Category: Biomarker

THU581 Possible Markers For Myalgic Encephalomyelitis / Chronic Fatigue Syndrome Developed In Long Covid: Utility Of Serum Ferritin And Insulin-like Growth Factor-I

Abstract:

Almost three years have passed since coronavirus disease 2019 (COVID-19) pandemic broke out, and along with the number of acute COVID-19 patients, the number of patients suffering from chronic prolonged symptoms after COVID-19, long COVID, or post COVID-19 condition, has also increased.

We established an outpatient clinic specialized for COVID-19 after care (CAC) in Okayama University Hospital in Japan in February 2021. Our recent study has revealed that the most common symptom is “fatigue”, a part of which potentially may develop into myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). However, the pathogenesis and specific prognosticator have yet to be elucidated. The aim of this study was to elucidate the clinical characteristics of patients who developed ME/CFS after COVID-19.

This retrospective observational study investigated the patients who visited our CAC outpatient clinic between February 2021 and March 2022. Of the 234 patients, 139 (59.4%) had fatigue symptoms, of whom 50 (21.4%) met the criteria for ME/CFS (ME/CFS group), while other 89 did not (non-ME/CFS group); 95 patients had no fatigue complaints (no-fatigue group). Although the patients’ backgrounds were not significantly different among the three groups, the ME/CFS group presented the highest scores on the self-rating symptom scales, including the Fatigue Assessment Scale (FAS), EuroQol, and Self-Rating Depression Scale (SDS).

Of note, serum ferritin levels, which were correlated to FAS and SDS scores, were significantly higher in the ME/CFS group (193.0 μg/mL; interquartile range (IQR), 58.8-353.8) than those of non-ME/CFS (98.2 μg/mL; 40.4-251.5) and no-fatigue (86.7 μg/mL; 37.5-209.0) groups, and this trend was prominent in the female patients. Endocrine workup further showed that the ME/CFS group had higher thyrotropin levels but lower growth hormone levels in the serum, and that insulin-like growth factor (IGF)-I levels were inversely correlated with ferritin levels (R = -0.328, p < 0.05).

Collectively, we revealed that serum ferritin levels could be a possible predictor for developing ME/CFS related to long COVID, especially in female patients. Earlier studies have suggested that hyperferritinemia is a clinical feature in the patients of long COVID, in which hepcidin-like effects could also be involved. Our present study also uncovered a relationship between hyperferrinemia and endocrine disorders among patients developing ME/CFS after COVID-19, although further investigations are necessary to understand the characteristics of ferritin metabolism.

Presentation: Thursday, June 15, 2023

Source: Yukichika Yamamoto, Yuki Otsuka, Kazuki Tokumasu, Naruhiko Sunada, Yasuhiro Nakano, Hiroyuki Honda, Yasue Sakurada, Toru Hasegawa, Hideharu Hagiya, Fumio Otsuka, THU581 Possible Markers For Myalgic Encephalomyelitis / Chronic Fatigue Syndrome Developed In Long Covid: Utility Of Serum Ferritin And Insulin-like Growth Factor-I, Journal of the Endocrine Society, Volume 7, Issue Supplement_1, October-November 2023, bvad114.1370, https://doi.org/10.1210/jendso/bvad114.1370 (Full text available as PDF file)

Unsupervised cluster analysis reveals distinct subtypes of ME/CFS patients based on peak oxygen consumption and SF-36 scores

Abstract:

Purpose: Myalgic encephalomyelitis, commonly referred to as chronic fatigue syndrome (ME/CFS), is a severe, disabling chronic disease and an objective assessment of prognosis is crucial to evaluate the efficacy of future drugs. Attempts are ongoing to find a biomarker to objectively assess the health status of (ME/CFS), patients. This study therefore aims to demonstrate that oxygen consumption is a biomarker of ME/CFS provides a method to classify patients diagnosed with ME/CFS based on their responses to the Short Form-36 (SF-36) questionnaire, which can predict oxygen consumption using cardiopulmonary exercise testing (CPET).

