Open Medicine Foundation (OMF)

[Hutan]

Hmm, on the idea that 'co-morbidities are central in ME/CFS'. Of course it depends on what you define as a comorbidity, but asthma was mentioned, and I don't think we have seen good evidence for that being central in ME/CFS. Similarly for depression. I think the doctors mentioned who believe that co-morbidities are central probably don't understand ME/CFS very well.

I do like Katherine's approach of taking people reporting IBS in the UK Biobank and comparing various biological measures between e.g. people reporting IBS and ME/CFS in the UK Biobank. That does seem like a good way to identify some potentially important differences. But, to me, that approach is the exact opposite of assuming that co-morbidities are central - it's a way of teasing out the noise in the symptoms that people present with.

The PEM project (predicting PEM from data from wearables and biometric data) sounds good.

 

[Peter T]

Hmm, on the idea that 'co-morbidities are central in ME/CFS'. Of course it depends on what you define as a comorbidity

In relation to ME/CFS can we always say what is a co-morbidity and what a symptom of the ME/CFS?

Now I am not working and in control of my diet, I only experience migraines type headaches when in PEM, so do I suffer from migraines as a condition in itself or are my headaches part and parcel with PEM, so a symptom of the ME/CFS itself.

 

[forestglip]

Statistics 101 from OMF Science Wednesdays:

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Statistics is considered a mathematical way to describe data and test hypotheses. Some of the fundamental components of statistics include p-values, regressions, and confidence intervals.

What is a p-value?
In the world of research and statistics, the word “significant” has a specific meaning. In general, a result is considered significant if there is a low likelihood that it can be explained by random chance and is therefore due to what is being tested. Calculating a p-value when analyzing a set of data is a way of determining if the results are significant.

In order for a p-value to have meaning, part of the study design process should include setting a significance level. This is typically a value around 0.05, and a p-value lower than the significance level indicates that the results are significant.

What is a regression analysis?
A regression analysis is a way of analyzing the relationship between a dependent variable and at least one independent variable. In other words, a regression analysis tries to predict one parameter (the dependent variable) based on another parameter (the independent variable).

Taking a regression analysis one step further, statistics can also describe the strength of the relationship between the variables through a correlation coefficient. Correlation coefficients range from -1 to 1. A coefficient of -1 would mean the variables are perfectly negatively correlated—when the independent variable goes up by a unit, the dependent variable goes down by a unit. A perfect positive correlation—when the coefficient equals 1—would mean both the independent and dependent variable move in the same direction, and a coefficient of 0 indicates there’s no relationship between the variables.

What is a confidence interval?
A confidence interval is the range of values in which a parameter is likely to fall if you repeated the test. For example, for a 95% confidence interval, you would be 95% confident that a retest would produce a value that falls within the specified range. This interval can therefore be a way of describing the data, including how variable—or how spread out—it is.

What does this mean for evaluating and understanding research results?
Different statistical tests are needed for different types of research, and there are many ways to introduce bias to statistics, making proper analysis complicated. To understand whether a research study has used the proper statistical tests, one general guideline is as follows: the number of groups being compared has large implications for which test is appropriate. For example, a t-test should only be used when comparing the averages of two groups, but comparing three or more groups requires a different test like an analysis of variance (ANOVA). There are many types of statistical tests and nuances that go into this, but this guideline can at least serve as a starting point.

In the ME/CFS and Long COVID research world, data analysis is further complicated by the concept of multiple testing. Testing multiple hypotheses at a time or looking at subsets can introduce bias to the statistics. Therefore, research studies doing multiple testing should either use a stricter significance level—lower than the typical 0.05—or incorporate post-hoc corrections for their p-values (e.g., Bonferroni correction).

OMF’s Computational Research Center for Complex Diseases, directed by Dr. Wenzhong Xiao, has extensive expertise in statistics, especially in the context of biomedical research. Having this expertise within the OMF collaborative research model contributes to the scientific rigor of OMF’s research portfolio. Read more about the computation center on our website.

 

[Utsikt]

What is a p-value?
In the world of research and statistics, the word “significant” has a specific meaning. In general, a result is considered significant if there is a low likelihood that it can be explained by random chance and is therefore due to what is being tested.

Is the last part accurate? Could it not be due to variables they have not been able to control? I also think explaining corrections for multiple testing might also be warranted here, because a low p-value is bound to show up randomly if you test enough things.

 

[jnmaciuch]

Is the last part accurate? Could it not be due to variables they have not been able to control?

