A reader sent me this email (actually another one did too). The issue was fixed but not in the obvious way:
BUG: Checking and “Add elected items…”
This was actually a coding error, the checkbox should not be there. To do what the user intended:
Now to pick bacteria for KEGG Vectors
Below is the revised page fixing the problem. Notice the RED RECTANGLE
Clicking this will take you to the page listing the bacteria for this item, example below
On this page the checkboxes work. You see all of the bacteria associated that you have and thus can target the specific one (likely Brochothrix thermosphacta that is running well above the top of the normal range) that is causing the KEGG Vector (Product, Enzyme, Module) to be of concern.
While working on the last long COVID post, another Long COVID person contacted me. He was definitely frustrated (in the same way that I have seen people with ME/CFS be frustrated over the last decades).
I’ve literally consulted with over 7 doctors (internist, hematologist, endocrinologist, ENT, GI specialist, cardiologist, & neurologist) over 3 weeks period and still have 4 consultations to go! All those doctors did is to request for more bloodwork and scans and then tell me that it’s all in my head (using smooth words) and send me home!
Long Haul Covid Patient
Recap on the Literature
The Microbiome and COVID have strong relationships. The microbiome prior to COVID impacts the severity. The severity of the symptoms correlates with the microbiome changes. This leads naturally to Long COVID being a continuation of this theme.
A new study used fecal samples were collected at least 38 days following diagnosis. By common belief, the patients are fully recovered — except their microbiome are not! What is the difference? It depends on where COVID presentation.
positive detection of SARS-COV-2 RNA from the respiratory tract, defined as respiratory positive (RP)
failure to detect in the respiratory tract, but had covid, is negative
They found changes in “13 phyla, 18 classes, 44 orders, 88 families, 234 genera, 1 phylum, 1 class, and 1 order were significant”. To put it simply, look at the changes below — they are NOT minor but major shifts!
My ongoing long haul symptoms: – Vertigo/lightheadedness. Can’t maintain proper balance and head feels heavy 🙁 I was walking into furniture in my home! It’s difficult to drive a car or even fast walk or go down the stairs. I feel as if my head is heavy and will fall. – Mood swings/anhedonia. No more feeling of happiness or pleasure. Low serotonin? Low dopamine? – Brain fog/memory loss/loss of concentration. I’m back to work and it has been extremely difficult to get tasks done. – Occasional blood in stool. Ulcers? Never had GI bleeding in my life! – Early evening fatigue. Feel extremely tired past 8pm. I also wake up super early (5-6am) and can’t go back to sleep. – Blurry vision during night. – Slight shakiness/tremors in hands and legs. Low iron? Low dopamine? – Low libido/sex drive despite getting morning erections.
In his own words
Pro Forma Analysis
I am going to do the same process as with the other Long COVID person. First, we have two lists of bacteria available, the number of studies are few but slowly increasing.
The earlier sample had just 7 out of range, the latest sample increased to 16, implying the microbiome is drifting further away from normal. Comparing samples, we found the following concerningly high on both samples:
Three items were out of range, one in common with the other Long COVID but in the opposite direction a-Galactosidase was high, and the other was low.
KEGG Bacteria Products Out of Range
The earlier sample had 9 out of range, the latest sample has 145!! Another indication of shifting further away from normal 🙁
KEGG Modules Out of Range
Just one in each sample, nothing in common.
KEGG Enzymes Out of Range
The earlier sample had 8 out of range, the latest sample has 139!! Another indication of shifting further away from normal 🙁
Kegg Suggestions
Where there are so many items with issues, I usually do not bother looking at them individually. Instead, I look at what can be computed to address them. Because every item is low, we do not need to look at trying to reduce anything — just add,
KEGG Suggested Probiotics
This is done by seeking out probiotic bacteria producing enzymes etc that are not being produced enough by existing bacteria. These can be viewed as a biological supplement producing items not available as regular supplements. The retail probiotics Sun Wave Pharma/Bio Sun Instant and Prescription Assist appear to be good choices (if available).
These are the same ones as with the other Long COVID person. What surprised me was that the earlier sample had a higher value list than the latest sample. This implies that the new overgrowth are providing the material to stablize the microbiome (unfortunately, in the current state of dysfunction)
A common mistake is to slip into a homeopathic thinking, “oh, I am taking some — that is enough”. In general I recommend starting low and increasing to the maximum dosages used in clinical studies. See this page for amounts used and links to the study or clinical trial.
KEGG Suggested Supplements
I tossed in the prior review for reference, two supplements are in common with all three samples.
Earlier Sample
Latest Sample
Other Person
beta-alanine iron L-Cysteine L-glutamine L-Lysine L-Proline Molybdenum NADH Vitamin B-12
beta-alanine D-Ribose iron L-Histidine L-Lysine L-Phenylalanine L-Tryptophan magnesium
We similarly identify supplements that are available retail (defined as being available on Amazon.com)
Suggestions
Using the last Long COVID post as a model, I jump directly to suggestions using the latest results. EXCEPT – I selected everything — including antibiotics and antivirals. No antibiotic made it high on either list.