Methods: Two datasets were used in the study. The first contained SF-36 responses from 2,347 validated records of ME/CFS diagnosed participants, and an unsupervised machine learning model was developed to cluster the data. The second dataset was used as a validation set and included the cardiopulmonary exercise test (CPET) results of 239 participants diagnosed with ME/CFS. Participants from this dataset were grouped by peak oxygen consumption according to Weber’s classification. The SF-36 questionnaire was correctly completed by only 92 patients, who were clustered using the machine learning model. Two categorical variables were then entered into a contingency table: the cluster with values {0,1} and Weber classification {A, B, C, D} were assigned. Finally, the Chi-square test of independence was used to assess the statistical significance of the relationship between the two parameters.

Findings: The results indicate that the Weber classification is directly linked to the score on the SF-36 questionnaire. Furthermore, the 36-response matrix in the machine learning model was shown to give more reliable results than the subscale matrix (p – value < 0.05) for classifying patients with ME/CFS.

Implications: Low oxygen consumption on CPET can be considered a biomarker in patients with ME/CFS. Our analysis showed a close relationship between the cluster based on their SF-36 questionnaire score and the Weber classification, which was based on peak oxygen consumption during CPET. The dataset for the training model comprised raw responses from the SF-36 questionnaire, which is proven to better preserve the original information, thus improving the quality of the model.

Source: Lacasa M, Launois P, Prados F, Alegre J, Casas-Roma J. Unsupervised cluster analysis reveals distinct subtypes of ME/CFS patients based on peak oxygen consumption and SF-36 scores. Clin Ther. 2023 Oct 4:S0149-2918(23)00352-1. doi: 10.1016/j.clinthera.2023.09.007. Epub ahead of print. PMID: 37802746. https://www.clinicaltherapeutics.com/article/S0149-2918(23)00352-1/fulltext (Full text)

HERV activation segregates ME/CFS from fibromyalgia and defines a novel nosological entity for patients fulfilling both clinical criteria

Abstract:

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia (FM) are chronic diseases with poorly understood pathophysiology and diagnosis based on clinical assessment of unspecific symptoms. The recent post-COVID-19 condition, which shares similarities with ME/CFS and FM, has raised concerns about viral-induced transcriptome changes in post-viral syndromes. Viral infections, and other types of stress, are known to unleash human endogenous retroviruses (HERV) repression that if maintained could lead to symptom chronicity. This study evaluated this possibility for ME/CFS and FM on a selected cohort of female patients complying with diagnosis criteria for ME/CFS, FM, or both, and matched healthy controls (n=43).

The results show specific HERV fingerprints for each disease, confirming biological differences between ME/CFS and FM. Unexpectedly, HERV profiles segregated patients that met both ME/CFS and FM clinical criteria from patients complying only with ME or FM criteria, while clearly differentiating patients from healthy subjects, supporting that the highly prevalent comorbidity condition must constitute a different nosological entity.

Moreover, HERV profiles exposed significant quantitative differences within the ME/CFS group that correlated with differences in immune gene expression and patient symptomatology, supporting ME/CFS patient subtyping and confirming immunological disturbances in this disease. Pending issues include validation of HERV profiles as disease biomarkers of post-viral syndromes and understanding the role of HERV during infection and beyond.

Source: Karen Gimenez-Orenga, Eva Martin-Martinez, Lubov Nathanson, Elisa Oltra. HERV activation segregates ME/CFS from fibromyalgia and defines a novel nosological entity for patients fulfilling both clinical criteria. bioRxiv 2023.10.05.561025; doi: https://doi.org/10.1101/2023.10.05.561025 https://www.biorxiv.org/content/10.1101/2023.10.05.561025v1 (Full text available as PDF file)

 

Long read sequencing characterises a novel structural variant, revealing underactive AKR1C1 with overactive AKR1C2 as a possible cause of unexplained severe fatigue

Abstract

Background: Causative genetic variants cannot yet be found for many disorders with a clear heritable component, including chronic fatigue disorders like myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). These conditions may involve genes in difficult-to-align genomic regions that are refractory to short read approaches. Structural variants in these regions can be particularly hard to detect or define with short reads, yet may account for a significant number of cases. Long read sequencing can overcome these difficulties but so far little data is available regarding the specific analytical challenges inherent in such regions, which need to be taken into account to ensure that variants are correctly identified.

Research into chronic fatigue disorders faces the additional challenge that the heterogeneous patient population likely encompasses multiple aetiologies with overlapping symptoms, rather than a single disease entity, such that each individual abnormality may lack statistical significance within a larger sample. Better delineation of patient subgroups is needed to target research and treatment.

