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Metabolome of chronic fatigue syndrome - Jos W. M. van der

Discussion in 'Latest ME/CFS Research' started by medfeb, Jan 27, 2017.

  1. medfeb

    medfeb Senior Member

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    From Tate Mitchell via Co-Cure. Is this already posted? And should this be somewhere else?

    http://www.pnas.org/content/early/2017/01/25/1618447114.extract

    Metabolome of chronic fatigue syndrome
    Megan E. Roerink, Ewald M. Bronkhorst, and Jos W. M. van der Meer

    Naviaux et al. (1) report on a distinct metabolic signature present in patients who have myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) compared with healthy controls. Metabolic pathway analysis is a growing field of interest, and could offer relevant pathophysiological or diagnostic clues in complex illnesses such as CFS. However, reviewing the patient selection and statistical methods used, we have some concerns.

    First, the largest difference in metabolites was caused by a decrease of plasma sphingo- and glyco- sphingolipids in patients who had CFS. Sphingolipids have a broad range of action, and, currently, it is not completely clear which external factors can influence plasma concentrations. However, we do know that physical activity and exercise play a role and, for example, can lead to an increase in sphingoid base-1 phosphates (2). This knowledge could be relevant in the current patient category because patients with CFS usually are less physically active than healthy controls (3), which is also reflected, to some extent, by the lower Karnofsky score in patients. Unfortunately, physical activity was not measured in the current study, and controls were not selected based on their activity levels. Therefore, the difference in metabolic signature is likely to be at least partially due to differences in physical activity, as opposed to being a patient with ME/CFS or not. Furthermore, it would be interesting to have additional information on lifestyle factors such as diet and use of medication. Patients who have CFS often use antidepressants and different groups of over-the-counter drugs, which could be influencing plasma metabolites (4, 5).

    Second, we have concerns regarding the statistical methods used. To start, the number of metabolites measured in this study is extremely high (n=612) compared with the number of included individuals (n=84). To generate a diagnostic model, the authors first use a nondescribed method to select 60 variables with a large discriminative value. Next, groups of five to 15 metabolites were selected and entered as candidate diagnostic classifiers. The performance of this model was tested on the same dataset that was used to design it, and yielded an area under the curve of 94% for males and 96% for females. This method is by no means reliable. Using this approach, selecting 60 of 612 predictors guarantees a very high level of apparent predictive performance, even if all predictors were just random variables. The set of metabolites should be tested in an independent cohort or at least be corrected for optimism that covers the entire selection of predictors, from the original 612 to the final set using internal validation methods. Only then proper insight into its diagnostic value for predicting CFS can be obtained.

    1 Naviaux RK, et al. (2016) Metabolic features of chronic fatiguesyndrome. Proc Natl Acad Sci USA 113(37):E5472–E5480.
    2 Baranowski M, Charmas M, Długołecka B, Gorski J (2011) Exercise increases plasma levels of sphingoid base-1 phosphates in humans. Acta Physiol (Oxf) 203(3):373–380.
    3 Vercoulen JH, et al. (1997) Physical activity in chronic fatigue syndrome: Assessment and its role in fatigue. J Psychiatr Res 31(6):661–673.
    4 Lewith G, Stuart B, Chalder T, McDermott C, White PD (2016) Complementary and alternative healthcare use by participants in the PACE trial of treatments for chronic fatigue syndrome. J Psychosom Res
    87:37–42.
    5 Boneva RS, Lin JM, Maloney EM, Jones JF, Reeves WC (2009) Use of medications by people with chronic fatigue syndrome and healthy persons: A population-based study of fatiguing illness in Georgia. Health Qual Life Outcomes 7:67.
     
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  2. hixxy

    hixxy Senior Member

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    Australia
    It was posted here somewhere but I can't for the life of me figure out where. I remember a discussion about it.
     
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  3. Kati

    Kati Patient in training

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    medfeb, Valentijn, Gemini and 5 others like this.
  4. hixxy

    hixxy Senior Member

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    Esther12 likes this.
  5. Solstice

    Solstice Senior Member

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    Funny how almost noone gave it a second look on that thread. So accustomed to the bullshit spewed they just ignore it.

    http://sci-hub.cc/doi/10.1073/pnas.1618984114

    You could say v.d. Meer et al got schooled in Naviaux' reply though.
     
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  6. J.G

    J.G

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    Yeah. I find the letter by Roerink a feeble attempt at methodological criticism to begin with and the Naviaux response totally blasts their points out of the water. Multiple lines from the Naviaux response are worth picking up on, but these pack the greatest punch:
    Ouch. That's the scientific equivalent of a "sick burn". Did the Roerink group not consult the supplemental materials in the first place? Or is the gap in scientific understanding so wide that they just couldn't wrap their heads around the methodology? Either way, Roerink et al don't come off well. If you're gonna go through the trouble of formally critiquing someone else's research, at least get your facts straight...
     
    Last edited: Jan 28, 2017
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  7. Valentijn

    Valentijn WE ARE KINA

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    Quite amusing to see the quacks complain about statistics, given the shenanigans they frequently pull. A recent Dutch paper, for example, presumed that the drop-outs who received no treatment at all would have performed as well as other patients with similar characteristics. The untreated were thereby counted as successes, and used to inflate the numbers to achieve statistical significance.
     