12%ile and COVID-19
Remember, we are restricting to only those reported for active COVID and with the same direction of shift
12%ile and Long COVID
We have a much longer list of bacteria selected — which likely reflect that it is long COVID.
Quick Kaltoft-Moldrup suggestions
A very short list – this happen because we do not have studies reporting what modifies many of these bacteria.
The earlier sample had just a half dozen bacteria picked. The latest had moreEarlier had only a few (not even 20 Add Suggectios)
Reminder – The WHY for suggestions
On the suggestion line, you may see a 📚. Clicking it will show the source of the recommendation and why. Remember the more positive impact (by number of studies reporting the same), the greater the confidence shown. It is the confidence that it will shift in the desired direction. It is not which works better.
Putting Suggestions Together
With much bigger lists of bacteria, we also run the risk of more complexity and contrary suggestions [for example, bifidobacterium longum bb536 (probiotics) was a take, but bifidobacterium longum (probiotics) was an avoid]. I find myself giving the 12%ile and Long COVID Suggestions the greatest credence. It has some interesting
What seems to be reoccurring – make sure you look up dosages where available. Start low and work up to the maximum dosages used in clinical trials (after consulting with your medical professional)
My read of the data is to avoid all Lactobacillus and Bifidobacterium probiotics. You have above the median amounts of both of them 85%ile and 66%ile respectively – you do not need more, in fact, they likely contribute to the dysfunction! Miyarisan, Prescript Assist (or Equilibrium), aor / probiotic-3 and Sun Wave Pharma/Bio Sun Instant appears to be the best retail candidates for probiotics.
This is a MODEL not a PROTOCOL
This is directed to people reading this post and saying “I will do what is described”. What is the difference? A Protocol comes from clinical experience and is a defined set of actions that are repeated for each patient. A model is a theoretical way to generate candidate actions that may help. This is not a model for Long COVID patients, it is a model for one person’s microbiome. Every Long COVID patient will have a different microbiome and thus different candidate actions. You can see this by looking at the next post on Long COVID microbiome.
An analogy, Long COVID can be compared to a headache. There are at least 17 types of headaches. You may need to see a dentist (tooth issues), or take a antihistamine (allergy) or take oxygen or …. Details drives the treatment.
To help illustrate this, I have put the bacteria targeted from each of them below. You will note a lot is in common.
Female Prior Long COVID
Male This Post
Logical Treatment Options based on test results
Above you read about this user’s frustrations with the medical professionals. The root problem is that profession runs on encyclopedic knowledge (often photographic memory) that looks for a match and retrieves it as treatment. I term this as cook-book medicine. If there are no matches, then we typically see “deer in the headlights“, a deer with a MD.
Microbiome Prescription builds predictive modelling with a wide variety of suggestion options. Most of the options do not require a prescription, the user is in primary control. There is a full chain-of-evidence to the basis of the suggestions for people to review (yes, some people may need to upgrade their reading skills). The core facts are your microbiome.
Improvements can be objectively measured (instead of a vague “do you feel better”). Feeling better is important, but it should not be the sole criteria.
Questions and Answers
For many of these questions I went to the “See Impact..” with the specific sample.
Q:I’m currently on bovine Colostrum, is it ok to keep taking it?
A: Bovine Colostrum is not the database, the closest match is whey. It has no known impact – so you can assume it is safe.
Q:I’m eating boiled and cooled down potato every day as source of resistant starch. Is it ok?
A: Yes, That is actually the product that was used in most of the studies cited.
Q: Which nuts is best for my case (pistachio, hazelnut, cashew, brazillian nut, etc.)? I’ve been eating walnuts and almonds for several months.
A: I checked the types that we have data for:
Peanuts – no known impact — so you can assume it is safe.
Walnuts – positive impact
Generic nuts – no known impact — so you can assume it is safe.
Q:My vitamin D turned out high-normal. My 25-hydroxy vitamin D reading is 29.1 ng/ml & reference range (20 – 40). The suggestions included Vitamin D.
A: Vitamin D supplement are estimated to be a net negative benefit (same numbers as above). I looked at the citations used to make that decision and see a mixed impact on different items according to different studies. The results illustrates why suggestions may change from month to month. If new studies are added (which happens monthly) then the impact estimates change. My goal is deliver the best estimate from current studies – a moving target,
Whatever happens, I just want you to know that you have helped me a lot even before getting Covid-19… One word, thank you Ken 💐
REMINDER: These are suggestions generated by an artificial intelligence program. Before implementing, they should be reviewed by your medical professional.
The Microbiome and COVID have strong relationships. The microbiome prior to COVID impacts the severity. The severity of the symptoms correlates with the microbiome changes. This leads naturally to Long COVID being a continuation of this theme.
