Category: 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)

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)

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)

Unequal access to diagnosis of myalgic encephalomyelitis in England

Abstract:

Background People with Myalgic Encephalomyelitis (ME/CFS; sometimes referred to as chronic fatigue syndrome) experience very poor health-related quality of life and only rarely recover. ME/CFS has no curative treatment and no single diagnostic test. Public health and policy decisions relevant to ME/CFS require knowledge of its prevalence and barriers to diagnosis. However, people with ME/CFS report lengthy diagnostic delays and widespread misunderstanding of their symptoms. Published prevalence estimates vary greatly by country, gender, age and ethnicity.

Methods Hospital Episode Statistics data is routinely collected by the NHS in England together with patient age, gender and ethnicity. This data, downloaded from the Feasibility Self-Service of NHS DigiTrials, was used to stratify individuals with the ICD-10 code that best reflects ME/CFS symptoms (G93.3; “Postviral fatigue syndrome”) according to their age, self-reported gender and ethnicity, General Practice and NHS England Integrated Care Board (ICB).

Results In all, 100,055 people in England had been diagnosed with ME/CFS (ICD-10:G93.3) between April 1 1989 and October 7 2023, 0.16% of all registered patients. Of these, 79,445 were females and 20,590 males, a female-to-male ratio of 3.88:1. Female relative to male prevalence peaked at about 6-to-1 in individuals’ fourth and fifth decades of life. Prevalence varied widely across the 42 ICBs: 0.086%-0.82% for females and 0.024%-0.21% for males. White individuals were approximately 5-fold more likely to be diagnosed with ME/CFS than others; black, Asian or Chinese ethnicities are associated with particularly low rates of ME/CFS diagnoses. This ethnicity bias is stronger than for other common diseases. Among active English GP practices, 176 (3%) had no registered ME/CFS patients. Eight ICBs (19%) each contained fewer than 8 other-than-white individuals with a G93.3 code despite their registers containing a total of 293,770 other-than-white patients.

Conclusion Those who are disproportionately undiagnosed with ME/CFS are other-than-white ethnic groups, older females (>60y), older males (>80y), and people living in areas of multiple deprivation. The lifetime prevalence of ME/CFS for English females and males may be as high as 0.92% and 0.25%, respectively, or approximately 390,000 UK individuals overall. This improved estimate of ME/CFS prevalence allows more accurate assessment of the socioeconomic and disease burden imposed by ME/CFS.

Source: Gemma L. Samms, Chris P. Ponting. Unequal access to diagnosis of myalgic encephalomyelitis in England. medRxiv 2024.01.31.24302070; doi: https://doi.org/10.1101/2024.01.31.24302070 https://www.medrxiv.org/content/10.1101/2024.01.31.24302070v1.full-text (Full text)

Correspondence: Inaccurate reference leads to tripling of reported FND prevalence

Highlights:

  • Perez et al asserted that FND is the “2nd most common” diagnosis in outpatient neurology.
  • Stone et al (2010), cited by Perez et al, does not support the “2nd most common” claim.
  • In Stone et al, a broad “functional/psychological” category was the second most common
  • •

    FND is not synonymous with the “functional/psychological” category in Stone et al.

To the editor:

An article in NeuroImage: Clinical, “Neuroimaging in functional neurological disorder: state of the field and research agenda” (Perez et al., 2021), cited a prominent paper (Stone et al., 2010) as evidence for the assertion that functional neurological disorder (FND) is the “2nd most common outpatient neurologic diagnosis.” Although studies have yielded varying FND prevalence rates, the claim that it is the second-most common diagnosis at outpatient neurology clinics represents an erroneous interpretation of the findings of the referenced 2010 paper.

FND is the current name for what was formerly called conversion disorder, the diagnosis previously given to patients believed to have psychogenic motor and gait dysfunctions, sensory deficits, and non-epileptic seizures. According to the 2013 edition of the Diagnostic and Statistical Manual of Mental Disorders and as noted in Perez et al, FND is not a diagnosis of exclusion but requires the presence of specific “rule-in” clinical signs believed to be incompatible with known neurological disease. Some of these clinical signs have long been used by neurologists and other clinicians to help them identify cases of conversion disorder.

