Can you clarify further? The title of the article is "Dr. Ian Lipkin and Dr. Mady Hornig, Use Deep Sequencing and Proteomics to Hunt CFS Viruses." If Dr. Lipkin's proteomics uses new technology, and the criteria are key here, then why are they not key to the complex types of biomarker studies (such as proteomics and genetics) that you were discussing?
Oh, that's interesting... I didn't know that Lipkin was doing proteomics for his pathogen study...
It sounds more complex and more thorough than I thought it was going to be. (I'm struggling to find any info about any of the ongoing studies.)
I guess Lipkin could throw up some very interesting results.
Lipkin might even be able to define sub-groups based on his findings, if he's doing a study that complex.
Ember, I'm not disagreeing with what Lipkin said...
Alex and I were exploring the subject theoretically, and talking about possibilities for the future.
I'll try and explain the angle that I was coming from...
I agree that the tightest diagnostic criteria will bring us the best results.
And I'm in favour of using Fukuda, CCC and ICC alongside each other, or even CCC and ICC without Fukuda.
Some of the researchers (Lipkin? Mikovits?) have also been selecting for sudden-onset patients, with viral-like onset, and some specific symptoms (I can't remember the details), and I think that's really helpful as well.
The more of this careful selecting of the most homogeneous group of patients, the better, as far as I'm concerned.
But we had been discussing the heterogeneous nature of 'CFS', and discussing whether we could be certain that even all 'ME' patients have exactly the same disease.
So then we went on to talk about biomarker research, and how it could be a game changer.
If a researcher was to design a complex enough proteomic or genetic study, with a large enough number of patients, then the huge amount of data that was amassed could be processed with IT.
And this could lead us to a deeper understanding about subgroups, or separate cohorts.
And it could separate patients into groups based on the data.
(And if all the data was checked against symptoms, diagnostic criteria, family history, health history, type of onset, etc, then there could be potential for learning a lot more than we already know.)
So, for example, if you had a single cohort of patients based on the CCC, but then when doing a genetic study, you found that this group of CCC patients could be subdived into three separate groups, based on the data, then what would this tell us?
It could tell us that there are three different diseases, or three different triggers for the same disease, or three different varieties of the same disease, or it could be the same disease, but with three different patient responses, or it could separate the groups for reasons that we currently have no insight into at all. (I'm talking hypothetically.)
We wouldn't know the answer straight away, but we could name each group CCC1 CCC2 and CCC3.
And then future research could investigate the response of each group separately, to treatments.
And each group could be investigated separately in all future research.
This could give huge benefits...
For example, if 80% of CCC1 patients responded to Rituximab, but only 10% of CCC2 and CCC3 responded, then this would mean that Rituximab could quickly be given to all ME patients who fitted the CCC1 cohort.
But if we were to lump all the (hypothetically new) CCC groups together then only 33% would have responded to Rituximab (in this imaginary situation), so obviously, it would be great to get the patients into separate groups based on the data.
Beyond that, if we were able to do a quick blood test to determine different subsets of CFS patients, then that could potentially supersede
all of the current diagnostic criteria completely, even potentially making them redundant. (Hypothetically.)
This sort of thing has actually already been going on. Jonathan Kerr suggested that he would be able to find successful therapeutic drugs based on his genetic findings. Because, if the genetics findings in a subset of his CFS patients were similar to those seen in a different illness, then the drugs for that different illness could be tested as a potential treatment for that subset of CFS patients. So in this instance, Jonathan Kerr was bi-passing some of the various diagnostic criteria, and sub-grouping CFS patietns purely based on his data. By sub-dividing CFS patients, based on sets of genetic data, appropriate treatments could be investigated for each of those cohorts, regardless of diagnostic criteria used.
But, yes, practically speaking, for the most helpful results, and for the most homogeneous patients samples possible, then like Lipkin says, patient selection is the key.