A systematic review of metabolomic dysregulation in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis/ (CFS/ME/SEID):Huth,Staines et al May 2020

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A systematic review of metabolomic dysregulation in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis/Systemic Exertion Intolerance Disease (CFS/ME/SEID)
  • Teilah Kathryn Huth,
  • Natalie Eaton-Fitch,
  • Donald Staines &
  • Sonya Marshall-Gradisnik


    Abstract
    Background

    Chronic Fatigue Syndrome/Myalgic Encephalomyelitis/Systemic Exertion Intolerance Disease (CFS/ME/SEID) is a complex illness that has an unknown aetiology. It has been proposed that metabolomics may contribute to the illness pathogenesis of CFS/ME/SEID. In metabolomics, the systematic identification of measurable changes in small molecule metabolite products have been identified in cases of both monogenic and heterogenic diseases. Therefore, the aim of this systematic review was to evaluate if there is any evidence of metabolomics contributing to the pathogenesis of CFS/ME/SEID.

    Methods
    PubMed, Scopus, EBSCOHost (Medline) and EMBASE were searched using medical subject headings terms for Chronic Fatigue Syndrome, metabolomics and metabolome to source papers published from 1994 to 2020. Inclusion and exclusion criteria were used to identify studies reporting on metabolites measured in blood and urine samples from CFS/ME/SEID patients compared with healthy controls. The Joanna Briggs Institute Checklist was used to complete a quality assessment for all the studies included in this review.

    Results
    11 observational case control studies met the inclusion criteria for this review. The primary outcome of metabolite measurement in blood samples of CFS/ME/SEID patients was reported in ten studies. The secondary outcome of urine metabolites was measured in three of the included studies. No studies were excluded from this review based on a low-quality assessment score, however there was inconsistency in the scientific research design of the included studies. Metabolites associated with the amino acid pathway were the most commonly impaired with significant results in seven out of the 10 studies. However, no specific metabolite was consistently impaired across all of the studies. Urine metabolite results were also inconsistent.

    Conclusion
    The findings of this systematic review reports that a lack of consistency with scientific research design provides little evidence for metabolomics to be clearly defined as a contributing factor to the pathogenesis of CFS/ME/SEID. Further research using the same CFS/ME/SEID diagnostic criteria, metabolite analysis method and control of the confounding factors that influence metabolite levels are required.
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    https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02356-2
 
But I thought NCNED had solved it all with their gold standard tests proving it's all to do with calcium?? [/end needless snark]

Things I found of note (my bolding).
Screening, inclusion and exclusion criteria

This systematic review was conducted to evaluate primary research that compared metabolite levels in biological samples (blood and urine) from CFS/ME/SEID patients with healthy controls (HC). All of the results from the literature search were imported into EndNote for storage and screening. Duplicate copies of papers were removed, and the following criteria were used by the authors to screen titles and abstracts: (i) all primary research comparing metabolite levels in blood or urine samples between CFS/ME/SEID patients and HC; (ii) CFS/ME/SEID diagnosis according to Fukuda, CCC or ICC; (iii) use of appropriate statistics to compare CFS/ME/SEID patients and HC cohorts; (iv) studies published between 1994 and 2019 to exclude non-Fukuda definitions prior to 1994; (v) human studies in adults aged 19 years and above; (vi) journal articles published in English; and (vii) free and paid full text publications of original research to exclude publication bias.
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Interesting to see ICC authors happy to use other criteria - of the eleven included papers, four used Fukuda only, and two used Fukuda and Oxford, and none of those mention PEM as being a requirement. I can understand why, no included paper used ICC.

Across the 11 studies included in this review, four different diagnostic criteria including 1994 Fukuda, CCC, Oxford and IOM were used to diagnose CFS/ME/SEID patients. The use of the different criteria in this review makes it inherently difficult to draw reliable conclusions from the results as each definition varies in the selection criteria for the symptoms, illness onset and duration of fatigue suffered [31]. The 1994 Fukuda definition was used in eight out of the ten studies. It has been argued that the 1994 Fukuda definition is limited by specificity due to its broad and non-specific criteria, which causes an inconsistent identification of CFS/ME/SEID cases for research purposes [31]. The IOM definition was used in one study as it was designed to allow for a broader clinical criteria to identify CFS/ME/SEID patients. However, the IOM is limited as it only has a few exclusionary conditions [32]. It has also been reported that when results for the IOM are compared to the 1994 Fukuda, the IOM criteria results in a higher prevalence rate and a classification system that is more heterogeneous [30, 32].
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This bolded line is inaccurate. The first reference is to a paper in which I can see no discussion of "a higher prevalence rate and a classification system that is more heterogeneous" and the second reference is to the Lenny Jason et al paper that uses a woeful description of PEM and then only applies it to the IOM, not the other selection criteria he discusses.

