Increasing Akkermansia: Probiotics and Kampo

This is a personal observation post and not my traditional aggregation of studies. Your experience may differ.

Known Sources:

Akkermansia Probiotic

This is available exclusively as Pendulum Akkermansia. We signed up for the subscription plan when it was first announced, and they have kept shipping at their low introductory price every month. Within I week of getting the first shipment, I read a few posts that “it did not persist”. Excuse me, the typical turnaround time for a microbiome test is 3-4 weeks. I suspect that someone is gas-lighting. Or, they may have been taking a hostile probiotic at the same time.

We subscribed to only one bottle and we decided that the wife would take it for the first two months (we rotate probiotics). The results were her Akkermansia muciniphila went from 4,530 to 64,920 (91%ile) — so, it appears to have persisted (or helped the native ones to prosper by creating a friendlier environment). I should point out that that the fact that she has some to start with may be a significant factor for this awesome increase (that is, the environment was not totally hostile).

She noticed some significant changes, to her the most significant one was a major improvement in sleep.

Kampo (Japanese Name)

This is also known as fang feng tong sheng san (Chinese: also may be named: Fang Feng Tong Sheng Pian (Fang Feng Tong Sheng Wan,  防风通圣片) – best names to use on Amazon) or Bofu-tsusho-san. A professional user of Microbiome Prescription reported a major increase of Akkermansia with his patients using this and forwarded two studies that illustrated this.

  • Bofutsushosan improves gut barrier function with a bloom of Akkermansia muciniphila and improves glucose metabolism in mice with diet-induced obesity. [2020]
  • Increase of Akkermansia muciniphila by a Diet Containing Japanese Traditional Medicine Bofutsushosan in a Mouse Model of Non-Alcoholic Fatty Liver Disease [2020]
A massive bloom!
As an image

So, what else does Kampo do?

For additional studies, see pub-med.

https://www.tsumura.co.jp/english/products/pi/JPR_T062.pdf

Other Approaches

Current Experiment

As mentioned above, we rotate probiotics as a standard practice in our household. I am starting both the Akkermansia and Kampo and plan to do a new microbiome report in a month (so 2 months until this post gets updated). I expect a significant jump in my Akkermansia. At last test, it was 570 or 0.05% (via Biomesight), below the lower end of Dr. Jason Hawrelak Recommended ranges.

Results:

A 7 to 8 fold increase was seen after a month. The probiotic was stopped for a week before the sample was taken.

May be an image of text that says "Low High Description Bacteria Distribution (KM) BiomeSight (KM) 2021-10- 19 Self (family) Akkermansiaceae 16 Concern? Count BiomeSight 269847 글 Bacteria 2022-01- 24 Self (genus) Akkermansia Percentile 10 Distribution 570 269847 Count 署 Bacteria 34.7 (species) Akkermansia muciniphila Percentile 16 Distribution 570 4160 雪 199384 34.6 53.3 Bactena Distribution 570 4160 38.5 38 64.7 4160 55.9"
Using BiomeSight algorithm on FASTQ file
May be an image of text that says "Low Description iption High Bacteria Distribution (KM) Thryve (KM) 2021-09- 09 TEKNT (family)Akkermansiaceae 16 Concern? Thryve Count 269847 2022-01- 24 self (genus) Akkermansia Bacteria Percentile 10 Distribution 637 Count 269847 (species) Akkermansia muciniphila 言 Bacteria 35.8 Percentile 16 Distribution 637 4226 199384 言 Bacteria 35.7 53.4 Distribution 631 4276 64.8 39.4 4203 55.9"
Using Ombre algorithm on FASTQ file

As an unintended side-effect, a lost of 7+ kilos over 7 weeks without changing diet (or dieting)!!! I have also taken high dosages of bifidobacterium infantis with it. It is known to encourage akkermansia too.

Before/Donor/After FMT Analysis

A user of my site that is active consulting on autism microbiome manipulation obtained permissions for me to do an analysis of one of his patients going through FMT. All of the microbiome testing was done via Biomesight (including the donor). This is specific type of data that I have been pleading to see if we can make predictive models of what could occur with FMT.

MeasurePriorDonorAfter
Taxonomy374406550
Elusive336
Rare4817
Sparse121533
Infrequent283768
Uncommon6689145

I did analysis at the Species, Genus, Family, Order and Class level trying many many approaches. This summarize my key findings.

The second sample was done one month after the FMT. Patient was very good for a couple of days, then “the war started”. New more severe autism symptoms appeared.

Do NOT expect it to reduce overgrowths!