Stone et al.,’s (2010) paper was one of several arising from the Scottish Neurological Symptoms Study (SNSS). The study reviewed records from multiple outpatient neurology clinics and reported that 209 of 3781 attendees, or less than 6 %, received diagnoses compatible with conversion disorder–in other words, what would now be called FND. In terms of ranking, this group of patients—labeled in the SNSS as having “functional” symptoms or diagnoses–was far down the list. The study found higher rates of many other conditions, including headache (19 %), epilepsy (14 %), peripheral nerve disorders (11 %), miscellaneous neurological disorders (10 %), multiple sclerosis/demyelination (7 %), spinal disorders (6 %) and Parkinson’s disease/movement disorders (6 %).

Earlier this year, a paper in the European Journal of Neurology (Mason et al., 2023) cited a different SNSS paper (Stone et al., 2009) to support the assertion that FND prevalence at outpatient neurology clinics was 5.4 %—far lower than the percentage needed to be the “2nd most common” diagnosis. Moreover, the authors of another paper (Foley et al., 2022) have recently issued a correction for the same misstatement of FND prevalence from the SNSS findings as the one identified in Perez et al.

The assertion that the SNSS found FND to be the “2nd most common” diagnosis at outpatient neurology clinics is based on a parallel and commonly repeated claim that the study found the prevalence in these settings to be 16 % (e.g. Ludwig et al., 2018). That rate is almost three times the 5.4 % prevalence recently highlighted in Mason et al. The extra patients included in this greatly expanded FND category were another 10 % collectively identified in the SNSS as having “psychological” symptoms or diagnoses. These “psychological” patients fell into a range of clinical sub-categories, among them hyperventilation, anxiety and depression, atypical facial/temporomandibular joint pain, post-head injury symptoms, fibromyalgia, repetitive strain injury, and alcohol excess. The SNSS paper cited in Perez et al reported that a combined grouping of the patients with “functional and psychological” symptoms or diagnoses had a prevalence of 16 % but did not provide any evidence that the 10 % included under the “psychological” label met, or could have met, the explicit FND requirement for rule-in clinical signs.

FND is not synonymous with the broader “functional and psychological” category in the SNSS and should not be presented as if it were. The post-hoc reinterpretation of previously reported data in a way that conflates FND with other complex conditions—almost tripling its apparent prevalence in the process–is an example of the phenomenon known as diagnostic creep. In any event, the SNSS results are a matter of record. Whatever future studies might determine about FND rates, the published findings cited by Perez et al and addressed in this letter do not support either the claim that it is the “2nd most common” diagnosis in outpatient neurology clinics or the related claim that its prevalence at these venues is 16 %.

Sincerely–

David Tuller (corresponding author)

Center for Global Public Health

School of Public Health

University of California, Berkeley

Berkeley, CA, USA

David Davies-Payne

Department of Radiology

Starship Children’s Hospital

Auckland, New Zealand

Jonathan Edwards

Department of Medicine

University College London

London, England, UK

Keith Geraghty

Centre for Primary Care and Health Services Research

Faculty of Biology, Medicine and Health

University of Manchester

Manchester, England, UK

Calliope Hollingue

Center for Autism and Related Disorders/Kennedy Krieger Institute

Department of Mental Health/Johns Hopkins Bloomberg School of Public Health

Johns Hopkins University

Baltimore, MD, USA

Mady Hornig

Department of Epidemiology

Columbia University Mailman School of Public Health

New York, NY, USA

Brian Hughes

School of Psychology

University of Galway

Galway, Ireland

Asad Khan

North West Lung Centre

Manchester University Hospitals

Manchester, England, UK

David Putrino

Department of Rehabilitation Medicine

Icahn School of Medicine at Mt Sinai

New York, NY, USA

John Swartzberg

Division of Infectious Diseases and Vaccinology

School of Public Health

University of California, Berkeley

Berkeley, CA, USA

Source: Correspondence: Inaccurate reference leads to tripling of reported FND prevalence. Neuroimage Clin. 2024 Feb 7;41:103537. doi: 10.1016/j.nicl.2023.103537. Epub ahead of print. PMID: 38330816. https://www.sciencedirect.com/science/article/pii/S2213158223002280 (Full text)

Psychometric evaluation of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) among adults with Long COVID, ME/CFS, and healthy controls: A machine learning approach

Abstract:

Long COVID shares a number of clinical features with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), including post-exertional malaise, severe fatigue, and neurocognitive deficits. Utilizing validated assessment tools that accurately and efficiently screen for these conditions can facilitate diagnostic and treatment efforts, thereby improving patient outcomes.