One study suggests that a core microbiota could predict COVID-19 severity in healthy subjects.27 Another study shows that the composition of the intestinal microbiota in the Chinese cohort is different between COVID-19 infected and un-infected controls, with symptom severity correlating with specific bacterial taxa.2
A new study used fecal samples were collected at least 38 days following diagnosis. By common belief, the patients are fully recovered — except their microbiome are not! What is the difference? It depends on how COVID presented.
positive detection of SARS-COV-2 RNA from the respiratory tract, defined as respiratory positive (RP)
failure to detect in the respiratory tract, but had covid, is negative
They found changes in “13 phyla, 18 classes, 44 orders, 88 families, 234 genera, 1 phylum, 1 class, and 1 order were significant”. To put it simply, look at the changes below — they are NOT minor but major shifts!
MRI showed pericarditis.Tachycardia stopped once I resumed H1 blockers
I had stopped out of paranoia when acutely ill, and B started antacid prescribed by cardiologist.
Severe constipation started at around April 2020. So we’re talking about over a year ago.
I had a break from constipation issue for about 6 months – november 2020 to may 2021.
Other ongoing symptoms are pain on the left side under the ribcage and internal vibrations, numbing of sensation “down there” (don’t feel much the need to go to the toilet,
Sex life is hampered too, nerve damage very likely according to myself and also gynecologist who thinks it’s postviral).
The entire first half of 2021 i adopted a diet based on green smoothies.
Other than 80% veg based smoothies and flaxseed i ate some veg stir fries and fresh salmon and some crisps (in small quantities!!! but every day a little pack).
Before covid i ate only crap, all sort of crap, so that this diet was for me a huge sacrifice.
But since covid junk food made me feel bad anyway, so slowly i accepted to change my diet.
I’m also taking a very long list of supplements. I had high cholesterol before covid, had it at 18 already despite being slim, but after covid it remained high and my sugar level got very high (not yet in the red). [This may no longer be true — the measurements were from a year ago]
Pro Forma Analysis
First, we have two lists of bacteria available, the number of studies are few but slowly increasing.
I see 34 Outliers using the Kaltoft-Moldrup ranges (which are usually bigger ranges than most testing labs use). This person mentions 50+ out of range from their lab. Well, that huge number is precisely what the study above reported. This is not a typical microbiome disruption.
“About 30% of COVID-19 patients also presented with cardiomyopathy (Arentz et al., 2020). 1α,25(OH)2D3 plays a crucial role in the prevention of cholesterol build-up in the arteries by preventing the conversion of macrophages to foam cells (Oh et al., 2009) and enhancing the cholesterol efflux from blood vessels (Yin et al., 2015)”
She has cholesterol build up and a form of cardiomyopathy (See What About Tachycardia-induced Cardiomyopathy?) So low Vitamin D, the literature and her lab results are in agreement. This suggest serious Vitamin D3 supplementation (see Clinical Therapeutic Dosages here, I would suggest at least 10,000 IU/day — to be discussed with her physician)
KEGG Bacteria Products Out of Range
Every single one of a list of 47 was low. Not enough being produced
KEGG Modules Out of Range
Nothing reported
KEGG Enzymes Out of Range
As above, a list of 48 items with every item being low
Kegg Suggestions
Where there are so many items with issues, I usually do not bother looking at them individually. Instead, I look at what can be computed to address them. Because every item is low, we do not need to look at trying to reduce anything — just add,
KEGG Suggested Probiotics
This is done by seeking out probiotic bacteria producing enzymes etc that are not being produced enough by existing bacteria. These can be viewed as a biological supplement producing items not available as regular supplements. The retail probiotics Sun Wave Pharma/Bio Sun Instant and Prescript Assist appear to be good choices (if available). The fall back by species are:
A common mistake is to slip into a homeopathic thinking, “oh, I am taking some — that is enough”. In general I recommend starting low and increasing to the maximum dosages used in clinical studies.
KEGG Suggested Supplements
We similarly identify supplements that are available retail (defined as being available on Amazon.com)
beta-alanine
D-Ribose
iron
L-Histidine
L-Lysine
L-Phenylalanine
L-Tryptophan
magnesium
Suggestions
One unique feature of Microbiome Prescription is that it not only identifies candidate issue areas, it also makes suggestions based solely on studies from the US National Library of Medicine. These suggests factor in side-effects on other bacteria. Every other site, has a blinkered thinking with their suggestions and do not consider side effects. Of course, there is one layer of side effects that only your MD can help — medical conditions you have. A suggestion may suggest peanut butter and you have an allergy to peanuts!
