Small correction that one group, hypertension, had a higher median BMI.The ME/CFS group has the highest median BMI of all the groups
Small correction that one group, hypertension, had a higher median BMI.The ME/CFS group has the highest median BMI of all the groups
Gosh this thread has been busy. I haven't caught up with it all yet.No I know. Just saying you get a lot of conflicting information on this topic. Which comorbidities would you allow?
We selected most common comorbidities in the UK Biobank cohort of ME/CFS.
Another recent study did analyse the circulating metabolome in relation to clinical outcomes and found that accumulated lipids, particularly the triglyceride-to-hosphatidylcholines ratio or total fatty acid were predictive of ME/CFS with biomarker potential (18). This agrees with the report of elevated circulating triglycerides (4) and indicates that lipid handling abnormalities may contribute to the clinical syndrome.
A very recent study by members of our team found that accumulated lipids, particularly the triglyceride-to-phosphatidylcholine ratio or total fatty acid were predictive of ME/CFS which lends confidence to our interpretation (18).
There was a study that looked at correlations between ME/CFS and all sorts of health conditions in the UK Biobank data. There is a thread on it somewhere here. I remember poking around in the data, but I think I felt in the end that the ME/CFS labelling was probably too noisy to tell us much. I don't think many correlations that we might have expected, things like allergies and asthma, showed up,Maybe there's some genetic predisposition overlap for conditions related to sensitivities and allergies like hay fever, asthma, IBS and migraine that also predispose to ME/CFS. I don't think DecodeME indicated any overlap with such conditions though.
I think something can only be considered part of the ME/CFS syndrome if it started at the same time as the ME/CFS and the severity/occurrence fluctuates in severity in some way in parallel with the core ME/CFS symptoms.
Given this goal, the results as presented are only suggestive rather than compelling, and there are a number of challenges that the authors have not fully addressed in the design of the study:
1. The UK Biobank population is self-reported from patients' memory of a previous condition, often at a distance of many years. The ME/CFS patients are coming from a pain questionnaire and a verbal interview cohort, and are believed to have high rates of misdiagnosis. Given the challenge of accurate diagnosis, the risk of overfitting models to a single dataset, and lack of understanding of metabolomic variability over an extended period, replication in a totally disjoint ME/CFS cohort really should be performed.
Reviewer 2 doesn't seem very impressed. They say a study on ME/CFS would be welcome 'if it has been properly structured in the statistical field'. They note that there should have been comparisons with patients with comorbid diseases and not with healthy populations, and that there is 'an error in the discriminant analysis in this regard'. So, they seem to be saying that there was an error in the choice of comparators, which is what I am saying.Are they novel and will they be of interest to others in the community and the wider field?
The use of biological databanks is an important resource to learn more about ME/CFS. Any study in this field is to be welcomed if it has been properly structured in the statistical field.
Is the work convincing, and if not, what further evidence would be required to strengthen the conclusions?
Considering the difficulties of the etiology of the disease, an appropriate initial analysis is complex. It would be appreciated to introduce the initial hypotheses and the process that has led to perform the statistics in this way. It remains to be concluded whether this is the best method. There are important gaps in the conclusions, actually ME/CFS patients have comorbid diseases and some results should be compared with patients with such diseases and not with healthy populations. There is an error in the discriminant analysis in this regard.
On a more subjective note, do you feel that the paper will influence thinking in the field?
It should be made clear that we have not compared the analyses, for example, of VLDL with respect to the population with hypercholesterolemia. There is no justification for this data to infer the disease.
I think we did do what Reviewer #2 was asking so perhaps this clarification will help: ME/CFS patients have co-morbid diseases but not all the same comorbid disease, this means that any cohort of ME/CFS patients has high heterogeneity due to individuals in that cohort having various different co-morbid diseases. The co-morbid diseases may have conflicting impacts on biomarker data that cancel them out. We conducted our analysis in a way that compares ME/CFS to the common comorbidities to highlight what may stand out in ME/CFS alone. An alternate path was taken by NIH recently, they whittled down a group of ~500 ME/CFS to 17 patients that did not have any co-morbidity in an attempt to identify a signature of purely ME/CFS. We’ve taken the path of keeping the large patient volume and instead of just comparing to healthy, we have compared to co-morbid diseases and the general population
We don’t refer to hypercholesterolemia in the manuscript, we aren’t attempting to infer that people have disease from the biomarker data either. We are trying to infer what biomarkers may be representing ME/CFS as distinct from commonly experienced co-morbidities of ME/CFS.
There is an obvious contribution to the field of knowledge about this serious disease, mainly with regard to the lipoprotein profile difference from controls and in my view a very important contribution about the influence from comorbidities which complicates diagnosis via specific biomarkers up till now. The final application of the algorithm and machine learning in achieving an improved rate of diagnosis emphasize the importance of this work.
Why do you find Supplementary Figure 7 reassuring?The sensitivity analysis and supplementary figure 7 are reassuring.
What does that even mean 'we created another cohort with 354 ME/CFS individuals with or without hypertension, depression, asthma, IBS, hay fever, hypothyroidism or migraine'? It's not clear how that cohort differs from the full cohort where all the individuals presumably also are 'with or without' the 7 conditions. Because 'with or without' is pretty all encompassing.To thoroughly investigate the impact of comorbid conditions in ME/CFS requires stratifying the cohort into groups of isolated condition combinations, which can substantially reduce the sample size and the statistical power. For example, there were 211 ME/CFS individuals with a combination of depression and other comorbid conditions, and 24 individuals with depression only. We recognise that the other 265 comorbid conditions not analysed in this study may influence the biomarker associations. Therefore, we created another cohort with 354 ME/CFS individuals with or without hypertension, depression, asthma, IBS, hay fever, hypothyroidism, or migraine and performed association tests (Supplementary Fig. 7) and sensitivity analysis for this subset (Supplementary Data 9).
Maybe there's some genetic predisposition overlap for conditions related to sensitivities and allergies like hay fever, asthma, IBS and migraine that also predispose to ME/CFS. I don't think DecodeME indicated any overlap with such conditions though.
I think something can only be considered part of the ME/CFS syndrome if it started at the same time as the ME/CFS and the severity/occurrence fluctuates in severity in some way in parallel with the core ME/CFS symptoms. So for example if someone's PEM includes IBS flare ups or migraine headaches, that might indicate it's part of their ME/CFS, or at least closely linked with it. On the other hand if someone has had asthma since childhood, and their ME/CFS starts after an infection 20 years later, the asthma isn't part of their ME/CFS, it's a coincidental comorbidity.