A Periodic Review of PubMed for COVID Fecal Microbiome finally found some studies:
One paper reported very very good results!
The optimal eight oral microbial markers (seven faecal microbial markers) were selected by fivefold cross-validation on a random forest model, and the classifier based on the optimal microbial markers was constructed and achieved an area under the curve (AUC) of 98.06% (99.74% in the faecal microbiome).”
“The heatmap showed that the faecal microbial community in CPRs (Confirmed Patients Recovered) was different from that in CPs (Confirmed Patients) and HCs,(Healthy Controls)” [SP is suspected Patient, SPR is suspected Patient recovered] which appears to confirm my hypothesis that most infections will leave a “garbage state” in the microbiome which will generally shift slowly back to the healthy state. This return to a healthy state is not certain and when it fails to happen, then we have diagnosis such as long haul covid, chronic fatigue syndrome, and post-infection syndrome.
This post is for caregivers that are interest in low risk treatment that theoretically have a high probability of success and low cost.
Short Summary of Approach
The microbiome produces some 4000+ different chemicals. For many conditions, especially “untreatable”, it appears that imbalances in these chemical mixtures result in cells, including brain cells, malfunctioning.
Some drugs help — and often those drugs were seen to alter the microbiome, correcting some of these shifts. The stupid question is this, if we know the bacteria that are involved — then why not starve or feed to put it into better balance.
IMHO It works! In my 50’s I had a sudden onset of cognitive issues, including memory. A SPECT scan was read as Early Onset Alzheimer’s. I also had another diagnosis. That other diagnosis has a bacteria shift pattern reported in 1998 in Australia. Making changes to alter that pattern caused the cognitive issues to fade and disappear.
Steps
You need to have a microbiome sample (done by taking a little bit of a stool and sending it to a lab). Then the data need to be upload to the free citizen science site, Microbiome Prescription. Not all labs are supported (i.e. they do not make their data available in a suitable format); those that are supported are listed here (with discount codes).
Once the data is uploaded, there are two Quick Suggestions links that generates suggestions using Fuzzy Logic Artificial Intelligence techniques.
A reader contacted me about a disagreement and the cause was a bug in the code for Kaltoft-Moltrup — subsequently fixed. This post looks at the bacteria selected by each for similarities and differences — so people can better understand the difference (which is a little abstract).
I am going to use one the demo samples from BiomeSight (BiomeSight:2019-06-10 Self).
Extreme 3% picked 29 bacteria
KM picked 24 bacteria
I sorted their selections below in alphabetical order, 13 are in common (just over 50% of the KM choices).
Kaltoft-Moltrup
Extreme 3%
Actinomyces : Too High
Actinomyces : Too High
Actinomyces naturae : Too High
Anaerofilum : Too High
Actinomycetaceae : Too High
Bacillales Family X. Incertae Sedis : Too High
Bacillales Family X. Incertae Sedis : Too High
Bacteroides cellulosilyticus : Too High
Bacteroides denticanum : Too High
Bacteroides dorei : Too Low
Bacteroides intestinalis : Too Low
Bacteroides intestinalis : Too Low
Bacteroides rodentium : Too High
Bacteroides rodentium : Too High
Bacteroides sartorii : Too High
Bacteroides sartorii : Too High
Bacteroides thetaiotaomicron : Too High
Bacteroides vulgatus : Too Low
Bacteroides vulgatus : Too Low
Blautia : Too High
Blautia obeum : Too Low
Blautia obeum : Too Low
Brochothrix : Too High
Brochothrix thermosphacta : Too High
Chitinophagaceae : Too High
Clostridium paradoxum : Too High
Clostridium paradoxum : Too High
Coprobacillus : Too High
Coprococcus : Too High
Coprococcus : Too High
cunicula : Too Low
Dehalogenimonas : Too High
Desulfovibrio vietnamensis : Too Low
Johnsonella : Too High
Johnsonella : Too High
Johnsonella ignava : Too High
Johnsonella ignava : Too High
Lachnospira : Too High
Lactococcus : Too High
Lactococcus : Too High
Leuconostoc : Too High
Listeriaceae : Too High
Micrococcaceae : Too High
Oscillospira : Too High
Prevotellaceae : Too Low
Streptococcaceae : Too High
Streptococcus vestibularis : Too High
Streptococcus vestibularis : Too High
Sutterella : Too Low
Syntrophobacteraceae : Too High
Tetragenococcus : Too High
Thiothrix : Too High
Turicibacter sanguinis : Too Low
Looking at a chart of Prevotellaceae, we see that KM low is 2.25%, thus be this sample being between 2.25 and 3 resulted it being excluded on one and included on another. For Listeriaceae : Too High, KM used 95.6% instead of 97%.
For Sutterella, KM uses 22% for low, hence it included. This is reasonable because there is a distinctive drop off around that!