Looking at the lowest numbers of the recipient prior and the donor, we found that the post-FMT numbers had a clear pattern.

  • At the Class level, 97% was higher than the lowest of the two, 58% was higher than the highest
  • At the Order level, 96% was higher than the lowest of the two, 56% was higher than the highest
  • At the Family level, 95% was higher than the lowest of the two, 61% was higher than the highest
  • At the Genus level, 91% was higher than the lowest of the two, 51% was higher than the highest
  • At the Species level, 94% was higher than the lowest of the two, 47% was higher than the highest

This was shocking — 50% of the bacteria will be higher than either the donor’s or recipient’s levels. Many people will assume that the levels will magically average the two levels. The reality seen here is that only 50% of the time will the new level be between these two levels and 50%of the time it will be higher than either. This is unlikely to be a preferred outcome.

Unexpected Disappearances

There were several items where both the recipient and the donor had bacteria, they were gone in the post-FMT sample! This was not expected, of special interest is that Lactobacillus was wiped out.

  • Order: Puniceicoccales
  • Family: Clostridiales Family XVI. Incertae Sedis
  • Family: Lactobacillaceae
  • Family: Puniceicoccaceae
  • Genus: Alkalibacterium
  • Genus: Butyricimonas
  • Genus: Carboxydocella
  • Genus: Catonella
  • Genus: Lactobacillus
  • Genus: Macrococcus
  • Genus: Pelagicoccus
  • Genus: Turicibacter
  • Species: lingnae
  • Species: Streptococcus oralis
  • Species: Veillonella parvula
  • Species: Streptococcus pseudopneumoniae
  • Species: Carboxydocella ferrireducens
  • Species: Sutterella wadsworthensis
  • Species: Catonella morbi

Many New Kids showed up!

These are bacteria not seen in the recipient prior nor the donor sample

  • Class Level: Acidobacteria, Calditrichae,Chitinophagia,Flavobacteriia,Ktedonobacteria,
  • Order Level: Acidobacteriales, Calditrichales, Caulobacterales, Chitinophagales, Chroococcales, Desulfobacterales, Flavobacteriales, Kiloniellales, Nostocales, Oscillatoriales, Rhodocyclales, Rickettsiales, Streptosporangiales, Synechococcales, Syntrophobacterales, Thermogemmatisporales,
  • Family Level: Acetobacteraceae, Acidobacteriaceae, Anaplasmataceae, Calditrichaceae, Caulobacteraceae, Chitinophagaceae, Chroococcaceae, Clostridiales Family XII. Incertae Sedis, Cyanobacteriaceae, Cytophagaceae, Desulfobacteraceae, Dysgonamonadaceae, Flavobacteriaceae, Fusobacteriaceae, Hymenobacteraceae, Kiloniellaceae, Listeriaceae, Nostocaceae, Oceanospirillaceae, Oscillatoriaceae, Oxalobacteraceae, Prevotellaceae, Pseudanabaenaceae, Rhodanobacteraceae, Rhodocyclaceae, Rickettsiaceae, Rivulariaceae, Streptosporangiaceae, Synechococcaceae, Syntrophobacteraceae, Thermogemmatisporaceae, Thiotrichaceae, Verrucomicrobiaceae,
  • Genus Level: Acholeplasma, Acidaminobacter, Aminobacterium, Ammonifex, Anoxybacillus, Asticcacaulis, Bilophila, Caldithrix, Calothrix, Catenibacterium, Chroococcus, Cyanobacterium, Desulfofrigus, Desulfosporosinus, Dokdonella, Dysgonomonas, Edaphobacter, Ehrlichia, Emticicia, Escherichia, Fusibacter, Fusobacterium, Gillisia, Haemophilus, Insolitispirillum, Kushneria, Listeria, Luteibacter, Lysinibacillus, Marinospirillum, Microbacterium, Neisseria, Niastella, Novispirillum, Oleomonas, Olivibacter, Oscillatoria, Parapedobacter, Paraprevotella, Pelotomaculum, Pontibacter, Ralstonia, Rickettsia, Roseomonas, Sarcina, Sebaldella, Skermanella, Tepidanaerobacter, Tepidimicrobium, Thalassospira, Thermoanaerobacter, Thermogemmatispora, Thiothrix,
  • I will skip the species level…

Bottom line is that the microbiome has become much more diverse

Recent FMT aspects

FMT destabilizes the microbiome, there are “strain riots” in the guts. We can see this with all of the “New Kids” showing up because the existing occupants are busy dealing with each other. This can be seen by the post microbiome having a lot more taxonomical items (550 vs 374 before – a 47% increase), The microbiome, over time, will downsize and stabilize with a new normal. During this period, you want to entrench your desired items by feeding it the right things and avoiding the wrong thing.