Checking against COVID and LONG COVID Profiles
The studies report on the US National Library of Medicine and are coded for averages being statistically high higher or lower than controls. This does not mean that the values are extremes. Statistically, this presents some challenges. I decided to explore how many matches happened with different definitions (Kaltoft-Moldrup ranges, top/bottom 3,6,9,12,15 %ile) for COVID and LONG COVID
Process
For those who wish to do it themselves, go to advance suggestions and do settings like below.
We will be change Bacteria Slection and Explicit Bacteria
Then click the suggestions at the bottom. On the suggestion page, click Bacteria Details to see the bacteria that are picked
The results are below by bacteria. As we reduce how extreme values that are needed to be deemed “high” or “low”, we have more and more matches. C – active COVID; L – Long Haul COVID / Post COVID
C – active COVID, L – Long COVID (i.e. 30+days after infection)
Suggestions – 3 approaches
After viewing the table above, I decided to do 3 approaches:
12%ile and COVID-19
12%ile and Long COVID
Quick Kaltoft-Moldrup suggestions
I expect all to be similar but with some differences. I will cut off suggestions around .425 to prevent information overload (which happens easily with the microbiome)
12%ile and COVID-19
The lack of a fine graduation of Confidence implies that we do not have that many applicable studies for the bacteria identified as important. Also this is what they had, not currently have.
12%ile and Long COVID
This has the graduation of Confidence values that I like to see.
Quick Kaltoft-Moldrup suggestions
Reminder – The WHY for suggestions
On the suggestion line, you may see a 📚. Clicking it will show the source of the recommendation and why. Remember the more positive impact (by number of studies reporting the same), the greater the confidence shown. It is the confidence that it will shift in the desired direction. It is not which works better. Microbiome Prescription strives to be open on the basis of it’s logic and allow easy verification by people who are interested.
Putting Suggestions together
Remember that the purpose of the site is to create prescriptions — suggestions to correct microbiome shifts. The suggestions attempt to be adjusted for side-effects on other bacteria. Labs suggestions are based on blinkered analysis, “You are too high in X, Z reduces X so we recommend it” – which often ignores the fact that X also increases Y which is also too high.
Suggestions are computed in two different ways with no overlap of source data (KEGG based on genes, and studies where substances were tested). Items that are on both sets of recommendations are definitely things to consider. There are items that may be only on KEGG suggestions because no one has done studies on them.
Remember, you can get opinions on over 3000 items in our database by going to the bottom of this list:
Iron was suggested by KEGG. I wanted to check it’s impact using the data from studies and was pleased with the result.
Iron was suggested by KEGG and appears to have a beneficial impact using studies data
I also confirmed magnesium was also a positive (and magnesium deficient, a negative)
This is a MODEL not a PROTOCOL
This is directed to people reading this post and saying “I will do what is described”. What is the difference? A Protocol comes from clinical experience and is a defined set of actions that are repeated for each patient. A model is a theoretical way to generate candidate actions that may help. This is not a model for Long COVID patients, it is a model for one person’s microbiome. Every Long COVID patient will have a different microbiome and thus different candidate actions. You can see this by looking at the next post on Long COVID microbiome.
An analogy, Long COVID can be compared to a headache. There are at least 17 types of headaches. You may need to see a dentist (tooth issues), or take a antihistamine (allergy) or take oxygen or …. Details drives the treatment.
Another post COVID person just contacted me, with their samples, so a second COVID post is also available Both this person’s sample, and the recent study confirmed my suspicion that Long COVID is a Post-Infection Syndrome. Post-Infection Syndromes are, IMHO, infection altered microbiomes that failed to return to normal.
Dialog Notes with User
Q: “I’ve got a huge issue/reservation with a part of the concept: the norm in the distribution of the data base might be far from normal. And even further, what is normal might not be optimal at all. What is normal reflects an average diet, but maybe an optimal diet would lead to an outlier sample, how do you address that issue….”
A: I do not use a bell curve, See this post for where I have evolved to. It’s based on Percentile and shape of distributions
Q: As a laywoman looking at the data my immediate focus went to methane-sibo. This matches my current issues and I’m surprised you didn’t mention it.
REMINDER: These are suggestions generated by an artificial intelligence program. Before implementing, they should be reviewed by your medical professional.
A reader had initial success from modifying the microbiome but it did not persist.
The reason I ended up at your website doing research into the connection microbiome and ME/CFS was that firstly I tried Miyarisan and it turned out to be one of the best things I ever tried, MY headaches and brain fog were early completely gone and I had a lot more energy. Unfortunately this wonder only lasted about 6 weeks till I overdid it and crashed and with that crash Miyarisan lost it’s effect on me.
The other thing was Nystatin, which I was given for the candida found in my gut last year and right on from the first pills I took, it gave me more energy ( so I doubt it had anything to to with the candida, but rather must have changed something else in my gut for the better). This lasted about 10 weeks and then pooped out and was not reproducible.