Methods: We use nanopore sequencing in a case of unexplained severe fatigue to identify and fully characterise a large inversion in a highly homologous region spanning the AKR1C gene locus, which was indicated but could not be resolved by short-read sequencing. We then use GC-MS/MS serum steroid analysis to investigate the functional consequences.

Results: Several commonly used bioinformatics tools are confounded by the homology but a combined approach including visual inspection allows the variant to be accurately resolved. The DNA inversion appears to increase the expression of AKR1C2 while limiting AKR1C1 activity, resulting in a relative increase of inhibitory neurosteroids and impaired progesterone metabolism.

Conclusions: This study provides an example of how long read sequencing can improve diagnostic yield in research and clinical care, and highlights some of the analytical challenges presented by regions containing tandem arrays of genes. It also proposes a novel gene associated with a specific disease aetiology that may be an underlying cause of complex chronic fatigue and possibly other conditions too. It reveals biomarkers that could be assessed in a larger cohort, potentially identifying a subset of patients who might respond to treatments suggested by the aetiology.

Source: Julia Oakley, Martin Hill, Adam Giess, Mélanie Tanguy, Greg Elgar. Long read sequencing characterises a novel structural variant, revealing underactive AKR1C1 with overactive AKR1C2 as a possible cause of unexplained severe fatigue. ResearchSquare [Preprint] https://www.researchsquare.com/article/rs-3218228/v2 (Full text)

Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes

Abstract:

Background: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia have overlapping neurologic symptoms particularly disabling fatigue. This has given rise to the question whether they are distinct central nervous system (CNS) entities or is one an extension of the other.

Material and methods: To investigate this, we used unbiased quantitative mass spectrometry-based proteomics to examine the most proximal fluid to the brain, cerebrospinal fluid (CSF). This was to ascertain if the proteome profile of one was the same or different from the other. We examined two separate groups of ME/CFS, one with (n = 15) and one without (n = 15) fibromyalgia.

Results: We quantified a total of 2083 proteins using immunoaffinity depletion, tandem mass tag isobaric labelling and offline two-dimensional liquid chromatography coupled to tandem mass spectrometry, including 1789 that were quantified in all the CSF samples. ANOVA analysis did not yield any proteins with an adjusted p value <.05.

Conclusion: This supports the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities.

Key message: ME/CFS and fibromyalgia as currently defined are not distinct entities. Unbiased quantitative mass spectrometry-based proteomics can be used to discover cerebrospinal fluid proteins that are biomarkers for a condition such as we are studying.

Source: Schutzer SE, Liu T, Tsai CF, Petyuk VA, Schepmoes AA, Wang YT, Weitz KK, Bergquist J, Smith RD, Natelson BH. Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes. Ann Med. 2023 Dec;55(1):2208372. doi: 10.1080/07853890.2023.2208372. Epub 2023 Sep 18. PMID: 37722890. https://www.tandfonline.com/doi/full/10.1080/07853890.2023.2208372 (Full text)

Investigating the potential role of circulatory extracellular vesicles in myalgic encephalomyelitis/ chronic fatigue syndrome

Abstract:

ME/CFS is a debilitating disease thought to affect millions of individuals. Still, the etiology of ME/CFS is unknown, and there are no standard treatments or established biomarkers. The current symptom-based diagnosis is extensive, and the use of different diagnostic criteria contributes to heterogeneity among patients and may problematize the comparison of findings. Thus, the discovery of a biomarker for ME/CFS is urgent and would benefit both patients and the ME/CFS research field.

Extracellular vesicles (EVs) are membrane limited vesicles secreted by all cells to the extracellular environment and can be collected through biofluids. EVs serve many functions, including transferring functional proteins, lipids, and nucleic acids between cells, thus mediating cell-to-cell communication. EV secretion and cargo may reflect disease state and EVs thus pose great potential as source of minimally invasive biomarkers.

The primary aim of this project was to study EVs in plasma from ME/CFS patients and assess the potential of EVs as source of biomarkers for the disease.

Using size exclusion chromatography, EVs were enriched from plasma from ME/CFS patients (n = 20) and healthy controls (n=20). Success of EV isolation was determined in representative patient- and control EV pools (n=5) using western blotting and transmission electron microscopy. Western blot experiments for detection of EV markers CD9, CD63 and TSG101, and albumin, were optimized and confirmed enrichment of EVs and presence of non-EV eluates in the isolated samples.