Personally, I would suggest a new sample every 6 weeks to monitor the stabilization.

Is FMT Worth the Risk?

FMT is effectively an organ transplant. Like organ transplants, there are significant risks of rejection and no way to undo it once it happens. From correspondence with many people who have tried it for ME/CFS, my feelings are that it is not a magic bullet. It is closer to playing Russian roulette, but with 5 of the 6 bullet chambers having bullets in the chambers.

I just spent 90 minutes zooming to the consultant involved with this autistic child. We both agreed that FMT for autistic children is not a wise course. The consultant is scratching their head on what to help this child recover from this situation.

Some prior posts on FMT

Hydration and the Microbiome

If you are dehydrated you will typically see a change of stools. Seeing hard pebbles and dry stools is a logical consequence of the body rationing water. Recently, we got a new smart scale ($30 on Amazon) that comes with a smart phone app and does multiple measures, including hydration. I have been running below the desired range, so I am busy hydrating with Gerolsteiner Mineral Water (from Traders Joe) etc.

Conceptually, the availability of water should impact the microbiome. So tonight I started to search out studies to confirm my speculation.

Effect of Increased Daily Water Intake and Hydration on Health in Japanese Adults, 2020

There were five out of the 108 types of intestinal bacterium at the genus level that showed significant differences within and between groups due to water supplementation, and 17 out of 564 species identified by the homology search of OTU representative sequences in the DDBJ 16S database showed the same behavior… Of these, a slight correlation was seen in the intervention group between changes in blood pressure and changes in the Psudoflavonifractor capillosus bacterial count (R = 0.42) Kineothrix sp. (R = 0.36), Feacalibacterium prausnitzii (R = 0.38), and Ruminococcaceae (R = 0.34) showed weak correlations between changes in body temperature and changes in bacterial count

Some related studies that implies impact on the microbiome but failed to do microbiome analysis with the study

Dehydration is often reported as a contributor to various conditions

Bottom Line

Hydration is an often overlooked aspect of improving the microbiome. It has impacts on blood pressure, body temperature, mood and cognitive ability. While we have few studies on it’s microbiome impact, we can likely assume safely that improved hydration will improve the microbiome. It should not be left to a “I drink enough water, I do not feel dehydrated”, but with actual measurements. Improving it is actually a slow process — my goal is to shift from my current level below, to at least 55%.

Revisions of Symptom Entry and Management

I just pushed an update that included features requested by a reader. The main items are:

  • Ability to enter symptoms when you do a test (“Pending Sample”)
  • Ability to copy symptoms from one sample to another
  • Ability to delete all symptoms for a sample with one click
  • Generation of a short list of possible symptoms from your bacteria

All of these options are shown for each sample via the dropdown:

Entering Symptoms

This has had one minor change, a new choice appears, “Pending Sample”. It allows you to enter symptoms when you do the sample (instead of trying to remember them 4 weeks later). Also two buttons are added to make status easier. Remember to use the Search box to filter

using Search
Clicking [Show All Items]


Clicking [Show Picked Items]


Quick Symptoms Additions

This takes your forecast symptoms with sufficient significance, removes any symptoms already entered, then show symptoms to consider.

If you check a few and click [Add]

These items will disappear from this list.

Symptoms Management

This allows you to copy symptoms between samples (for example, the same sample may be processed by multiple labs). Just select the sample you wish to copy from and click [Copy]

If you realized that you made a mistake, you do not have to uncheck item by item. you can single click all of the items.

Addendum

The algorithm for predicting symptoms has been revised (hopefully improved). There may be a further change, have some complex modelling to add. Also the ability to change from sample to sample has been added

From Bacteria prediction
From Consensus Report

Colonoscopy prep may be a blessing

A reader mentioned getting scheduled for one. Coincidentally, a week ago, I had a natural clearing out of the bowels (source unknown). I suspect that both scenario may produce the same opportunity to do some restructuring.

The first item to be aware of, gravity, bacteria further up the flow will repopulate (for better or worst). For myself, once it became clear that the bowels were being deeply emptied, I loaded up on herbs that impact many of the bad bacteria for a day (several capsules of each, several times a day).

The rationale was to thin out the bacteria that are up-channel.