But these two times that I felt I got energy because of some changes in my gut, were very rare in the way the they just generally provided a relief in all symptoms, as I was just overall feeling better and had more energy, but without crashing. Most of the times I have trouble, because I am easily overstimulated and most things that give me energy give me instant fatigue rebound, so Miyarisan and Nystatin really were different and made me try to work on my gut.
She attached her tests and summarized them as “As to my tests, I guess the most notable things are my low TH1(Interferon Gamma), my low glutathione, high TGF beta, my decreased SOD activity.”
Reminder that recovery is a journey
In an earlier youtube review of another ME/CFS patient, I used the graphic below
I used this model for my last flare and can be seen by the list of posts below on CFS Remission. Each report was associated with a new microbiome test and a change of supplements etc to address the changes that the prior changes caused.
At this point we get some very interesting results. First, the bacteria by themselves do not match any symptoms.
But when we go over to the KEGG components that the bacteria produces, we see the type of predictions that we would expect
Conclusion: She does not have the typical ME/CFS bacteria shifts but she has the typical jacked metabolites imbalance seen in people with ME/CFS. Same crime — different crime family!
Action Plan
At this point, we have identify major items of concern.
Hand Picked Suggestions
I am going to run it two ways — first with the extreme outliers shown above, then including Firmicutes (which I rarely do)
If your physician is willing to prescribe “off-label” also do alternating every two weeks between a PPI and atorvastatin (prescription). PPI is over the counter in some places and includes:
omeprazole (Prilosec, Prilosec OTC, Zegerid)
lansoprazole (Prevacid)
pantoprazole (Protonix)
rabeprazole (Aciphex)
esomeprazole (Nexium)
dexlansoprazole (Dexilant)
For items from the suggestions above, I would suggest going with handpicked suggestion list without firmicutes.
I would suggest an initial retest at 4 weeks or so, a full cycle of a PPI and atorvastatin, at the same time a cycle of alternating licorice and triphala. We want to see if this has caused a downward movement of the two species of concern.
I am a strong advocate on doing alternative pulses. It is what C. Jadin does for antibiotics (changing them every month) and I also have read several modelling studies that found rotation had better success than continuous. The english explanation is simple: for anything you may take — 90% of the bacteria may be killed and 10% survive (resistant). If you keep up with the same, then that 10% slowly regrows as resistant to whatever you are using. Changing between two things that are 90% effective (and different), then it becomes 99% killed and 1% survive.
As you have witnessed, 6 weeks with one item and then the resistors recovered your dysfunction, for another substance it lasted 10 weeks. We want to keep to 2 weeks on and then rotate.
I checked the parent taxa on these two, and I see Carthamus tinctorius L (Safflower) inhibits one of them – so using safflower oil may help. There is no simple parent for the other.
As always, consult with your medical professional before implementing.
The key for creating an upload is identifying the taxon and the percentage of bacteria for a taxon.
Example
A reader wanted to upload his results and the lab would not provide a suitable CSV file. What they had was a page like shown below
Example if a 3rd party report
The starting point is simple, hand copy the data to Excel or equivalent (in many cases you can just copy the page or report and paste into Excel
The next steps
Delete any lines that do not have a measurement
Copy the percentage (or compute it) into a new column.
For those familiar with excel I used these 3 formulas
C: =FIND(” “,A3)
D: =LEFT(A3,C3)
E: =SUBSTITUTE(MID(A3,C3,10),”%”,””) * 1
The result is partial success with a few errors like below
I forgot to delete the ND – none detectable
For those caused by compound names, I need to count the characters to the last space and update Column C (I cheated by counting from the end and using =LEN(A25)-6. We ended up with
Names and Numbers. Note we have a “o” which is not a ND, so we replace with 0.0001
Also “Unclassified” should be deleted.
The Long Lookup
The next stage is time consuming… looking up the taxon numbers for each bacteria. There are two sources:
Lookup on NCBI, i.e. https://www.ncbi.nlm.nih.gov/search/all/?term=lactobacillus
Alternatively use https://microbiomeprescription.com/Library/Lookup
Add a new column before the percentage and put the taxon numbers there.
Next copy rows E and F to a new worksheet (remember to paste as values) and save as CSV file
The file should look like below.
Now Insert your email address as the first line. resulting in:
The nature of data for the microbiome is not a straight line, nor a bell curve. Finding associations is challenging with often poor results I know from years working as a statistician that finding a “magical data transformation” is the key to finding associations. However, a ongoing issue is over-fitting the data when people try formula at random. I have tried a variety of methods from machine learning — with poor results in general.
I put my lateral thinking cap on. Instead of using a defined explicit formula — instead an intrinsic transformation: the percentile of the readings. To do this approach, you need a large sample size – fortunately I have such with over 1500 pairs of data points being common. A similar approach was discussed in Percentile Regression: A Parametric Approach 1978, Journal of the American Statistical Association, but never gained popularity.
