The weekly research round-up includes recent publications about ME/CFS and Long Covid. We highlight the studies that have particularly caught our interest and follow these with the full list of publications together with their abstracts (summaries).
It’s been a quieter week, with six new ME/CFS studies and twenty-three new Long Covid studies this week.
The ME Association maintains a comprehensive index of published research on ME/CFS and Long Covid that is free to use and updated weekly.
Audio Commentary by Dr Katrina Pears
We have highlighted one of the ME/CFS studies in more detail below:
Paper four (4) is a preprint study (meaning it has not been peer-viewed and the science verified) on machine-based learning models to discriminate metabolites (intermediate products of metabolic reactions) from ME/CFS patients from controls. In doing this work the study works towards finding biomarkers for diagnosis.
This study used a metabolomic dataset of 26 ME/CFS patients and 26 healthy controls. Metabolomics studies are large scale, as several thousand metabolites can be measured in a single sample (blood or urine), this can be used to access what is happening at a cellular level (an overview of metabolomics can be found here).
The dataset used in this study contained 768 metabolites, classified into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics.
The researchers used a range of different statistical methods and algorithms (four approaches were tested) to separate people with ME/CFS and healthy controls. The model known as Random Forest Classifier (RFC) was found to perform best on this task.
The results found that the metabolites of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis.
A few things to note from this study:
- The sample was restricted to females only due to the high prevalence in the female sex, (and also necessary due to the small sample size which would not be able to address gender related differences) as well as reported differences at a molecular level in other studies, but this is also a draw back as we don’t know if results would vary in males.
- The dataset used came from previous research conducted by Germain et al., 2020. Therefore, no new analysis or investigation occurred, and relied solely on the accuracy and methodological approaches of the previous researchers.
- All ME/CFS patients were diagnosed using the CDC criteria, this is the most widely used criteria.
- No independent verification of the results, i.e. no one else processed the data to confirm the results.
- A huge range of metabolites have been found to be altered in ME/CFS, so it is interesting that only four were noted to be the most important in this study. (See a previous systematic review on metabolomic dysregulation by Huth et al., 2020).
A lot of complex statistical methods were used in this research. However, the main drawback and concerns about this study is that this is a very small dataset to make such claims. Much bigger datasets are needed to verify methods, especially which metabolites can distinguish a ME/CFS group. Furthermore, multi-centre investigations with different data and samples are needed to validate this work, however, metabolomic studies are likely to help us make a breakthrough as they are high data output and when combined with statistical methods can help to identify trends.
You may also be interested in reading Paper two (2) which aims to separate ME/CFS from healthy control and MS based on the theory that those with ME/CFS have altered T-cell receptor diversity. Unfortunately, the different groups could not be distinguished in this research. Dr Charles Shepherd has provided a brief summary on the role of T cells, which can be found here. Furthermore, this research used samples from the UK ME/CFS biobank (UKMEB) whose running costs are funded by the MEA Ramsay Research Fund.
In the Long Covid reference section you may also be interested in Paper three (3) which is a Cochrane review of microclots and apheresis. There is a plain language summary available on this paper and a comment by Dr Charles Shepherd which can be found here.
ME/CFS Research References and Abstracts (13 – 19 June)
Helen Leach, Helen Atherton, Abi Eccles and Carolyn Chew-Graham.
British Journal of General Practice 2023; 73 (suppl 1): bjgp23X733689.
Background: Restrictions due to the COVID-19 pandemic resulted in a sudden shift to a predominantly remote consulting model in primary care from March 2020. Little evidence exists examining the experience of remote consulting for people living with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) or fibromyalgia, with the current literature focusing on the challenges faced by clinicians and people living with these conditions. Clinical guidance highlights the importance of building therapeutic relationships and personalising care, but it is unclear how this translates into a remote or virtual consulting space.
Aim: To explore how people living with CFS/ME and fibromyalgia experience remote consulting with a primary care clinician, including synchronous and asynchronous methods, that is, e-consultation platforms, email, video, and telephone.
Method: Semi-structured interviews are being recorded and analysed thematically using a Foucauldian theoretical framework. Participants have been recruited across the West Midlands from a range of backgrounds.
Results: Recruitment is still ongoing. Preliminary analysis indicates that remote consulting is acceptable for these groups of patients, but only if they feel validated, listened to, and with a clinician who considers a holistic view with continuity of care.
Conclusion: Remote consulting has presented new challenges for primary care, and it is important to identify which groups of patients are most suited. This study explores the views from a group of patients that are associated with some complexity, and complements the literature that explores the ability to deliver relationship-based care when consulting digitally/remotely. Recommendations from the findings will be created for use by patients and clinicians alike.