For Coprobacillus, it looks like I need to do some adjustments of the KM, a chunk of unusual data caused a “step” that incorrectly triggered the high computation.
Bottom Line
We have good overlap with the differences being due to the curves being different. With the extreme 3% approach, we are insensitive to the difference of shapes. With KM we are sensitive (and some parameters to the algorithm needs a little adjustment).
This is an analysis using a standard flow that I tend to use… The analysis was done using data from Thryve and uploaded to Microbiome Prescription.
Microbiome Functional Abnormalities
With the recent addition of KEGG information, my focus has shifted from a naive “this bacteria is too high or too low” to this enzyme or end-product is too high or too low. Why? Bacteria can substitute for each other.
Consider building a wall on a house. If you grow up in the US. Northwest, it will be 90% wood and 10% gypsum board. But walls may be built with steel framing, concrete siding, bricks, stones, logs, concrete blocks etc. Is a wall not built of 90% wood unsafe? No, the question should be how does the wall function for structure strength, insulation etc. It is the same with bacteria.
Three Functional Checks
We use the three items are at the bottom of Changing Your Microbiome
End Product Abnormalities
At this point, the reader should copy and google information about each end product.
“Indole may act as an interspecies signaling compound.” [src] translation: the bacteria internet is flaky
Bacteriocin: Lasso peptide – “It has varieties of biological activities, among which the most important one is its antibacterial efficacy.” [src] in other words is suppresses other bacteria
So the story appears to be bad communications between bacteria and the microbiome being bias towards those that tolerate the natural antibioitic, Bacteriocin: Lasso peptide.
Clicking thru to the bacteria involved, we find that the antibiotic effect is due to one bacteria, shown below. The Indole appears to be due to low Alistipes level (most common source, and has a low value)
Source for Biogenic amines and Bacteriocin: Lasso peptideKEGG Enzyme Outliers
I dropped filtering to 90% and only the enzymes show issue.
Core Supplements
The purpose of this is to identify items that may compensate for low amounts. There were none.
There are no significant lows ( < 25% of Median) and we note two high items: GABA and DAO
Predicted Symptoms
There was only one item that had strong likelihood:
“The gut microbiota of children with CP and epilepsy consuming a liquid diet had elevated levels of symbiotic pathogens and diminished intestinal barrier protection bacteria, relative to a general diet group. These differences in bacterial microbiota were associated with GI dysfunction symptoms.”
“The increased risk of “Neurodegenerative diseases” in CPE patients was probably attributed to Streptococcus, Parabacteroides, and Bacteroides ” [2019]
I added CP to the database with the bacteria shifts reported. And then ran for matches to the reported patterns
Values in top or bottom 9%. A good match for PubMed report
The top results for probiotics are shown below
In terms of diet additions:
As well as:
Vitamin D, Blackberries, Limes, Oranges are in the Flavonoids list
And diet avoidance
We note that some species of lactobacillus are on the to take and others to avoid. Different species produce different bacteriocins (natural antibiotics against other bacteria)
Bottom Line
The above changes will likely have a positive impact and should slow neurodegeneration. As always, these suggestions should be review by knowledgeable medical professional before starting. These are machine learning suggestions that is blinkered in terms of factors considered.
One person who has had many samples of time. Typically that person is looking for outliersto reduce or disappear
A family with samples from different members. Typically one person is challenged and since the family group has shared DNA and diet — the hope is that the bacteria grouping causing the challenge will be identified. Once identified, it may be actionable.
This scenario will have more tools added over the next weeks.
If you have two or more samples uploaded, you will see the top two items on the Available Samples page. These may be collapsed into one over the next few weeks.
Clicking the right button of these two will take you to a sample selection page.
Clicks TWO or more samples
The program will list all items below that matches all samples OR all samples except 1 (but at least two).
I selected a group of 5 samples from when I was having a ME/CFS flare.
The lack of dehydrogenases above would account for high lactate(brain fog) and agrees with research [Nicotinamide adenine dinucleotide (NADH) in patients with chronic fatigue syndrome]
IMHO, it correctly identified what was wrong with me.
KEGG Modules takes a bit of research to understand.
Doing some research, I found “. L-tryptophan is produced in the shikimate pathway from chorismate” Which lead to many ME/CFS articles on PubMed.
Again, it may take some research to understand what this is.
This informed me which bacteria were constantly high over 5 samples
This set of tools does not give immediate answers; it gives you leads to investigate. For myself, the findings plus the use of PubMed studies weaved a story of what happened that agrees with the literature. This is very important because ME/CFS contains dozens of subsets. Often I have seen that what is helpful for one subset is harmful for another. I suspect this also applies to other conditions, such as ASD/Autism.
In this case, it identified one key family to reduce. Identified enzymes that I was short on. Lead to a possible supplements that I should consider because of the dysruption.