This post gives a walk thru of the process being done on 14,374,869 possible associations that we have (excluding symptoms and conditions)
Example
I picked one of my initial good results and will walk thru charts showing how charts change according to the approach. First the raw numbers plotted
We see a relationship which looks weak (flat) if you do not do the R2 calculations
Then we chart of log of the raw numbers (log of the values worked well to determine the Kaltoft-Moldrup normal ranges – KM is based on different moments of the resulting curves)
The pattern is stronger (20% higher R2)
The new way is shown below, using the intrinsic transformation to percentile
Plotting Percentile against Percentile (52% higher R2 than original)
Bottom Line
Finding associations as illustrated above, means we can tease information from our data. For example, for B12 levels, we have a strong association to Glycolysis (Embden-Meyerhof pathway), glucose => pyruvate. This means that the bacteria associated with that is likely associated with B12 production. For example, a few of some 2000+ strains associated with this module.
Faecalibacterium prausnitzii
Bacteroides vulgatus
Bacteroides uniformis
Parabacteroides distasonis
Bacteroides caccae
Bacteroides dorei
Bacteroides thetaiotaomicron
Bacteroides ovatus
Roseburia intestinalis
Flavonifractor plautii
Bacteroides fragilis
Odoribacter splanchnicus
Alistipes finegoldii
Eggerthella lenta
Additionally, it means that where there is a relationship between bacteria but we know nothing about how to modify one of the bacteria and something about the other; then we can propose suggestions by association. This will be coming soon to Microbiome Prescription – the citizen science site.
Hey do you think microbiota dysbiosis could cause circadian disturbance? Most articles go in an opposite direction and say its lifestyle causing circadian disturbance…But my disturbance is resistant to lifestyle… I just have primary circadian problem that might be even my worst symptom… Most resistant and almost lifelong.
Asked by a Reader
In keeping to “gold standard” of information instead of bloggers’ urban myths and ideologies, I head over to the National Library of Medicine studies.
“gut microbial metabolites influence central and hepatic clock gene expression and sleep duration in the host and regulate body composition through circadian transcription factors”[2020]
“Findings have suggested that gut microbiota play a major role in regulating brain functions through the gut-brain axis. A unique bidirectional communication between gut microbiota and maintenance of brain health could play a pivotal role in regulating incidences of neurodegenerative diseases. ” [2021]
First, a more precise definition of circadian rhythms from the above study.
A fundamental part of eukaryotic life, circadian rhythms are endogenous, entrainable biological processes that oscillate in a 24-hour period in concert with the circadian environment of the earth. Circadian rhythms can be found at an intracellular level and have the ability to impact all aspects of metabolism (11). The mammalian circadian rhythm is orchestrated by a master clock, located in the suprachiasmatic nucleus (SCN) of the hypothalamus (12). The master clock follows the 24-hour light-dark cycle (the diurnal cycle) and coordinates the release of neurotransmitters such as serotonin and norepinephrine. Serotonin and norepinephrine are present at higher levels during wakefulness, while melatonin peaks during the night, regardless of the diurnal or nocturnal sleep cycles across species… The peripheral circadian clock is a system of organs within the 22 body which collect environmental and internal signals in order to direct the expression of circadian clock genes
And then we read:
“food intake can disassociate peripheral clock periodicity from the master clock; when this happens, greater immune system activation and metabolic dysfunction occur”
“Dysbiosis and metabolic consequences resulting from circadian clock disruption may be due to increased permeability of the intestinal epithelial barrier “
“gut microbial metabolites such as the short-chain fatty acids butyrate and acetate may influence clock gene expression“
“Leone et al. found that a lack of gut microbiota, and consequently a deficit of microbial metabolites, resulted in markedly impaired central and hepatic circadian clock gene expression (40), suggesting the possibility that gut microbiota play a role in propagating circadian rhythm at the molecular level”
“Serotonin deficiency elicits the loss of the circadian sleep-wake rhythm”
“The microbes of the gastrointestinal tract exhibit circadian rhythm, and their composition oscillates in response to the daily feeding/fasting schedule.
The characteristics of the gastrointestinal microbiome and metabolism are related to the host’s sleep and circadian rhythm. Moreover, emotion and physiological stress can also affect the composition of the gut microorganisms. The gut microbiome and inflammation may be linked to sleep loss, circadian misalignment, affective disorders, and metabolic disease.
