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.
Thryve is what I am starting to use. Their reports may be processed here for independent suggestions. I would also recommend
” No consistent association between a vegan diet or vegetarian diet and microbiota composition compared to omnivores could be identified. Moreover, some studies revealed contradictory results. This result could be due to high microbial individuality, and/or differences in the applied approaches. Standardized methods with high taxonomical and functional resolutions are needed to clarify this issue. “
I have seen that also in extracting facts to the database. While diet (based on these studies) is still on the suggestions list, it is not recommended to use. Specific food is a very different question. Diets tend to be nebulous collections of foods making things very undefined.
I recall reading reviews of difference of reports by bloggers who took two samples from the same stool and sent them to different analysis labs. There are a dozen possible explanation for those differences.
Due to the demise of uBiome, a number of former users downloaded their FASTQ data files and processed that data through different providers that will determine the bacteria taxonomy from FastQ files. Most of us naively believed that the reports would be similar – after all it is digital data in and thus similar taxonomy would be delivered… It appears that things are a lot more complex than that.
What is in a FastQ File
A taxonomy download may be 20-30,000 bytes. This contains the bacteria name and hopefully the taxa number with the percentage or count out of a million. The FastQ file is the result of a machine reading the DNA bits of bacteria in your microbiome. It is a lot bigger. DNA bits are represented by 4 characters (A,T,C,G) The typical data would be 170,000,000 bytes (170 Megs).
If you examine the text, yes text, you will see line after line with:
These strings have been matched to certain bacteria, just like your DNA would match to you (and other people closely related). If you go over the US National Library of Medicine, you will find information on these sequences, like this for Bacillus subtilis , a common probiotic.
So, the process is matching up to a reference set. At this point of time we walk into the time trap!
A firm like uBiome may have gotten the latest values when it was started. I suspect a business decision was made not to constantly update them. Why you ask? The answer is simple, to maintain consistency and comparability from sample to sample over time. If they use newer ones, then they should reprocess the old ones to be consistent, but then reports will change in minor or major ways — resulting in support emails and phone calls. Support can be a major expense. So keep to what we started with. I suspected that with uBiome Plus, they were working on using new reference values, after all it was a different test!!
Each provider has a different set of reference sequences. Their sequences may be proprietary (not in the publish site above). This means that to compare results, you need to use the same reference sequences to match with your FastQ microbiome data. If not, it may result in a “bible” by taking page 1 from King James Bible, page 2 from the Vulgate, page 3 from Tynsdale’s translation, etc. Things become a hash.
Another issue also arises, bacteria get renamed or refined. The names used in an older reference library may not match the names in a latter reference library.
For myself, I have the FastQ for all of my uBiome tests and my Thryve Inside tests. I will continue on requiring these FastQ files from testing firms so I can keep the ability to compare samples to each other overtime by running them through the same provider.
I have created a page to allow comparison between FastQ files processed to taxonomy by different provider. The button to get to it, is at the top of the Samples Page – “FastQ Results Comparison”
This takes you to a list of all of your samples. Note that I have 4 samples with the same date below. It is actually just 1 FastQ file interpreted by four different providers. There are additional providers.
This produces a report showing the normalize count (scaled to be per million). I also have the raw count on the page as tool tips over each numbers.
Who has the right numbers?
Without full disclosure by all of the providers, it is difficult to tell.
Fortunately, researchers use the same process for each study. That means that the results are relatively independent of the process used. It does mean that Study A may find some bacteria are high or low and this is NOT reported in Study B. The why may be very simple, that bacteria was never looked for. Things get fuzzy. With the distribution of bacteria known for a particular method, then we can determine if it is high or low… but that means sufficient samples with that method. With uBiome, we had a large number of samples from this one provider and that allow us to make some good citizen science progress.
Bottom Line on why the difference
Different reference libraries
Change in bacteria classifications (same sequence, different name)
A rich 16s taxonomy report may contain thousands of species. Every documented modifier increases and decreases dozens of species. While we have 1800 potential modifiers, the challenge of finding a perfect modifier is very hard, if not improbable.
This post describes a variety of approaches that could be taken. In the ideal site, all of these choices should be available for a very knowledgeable consumer or medical professional to select the best candidate.
This means that some expert says what they, usually based on clinical experience, believe to be a healthy microbiome. Typically this has the risk of being a regionalized definition of a healthy microbiome — where the diet and the dna of the region are intrinsically included.
One example is Jason Hawrelak in Hobart, Tasmania, Australia. If you are from Chennai, India and a vegetarian, many of his proportions may not apply. See this post for the discussion on how microbiome varies by country, dna and even longitude.
Ideal species proportions Example
Total butyrate producers
Filter by medical conditions or symptoms
We may have 1000 species. From published studies, we know that the average of people with a condition compare to controls may be high or low (and occasionally both). Some of the people with the condition may have a normal value — it is the group that has a low or high value. These values may be connected to the diet, age or other confounders of the group being studied.
Repeatability of results
We may have 20 studies on the microbiome associated with Facebookitis. Some studies report the same species, other species are only reported in just one study. I could assume that the more studies that a species is mentioned, the more reliable that species is associated. So we may have Facebookamina found in 12 studies and Twitteramina in just 1. We have a multitude of choices: use only the species over some threshold (say the median number), create a new weighting for the number of studies (for example, Log(Number of Citations)), etc. This is one of the challenges of building your algorithm.
Of the 1000 species, perhaps 30 are reported high or low in studies for Facebookitis. In our sample of 1000 species we find that we have 20 of them. Of these 20, we find that 12 have matching shifts to the studies. It is these 12 that are our candidates to shift. We ignore everything else.