Joshua J Dibble, Ben Ferneyhough, Matthew Roddis, Sam Millington, Michael D Fischer, Nick J Parkinson, Chris P Ponting.
Objective: Myalgic Encephalomyelitis (ME; sometimes referred to as Chronic Fatigue Syndrome or CFS) is a chronic disease without laboratory test, detailed aetiological understanding or effective therapy. Its symptoms are diverse, but it is distinguished from other fatiguing illnesses by the experience of post-exertional malaise, the worsening of symptoms even after minor physical or mental exertion. Its frequent onset after infection might indicate that it is an autoimmune disease or that it arises from abnormal T-cell activation.
Results: To test this hypothesis, we sequenced the genomic loci of a/d, b and g T-cell receptors (TCR) from 40 human blood samples from each of four groups: severely affected people with ME/CFS; mildly or moderately affected people with ME/CFS; people diagnosed with Multiple Sclerosis, as disease controls; and, healthy controls. Seeking to automatically classify these individuals’ samples by their TCR repertoires, we applied P-SVM, a machine learning method.
However, despite working well on a simulated data set, this approach did not partition samples into the four subgroups, beyond what was expected by chance alone. Our findings do not support the hypothesis that blood samples from people with ME/CFS frequently contain altered T-cell receptor diversity.
Mitra Khalafbeigi, Farzaneh Yazdani, Florence Genis, Ka Yan Hess, Samita Kirve.
Irish Journal of Occupational Therapy, 2023.
Purpose: Female adults diagnosed with myalgia encephalomyelitis (ME) and chronic fatigue syndrome (CFS) often are marginalised because their condition is not fully recognised by medical and health-care systems. The purpose of this small-scale study was to explore the lived experiences of adult females with ME/CFS in England in relation to contributing factors that impact their occupational participation.
Design/methodology/approach: A qualitative study design using semi-structured interviews was used with nine female adult participants who were selected using a purposive sampling method. A Thematic Networks tool was used to analyse data.
Findings: Four organising themes were identified: impairment-, person-, environment- and society-related factors. Two global themes, invisibility and diagnosis stigma, were identified as the overarching issues that female adults with ME/CFS face in occupational participation.
Originality/value: Many of the issues that contribute to lack of participation by this population are associated with environmental factors which are secondary to their illness.
Yagin, F.H.; Alkhateeb, A.; Raza, A.; Samee, N.A.; Mahmoud, N.F.; Colak, C.; Yagin, B.
Preprints.org 2023, 2023071585.
Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating disease with a significant global prevalence of over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. A multidisciplinary approach is essential for diagnosing, treating, and managing this complex disease.
The current study aims at employing explainable artificial intelligence (XAI) and machine learning (ML) techniques to identify discriminative metabolites for ME/CFS.
Material and Methods: The present study used a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites, classified into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics.
Random forest-based feature selection and Bayesian Approach based-hyperparameter optimization were implemented on the target data. Four different ML algorithms [Gaussian Naive Bayes (GNB), Gradient Boosting Classifier (GBC), Logistic regression (LR) and Random Forest Classifier (RFC)] were used to classify individuals as ME/CFS patients and healthy individuals. XAI approaches were applied to clinically explain the prediction decisions of the optimum model. Performance evaluation was performed using the indices of accuracy, precision, recall, F1 score, Brier score, and AUC.
Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis.
The RFC learning model outperformed GNB, GBC, and LR in ME/CFS prediction using the 1000 iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC.
Conclusion: RFC model proposed in this study correctly classified and evaluated ME/CFS patients through the selected biomarker candidate metabolites. The methodology combining ML and XAI can provide a clear interpretation of risk estimation for ME/CFS, helping physicians intuitively understand the impact of key metabolomics features in the model.
Lawrence D. Hayes, Nilihan E.M. Sanal-Hayes, Marie Mclaughlin, Ethan C.J. Berry, Nicholas F. Sculthorpe.
The American Journal of Medicine, 2023.
Purpose: Postural sway and physical capacity had not previously been compared between people with long COVID and people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Therefore, this study determined postural sway and physical capacity in people with long COVID (∼16 month illness duration; n=21) and ME/CFS (∼16 year illness duration; n=20), versus age-matched healthy controls (n=20).
Methods: Postural sway was during a 30 s static stand test. Physical capacity was determined using the timed up and go test and five times sit to stand test. Throughout, participants wore isoinertial measurement units.
