable indicator for a true ME/CFS diagnosis. HRQoL indicators were consistently lower in ME/CFS and non-ME/CFS cases compared to controls, with ME/CFS cases having lower scores in most domains. Having higher symptom severity scores and perceived poorer health were the significant affecting factors of lower HRQoL. Although self-report can be used as screening to identify cases in populations, we suggest studies of ME/CFS should include appropriate medically confirmed clinical diagnosis for validity. Further large-scale population-based studies with simultaneous medical assessment are suggested to further characterize validity parameters of self-reported diagnosis.

Source:Enkhzaya Chuluunbaatar-Lussier, Melody Tsai, Travis Boulter, Carola Munoz, Kathleen Kerr, Luis Nacul. Epidemiology of Myalgic Encephalomyelitis among individuals with self-reported Chronic Fatigue Syndrome in British Columbia, Canada, and their health-related quality of life. medRxiv 2024.05.16.24307437; doi: https://doi.org/10.1101/2024.05.16.24307437 https://www.medrxiv.org/content/10.1101/2024.05.16.24307437v1 (Full study available as PDF file)

Interdisciplinary, collaborative D-A-CH (Germany, Austria and Switzerland) consensus statement concerning the diagnostic and treatment of myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

in English, German

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe, chronic multisystemic disease which, depending on its severity, can lead to considerable physical and cognitive impairment, loss of ability to work and the need for nursing care including artificial nutrition and, in very severe cases, even death.

The aim of this D-A-CH (Germany, Austria, Switzerland) consensus statement is 1) to summarize the current state of knowledge on ME/CFS, 2) to highlight the Canadian Consensus Criteria (CCC) as clinical criteria for diagnostics with a focus on the leading symptom post-exertional malaise (PEM) and 3) to provide an overview of current options and possible future developments, particularly with regard to diagnostics and therapy. The D-A-CH consensus statement is intended to support physicians, therapists and valuer in diagnosing patients with suspected ME/CFS by means of adequate anamnesis and clinical-physical examinations as well as the recommended clinical CCC, using the questionnaires and other examination methods presented.

The overview of the two pillars of therapy for ME/CFS, pacing and symptom-relieving therapy options, is intended not only to provide orientation for physicians and therapists, but also to support decision-makers from healthcare policy and insurance companies in determining which therapy options should already be reimbursable by them at this point in time for the indication ME/CFS.

Source: Hoffmann K, Hainzl A, Stingl M, Kurz K, Biesenbach B, Bammer C, Behrends U, Broxtermann W, Buchmayer F, Cavini AM, Fretz GS, Gole M, Grande B, Grande T, Habermann-Horstmeier L, Hackl V, Hamacher J, Hermisson J, King M, Kohl S, Leiss S, Litzlbauer D, Renz-Polster H, Ries W, Sagelsdorff J, Scheibenbogen C, Schieffer B, Schön L, Schreiner C, Thonhofer K, Strasser M, Weber T, Untersmayr E. Interdisziplinäres, kollaboratives D-A-CH Konsensus-Statement zur Diagnostik und Behandlung von Myalgischer Enzephalomyelitis/Chronischem Fatigue-Syndrom [Interdisciplinary, collaborative D-A-CH (Germany, Austria and Switzerland) consensus statement concerning the diagnostic and treatment of myalgic encephalomyelitis/chronic fatigue syndrome]. Wien Klin Wochenschr. 2024 Aug;136(Suppl 5):103-123. German. doi: 10.1007/s00508-024-02372-y. Epub 2024 May 14. PMID: 38743348. https://pubmed.ncbi.nlm.nih.gov/38743348/

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS.

Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequency-matched groups, were used to develop the XAI-based model. The dataset contained 832 metabolites, and after feature selection, the model was developed using only 50 metabolites, meaning less medical knowledge is required, thus reducing diagnostic costs and improving prognostic time. The computational method was developed using six different ML algorithms before and after feature selection. The final classification model was explained using the XAI approach, SHAP.

The best-performing classification model (XGBoost) achieved an area under the receiver operating characteristic curve (AUCROC) value of 98.85%. SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. Besides the robustness of the methodology used, the results showed that the combination of ML and XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS.

Source: Yagin FH, Shateri A, Nasiri H, Yagin B, Colak C, Alghannam AF. Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome. PeerJ Comput Sci. 2024 Mar 20;10:e1857. doi: 10.7717/peerj-cs.1857. PMID: 38660205; PMCID: PMC11041999. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041999/ (Full text)

Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME

Abstract:

The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as long COVID. With an increasing number of people experiencing these symptoms, early intervention is crucial. In this study, we introduce a novel method to detect the likelihood of PASC or Myalgic Encephalomyelitis (ME) using a wearable four-channel headband that collects Electroencephalogram (EEG) data. The raw EEG signals are processed using Continuous Wavelet Transform (CWT) to form a spectrogram-like matrix, which serves as input for various machine learning and deep learning models. We employ models such as CONVLSTM (Convolutional Long Short-Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional Long short-term memory). Additionally, we test the dataset on traditional machine learning models for comparative analysis.

Our results show that the best-performing model, CNN-LSTM, achieved an accuracy of 83%. In addition to the original spectrogram data, we generated synthetic spectrograms using Wasserstein Generative Adversarial Networks (WGANs) to augment our dataset. These synthetic spectrograms contributed to the training phase, addressing challenges such as limited data volume and patient privacy. Impressively, the model trained on synthetic data achieved an average accuracy of 93%, significantly outperforming the original model.

These results demonstrate the feasibility and effectiveness of our proposed method in detecting the effects of PASC and ME, paving the way for early identification and management of the condition. The proposed approach holds significant potential for various practical applications, particularly in the clinical domain. It can be utilized for evaluating the current condition of individuals with PASC or ME, and monitoring the recovery process of those with PASC, or the efficacy of any interventions in the PASC and ME populations. By implementing this technique, healthcare professionals can facilitate more effective management of chronic PASC or ME effects, ensuring timely intervention and improving the quality of life for those experiencing these conditions.

Source: Harit Ahuja, Smriti Badhwar, Heather Edgell, Lauren E. Sergio, Marin Litoiu. Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME. Front. Hum. Neurosci. Sec. Brain-Computer Interfaces, Volume 18 – 2024 | doi: 10.3389/fnhum.2024.1359162 https://www.frontiersin.org/articles/10.3389/fnhum.2024.1359162/full (Full text)

NICE guideline on ME/CFS: robust advice based on a thorough review of the evidence

Abstract:

In 2021, the National Institute for Health and Care Excellence produced an evidence-based guideline on the diagnosis and management of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), a disabling long-term condition of unknown cause. The guideline provides clear support for people living with ME/CFS, their families and carers, and for clinicians. A recent opinion piece published in the journal suggested that there were anomalies in the processing and interpretation of the evidence when developing the guideline and proposed eight areas where these anomalies were thought to have occurred. We outline how these opinions are based on a misreading or misunderstanding of the guideline process or the guideline, which provides a balanced and reasoned approach to the diagnosis and management of this challenging condition.

Source: Barry PW, Kelley K, Tan T, et al. NICE guideline on ME/CFS: robust advice based on a thorough review of the evidence. Journal of Neurology, Neurosurgery & Psychiatry Published Online First: 28 February 2024. doi: 10.1136/jnnp-2023-332731 https://jnnp.bmj.com/content/early/2024/02/28/jnnp-2023-332731 (Full text)