Tag: metabolomics

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)

Untargeted Metabolomics and Quantitative Analysis of Tryptophan Metabolites in Myalgic Encephalomyelitis Patients and Healthy Volunteers: A Comparative Study Using High-Resolution Mass Spectrometry

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic, complex illness characterized by severe and often disabling physical and mental fatigue. So far, scientists have not been able to fully pinpoint the biological cause of the illness and yet it affects millions of people worldwide.

To gain a better understanding of ME/CFS, we compared the metabolic networks in the plasma of 38 ME/CFS patients to those of 24 healthy control participants. This involved an untargeted metabolomics approach in addition to the measurement of targeted substances including tryptophan and its metabolites, as well as tyrosine, phenylalanine, B vitamins, and hypoxanthine using liquid chromatography coupled to mass spectrometry.

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Source: Abujrais S, Vallianatou T, Bergquist J. Untargeted Metabolomics and Quantitative Analysis of Tryptophan Metabolites in Myalgic Encephalomyelitis Patients and Healthy Volunteers: A Comparative Study Using High-Resolution Mass Spectrometry. ACS Chem Neurosci. 2024 Sep 20. doi: 10.1021/acschemneuro.4c00444. Epub ahead of print. PMID: 39302151. https://pubs.acs.org/doi/10.1021/acschemneuro.4c00444 (Full text)

Fast Targeted Metabolomics for Analyzing Metabolic Diversity of Bacterial Indole Derivatives in ME/CFS Gut Microbiome

Abstract:

Disruptions in microbial metabolite interactions due to gut microbiome dysbiosis and metabolomic shifts may contribute to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and other immune-related conditions. The aryl hydrocarbon receptor (AhR), activated upon binding various tryptophan metabolites, modulates host immune responses. This study investigates whether the metabolic diversity-the concentration distribution-of bacterial indole pathway metabolites can differentiate bacterial strains and classify ME/CFS samples.

A fast targeted liquid chromatography-parallel reaction monitoring method at a rate of 4 minutes per sample was developed for large-scale analysis. This method revealed significant metabolic differences in indole derivatives among B. uniformis strains cultured from human isolates. Principal component analysis identified two major components (PC1, 68.9%; PC2, 18.7%), accounting for 87.6% of the variance and distinguishing two distinct B. uniformis clusters. The metabolic difference between clusters was particularly evident in the relative contributions of indole-3-acrylate and indole-3-aldehyde.

We further measured concentration distributions of indole derivatives in ME/CFS by analyzing fecal samples from 10 patients and 10 healthy controls using the fast targeted metabolomics method. An AdaBoost-LOOCV model achieved moderate classification success with a mean LOOCV accuracy of 0.65 (Control: precision of 0.67, recall of 0.60, F1-score of 0.63; ME/CFS: precision of 0.64, recall of 0.7000, F1-score of 0.67).

These results suggest that the metabolic diversity of indole derivatives from tryptophan degradation, facilitated by the fast targeted metabolomics and machine learning, is a potential biomarker for differentiating bacterial strains and classifying ME/CFS samples.

Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (ST002308, DOI: 10.21228/M8G13Q; ST003344, DOI: 10.21228/M8RJ9N; ST003346, DOI: 10.21228/M8RJ9N).

Source: Tian H, Wang L, Aiken E, Ortega RJV, Hardy R, Placek L, Kozhaya L, Unutmaz D, Oh J, Yao X. Fast Targeted Metabolomics for Analyzing Metabolic Diversity of Bacterial Indole Derivatives in ME/CFS Gut Microbiome. bioRxiv [Preprint]. 2024 Jul 29:2024.07.29.605643. doi: 10.1101/2024.07.29.605643. PMID: 39131327; PMCID: PMC11312560. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11312560/ (Full text)

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)

Reinforcing the Evidence of Mitochondrial Dysfunction in Long COVID Patients Using a Multiplatform Mass Spectrometry-Based Metabolomics Approach

Abstract:

Despite the recent and increasing knowledge surrounding COVID-19 infection, the underlying mechanisms of the persistence of symptoms for a long time after the acute infection are still not completely understood. Here, a multiplatform mass spectrometry-based approach was used for metabolomic and lipidomic profiling of human plasma samples from Long COVID patients (n = 40) to reveal mitochondrial dysfunction when compared with individuals fully recovered from acute mild COVID-19 (n = 40).