I then started to repopulate, the first item, an E.Coli probiotic, symbioflor 2 e.coli probiotics, stopped the natural clearing within 2 hrs (not surprising given what made Mutaflor, the alternative E.Coli probiotic, famous back in 1918). I then proceeded to load up on only probiotics that are known to persist (see thus post, Studies on Probiotic Persistence). For myself, I noticed some improvements (but really I do not have much wrong to improve).

This simple approach conceptually should work with colonoscopy prep. If moviprep (prescription)  is being used, we have a pretty good profile on it’s impact from BMJ’s Effects of bowel cleansing on the intestinal microbiota

That’s it — seize the opportunity!

Which brand of Herbs? A reader asked

Long ago we changed over to making our own pills/capsules using Organic Bulk Purchases. It has the following benefits:

  • Much lower cost per capsules
  • It is organic ingredients
  • Often you can also make tea from it (depends if it is powdered or not)
  • No fillers or creative mixology from brands.

A few examples

Organic India Triphala Powder - Immune Support, Digestion, Adaptogen, Colon Cleanse, Nutrient Dense, Vegan, Gluten-Free, Kosher, USDA Certified Organic, Non-GMO, Triphala Powder Organic - 1 Lb Bag
454 g for $16, versus 41 g for $15 as retail capsules
220 g for $7 versus 90 g for $19.00 as prepared capsules

One Readers Feedback

The experiment was an abject failure, unfortunately. 
I cleared the bowels and took no action the day of colonoscopy (Monday), other than to eat a dinner that included some fermentable fiber veggies. 
The next day (Tuesday) I took the baby scoop of Custom Probiotics D Lactate Free, which includes l rhamnosus. Continued eating meats and fermentable fiber veggies, introducing some shitaakes.
SIBO symptoms roared back with a vengeance that seemed almost too fast to be possible. By Tuesday night I had gross SIBO burps and an upset tummy. 
Wednesday I took a heaping baby spoonful of d-lactate free probiotics and suffered even more upset. By Thursday I forewent the probiotics but continued eating fermentable fibers, with a violent, burning, smelly bowel clearing that evening. I also became uncharacteristically emotionally reactive. 

Felt better Friday and switched to non-fermentable fibers. I had an mHBOT session that typically “resets” me, but after a low-fermentable fiber dinner, I got hit with a wave of brain fog I haven’t had for some time since doing rifaximin in Sept, continuing HBOT, and taking continued boswellia and Atrantil. 
I fogged out again after my morning Americano today, which is usually fine. Having unhappy poops again. A bit frustrated—I seem to have sent myself in the wrong direction and don’t totally understand why. I’ll add theanine for mast-cell control and start neem. Considering a rifaximin round. 
One theory—I have high mycophenolic acid in urine, and noted rats without microbiome do better when inundated with mycophenolic acid. Wonder if this is an angle to explore. 
Anyway! I thought you may be interested in the results—hope this wasn’t an overshare! I figure we’re all learning together.  

My comment: It is clear that the clearing set things up for a change – unfortunately, it went in the wrong direction. One of the items that I cited, “load up on only probiotics that are known to persist (see thus post, Studies on Probiotic Persistence).” was not done — Custom Probiotics D Lactate Free is not known to persist.

Producers of Butyrate, D-Lactic Acid and Histamines

These are often asked for with people often running off old research. Often the research is based on seeing an increase of one of these when a specific bacteria increase… hence. this bacteria must be producing it! This was the best that we could do from old, last millennium technology and lab tests. We have enter a new world that is summarized on Kyoto Encyclopedia of Genes and Genomes. Instead of measuring substances (never with a pure culture of a specific bacteria), we can look at the gene in each bacteria and what they produce. In other words, we can get more accurate information that is truly bacteria specific.

https://www.kegg.jp/entry/C00388

These pages list the enzymes that produces it, for example 1.4.3.22        2.1.1.8         2.3.1.-         4.1.1.22        
6.3.2.18. We can then look to see if the enzyme consumes (substrate) or produces it.

https://www.kegg.jp/entry/2.1.1.8 This CONSUMES histamine
https://www.kegg.jp/entry/4.1.1.22 this PRODUCES histamine

Then it is just a matter of looking up which bacteria has these enzymes!

Specific Species and Strains

The three files below are directly from KEGG data

Genus of the producers

Unfortunately, no lab detects all of these strains and often report “unknown Species in Some Genus“. I have seen that being as high as 45% not being identified in some samples. A work around is to look at the genus numbers — which are usually pretty complete for most labs. This is a two sided coin, because some members of a genus may be a producer and some may not be. We do not know the ratio for a genus between these two. This approach will identify potential candidates, but not definite candidates… more fuzzy logic.