On the other hand, peripheral clocks are found in the nucleus of almost every single cell (eg, enterocyte, hepatocyte, myocyte, adipocyte), and they show circadian rhythms and oscillations that are dependent and independent of the circadian rhythms from the master clock. While the master clock responds mainly to light/dark cycle, peripheral clocks respond to other zeitgebers (eg, temperature, diet, timing, and content of food intake), which indirectly regulate the central clock … However, Parabacteroides, Lachnospira, and Bulleida were specific to the human GI tract. Lachnospira was unique in that it was the dominant species that were affected by time and behavior (energy consumption early during the day) [114]. However, it is not fully understood why some species increase with clock time throughout the day. One of the theories is that some species are bile resistant, so they increase during the day as the food is ingested, and bile is secreted (eg, Oscillospira and )
“We found that up to 20% of all commensal species in mice and humans undergo diurnal fluctuations in their relative abundance, resulting in rhythmic changes of the entire bacterial community over the period of one day. For instance, the common mouse and human commensal genus Lactobacillus increases in relative abundance during the resting phase (the light phase in a mouse) and declines during the active phase.”
Bottom Line
Time of day, time of year, eating time and diet impacts intra-day microbiome population and thus the metabolites being produced. Some of these metabolites have been shown to impact circadian cycle in recent studies. A few bacteria pulled from the studies cited above include:
Fusobacterium
Porphyromonas,
Prevotella
Bacteroides acidifaciens,
Lactobacillus reuteri,
Peptococcaceae
Eggerthella,
Anaerotruncus,
Desulfovibrio,
Roseburia,
Ruminococcus
Time of year impacts (and may be a factor for Seasonal Affective Disorder – SAD)
Helicobacter,
Bacillus,
Stenotrophomonas
Proteobacteria,
Lactobacillus
Romboutsia
I was unable to find any 16s clinical studies on SAD
Advice for taking samples
Record the day of the week, time of day, and if female, where you are in your cycle for stool samples. For best consistency (i.e. seeing what actually changed between samples) — make sure all follow up control for these factors as much as possible.
By same data, I mean the same FASTQ files, a detail file of the parts of your sample returned by a 16s machine. This is then processed through software to infer the bacteria. The result is two different reports. If you pass the same files to other providers, you will like get even more different reports. For why, see this post from 2019, The taxonomy nightmare before Christmas…
This post is going to look an actual example.
What a FASTQ file looks like… the letters CGAT mean adenine (A), cytosine (C), guanine (G), and thymine (T) – parts of DNA
Krona View
At this level, they look similar – but there is often a 25% difference between the numbers of a species.
ThryveBiomeSight
Comparing Samples
At the class level you can see some dramatic changes in counts and percentile. At present, I am using percentiles from aggregations of all labs sources.
When I hit 1000 samples from a specific lab, I will doing lab specific percentiles. Current counts — thus we are using an aggregate for percentile for all labs
From https://microbiomeprescription.com/Upload/Statistics
For items of concern, you can actually drill down manually on the bacteria. For example for Bacilli above.
You can also get the percentile that is lab specific by going to https://microbiomeprescription.com/Library/Statistics?taxon=91061 with no sample and then changing to the lab as shown below.
We find that we are at the 20%ile for biomesight specific samples and 2.4%ile for thryve specific samples. For explanations, you will need to ask the questions to the lab — microbiome prescription just presents the data.
The bottom line is that you want to always use the same lab software for comparing samples. Ideally, the same lab for the physical processing. Comparing the same sample that is processed by two different pieces of software results interpretation challenges.
To give a more human context — take a book and ask two people to retell it aloud, one is from the rural areas of Scotland (with thick Scottish accent) and the other from Mumbai India (with thick Marathi accent) with a third person (a native from Bermuda) trying to recall what they heard…. Different choice of words in the retelling with different intonations. That is the human reality — which also applies to labs.
When this site was started, there was one dominant player in retail-provider: uBiome. In June 2018, the first ThryveInside sample was uploaded, A year later, in May 2019, the first American Gut sample. A year later, in July 2020, BiomeSight started rolling in significant numbers — for 10 months, BiomeSight was the most frequent upload type every month. At present, I support 8 upload types and provide an API for any lab that wishes to do a direct transfer. BiomeSight lead the way here. Statistics are here for those interested.
The first three labs, uBiome, Thryve and American Gut, all used the NCBI Bacteria Taxonomy systems. These are number and thus easy to store in the database and economic to do analysis on. This is a critical foundation. There are problems using names, because names change overtime. One bacteria has 237 different names. As illustrated below — same bacteria was discovered by many different people. Each person gave it a name and published papers using that name. In time (especially with DNA techniques) it was realized that they were all the same!!
NCBI is an unique identifier just like social security number is for American. Unfortunately, Canadians have SIN numbers. Other nations have Person Numbers. The same thing has happened with lab equipment. The problem is matching identities. With non-Americans in the US, some are issued TIN numbers (and thus we are good for US identity), others do not have TIN numbers. A person is like a bacteria.
Case Studies With Microba and BiomeSight
Microba does not use NCBI numbers. Microba uses the Genome Taxonomy Database (GTDB https://gtdb.ecogenomic.org/) for taxonomic classification. The question arises, who attempts the mapping of the GTDB identifiers to NSBI — Microba or MicrobiomePrescription or no-one?