I think a more accurate way to say it is that a p-value below the cutoff lets you reject the null hypothesis that there is no (population-wide, i.e. not due to random sampling) difference between the two groups you are testing with respect to your outcome variable. But you’d be right that this doesn’t eliminate the possibility of confounders driving that between-group difference

 

[forestglip]

Dr. Armstrong and his team at OMF’s Melbourne ME/CFS Collaboration are investigating the link between neuroinflammation, cerebral blood flow, and dysregulated hormones in ME/CFS, POTS, and Long COVID.

To hear more about the study from Dr. Chris Armstrong, Dr. Xiaoyun Wang and Jamie Elliott, check out this interview.

 

[Hutan]

14 minute video
Both of the young researchers have family members with ME/CFS and clearly have a genuine interest in contributing to the understanding of the illness.

Malopus project on brain function in ME/CFS and LC. They will be using high-resolution MRI scanning and PET-scan tracers. They need 26 female participants in each group (presumably 3 groups) - they already have 40 participants and welcome more. They will look for neuroinflammation and at functional states. It sounds as though they will be sampling for peripheral hormones and recording symptoms.

They hope to provide some definitive results from a good sized study - replicating lots of small studies, and enabling linking of different sorts of results.

9 mins Xiaoyun - aims to compare hypothalamus metabolites in patient and control groups before and after exertion - and relate differences to neuroinflammation, blood hormones and symptoms. She hypothesises that glutaminergic signalling is involved. It sounds to be very much the HPA axis focussed, a lot of talk about endocrine issues. I get the impression that Xiaoyun thinks there are hormonal abnormalities - no doubt cortisol. I'm rather skeptical about that, but it sounds like an interesting study that should give some clear answers. There is no detail as to what neuroinflammation means.

11.30 mins Jamie - functional MRI. How blood flow is regulated and specific nerve activity under various tasks and conditions i.e. decision-making exertion.


Thanks OMF and Chris @MelbME. It's great to see two committed young researchers and best wishes to them. Some questions are:
* the PET tracers - can we know now exactly will they be measuring (or is that confidential for now), and so get a better sense of what is meant by neuroinflammation?
* have the issues around the idea of low cortisol been taken on board? e.g. the problems with the literature, and the impact of lifestyle on some measures of cortisol e.g. the timing of the daily peak?
* is the only exertion in this study cognitive exertion?

 

[forestglip]

* the PET tracers - can we know now exactly will they be measuring (or is that confidential for now), and so get a better sense of what is meant by neuroinflammation?
* have the issues around the idea of low cortisol been taken on board? e.g. the problems with the literature, and the impact of lifestyle on some measures of cortisol e.g. the timing of the daily peak?
* is the only exertion in this study cognitive exertion?

I haven't watched the video, but your summary sounds a lot like the things discussed in a previous video that I summarized in an earlier post. Bolded the parts that relate to a couple of your questions:

Danielle Meadows interviews Chris Armstrong about an ongoing study using neuroimaging in long COVID, ME/CFS, and POTS to look at cerebral blood flow and neuroinflammation. Also they want to study involvement of the HPA axis, which includes the hypothalamus that produces a variety of hormones, and these hormones have had some evidence of being dysregulated in ME/CFS and LC. They want to see if altered blood flow or neuroinflammation is affecting the hypothalamus, causing these hormone issues.

They reference an ongoing study on neuroinflammation by Jonas Bergquist. Bergquist's study is looking at microglia neuroinflammation using a marker called TSPO. Conversely, Armstrong's study is looking at a tracer called SMBT-1, which would show inflammation on astroglia (AKA astrocytes). This type of inflammation has never been studied in ME/CFS or LC.

Armstrong is also using MRS (magnetic resonance spectroscopy) to look at levels of neurotransmitters, such as glutamate, around the hypothalamus, which may be causing dysregulation in this region. They will also measure peripheral hormone levels, to see if they relate to these neurotransmitter levels.

Also tracking a tracer of blood to measure blood flow through the brain.

The driving force behind this project is one of Armstrong's PhD students, Ellen (sp?), who has developed a hypothesis about glutamate receptors being altered in the brain, and possibly playing some role in brain dysregulation. Otherwise, they also just want to find links between neuroinflammation, blood flow, and hormones.

Study will also have participants do hand grip exercises while in the scanner to see if there is some alteration in blood flow due to exertion. Also they will look at blood before and after this minor exertion, in case there are any meaningful differences.

Currently awaiting ethics approval, but plan to provide details on enrollment through OMF and StudyME. [So sign up if you want to be notified about enrollment in this or other studies!]

 

[Hutan]

Perfect, thanks for that information @forestglip