Bottom Line

All of this information is freely available on Kyoto Encyclopedia of Genes and Genomes, it just take diligence to extract and assemble it. If you see items on a labs reporting that is NOT on these lists, challenge the lab to produce evidence. I suspect most labs will produce studies with circumstantial evidence only, “because crime goes up when this ethnic group moves in — it must be that ethnic group” — ignoring that a different ethnic group may be behind it. An example of a drug problem in one city at one time. Most of the drug dealers were African-Americans, but the importers were Vietnamese. With KEGG, we are looking for the fingerprints on the gun or drug packages intercepted,

IBS/SIBO/Constipation etc etc etc

A reader requested an education analysis using the tool at Microbiome Prescription.

Back story-long history of gut issues/IBS.Terrible time post partum 22 years ago with sleep issues  and mental health. I’ve been down the hell hole of modern medicine on all kinds of meds for years. Finally diagnosed with SIBO in 2019 and then no luck with treatment. More recently had bad reaction to high doses of Vitamin D-gut issues worse- total body pain, inflammation,  food intolerances,  terrible insomnia. I will add-horrific constipation not resolved unless I take herbal formulas [Microbe Formulas  Bowel Mover and Dr. Christophers Lower Bowel Formula. I try to rotate. No specific order. I do think the garlic in the Microbe formulas does help.]

She also mentioned mold and Lyme markets. From Citizen Science we have Comorbid: Mold Sensitivity / Exposure and Infection: Lyme. I will do those separate at the bottom.

I am going to do layers of suggestion and see what evolves. We start with Dr. Jason Hawrelak criteria for a healthy gut. We find a lot of issues as shown below

TaxonomyRankLowHighYour ValueStatus
Bacteroidiaclass03561.543Not Ideal
Akkermansiagenus130.005Not Ideal
Bacteroidesgenus02058.983Not Ideal
Bifidobacteriumgenus2.550.014Not Ideal
Blautiagenus5106.56Ideal
Desulfovibriogenus00.250.004Ideal
Eubacteriumgenus0150.007Ideal
Lactobacillusgenus0.0110.005Not Ideal
Methanobrevibactergenus0.00010.020Not Ideal
Roseburiagenus5100.795Not Ideal
Ruminococcusgenus0154.796Ideal
Proteobacteriaphylum045.34Not Ideal
Bilophila wadsworthiaspecies00.251.08Not Ideal
Escherichia colispecies00.010.047Not Ideal
Faecalibacterium prausnitziispecies10150Not Ideal

The key suggestions computed by the AI are shown below. We will add more suggestions and end up with a consensus report joining all of these set of suggestions into a single report

A second approach is not to limit to a few key items, instead look for odd items across all bacteria. We use Kaltoft-Moltrup Ranges and get the following bacteria being identified:

Bacteria NameAnalysis
  BacteroidaceaeToo High
  BacteroidesToo High
  Bacteroides caccaeToo Low
  Bacteroides gallinarumToo Low
  Bacteroides paurosaccharolyticusToo High
  Bacteroides thetaiotaomicronToo High
  biotaToo Low
  Blautia hydrogenotrophicaToo Low
  CatonellaToo Low
  Catonella morbiToo Low
  ErysipelothrixToo High
  Erysipelothrix murisToo High
  FaecalibacteriumToo Low
  LactobacillaceaeToo Low
  Parabacteroides distasonisToo Low
  RuminococcaceaeToo Low
Many of these are common with IBS
Suggestions generated

Our third pass, is using US National Library of Medicine studies that identify certain bacteria associated with IBS. We will use IBS but widen the criteria used to extreme 6%. Some of the bacteria are cited above, and some are new.

Bacteria NameAnalysis
  BacillusToo Low
  BacteroidaceaeToo High
  BacteroidesToo High
  Bacteroides ovatusToo High
  Bacteroides thetaiotaomicronToo High
  Bacteroides uniformisToo Low
  Bacteroides vulgatusToo High
  ClostridiumToo High
  DesulfovibrioToo Low
  Dialister invisusToo High
  EnterococcusToo Low
  FaecalibacteriumToo Low
  RuminococcaceaeToo Low

We notice that   soy,  Cacao and  lactobacillus casei (probiotics) seem to be included every time, although we have different bacteria being selected.

Going over to citizen science, we see four matches for SIBO

Bacteria NameAnalysis
  Escherichia albertiiToo High
  PaenibacillusToo Low
  Serratia entomophilaToo High
  Symbiobacterium toebii Rhee et al. 2002Too Low

Unfortunately the information we have for this is very limited.