In this study, we generated a series of random forest machine learning algorithms to evaluate the psychometric properties of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) in classifying large groups of adults with Long COVID, ME/CFS (without Long COVID), and healthy controls.

We demonstrated that the DSQ-SF can accurately classify these populations with high degrees of sensitivity and specificity. In turn, we identified the particular DSQ-SF symptom items that best distinguish Long COVID from ME/CFS, as well as those that differentiate these illness groups from healthy controls.

Source: McGarrigle WJ, Furst J, Jason LA. Psychometric evaluation of the DePaul Symptom Questionnaire-Short Form (DSQ-SF) among adults with Long COVID, ME/CFS, and healthy controls: A machine learning approach. J Health Psychol. 2024 Jan 28:13591053231223882. doi: 10.1177/13591053231223882. Epub ahead of print. PMID: 38282368. https://pubmed.ncbi.nlm.nih.gov/38282368/

Frequency and characteristics of chronic fatigue syndrome in multiple sclerosis patients at a university hospital in Eastern Saudi Arabia

Abstract:

BACKGROUND: Multiple sclerosis (MS) is a chronic, inflammatory demyelinating disease that affects various parts of the central nervous system. Fatigue, a common symptom, transient, prolonged, or chronic experienced by individuals with MS, can significantly impact daily functioning. It can be associated with underlying pathological processes or can have an idiopathic cause, such as chronic fatigue syndrome (CFS). The study aimed to assess the presence and etiology of fatigue in MS patients and its relationship with CFS.

MATERIALS AND METHODS: This cross-sectional study was conducted in the Eastern Province of Saudi Arabia. Data were collected using a questionnaire from a sample of 225 MS patients receiving care at our university hospital. The questionnaire included the Centers for Disease Control and Prevention (CDC) criteria for diagnosing CFS and the Expanded Disability Status Scale to evaluate fatigue in MS patients.

RESULTS: Of the total of 225 MS patients who participated in this study, 87.1% were diagnosed with relapsing-remitting MS, 6.7% with primary progressive MS, 3.6% with clinically isolated syndrome, and 2.7% with secondary progressive MS. About 53% had experienced fatigue that persisted for over 6 months. Analysis of CFS diagnosis revealed that 7.3% of patients met both CDC criteria and self-reported answers while 17.5% reported having CFS despite not meeting the CDC criteria. These findings highlight a significant lack of agreement between patient-reported diagnoses and established criteria, indicating poor agreement (P = 0.028).

CONCLUSION: The study found an association between CFS and MS, and a significant impact on daily functioning. The study revealed lack of agreement between patient-reported diagnoses and established criteria for CFS. This emphasizes the need for a standardized approach to diagnosis and evaluation of fatigue in MS patients.

Source: AlAmri, Abdullah S.; AlShamrani, Foziah J.; AlMohish, Noor M.; Zafar, Azra S.; Alnaaim, Saud A.1; Alazman, Hatem A.; Al-Ghanimi, Ibrahim A.2; AlNahdi, Abdullah A.; AlDawsari, Fahad A.; AlMatrafi, Shahad B.3; Alzahrani, Ghaida R.3; Alnamlah, Muna S.; Alkhalifa, Rawan A.. Frequency and characteristics of chronic fatigue syndrome in multiple sclerosis patients at a university hospital in Eastern Saudi Arabia. Journal of Family and Community Medicine 31(1):p 63-70, Jan–Mar 2024. | DOI: 10.4103/jfcm.jfcm_73_23 https://journals.lww.com/jfcm/fulltext/2024/31010/frequency_and_characteristics_of_chronic_fatigue.9.aspx (Full text)

Systems thinking, subjective findings and diagnostic “pigeonholing” in ME/CFS: A mainly qualitative public health study from a patient perspective

Abstract:

in English, German

Background: ME/CFS (Myalgic encephalomyelitis/chronic fatigue syndrome) is an illness that is predominantly viewed as a neuroimmunological multisystem disease, which is still unknown to many doctors in Germany or which they classify as a psychosomatic disease. From their perspective, ME/CFS patients report significant deficits in terms of medical treatment and a doctor-patient relationship (DP relationship) that is perceived as problematic. The aim of the present study is to more precisely analyse the process of finding a diagnosis as an influencing factor on the DP relationship in ME/CFS from the point of view of those affected.