Untargeted metabolomic analysis using CE-ESI(+/-)-TOF-MS and GC-Q-MS was performed. Additionally, a lipidomic analysis using LC-ESI(+/-)-QTOF-MS based on an in-house library revealed 447 lipid species identified with a high confidence annotation level. The integration of complementary analytical platforms has allowed a comprehensive metabolic and lipidomic characterization of plasma alterations in Long COVID disease that found 46 relevant metabolites which allowed to discriminate between Long COVID and fully recovered patients.

We report specific metabolites altered in Long COVID, mainly related to a decrease in the amino acid metabolism and ceramide plasma levels and an increase in the tricarboxylic acid (TCA) cycle, reinforcing the evidence of an impaired mitochondrial function. The most relevant alterations shown in this study will help to better understand the insights of Long COVID syndrome by providing a deeper knowledge of the metabolomic basis of the pathology.

Source: Martínez S, Albóniga OE, López-Huertas MR, Gradillas A, Barbas C. Reinforcing the Evidence of Mitochondrial Dysfunction in Long COVID Patients Using a Multiplatform Mass Spectrometry-Based Metabolomics Approach. J Proteome Res. 2024 Apr 2. doi: 10.1021/acs.jproteome.3c00706. Epub ahead of print. PMID: 38566450. https://pubmed.ncbi.nlm.nih.gov/38566450/

Metabolomic and immune alterations in long COVID patients with chronic fatigue syndrome

Introduction: A group of SARS-CoV-2 infected individuals present lingering symptoms, defined as long COVID (LC), that may last months or years post the onset of acute disease. A portion of LC patients have symptoms similar to myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), which results in a substantial reduction in their quality of life. A better understanding of the pathophysiology of LC, in particular, ME/CFS is urgently needed.

Methods: We identified and studied metabolites and soluble biomarkers in plasma from LC individuals mainly exhibiting ME/CFS compared to age-sex-matched recovered individuals (R) without LC, acute COVID-19 patients (A), and to SARS-CoV-2 unexposed healthy individuals (HC).

Results: Through these analyses, we identified alterations in several metabolomic pathways in LC vs other groups. Plasma metabolomics analysis showed that LC differed from the R and HC groups. Of note, the R group also exhibited a different metabolomic profile than HC. Moreover, we observed a significant elevation in the plasma pro-inflammatory biomarkers (e.g. IL-1α, IL-6, TNF-α, Flt-1, and sCD14) but the reduction in ATP in LC patients. Our results demonstrate that LC patients exhibit persistent metabolomic abnormalities 12 months after the acute COVID-19 disease. Of note, such metabolomic alterations can be observed in the R group 12 months after the acute disease. Hence, the metabolomic recovery period for infected individuals with SARS-CoV-2 might be long-lasting. In particular, we found a significant reduction in sarcosine and serine concentrations in LC patients, which was inversely correlated with depression, anxiety, and cognitive dysfunction scores.

Conclusion: Our study findings provide a comprehensive metabolomic knowledge base and other soluble biomarkers for a better understanding of the pathophysiology of LC and suggests sarcosine and serine supplementations might have potential therapeutic implications in LC patients. Finally, our study reveals that LC disproportionally affects females more than males, as evidenced by nearly 70% of our LC patients being female.

Source: Saito Suguru, Shahbaz Shima, Luo Xian, Osman Mohammed, Redmond Desiree, Cohen Tervaert Jan Willem, Li Liang, Elahi Shokrollah. Metabolomic and immune alterations in long COVID patients with chronic fatigue syndrome. Frontiers in Immunology, Vol 15, 2024. DOI=10.3389/fimmu.2024.1341843  https://www.frontiersin.org/articles/10.3389/fimmu.2024.1341843/full (Full text)

In vitro B cell experiments explore the role of CD24, CD38 and energy metabolism in ME/CFS

Abstract:

Disturbances of energy metabolism contribute to clinical manifestations of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Previously we found that B cells from ME/CFS patients have increased expression of CD24, a modulator of many cellular functions including those of cell stress.

The relative ability of B cells from ME/CFS patients and healthy controls (HC) to respond to rapid changes in energy demand were compared. CD24, the ectonucleotidases CD39, CD73, the NAD-degrading enzyme CD38 and mitochondrial mass (MM) were measured following cross-linking of the B cell receptor (BCR) and co-stimulation with either T cell dependent or Toll-like receptor-9 dependent agonists. Levels of metabolites consumed/produced were measured using 1H-NMR spectroscopy and analysed in relation to cell growth and immunophenotype.