Going back to US National Library of Medicine for SIBO, we get NO BACTERIA matches at all. My conclusion is that it may be atypical SIBO.

At this point, I want to check some specific items that she cited. There is a tool for that

  • For Vitamin D, we appear to have adverse effects
    • Categoric Sum:1
    • Categoric Average:0.1
    • Log(Count) Sum:-8.4
    • Log(Count) Avg:-0.6
  • For Garlic, we have a definite positive effect
    • Categoric Sum:3
    • Categoric Average:0.3
    • Log(Count) Sum:7.4
    • Log(Count) Avg:0.8

These predictions are solely from the microbiome and agree with what she has experienced.

Some Predictions

Above tested two substances that had been tried and the prediction appear to agreed with her experience. She asked about an items she was planning to take or recently started.

  • lactoferrin – which does not match any item, I selected iron and the results suggested that it will not improve matters. This looks at all undesired shifts.
    • Categoric Sum:0
    • Categoric Average:0
    • Log(Count) Sum:-10
    • Log(Count) Avg:-0.8

I also check the merge consensus report (see bottom) where we are selecting only the bacteria of concern. It is also an avoid

Consensus Report

Each of the above list of suggestions are stored on the server (for 24 hours) and we can see all of them together.

Our top suggestions (i.e. items that moves everything above in the right direction without exceptions)

Safest Takes

The complete list is below for the person to explore in more details. There are 400+ items that have good or bad impact.

My non-medical profession selection would be the following as shown below, using dosages from clinical studies,

ItemDosage
resveratrol (grape seed/polyphenols/red wine)2000   mg/d
soy20 gm/day
Cacao2000   mg/day
barley60 gm/day
lactobacillus casei (probiotics)48000   MCFU/day
(48 BCFU)
fructo-oligosaccharides (prebiotic)15 gm/day
walnuts75 gm/day
folic acid,(supplement Vitamin B9)5   mg/day
N-Acetyl Cysteine (NAC), 2400   mg/day
vitamin b3 (niacin)1000 mg/day
lactobacillus reuteri (probiotics)5 MCFU

As always, this is produced from a computer AI model and not clinical experience. Before any change is done, it should be discussed with your medical professional. Some items, like 1000 mg of niacin per day may require testing (see this summary on niacin from the National Institute of Health)

Mold and Lyme Markers

Lyme is always a fuzzy area –if the person had ever had EBV and their microbiome is off, then false positives are well reported in the literature.

Mold Sensitivity
Lyme associated

The suggestions are shown below, there are a few matches with the above

Safest Take

I did a side by side comparison and found that there was a lot of disagreement between the sets of suggestions. That is not unexpected, because the bacteria selected determines the suggestions.

My gut feeling is that the IBS/SIBO is the preferred one — the citizen science did not have a single item auto checked, I had to go with the secondary items 💡 to get suggestions. This implies a weak match. Second, the IBS/SIBO included the gold standard bacteria identified from formal clinical studies. In short, likely better quality of information.

For those that are interested in how I created the above comparison, see this video — just change the URL to Source and enter a name in the column before pasting between sheets.

Analysis of a Long Covid Microbiome Sample

The reader describes their situation below:


I am living with LongCOVID following infection in March / April 2020. I contracted COVID-19 in the workplace, employed as a pharmacist at an NHS hospital in South Wales, U.K.

I  shared my story with WalesOnline at the latter stages of 2020 due to the lack of awareness around LongCOVID, and I share with you below for your interest.

📺 ‘Super-fit pharmacist who has ‘long Covid’ now left breathless by short walks’

📺 ‘30-year-old fitness fanatic with long Covid details his horrendous list of symptoms’

Unfortunately, I am still troubled by GI symptoms and despite improving over the past few months, I’m still having difficulty with bowel urgency / diarrhoea and mild abdominal pain. I lost 10kg in 10 weeks between July – Sept. 2020 (72kg at my lowest); thankfully this has recovered and I have gained weight, albeit chubbiness, weighing 88kg last week. I was diagnosed at the start of 2021 with ‘post-viral IBS’ and ‘leaky gut syndrome’, but GI clinicians are at a loss of how to proceed with my symptoms, hence my purchase of the BiomeSight kit. I have tried numerous diets (FODMAP, dairy-, gluten-free), again, to no avail.

I approach my 20th month since first being infected and I am still quite a distance from where I was pre-COVID doing all I possibly can to recover, so I would be extremely grateful for your insight, not only to help myself, but others in a similar situation.