Method: As part of an explorative qualitative survey, 544 ME/CFS patients (> 20 years; 455 ♀, 89 ♂) with a medical diagnosis of ME/CFS were asked in writing about their experiences with regard to the process of finding a diagnosis. The sampling was previously done by self-activation and via the snowball principle. The questionnaire to be answered was structured analogously to a focused, standardized guideline interview. The evaluation was carried out as part of a qualitative content analysis according to Mayring. Some of the results were subsequently quantified.

Results: The participants described what they saw as the inadequate process of making a diagnosis as a central factor in a problematic DP relationship in ME/CFS. From their point of view, many doctors deny the existence of ME/CFS or classify it as a solely psychosomatic illness, insist on their level of knowledge, ignore patient knowledge and disregard scientific information provided. They follow the standard program, think in “pigeonholes” and are incapable of systemic thinking. This has a significant impact on the DP relationship.

Discussion: From the point of view of ME/CFS patients, the process of making a diagnosis and the recognition of ME/CFS as a neuroimmunological multisystem disease are the central aspects of a DP relationship that they experience as problematic. In the past, findings classified as “subjective” and thus ignored, the pigeonholing that is characteristic of biomedically oriented medicine and a healthcare system that opposes systemic thinking when making a diagnosis have all been identified as factors that may have a significant impact on the DP relationship.

Source: Habermann-Horstmeier L, Horstmeier LM. Systemisches Denken, subjektive Befunde und das diagnostische „Schubladendenken“ bei ME/CFS – Eine vorwiegend qualitative Public-Health-Studie aus Patientensicht [Systems thinking, subjective findings and diagnostic “pigeonholing” in ME/CFS: A mainly qualitative public health study from a patient perspective]. Dtsch Med Wochenschr. 2023 Dec 14. German. doi: 10.1055/a-2197-6479. Epub ahead of print. PMID: 38096913. https://pubmed.ncbi.nlm.nih.gov/38096913/

IgG Antibody Responses to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Their Effective Potential for Disease Diagnosis and Pathological Antigenic Mimicry

Abstract:

The diagnosis and the pathology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) remain under debate. However, there is a growing body of evidence for an autoimmune component in ME/CFS caused by the Epstein-Barr virus (EBV) and other viral infections.
In this work, we took advantage of a large public dataset on the IgG antibodies to 3,054 EBV peptides to understand whether these immune responses could be used as putative biomarkers for disease diagnosis and triggers of pathological autoimmunity in ME/CFS patients using healthy controls (HCs) as a comparator cohort. We then aimed at predicting disease status of study participants using a Super Learner algorithm targeting an accuracy of 85% when splitting data into train and test datasets.
When we compared data of all ME/CFS patients or data of a subgroup of these patients with non-infectious or unknown disease trigger to the dataset of HC, we could not find an antibody-based classifier that would meet the desired accuracy in the test dataset. In contrast, we could identify a 26-antibody classifier that could distinguish ME/CFS patients with an infectious disease trigger from HCs with 100% and 90% accuracies on the train and test sets, respectively.
We finally performed a bioinformatic analysis of the EBV peptides associated with these 26 antibodies. We found no correlation between the importance metric of the selected antibodies in the classifier and the maximal sequence homology between human proteins and each EBV peptide recognized by these antibodies.
In conclusion, these 26 antibodies against EBV have an effective potential for disease diagnosis of a subset of patients, but they are less likely to trigger pathological autoimmune responses that could explain the pathogenesis of ME/CFS.
Source: Fonseca, A.; Szysz, M.; Ly, H.T.; Cordeiro, C.; Sepúlveda, N. IgG Antibody Responses to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Their Effective Potential for Disease Diagnosis and Pathological Antigenic Mimicry. Preprints 2023, 2023111523. https://doi.org/10.20944/preprints202311.1523.v1 https://www.preprints.org/manuscript/202311.1523/v1 (Full text available as PDF file)