Most of the content was originally posted on https://cfsremission.com/ with the pages on the left being a restructuring of selected posts from over a thousand posts on that site.
Recommended Site For Testing
If you have ME/CFS or other financially disastrous condition, there is always a nasty cost factor for testing. My usual recommendation is for the cheapest, high quality provider that provides information for upload to my analysis site. Some sites provide a mountain more of information — but the benefit from that extra information is almost nothing (and it adds $$$$ and complexity).
uBiome.com is shutting down. This had been my personal usual site because using a variety of techniques, the cost was $25/sample. Don’t order from there.
BiomeSight.com (EU based) is an excellent buy using our discount code [MICRO]. They have also automated data transfer to our analysis site.
Thryve is what I am starting to use. Their reports may be processed here for independent suggestions. I would also recommend
A reader sent me the message below and gave permission to use his sample. I had, about a year ago, wrote The taxonomy nightmare before Christmas… that looks at the differences between lab results using the sample sample (as represented by a FASTQ digital file). We now try one more variation.
Last september I did (again) test my microbiome with Thryve. Because I had some general doubts about the validity of stool samples, I ordered two tests and took two different samples of the same stool and send them in under two different names. …the results confirmed my doubts as I got different bacteria levels of the ten strains Thryve shows in their overview.
% sample 1
% sample 2
So I do not doubt the reliability of each sample, but see that the validity of the sample is the problem. The results of a sample seem to be more or less random and not representative of the microbiome in general. …so I think that any advice given, based on the results of one sample is arbitrary. If we are to take the importance of the microbiome seriously, we will have to consider a new way of getting a representative sample to have a solid base for interventions concerning our health.
The typical sample seems to contain a round a 100,000 bacteria and is usually reported out of a million (scaled up). “Bacteria in faeces have been extensively studied. It’s estimated there are nearly 100 billion bacteria per gram of wet stool. ” [src] The sample that you sent it was likely no more than one milligram.
To use the “if I was a Martian” model… It is like a spaceship abducting a boatload of people in the Mediterranean…. If the boat is a cruise ship full of fat diabetic elderly Americans you will get one result. If the boat are full of starving Nigerians children trying to become refugees in Europe, a very different result. That is a disturbing concept when you mind is fixed on a deterministic precise definitive result. It’s a sample folks! For most industrial processes, dozens (or hundreds) of samples are required to get quality assurance. For the nerds, some readings:  [Wikipedia]
Example: Two employees working for the same company at the same job earning the same amount and living in the same community. You stop each of them and take a sample of how much money they have in their wallet. Would you expect them to have the same amount? Would they have the same number of pennies? dimes? quarters? Credit Cards?
I would expect differences in samples to increase as you move down the rank. It is similar to asking at one level [European, African, Asian] on the abducted ship above. At the next level [Swede, Dane, Italian, etc] , the counts between sample will diverge as you do more detail classification.
This is an illustration on why I do fuzzy logic on predicting symptoms with good success according to readers. Using studies from PubMed have been reported to produce poor results according to readers.
When the two samples are used to predict symptoms, we have a strong convergence. While the actors may be different, their impact are similar.
Adjusting for Natural Variation
Using counts without context is a good way to get upset without justification. I use percentiles to provide context and have a comparison page (which I need to revise). At the phylum level we see general agreement between the samples. One rare phylum was lacking in one sample (not found in 30% of Thryve Samples but only 6% of BiomeSight – hint: download the FASTQ files and process them thru BiomeSight [for free!]).
Medical Condition Matches
Going over to Pub Med Medical condition matches, we see a striking similarity between the samples as shown below. So for detecting medical conditions — they are almost identical to each other (despite the differences in bacteria)
End Products Predictions
Again, we have strong agreement between the samples using 3 buckets.
Both below 12%ile (i.e. Low)
Both below 82%ile (i.e. High)
Both in normal range
This means for this type of diagnostic evaluation — they appear to be the same.
There are several questions that need to be asked (and an answer to one):
To the folks at Thryve (and Biomesight.com), why are the numbers so different?
The critical difference between the information lab providers and my site is interpretation sophistication.
So, to answer the reader’s question “The numbers are in major disagreement, but the diagnostic significance of the whole sample is in strong agreement”. Doing the lab analysis is worth it — just ignore the lab’s “value added” suggestions/information.
I have just completed a series of charts showing timelines (over 1000 new charts for most users). Timeline are important because they show how things are changing. Showing a time line can be complicated because numbers from different lab software are not comparable (see Taxonomy Nightmare post) even when the same analysis data file (FastQ) is used!