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  8. Kati

    Kati Patient in training

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    My apologies: the response I added was added on the wrng thread, Fluge and Mella's paper, when it should have been added to the Naviaux paper. I added the correspondance links to the Naviaux et al Thread.

    If you are confused, don't worry about it. i am too :bang-head: :thumbdown: And my brain hurts.
     
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  9. Solstice

    Solstice Senior Member

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    That's fogged up!
     
  10. Gijs

    Gijs Senior Member

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    I wish they were just as critical to the Pace trial :)
     
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  11. Gijs

    Gijs Senior Member

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    Reply to Roerink et al.: Metabolomics of chronic fatigue syndrome
    -----------------------------------------------------------------
    Robert K. Naviaux(a,b,c,d,*) and Eric Gordon(e)
    a The Mitochondrial and Metabolic Disease Center, University of
    California, San Diego School of Medicine, San Diego, CA 92103-8467;
    b Department of Medicine, University of California, San Diego School
    of Medicine, San Diego, CA 92103-8467;
    c Department of Pediatrics, University of California, San Diego
    School of Medicine, San Diego, CA 92103-8467;
    d Department of Pathology, University of California, San Diego
    School of Medicine, San Diego, CA 92103-8467;
    e Gordon Medical Associates, Santa Rosa, CA 95403
    * Correspomding author. Email: rnaviaux@ucsd.edu


    We thank Roerink et al. (1) for their comments. We respond to their two
    points in order. Their first point asked about the effect of physical
    activity on sphingolipids. The sphingolipid response to exercise is
    complex. It differs in healthy trained and untrained individuals and has
    not yet been studied using methods that can distinguish the classes of
    sphingosines, ceramides, sphingomyelins, and glycosphingolipidsmeasured
    in our analysis of myalgic encephalomyelitis (ME)/chronic fatigue
    syndrome (CFS) (2). Our study shows specifically that ceramides were
    decreased in ME/CFS (2). However, the small study (n=10 per group) by
    Baranowskietal.(3) that was cited by Roerink et al. (1) showed that
    ceramides were the same in trained and untrained subjects and did not
    change with acute exercise (figure 1 of ref. 3). All subjects in our
    first study of ME/CFS (2) were ambulatory. We did not specifically
    control for physical activity because the capacity for physical activity
    is a fundamental difference between patients with ME/CFS and controls.
    We wished to distinguish 'pathological fatigue' that is chronic and
    prevents activity at baseline in patients with ME/CFS from
    'physiological fatigue' that is transient and caused by activity in
    non-CFS subjects and is not present at baseline. Others have also
    focused on this distinction (4). Metabolomic analysis showed that ME/CFS
    is characterized by differences in 20 different metabolic pathways
    (figure 1E of ref. 2), and not just sphingolipids. With regard to the
    broader metabolic effects of exercise, a recent large metabolomics study
    (n=277) found that only 11 of 591 metabolites were significantly
    correlated with physical activity in healthy subjects (5). Increased
    physical activity was associated with a decrease in isoleucine, valine,
    and glucose, whereas sedentary behavior produced an increase (5). In
    ME/CFS, isoleucine, valine, and glucose were not changed (2). The
    absence of metabolic abnormalities in ME/CFS known to be associated with
    decreased physical activity is strong evidence that physical activity
    alone cannot explain the distinct pattern of metabolic abnormalities
    found in ME/CFS (2).

    Second, Roerink et al. (1) raise questions about the standard
    statistical methods used in metabolomics studies. We used multivariate
    analysis by partial least squares discriminate analysis (PLSDA), area
    under the receiver operator characteristic (AUROC) curve analysis,
    random forest methods for biomarker discovery, repeated double
    cross-validation, and permutation analysis. A convenient implementation
    of these methods is available at www.metaboanalyst.ca, and nice reviews
    are available (6). Roerink et al. (1) incorrectly state that the method
    for selecting the top 60 metabolites was 'nondescribed' in our paper
    (2). We state that we used PLSDA in both the Results and Materials and
    Methods sections. Roerink et al. (1) then incorrectly implied that we
    used 60 metabolites in the AUROC analysis. We did not. We used eight
    metabolites in males and 13 metabolites in females (figure 2 and table 4
    of ref. 2). Ultimately, the best way to confirm the scientific accuracy
    of our metabolic findings in ME/CFS is to repeat the study using fresh
    samples collected from an independent cohort of patients and controls.
    This study is currently underway.

    1. Roerink ME, Bronkhorst EM, van der Meer JWM (2016) Metabolome of
    chronic fatigue syndrome. Proc Natl Acad Sci USA,
    10.1073/pnas.1618447114.
    2 Naviaux RK, et al. (2016) Metabolic features of chronic fatigue
    syndrome. Proc Natl Acad Sci USA 113(37):E5472-E5480.
    3 Baranowski M, Charmas M, Długołecka B, Gorski J (2011) Exercise
    increases plasma levels of sphingoid base-1 phosphates in humans. Acta
    Physiol (Oxf) 203(3):373-380.

    --------
    (c) 2017 National Academy of Sciences of the United States of America

    http://www.pnas.org/content/early/2017/01/25/1618984114.extract
     

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