Of the 10 we do not have, 3 are low. Do we deem this to be a low? If these three are only seen in 4% of the population do we still deem them to be a low? There is only a 12% chance that these will be reported. Is this noise or significant?
We could do this process for multiple other conditions that we have. I tend to avoid tossing in every condition because conditions often are interconnected. My preference is to always start with the most annoying condition or symptoms.
Weighting of one bacteria shift to another
There are many ways of weighing – giving a value to the amount of shift. The weight is important when we try finding modifiers because we are trying to estimate the net expected benefit for each modifiers so we can select the best modifiers.
The classic approach is a naive: how many do you have compared to the average. I do not recommend this approach. Let us consider some factors involved:
We find that only 60% of people have any of this bacteria. We can compute the average two ways:
Average over those who have it: Say 0.5 %
Average over every one (so those who do not have it is counted as a zero). This means that the average is now 0.3%
If your value is 0.48%, are you high or normal?
If the average for a different bacteria (say genus) is 20% and your value is 22% and you are 0.6% for the prior bacteria (using 0.5% average). At a per million level you are 20,000 high for one and 1000 for the other – do you give a weight of 20,000 and 1000? But one is 10% higher and the other is 20% higher. Surely you should give the one with a bigger shift, a greater weight? Picking the weighting is another step of developing the algorithm.
If one of the bacteria happens to be Clostridium difficile, you likely want it to be zero. This seems like an exception to any logic you developed above.
The above methods are mechanical. People often have experience or beliefs. Hand picking means going thru and selecting the species one by one after looking at the literature and association for each species that are outside of the expected range.
The expected range can be computed many ways. The classic lab approach is to compute the average and then the standard deviation. The normal range becomes mean – 2 std dev to mean + 2 std dev. IF THIS WAS A TRUE BELL CURVE, this means that 5% are above and 5% are belove. I strongly do not recommend this approach.
I have moved onto actual percentiles of the labs. So if you want to use the 5% criteria, you look it up against the actual data.
My own preference is 10% with the additional criteria that a strongly supporting (correlated) species must also be 10%. The goal is to identify a species-conspiracy and address (arrest) them as a whole.
This is one more decisions that you need to make in developing an algorithm.
Modifiers are the same situation as diseases and symptoms. Multiple studies with different results. Existing diet, DNA, etc may be confounders of the published studies.
Rather than repeat the discussions above (how many studies reported the same thing etc.), just re-read above. A major confounder is that different studies may not have tested for the same things — a no change report will often be omitted from the studies…. leaving what was tested for being uncertain.
Where are we:
We have a collection of bacteria shifts which may be due solely to diet or DNA with no association to any condition or symptoms | given diet and DNA.
We need to identify which bacteria to change (a simple true/false)
We need to give a value/weight for how relatively important each bacteria is to change.
We have modifiers which are likely to impact these bacteria (a simple true/false)
We need to give a value/weight for how certain each bacteria is to be change.
Now we need to optimise across all of these variables to get the optimal suggestions. The item to be optimized is the estimated weighted shift of taking a set of modifiers against a set of bacteria. The key word is estimated.
This is intended to allow you to better choice between alternatives – for example Aspirin versus Paracetamol (acetaminophen). I am sure people will find more uses for it.
The process is simple, search for each item, and put a check beside it. Select the Compare Impact radio button and then click the submit button below it.
This will take you to a page listing the impact side by side. In this case we seel that their impacts are similar, but different on a few items. At the family level there are a few differences
This is intended when you are prescribed drugs to treat some conditions and wish to reduce the impact on the microbiome by counteracting the drug or drugs impact on the microbiome.
For this example, we pick lovastatin (a statin), Famotidine (Pepcid AC).
We may wish to first see how much impact they have together (do they reinforce or counteract each other)
Just pressing back, and changing radio buttons, and submit produces suggestions.
The suggestions are done by creating a virtual microbiome report based on the above shifts and running that through our AI engine.
The suggestion page is the new format with the long lists hidden until you ask to see them.
The Take or Avoid list is defaulted to 100 items (which is one reason that I toggle visibility). Remember – none of these items are guaranteed to work, nor do you need to take all of them. Each item increases your odds…
The avoid list values are a lot higher, and thus you may wish by reducing any of these items that you are taking.
By uploading, you consent to allow your microbiome data and symptoms to be made available to citizen scientists for further discoveries.
Required consent is cited above. 3rd party is responsible to obtain consent.
The structure is simple:
The key is issued by us and identifies where the data is coming from (“source”)
logon and password are the authentication pair that you generate. These are used for logging on. Logon and Password should be the same for all samples from the same user (so we can display on a timeline).
Probability is an estimate whether something may help, not how much it can help. The relative help between two items is rarely found in any study.
Probability is based on the number of studies reporting that something shifts a bacteria in the desired way. A single report that Blue Cheese reduces Xeonella may get a value of 0.1 while 10 reports that barley reduces Boozella would get a value of 1.0 (if 10 reports were the maximum number of reports reported).
Studies often contradict each other – typically caused by a confounder that the study ignored. https://en.wikipedia.org/wiki/Confounding. To address this, we aggregate the number of reports with scaling. For example:
6 reports showing desired changes
2 reports showing undesired changes
Could be computed as 6-2=4. Due to this increase uncertainty, we do other methods for example:
exp (log(6/8) + log(2/8))
Once we compute all of these numbers, we then scale them so the maximum value is +1.