A reader asked which one to use. They can be compatible prices, especially this weekend with Black Friday specials A cost item that should also be factored in is shipping costs to and from. In the US, Thryve comes with a postage paid return package.
The Numbers
The upload page gives raw numbers. I am also going to dive a little deeper into the numbers
Elusive 1%
Adjusting for number of samples, they appear very similar.
Elusive 2%
These charts are those between 1% and 2% in occurance
Elusive 2-4%
This is the count between 2 and 4% Frequency. BiomeSight appears to have an edge.
Elusive 4-8%
As above, BiomeSight curves appears better. 100 is at the 84%ile with Thryve and at the 41% with Microbiome; in other words, Biomesight report more in this range per sample. This is important for the AI analysis, because we need a threshold count before we can detect patterns.
Elusive 8-16%
Thryves now pulls ahead. Biome Sight has 70 bacteria count at the median, while Thryve has 90
But wait! Does it report on what you are interested in?
In my last person analysis, there was two probiotic recommendations:
I would like to see those counts on my next sample…. so clicking on the above links, I see that stats:
Ouch, BiomeSight is the only one that reports either! Looking at the parent group, I see BiomeSight again reports better
Bottom Line
There is no clear better or worst — it depends on your needs.
BiomeSight offers free processing of Thryve FASTQ files which is big Kudo to Rose at BiomeSight. Thryve offered free processing once upon a time, but it does not appear to be offered any more (or it is sufficiently hidden that I cannot find it).
The new kid on the block, nirvanabiome, which uses CosmosID.com, is 3x as expensive and does not appear to report any more bacteria types (which is surprising given their claims, I expected counts close to Xenogenes shown at the top of this post). I do not have sufficient samples via CosmosID/Nirvanabiome to do more analysis.
nirvanabiome also appears to be targeting the Autism market. I have a separate blog on Autism and the Microbiome, so I am interested if they will produce actual beneficial results or if this is “the best of intentions, the worst of execution” scenario.
I have implemented an upload for Microba, an Australian firms that claims “With the most comprehensive microbiome test available”. Instructions on how to do a download and upload is in this
I have tried several times in the past to do it. One of the biggest problems is that they do not use NCBI reference numbers or names. In fact, many of the bacteria they name — you will not find a single study on PubMed with that name. In other words — valueless information.
I have a mapping of their interesting names to NCBI names on line (and it will grow as samples are added and new names are added). The mapping is located here. I have repeatedly email them to make a download with NCBI taxon numbers available without success.
Left side is their name, right side is NCBI name. CAG-### is very vague.Some additional “delights”
Only Selected Layers are Reported
They report only on the Phylum, Family, Genus and Species levels. Excluded are Orders, Classes and Strains. After the mapping, we are typically left with less than 100 bacteria taxonomy versus many more from other providers. I do not know how they define “With the most comprehensive microbiome test available”. Most means better than ALL…
This is made worse by the use of atypical names for bacteria. If you are high “Peh17” and go to PubMed to see what will lower it, or what conditions are associated with it — you hit a blank page. They may provide advice — but the basis of that advice cannot be independently checked.
The sum of all Species/Genus/Family is 100%. This implies that they have identified every bacteria — impossible. They have scaled the numbers of the bacteria that they detected to 100%. A person with actually 40% of one bacteria in their gut could see a report of 45%, 65%, 85% — depending on what other bacteria is there.
The report is to 0.01% that is 100 / 1,000,000, a coarser measurement than some other tests.
Bottom Line
For those of you who have already tested with Microba, you can upload and MicrobiomePrescription will do as much as it can with that information. If you decided to do a retest— I would not recommend using Microba for the reasons sited above. I have heard that the UK firm BiomeSight is making it easier for Australians to use their service. I have heard that duties and shipping costs makes Thryve Inside more expensive than BiomeSight.
Expect it to be a few days before 100% of your sample is ready — any new odd-ball names has to be researched and entered into the mapping table. At upload, you will likely be 80+% processed immediately.
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.
STRAINS
% sample 1
% sample 2
akkermansia
0,2
0,4
alistipes
0,02
7,2
bacteroides
0,02
3,4
bifido
2,6
1
blautia
10,3
3,3
eubacterium
7,2
4,1
faecalibact.
1,7
11,6
lactobac.
1,1
2,2
roseburia
1,9
2
ruminococcus
26,6
13,8
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.
Sampling Statistics
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: [2015] [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.
Bottom Line
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?
For users of my analysis site: https://microbiomeprescription.com/, for diagnostic purposes there are few differences! We have general agreement for:
End Product Production
Medical Studies Matches
Symptom Matches
Detecting high or low levels by percentile
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.
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.
This implies that for a greater chance of success and less risk, than DYI fecal transfer, that a lab that tests for possible infections AND for phage state may yield the best results.
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