To address the issues of different numbers, different symbols are used for different lab software. It is strongly recommented that you obtain the FastQ files from the lab that did the sample and process them thru:
The results should be 3 or more sets of reading for the single sample.
There are no magic most important number for all people. Nor can a single test provide a solution. Your microbiome changes over time, and as you attempt to change things, there will be unexpected shifts. If you are dealing with health issues, one test every 2-3 months is strongly recommended.
The data is recomputed at least once a week (there are a lot of numbers to recompute to keep the data current!).
The timelines are divided into 5 collections, as shown below
My own experience has been that I was able to improve (and in some cases, eliminate) medical concerns by using regular microbiome tests and altering diet, supplements etc to manipulate the microbiome. The challenge is identifying what is off, and then how to correct it.
This suite of timeline charts are intended to make that easier (although you may have many charts to look thru).
Bacteria Time Line
This collects the distributions for all bacteria types seen in all of your samples and allow you to inspect changes by time (and lab software!).
The bacteria rank goes down to Strains, when that is reported by the lab software.
End Product Timelines
End products being produced by the bacteria are estimated from available data. Since each lab reports different bacteria counts, there will be some variability. Again we have 3 style of display: Log, Value, % of highest value.
There are about 150 choices to explore. If a chart is blank, then there was no bacteria matches — this may not be an item of concern because we have partial knowledge only of what is produced by which bacteria.
Medical Conditions Timelines
This uses studies from PubMed which report ‘higher’ or ‘lower’ levels. We use quartiles (highest and lowest) to compute values. In general, readers have reported that these numbers are less accurate than symptoms (see section below). We have 3 style of display: Log, Value, % of highest value.
There are about 150 choices to explore
This is from this site’s Citizen Science Artificial Intelligence algorithms. Only symptoms that have at least 5 bacteria very statistically significant are shown. The number of items may increase or decrease with time. At present, it’s around 100.
Sample Profile Timeline
This displays a variety of general characteristics as shown below:
A brief summary
Bacteria Count: Number of bacteria identified. More diversity is usually good, but excessive diversity, especially of rare bacteria, usually indicate issues
Rarest 1%: Seen in less than 1% of uploaded samples
Rare 2%: Seen in less than 2% of uploaded samples
Rare 4%: Seen in less than 4% of uploaded samples
Unusual 8%: Seen in less than 6% of uploaded samples
Infrequent 16%: Seen in less than 16% of uploaded samples
Firmicutes-to-Bacteroidetes Ratio: Some people deem it significant for some conditions
Prevotella-to-Bacteroides Ratio: Some people deem it significant for some conditions
Overall Symptom Health: This is a measure summing all bacteria matches for all symptoms
Overall Medical Condition Health: This is a measure summing all bacteria matches for all medical conditions
This suite of charts gives a lot of analysis information. Most of these charts allow you to drill down immediately
FMTs have been tried for Chronic Fatigue Syndrome/ME with mixed success. The why of failures has been an ongoing interest of mine. We may now have a significant factor that has been ignored in these attempts.
Fecal microbiota transplantation (FMT) as a special organ transplant therapy, which can rebuild the intestinal flora, has raised the clinical concerns. It has been used in the refractory Clostridium difficile, inflammatory bowel disease, irritable bowel syndrome, chronic fatigue syndrome, and some non-intestinal diseases related to the metabolic disorders. But this method of treatment has not become a normal treatment, and many clinicians and patients can not accept it.
In addition to this, there was a podcast reporting success with FMT was associated with higher Phage Diversity in the donor. Phages are the police of the microbiome.
In this retrospective analysis, FMTs with increased bacteriophage α-diversity were more likely to successfully treat rCDI. In addition, the relative number of bacteriophage reads was lower in donations leading to a successful FMT. These results suggest that bacteriophage abundance may have some role in determining the relative success of FMT.
David Morrison requested this feature. The FastQ file is produced by the physical lab machine. This file is then pushed thru software to produce a list of taxonomies. Different 16s retail providers use different software and as a result – different reports. For back ground see this “Taxonomy Nightmare before Christmas” post.
Most of the Post-Covid19 Syndrome symptoms has a strong match to the symptoms seen with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This condition has no conventional medical treatment known. Treatments attempt to mitigate symptoms.
There is evidence that some people develop a long-term fatigue syndrome from coronavirus infections, Dr. Anthony Fauci said Thursday.
“There may well be a post-viral syndrome associated with Covid-19,” Fauci told a news conference organized by the International AIDS Society. The group is holding a Covid-19 conference as an add-on to its every-other-year AIDS meeting.