Results: Postural sway was worse (i.e. greater) in people with long COVID and ME/CFS than controls, but not different between long COVID and ME/CFS. Performance of the timed up and go test and five times sit to stand test were worse in long COVID and ME/CFS than controls, but not different between long COVID and ME/CFS. 87% and 13% of long COVID and ME/CFS participants exceeded the threshold for muscle weakness in the five times sit to stand test and timed up and go test, respectively.
Conclusions: These data suggest that both people with long COVID and people with ME/CFS have similarly impaired balance and physical capacity. Therefore, there is an urgent need for interventions to target postural sway and physical capacity in people with ME/CFS, and given the current pandemic, people with long COVID.
International Journal of Molecular Sciences. 2023; 24(15):11937.
Millions globally suffer from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The inflammatory symptoms, illness onset, recorded outbreak events, and physiological variations provide strong indications that ME/CFS, at least sometimes, has an infectious origin, possibly resulting in a chronic unidentified viral infection.
Meanwhile, studies exposing generalized metabolic disruptions in ME/CFS have stimulated interest in isolated immune cells with an altered metabolic state. As the metabolism dictates the cellular function, dissecting the biomechanics of dysfunctional immune cells in ME/CFS can uncover states such as exhaustion, senescence, or anergy, providing insights into the consequences of these phenotypes in this disease. Despite the similarities that are seen metabolically between ME/CFS and other chronic viral infections that result in an exhausted immune cell state, immune cell exhaustion has not yet been verified in ME/CFS.
This review explores the evidence for immunometabolic dysfunction in ME/CFS T cell and natural killer (NK) cell populations, comparing ME/CFS metabolic and functional features to dysfunctional immune cell states, and positing whether anergy, exhaustion, or senescence could be occurring in distinct immune cell populations in ME/CFS, which is consistent with the hypothesis that ME/CFS is a chronic viral disease.
This comprehensive review of the ME/CFS immunometabolic literature identifies CD8+ T cell exhaustion as a probable contender, underscores the need for further investigation into the dysfunctional state of CD4+ T cells and NK cells, and explores the functional implications of molecular findings in these immune-cell types.
Comprehending the cause and impact of ME/CFS immune cell dysfunction is critical to understanding the physiological mechanisms of ME/CFS, and developing effective treatments to alleviate the burden of this disabling condition.
Long-COVID Research References
- Association between duration of SARS-CoV-2 positivity and long COVID
- A Cross-Sectional Study of Symptom Prevalence, Frequency, Severity, and Impact of Long-COVID in Scotland: Part IPlasmapheresis to remove amyloid fibrin(ogen) particles for treating the post‐COVID‐19 condition
- Plasmapheresis to remove amyloid fibrin(ogen) particles for treating the post‐COVID‐19 condition
- Persistent post–COVID-19 neuromuscular symptoms
- The Impact of Post-COVID-19 Syndrome in Adolescents: A Pilot Study
- Consistency of inconsistency in long-COVID-19 pain symptoms persistency: A systematic review and meta-analysis
- Rehabilitation Perspectives in Long COVID-19
- Gastrointestinal symptoms of long COVID-19 related to the ectopic colonization of specific bacteria that move between the upper and lower alimentary tract and alterations in serum metabolites
- Does Long COVID Exist in Sub-Saharan Africa?
- Evolution of musculoskeletal symptoms in Long COVID: have world health systems been prepared to deal with the Long COVID syndrome?
- Culinary spices and herbs in managing early and long-COVID-19 complications: A comprehensive review
- Long COVID in Young Adults on a University Campus
- Understanding Public Perceptions and Information Sharing Patterns About Long Covid: A Qualitative Analysis of Twitter Data
- Clinical Trial to Test Safety and Efficacy of RNA-Based Immune Support in Long COVID Patients
- Is Cognitive Behavioural Therapy Effective for Post-COVID Fatigue?
- Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohort
- Long covid in persons with self-reported arthritis – symptoms, associated factors and functional limitations
- Long COVID in Young Patients: Impact on Lung Volume Evaluated Using Multidetector CT
- Long COVID, the Brain, Nerves, and Cognitive Function
- Amygdala and Insula Retraining (AIR) Significantly Reduces Fatigue and Increases Energy in People with Long COVID
- Epidemiology, clinical presentation, pathophysiology, and management of long COVID: an update
- Neuropsychological measures of post-COVID-19 cognitive status
- Challenges to delivering evidence-based management for long COVID
Dr Katrina Pears
The ME Association.