With cooperation from them (namely, they provided a reasonably complete list of the GTDB identifiers that they used), I was able to create a mapping table between those names and NCBI numbers that was not 100%, but sufficient to give meaningful results.
With BiomeSight.com, they added the numbers to their database. I always prefer the lab to take ownership of the mapping – there can be many nuisances specific to the lab equipment that they are using.
Popular Medical Tests that cannot be added to the data
There are two main reasons that these cannot be added:
They only measure selected bacteria (see below)
Their unit of measure is different. One counts the number of hex nut in a mixture of 1000 nuts; the other counts the number of packages of hex nuts (with a different number of nuts per package) in a carton of nuts. They are simply too different.
Lab Name
Bacteria Reported
Bioscreen (cfu/gm)
17
Biovis Microbiome Plus (cfu/g)
40
DayTwo
76
Diagnostic Solution GI-Map (cfu/gm)
34
GanzImmun Diagnostic A6 (cfu/gm)
72
GanzImmun Diagnostics AG Befundbericht
25
Genova Gi Effects (cfu/g)
28
Genova Parasitology (cfu/g)
7
InVitaLab (cfu/gm)
23
Kyber Kompakt (cfu/g)
11
Medivere: Darm Mikrobiom Stuhltest (16s limited)
16
Medivere: Darn Magen Diagnostik (16s Limited)
16
Medivere: Gesundsheitscheck Darm (16s Limited)
17
Metagenomics Stool (De Meirleir) (16s Limited)
53
Smart Gut (ubiome 16s – Limited Taxonomy)
23
Verisana (cfu/ml) aka (kbe/ml)
11
Viome (No objective measures)
29
For these test, users must transcribe whether the test indicated too high(↑) or too low (↓) levels. I give the ability to indicate how much…
How the labs represents varies greatly. Their units are not compatible.
Suggestions are based on these rough values and uses the same logic. A key limitation is that their normal ranges are likely computed assuming a bell curve and not Kaltoft-Moltrup Ranges. You may be acting on items that are in the typical ranges seen.
Issue of Missing Hierarchical Layers
If you look at “My Biome View” on Microbiome Prescription, you will see the hierarchy (per NCBI). Most labs do not give the full hierarchy in their reports. Often they will skip layers. The clearest example is Microba. They provide information in only 4 files.
But when this upload is viewed, you see all of the levels!
My Biome View
A more extreme example is the CosmosID’s PDF files, where they only list the species and strains!
The user who submitted this would see the following My Biome View…
Microbiome Prescription “completes” the data by summing up each level into the level above if missing. So I sum the count of all of the species in a genus to get the genus count if it was missing from the upload. There is an unfortunate gotcha. you may have 8000 in a genus and the sum of the species is 6000. If the lab provided the genus count, then we are good — no need to create a record with 6000. If we must create this level, then we are missing 2000 and higher levels are underreporting!.
This issue is also seen in some lab results. They scale the numbers so that the species that they report adds up to the count for the genus. What they do not report on is dropped from all of the parent levels.
When you use the Krona Chart, if there are no “unknown section” the0n this ignoring the not identified is a possible issue with the lab results. You can also do this on the My Biome View by comparing the numbers of the parent to the sum of the children – if they always match, then assume that the not identified are ignored.
Illustrates when the not identified is shown on a Krona Chart
Inconsistent Numbers
Above we have the case of the genus count being more than the sum of it’s species. This is a good state, because the numbers are more accurate. We have the unidentified bacteria being identified as least at the genus level.
I have also found cases where the sum of the species exceeds the genus. This can legitimately happen when alternative hierarchies are used. It becomes a problem when we attempt to keep everything in one hierarchy (“There can only be one!”)
From TV Series Highlander.
As a result, if the sum of the species (using NCBI hierarch) exceeds the genus, then we update the genus number for consistency (if we do not do that, then Krona charts can look bizarre — which a user emailed about).
Bottom Line
“Different strokes for different folks” is the problem. In accepting data from 9 different sources, I need to harmonize. The key that I play in is NCBI. This is a huge benefit because it is used with KEGG: Kyoto Encyclopedia of Genes and Genomes, which really enhances analysis.
Right Solution
It is simple, the labs should add to their websites equivalent pages seen on Microbiome Prescription — but only using their lab results. If their staff lacks the skills, I am a professional developer and can be contracted to do a lot of the backend coding (at my usual commercial rates ).
If you wish to be pro-active.
Verify that every bacteria shown on my biome view is shown on the lab results page. If it is not, they are skipping elements of the hierarchy
Verify that the count agrees, if not look at what is added up
Contact the provider and ask for automatic transfer to be implements. Code wise it is very simple, a few hours of work at most for most developers. What is needed is documented here, including a test site!
I cannot fix the root issue — inconsistent data. You are their customers and by being vocal, you can make a difference. If the upload is correct and complete — I make no modifications, it is only for problematic uploads.
Recent Comments