Fauci said the symptoms resemble those seen in patients with myalgic encephalomyelitis, or ME, once known as chronic fatigue syndrome.
“If you look anecdotally, there is no question that there are a considerable number of individuals who have a post-viral syndrome that in many respects incapacitates them for weeks and weeks following so-called recovery,” Fauci said.CNN
I am very familiar with ME/CFS as anyone who knows my story can attest. And I believe that while the model of why these symptoms are there is simple, the treatment is complex, not cookbook and must be individualized for each person.
COVID19 sends out chemical signals to the body to produce chemicals (metabolites) that its need OR which create a friendlier environment for it. The signals alters the microbiome (gut bacteria) to be a factory for its needs (Viralforming the gut). Once COVID is eliminated, the alterations should return to the prior state overtime— unfortunately a percentage that take a long time or never return. The best documented example is the Bergen’s Giardia Infection.
The microbiome consists of many co-operations between bacteria. Often there are over 2000 bacteria involved in various dialogs. Identifying the bacteria that are at abnormal (too high or low) is the start. The next step is modifying the bacteria by drugs, diet, supplements. At this point, we need to point out that there may be 100 or more abnormal bacteria that needs to be adjusted. Naive adjustments may make more bacteria abnormal.
My Proposed Process
This is the process that I have done with ME/CFS and it is likely that it may also work for many people with post-covid syndrome:
Rose Walbrugh and I are proud to announce one click sending of data from BiomeSight.com based in the UK to MicrobiomePrescription.com. After you get your BiomeSight data processed, you can send the data across without needing to download and upload. You will be sent an email with an automatic login link (no more making up and remembering passwords!).
Once the [Send] button is clicked, you will get an email like shown below that allows you to login and explore your data deeper.
Clicking the link will log you in automatically, and you will see your sample and it’s identified as originating with BiomeSight.
BiomeSight specific distributions of bacteria will automatically become available once there is a large enough sample.
As part of this celebration, a discount code “MICRO” is offered on BiomeSight services. This results in £60 off, which brings the price down to £89 per kit ($110). Local USA fulfillment is now setup. Expedited 2 day delivery at £4.95.
Microbiome Prescription is dedicated to working with labs to enrich user experience and knowledge. BiomeSight has stepped up to the plate for cooperation and win-win attitude.
I am currently working with BiomeSight.com to add Taxon numbers to their downloadable reports.
At first sight, this should be easy, the sample of their complete taxonomy looks like this:
The problem is that their software have forced items into an unnatural structure to make presentation easy. An item that is under Class — when you go to NCBI Taxonomy Browser may be listed as:
The result is that many many items have to be resolve by manual inspection of NCBI to find the apparent match and individually assigned. Example below, notice the “Group II” item which required working from existing matches for a line to identify probable candidates.
Update [MicrobiomeSight] Set OID=2731342 Where [Order]='Group II'
Update [MicrobiomeSight] Set OID=1643688 Where [Order]='Leptospirae'
Other issues concern differences of spelling and renaming, i.e. Cerasicoccales vs Cerasicoccus that was found….
While NCBI shows
We have a possible old/atypical name being used which obtuficates reports. This is one of the key reasons that I am pushing for taxon numbers in all uploads because without them, we would have massive inconsistencies.
After getting all of the Genus and Above resolved, I hit an issue with the species.. namely the list shown below remain unresolved. A few I did a google for and found no hits. Many had incomplete names.
Bacillus polyfermenticus in NCBI is Bacillus velezensis variant polyfermenticus
Candidatus Methylacidiphilum infernorum
All Phylum, Orders, Classes, Families and Genus had matching taxon assigned. At the Species level, 6445 were identified and 21 were not. This means 99.7% of species were given taxon numbers. I expect BiomeSight.com to offer uploadable formats soon, ideally with automatic transfer from their web site.
I just got out of the hospital for cellulitis where I was treated with IV antibiotics. My discharges notes said “take antibiotics to prevent diarrhea”. I asked which ones… blank faces. No one seem to have a concrete idea. So this is a review of the literature:
“Antibiotic-associated diarrhea (AAD) occurs in approximately 25% of patients receiving antibiotics.” 
” analyzing 25 randomized controlled trials of probiotics for the prevention of AAD … more than half of the trials demonstrated efficacy of the probiotic. ” So a random probiotic may have just a 50% chance of being effective. 
“We identified no evidence that a multistrain preparation of lactobacilli and bifidobacteria was effective in prevention of AAD or CDD. ”