Approach #1

As we have two microbiome profiles for COVID from the US Nation Library of Medicine, I will apply each one using 6%ile filter (values in the top or bottom 6%ile) to get a feel for the ground work. Then I will apply the ME/CFS for a third one (because of the similarity of Long COVID and ME/CFS).

For those not familiar with selecting

We end up with a short lists of bacteria (the titles links to the bacteria and studies reporting these shifts)

Active COVID 230 suggestions
Long COVID 280 suggestions
Chronic Fatigue Syndrome 230 suggestions

Almost everything is too low. Rather than examining suggestions from each of them, I will go directly to the consensus report. We hit a surprising 108 items on the safest take (items that will not shift any of the above in the wrong direction). Most are recommended in each case (Take Count = 3)

Safest Take

A few quick notes: Apples are very rich in pectin (some studies used apples and other pectin — I always try to keep data as reported and not do ‘well it’s just like…’ simplification). Similar with inulin and chicory.

The Safer text (some pro and some con) list was short and a bit of a mixed bag. With 108 items on safest, I would tend to ignore these. No need to include them.

On the avoid list we have “magnesium deficient diet” — which usually translates to magnesium rich or supplements.

I attach the complete list below of 304 different items.

Approach #2

This person is a pharmacist and thus looking at off-label drugs may be interesting for him to review. There are no accepted drugs for Long COVID, however, for ME/CFS often the top off-label drugs have often been used (with good results) by ME/CFS specialist (often at risk of professional censure). I have also added in CFS/ME with IBS (only Bacteroides Low was a match), and IBS to the consensus report.

The Criteria Selection being tried
Bacteria selected from IBS

The number of drugs that could influence these bacteria (good or bad) was almost 1300. I included some non prescription items to serve as a reference point (i.e. do drugs do better than some alternatives). In the small list of antibiotics at the top, I see several of the works for ME/CFS antibiotics — especially, those used by Cecile Jadin, MD: Tetracyclines, macrolides. Jadin does antibiotic rotation: 10 days on and 20 days off, then change to the next antibiotic. I have seen a few PubMed studies finding rotation was superior.

I noticed that several antibiotics often used for ME/CFS and IBS was on the avoid list: rifaximin (antibiotic)s, azithromycin,(antibiotic)s

The full list is attached

Approach #3

Above we worked on diagnosis, we are now going to switch to symptoms. My experience is that symptom-to-bacteria associations are much stronger than diagnosis to bacteria. Mileage will vary.

Oh have I mentioned that the symptom prediction from bacteria matches my symptoms almost completely? I think it’s 17 out of 20. Pretty incredible.

From a user in Europe by email on 11/11/2021

Below are his reported symptoms against predicted symptoms. It is interesting that many several predicted symptoms are autism related (which he does not have). This approach uses the bacteria that citizen science has associated to the symptoms (instead of clinical studies to the diagnosis). In theory, it will often be more sensitive for identifying the bacteria of concern.

See the video for how we do this. The final suggestions in Excel/csv format is below

Bottom Line

The intent of Microbiome Prescription site is to improve the odds of helping by working off studies on the US National Library of Medicines (at present, there are almost 6000 articles that we were able to harvest information from). We are very open on the where we get data, for example – for where we get the list of bacteria associated with a condition

From https://microbiomeprescription.com/citations/PubMedCitations?Code=LCV

And sources for how we know that something changes bacteria populations. In this case because of the high number of studies on inulin it will receive a high weight if certain bacteria are being targeted.

Example of what inulin impacts

We also try keeping faithful to the term used in the studies — apple contains large amounts of pectin, while some would just combined these to pectin (or apples), we attempt to keep the fine details. One related area that needs calling out is studies using items like  luteolin (flavonoid). If you click on these, you will go to a summary page with a link to foods containing it

We have a list of foods and amounts that contain it. It’s an extra step, but since these foods were not cited in the study, we “keep religion” and only cite what was used.

I am not licensed medically, and thus there is no clinical experience (or bias) for the suggestions. It is an uber-logical model.

With that said, this person needs to sit down with his significant other, look thru the lists and decide which options they wish to try. Being a trained pharmacist means that he can also evaluate the prescription options for risk and in some cases, try to game the system… for example: Atorvastatin … he may want to test for the conditions where it would be prescribed, if he is a little high — he may wish to use that as a “standard of care” rationale for getting a prescription — it’s an off label use (like Viagra was not intended for what it is prescribed for today).

As always, any planned action should be reviewed by their knowledgeable medical professional before starting.