EV enrichment was further validated through observation of intact EVs on transmission electron micrographs, however few CD63-positive EVs were observed. Through analysis of nanoparticle tracking analysis data, the isolated EV population primarily consisted of small EVs (< 200 nm). Within this EV population, meanand mode EV size was similar between cohorts, but the EV concentration was significantly elevated in samples from patients compared to controls (p = 0.006). However, statistical tests may have been influenced by high variation within the ME/CFS cohort.

Early-stage analysis of tandem mass spectrometry data identified 663 EV associated proteins. The majority of detected proteins overlapped with registered EV proteins, but only few differences could be observed between patient- and control derived samples. However, differential expression was not analyzed.

A biomarker for ME/CFS could not be suggested at this stage of the study, however increased EV concentration suggests abnormality in EV secretion in patients which strengthens their potential as source of biomarkers and further motivates EV research in ME/CFS and related diseases.

Source: Elena Støvring Yran. Investigating the potential role of circulatory extracellular vesicles in myalgic encephalomyelitis/ chronic fatigue syndrome. Master Thesis [University of Oslo] https://www.duo.uio.no/bitstream/handle/10852/103812/1/Masterthesis_ElenaYran_May15th2023.pdf  (Full text)

Increased gut permeability and bacterial translocation are associated with fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome: implications for disease-related biomarker discovery

Abstract:

Background: There is growing evidence of the significance of gastrointestinal complaints in the impairment of the intestinal mucosal barrier function and inflammation in fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome. However, data on intestinal permeability and gut barrier dysfunction in FM and ME/CFS are still limited with conflicting results. This study aimed to assess circulating biomarkers potentially related to intestinal barrier dysfunction and bacterial translocation and their association with self-reported symptoms in these conditions.

Methods: A pilot multicentre, cross-sectional cohort study with consecutive enrolment of 22 patients with FM, 30 with ME/CFS, and 26 matched healthy controls. Plasma levels of anti-beta-lactoglobulin antibodies (IgG anti-beta-LGB), zonulin-1 (ZO-1), LPS, sCD14, and IL-1β) were assayed using ELISA. Demographic and clinical characteristics of the participants were recorded using validated self-reported outcome measures. The diagnostic accuracy of each biomarker was assessed using the ROC curve analysis.

Results: FM patients had significantly higher levels of anti-β-LGB, ZO-1, LPS, and sCD14 than healthy controls (all P < 0.0001). In ME/CFS patients, levels of anti-β-LGB, ZO-1, LPS, and sCD14 were significantly higher than controls, but lower than in FM (all P < 0.01), while there was no significant difference in IL-1β level. In the FM and ME/CFS cohorts, both anti-β-LGB and ZO-1 correlated significantly with LPS and sCD14 (P < 0.001 for both). In the FM group, both anti-beta-LGB and ZO-1 were correlated significantly with physical and mental health components on the SF-36 scale (P < 0.05); whereas IL-1beta negatively correlated with the COMPASS-31 score (P < 0.05). In the ME/CFS cohort, ZO-1 was positively correlated with the COMPASS-31 score (P < 0.05). The ROC curve analysis indicated a strong ability of anti-β-LGB, ZO-1, LPS, and sCD14 to predictively distinguish between FM and ME/CFS from healthy controls (P < 0.0001).

Conclusions: Biomarkers of intestinal barrier function and inflammation were associated with autonomic dysfunction assessed by COMPASS-31 scores in FM and ME/CFS respectively. Anti-β-LGB antibodies, ZO-1, LPS, and sCD14 may be putative predictors of intestinal barrier dysfunction in these cohorts. Further studies are needed to assess whether these findings are causal and can therefore be applied in clinical practice.

Source: Franz Martin, Manuel Blanco Suárez2 Paola Zambrano, Óscar Cáceres Calle, Miriam Almirall, Jose Alegre-Martín, Beatriz Lobo, Ana María Gonzalez-Castro, Javier Santos, Joan Carles Domingo, Joanna Jurek, Jesús Castro-Marrero. Increased gut permeability and bacterial translocation are associated with fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome: implications for disease-related biomarker discovery. Front. Immunol., Sec. Mucosal Immunity, Volume 14 – 2023 | doi: 10.3389/fimmu.2023.1253121 https://www.frontiersin.org/articles/10.3389/fimmu.2023.1253121/abstract

WASF3 disrupts mitochondrial respiration and may mediate exercise intolerance in myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by various disabling symptoms including exercise intolerance and is diagnosed in the absence of a specific cause, making its clinical management challenging. A better understanding of the molecular mechanism underlying this apparent bioenergetic deficiency state may reveal insights for developing targeted treatment strategies.