Proliferating B cells from patients with ME/CFS showed lower mitochondrial mass and a significantly increased usage of essential amino acids compared those from HC, with a significantly delayed loss of CD24 and increased expression of CD38 following stimulation. Immunophenotype results suggested the triggering of a stress response in ME/CFS B cells associated with increased usage of additional substrates to maintain necessary ATP levels. Disturbances in energy metabolism in ME/CFS B cells were thus confirmed in a dynamic in vitro model, providing the basis for further mechanistic investigations.

Source: Christopher Armstrong, Fane F. Mensah, Maria Leandro, Venkat Reddy, Paul R. Gooley, Saul Berkovitz, Geraldine Cambridge. In vitro B cell experiments explore the role of CD24, CD38 and energy metabolism in ME/CFS. Front. Immunol. Sec. B Cell Biology, Volume 14 – 2023 | doi: 10.3389/fimmu.2023.1178882 https://www.frontiersin.org/articles/10.3389/fimmu.2023.1178882/abstract

Integrated ‘omics analysis for the gut microbiota response to moxibustion in a rat model of chronic fatigue syndrome

Abstract:

Objective: To observe the efficacy of moxibustion in the treatment of chronic fatigue syndrome (CFS) and explore the effects on gut microbiota and metabolic profiles.

Methods: Forty-eight male Sprague-Dawley rats were randomly assigned to control group (Con), CFS model group (Mod, established by multiple chronic stress for 35 d), MoxA group (CFS model with moxibustion Shenque (CV8) and Guanyuan (CV4), 10 min/d, 28 d) and MoxB group (CFS model with moxibustion Zusanli (ST36), 10 min/d, 28 d).

Open-field test (OFT) and Morris-water-maze test (MWMT) were determined for assessment the CFS model and the therapeutic effects of moxibustion.16S rRNA gene sequencing analysis based gut microbiota integrated untargeted liquid chromatograph-mass spectrometer (LC-MS) based fecal metabolomics were executed, as well as Spearman correlation analysis, was utilized to uncover the functional relevance between the potential metabolites and gut microbiota.

Results: The results of our behavioral tests showed that moxibustion improved the performance of CFS rats in the OFT and the MWMT. Microbiome profiling analysis revealed that the gut microbiomes of CFS rats were less diverse with altered composition, including increases in pro-inflammatory species (such as Proteobacteria) and decreases in anti-inflammatory species (such as Bacteroides, Lactobacillus, Ruminococcus, and Prevotella). Moxibustion partially normalized these changes in the gut microbiota.

Furthermore, CFS was associated with metabolic disorders, which were effectively ameliorated by moxibustion. This was demonstrated by the normalization of 33 microbiota-related metabolites, including mannose (P = 0.001), aspartic acid (P = 0.009), alanine (P = 0.007), serine (P = 0.000), threonine (P = 0.027), methionine (P = 0.023), 5-hydroxytryptamine (P = 0.008), alpha-linolenic acid (P = 0.003), eicosapentaenoic acid (P = 0.006), hypoxanthine (P = 0.000), vitamin B6 (P = 0.000), cholic acid (P = 0.013), and taurocholate (P = 0.002).

Correlation analysis showed a significant association between the perturbed fecal microbiota and metabolite levels, with a notable negative relationship between LCA and Bacteroides.

Conclusions: In this study, we demonstrated that moxibustion has an antifatigue-like effect. The results from the 16S rRNA gene sequencing and metabolomics analysis suggest that the therapeutic effects of moxibustion on CFS are related to the regulation of gut microorganisms and their metabolites. The increase in Bacteroides and decrease in LCA may be key targets for the moxibustion treatment of CFS.

Source: Chaoran LI, Yan Y, Chuwen F, Heng LI, Yuanyuan QU, Yulin W, Delong W, Qingyong W, Jing G, Tianyu S, Xiaowei S, Xue W, Yunlong H, Zhongren S, Tiansong Y. Integrated ‘omics analysis for the gut microbiota response to moxibustion in a rat model of chronic fatigue syndrome. J Tradit Chin Med. 2023 Oct;43(6):1176-1189. doi: 10.19852/j.cnki.jtcm.20231018.004. PMID: 37946480; PMCID: PMC10623263. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623263/ (Full text)

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Abstract:

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power.

We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as “prior information” into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels.

Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients.

Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Source: Brydges C, Che X, Lipkin WI, Fiehn O. Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts. Metabolites. 2023 Aug 31;13(9):984. doi: 10.3390/metabo13090984. PMID: 37755264; PMCID: PMC10535181. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535181/ (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)