In regard to ICC (and CCC).
It has been recommended that the revised criteria including the CCC or the ICC be used for research purposes. These two definitions allow for a more consistent identification of CFS/ME/SEID patients as they employ a more stringent set of criteria and consider the multisystemic nature of symptoms experienced by patients. For example, the ICC included revision of CFS/ME/SEID symptoms into categories including (a) neurological impairments, (b) immune, gastro-intestinal or genito-urinary impairment and (c) energy metabolism or transport impairments [7]. In addition, the ICC allows for the categorisation of CFS/ME/SEID patients according to symptom severity [7]. As no studies in this review used the ICC, no data were collected on the symptom severity of CFS/ME/SEID patients. This is a potential recruitment bias as the other definitions favour CFS/ME/SEID patients who can self-present to clinics, hospitals and universities for sample collection, thus excluding bed- or house- bound CFS/ME/SEID patients from participating. This sampling bias is likely to confound results as it limits CFS/ME/SEID participant selection to a mild or moderate illness severity.
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Quite frankly, I don't understand their reasoning - it seems to make no sense to me.
 
This seems a pretty weak paper. Why didn't they make a stronger recommendation on what to do moving forward. Their recommendations seem a bit wishy washy. I don't have the ability to read the paper in detail in full so I picked a few points. Here's one where they say
From the 11 studies included in this review, two studies used the same Nuclear Magnetic Resonance Spectrometry analysis method and two studies used the same Metabolon Mass Spectrometry method
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Sorry, but Hanson particularly states here the two Metabolon studies were not the same
Comprehensive Circulatory Metabolomics in ME/CFS Reveals Disrupted Metabolism of Acyl Lipids and Steroids
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7023305/
3.6. Reproducibility

In our previous study, blood processing was done differently, with blood tubes shipped overnight from a collecting location in New York City, compared to more rapid processing for the samples in this study [20]. Keeping live cells in a self-contained environment for an average delay of 24 h must have affected the amounts of some metabolites, potentially explaining differences in some metabolites between studies. Cells incubated overnight in plasma likely release into plasma, or absorb from plasma, a variety of metabolites. Our current study is more representative of the metabolites circulating in blood of ME/CFS subjects and controls. Results between our two studies can also not be precisely compared because not all the same metabolites were detected in both analyses achieved by Metabolon®. For instance, 458 metabolites with an HMDB ID are common between studies, with 25% of the data from Germain and colleagues [20] not present in this dataset, and conversely, 13% of the data in this dataset not present in Germain and colleagues [20].
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I think this field is still in it's infancy, and few standards exist that allow comparison across studies. Processing and handling of blood have a huge effect on some metabolites as Karl Morten has explained in one of his studies, and Hanson et al above. Fasting vs Non fasting, time of day, also have an effect. Until these are controlled across studies, and common standards are used, it will be hard to compare results. CLIA certified labs are starting to come on line which should help.

I think Karl Morten analysed 30,000+ metabolites in the mass spectrometer at Oxford, the largest number by far to date, but only a very low percentage of them are known metabolites. And when he took a second cohort collection from the UK ME/CFS biobank results were not repeatable with the polish cohort.

For those interested in metabolomics repeatability @Andy posted a video by Oliver Feihn in 2019 on this topic
https://www.s4me.info/threads/cente...itys-nih-funded-center.191/page-2#post-193193

This is an interesting paper on some of the challenges facing biomarker discovery using metabolomics. I thought including some of the detailed information such as in this 2018 paper would have made for better reading on why the ME/CFS studies have differences.
A Framework for Development of Useful Metabolomic Biomarkers and Their Effective Knowledge Translation
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6316283/
Abstract
Despite the significant advantages of metabolomic biomarkers, no diagnostic tests based on metabolomics have been introduced to clinical use. There are many reasons for this, centered around substantial obstacles in developing clinically useful metabolomic biomarkers. Most significant is the need for interdisciplinary teams with expertise in metabolomics, analysis of complex clinical and metabolomic data, and clinical care. Importantly, the clinical need must precede biomarker discovery, and the experimental design for discovery and validation must reflect the purpose of the biomarker. Standard operating procedures for procuring and handling samples must be developed from the beginning, to ensure experimental integrity. Assay design is another challenge, as there is not much precedent informing this. Another obstacle is that it is not yet clear how to protect any intellectual property related to metabolomic biomarkers. Viewing a metabolomic biomarker as a natural phenomenon would inhibit patent protection and potentially stifle commercial interest. However, demonstrating that a metabolomic biomarker is actually a derivative of a natural phenomenon that requires innovation would enhance investment in this field. Finally, effective knowledge translation strategies must be implemented, which will require engagement with end users (clinicians and lab physicians), patient advocate groups, policy makers, and payer organizations. Addressing each of these issues comprises the framework for introducing a metabolomic biomarker to practice.
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I wonder why this group produced 2 reviews (neuroimaging and metabolomics) in such a short period. Possibly a way to keep publishing during COVID?
 
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