We report that overexpression of Wiskott-Aldrich Syndrome Protein Family Member 3 (WASF3), here identified in a 38-y-old woman suffering from long-standing fatigue and exercise intolerance, can disrupt mitochondrial respiratory supercomplex formation and is associated with endoplasmic reticulum (ER) stress.

Increased expression of WASF3 in transgenic mice markedly decreased their treadmill running capacity with concomitantly impaired respiratory supercomplex assembly and reduced complex IV levels in skeletal muscle mitochondria. WASF3 induction by ER stress using endotoxin, well known to be associated with fatigue in humans, also decreased skeletal muscle complex IV levels in mice, while decreasing WASF3 levels by pharmacologic inhibition of ER stress improved mitochondrial function in the cells of the patient with chronic fatigue.

Expanding on our findings, skeletal muscle biopsy samples obtained from a cohort of patients with ME/CFS showed increased WASF3 protein levels and aberrant ER stress activation. In addition to revealing a potential mechanism for the bioenergetic deficiency in ME/CFS, our study may also provide insights into other disorders associated with fatigue such as rheumatic diseases and long COVID.

Source: Wang PY, Ma J, Kim YC, Son AY, Syed AM, Liu C, Mori MP, Huffstutler RD, Stolinski JL, Talagala SL, Kang JG, Walitt BT, Nath A, Hwang PM. WASF3 disrupts mitochondrial respiration and may mediate exercise intolerance in myalgic encephalomyelitis/chronic fatigue syndrome. Proc Natl Acad Sci U S A. 2023 Aug 22;120(34):e2302738120. doi: 10.1073/pnas.2302738120. Epub 2023 Aug 14. PMID: 37579159. https://pubmed.ncbi.nlm.nih.gov/37579159/

Evidence of a Novel Mitochondrial Signature in Systemic Sclerosis Patients with Chronic Fatigue Syndrome

Abstract:

Symptoms of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are common in rheumatic diseases, but no studies report the frequency of these in early systemic sclerosis. There are no known biomarkers that can distinguish between patients with ME/CFS, although mitochondrial abnormalities are often demonstrated.

We sought to assess the prevalence of ME/CFS in limited cutaneous SSc (lcSSc) patients early in their disease (<5 years from the onset of non-Raynaud’s symptoms) and to determine if alterations in mitochondrial electron transport chain (ETC) transcripts and mitochondrial DNA (mtDNA) integrity could be used to distinguish between fatigued and non-fatigued patients.

All SSc patients met ACR/EULAR classification criteria. ME/CFS-related symptoms were assessed through validated questionnaires, and the expression of ETC transcripts and mtDNA integrity were quantified via qPCR.

SSc patients with ME/CFS could be distinguished from non-fatigued patients through ETC gene analysis; specifically, reduced expression of ND4 and CyB and increased expression of Cox7C. ND4 and CyB expression correlated with indicators of disease severity.

Further prospective and functional studies are needed to determine if this altered signature can be further utilized to better identify ME/CFS in SSc patients, and whether ME/CFS in early SSc disease could predict more severe disease outcomes.

Source: van Eeden C, Redmond D, Mohazab N, Larché MJ, Mason AL, Cohen Tervaert JW, Osman MS. Evidence of a Novel Mitochondrial Signature in Systemic Sclerosis Patients with Chronic Fatigue Syndrome. International Journal of Molecular Sciences. 2023; 24(15):12057. https://doi.org/10.3390/ijms241512057 https://www.mdpi.com/1422-0067/24/15/12057 (Full text)

A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

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.

Source: Yagin, F.H., Alkhateeb, A., Raza, A., Samee, N.A., Mahmoud, N.F., Colak, C., & Yagin, B. (2023). A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Preprints. https://doi.org/10.20944/preprints202307.1585.v1 https://www.preprints.org/manuscript/202307.1585/v1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706650/ (Full text of completed study)