EU/JRC Technical Report related

Caution for other Long COVID Patients

Before COVID, you had a unique microbiome, COVID “infection formed” it to suit its needs. These changes caused symptoms, made it easier for secondary infection and allow “alternative community of bacteria” to become established. How it changed depends on what it was like before and which variant of the virus. While the above suggestions are likely similar to what your suggestions could be, it is really important to get your own microbiome sample to work from. There will be large differences between people. With this approach, we can be single person specific for a treatment plan.

P.S. This sample was done via Biomesight, a UK based firm

Suggestions from Symptoms are Changed

A few weeks ago, I stumbled on some algorithms that had good results for predicting symptoms from bacteria. The next logical step was using the associations to get suggestions. While working on a blog post, I was getting odd results and digging into why, I discovered both logic and computational errors. Two readers had also raised questions about apparent bizarre logic. They were right — my logic was too simplistic and needed revision as well as better exposure of the logic being used.

The corrected version is up now. The main differences are:

  • Auto checking check boxes will happen less
  • Possible additions (unchecked) are marked with a 💡 
  • For Premium users, you get to see more of the gears that are turning.

To explain the issue, let us look at some details shown when in professional mode.

The old logic made suggestions to move away from the Cohort number. So for Emticicia, because we were higher than the Cohort value, we would try to raise it. This revision tries to lower it towards the 50%ile instead. The conceptual logic of moving away from the cohort was correct ignoring the sample percentile was incorrect. This implementation revision should correct this. For the other two bacteria above, we see that the cohort was high and the shift was even higher — again, moving away from the cohort is the desired, but moving higher than 90%ile is likely a poor choice, in this case you want to really move it down a lot.

The other factor is taking into account the z-score, etc. Some pages may have no automatic check. If you just click thru, you may get this message:

Let us look at some of the automatic checked

Both the person’s percentile and the Cohort are high, one was below the cohort and one was above. Because they are both high, the logic is to move them down to the middle (ignoring which side of the cohort it was on). The last one was not checked despite being Very Strong because the sample percentile was so close to the 50%ile (middle value)

A third set of examples is below, which include the weight being visible (likely will be moved to a professional feature — mainly because it can be more complicated to interpret well)

To get an automatic check the weight needs to be at least 20, for the 💡 , at least 10. For Acidobacteria, it is low but it is also a considerable distance from the cohort average. If selected, we declare a negative value and thus attempt to increase it (potentially moving it much closer to the cohort typical value — i.e. increase symptom). On the flip side, at only the 10%ile, you do not really want to decrease it more. A dilemma – excluding it is actually the best path. It has significance for the symptom forecast but has no clear action for altering the associated bacteria.

For this last one, we see Collinsella is a middle peak, and the desired direction is to increase it (negative value). Remember that these weights are used in computing the weights for suggestions.

Bottom lIne

The site is always in a state of change — from new studies being added, new samples being uploaded (and many statistics recalculated daily) and tuning and adjust algorithms — in this case readers questions lead to looking at the working data and seeing potential issues to correct (as well as displaying those numbers so people may ask questions — leading to still better algorithms).

Ketogenic diet did not help a health issue, it created one

A reader reached out for an educational review of their 16s microbiome results. I usually try to make time to do an education review once a fortnight unless I am deep in coding or analysis issues. He provided a nice very detailed back story, which is verbatim below

My problem started out approaching eight years ago when I went on a ketogenic diet.  I had a massive energy collapse and weight loss (I was not overweight at the start), and I incorrectly believed this was about insufficient calories rather than macronutrients

It turns out I have some uncommon genetics that make my ability to process fats for energy inefficient, under conditions of severe catabolic stress.   Severe catabolic stress would be conditions like starvation, high fever, etc.   Keto diet mimics starvation and is an extremely catabolic stress to the body.    It looks like this diet was a suicide diet for me, and it started a set of symptoms that are persistent even after reverting to a higher carb diet.

The original symptoms were like CFS, but I have since been able to correct most of those by going to a higher carb diet, mostly using lower-glycemic whole food carbs like lentils, fruit, and vegetables.     This took about three years to figure out and I think some infections may have exploited the period of low energy.   Those infections may include dysbiosis as well as possibly some kind of brain infection.

During the first month of the diet I had some “event” where I tried to correct the energy collapse with a higher level of exercise, and after one high-intensity running session I spent the entire night drinking a liter of water every hour.  I was desperately thirsty and was unable to quench the thirst.   

The high levels of water probably induced an electrolyte imbalance, further aggravating the underlying cause.   I should have made a trip to the emergency room, but I did not.  After that one night I started with a tinnitus that has been with me 24×7 for approaching eight years.   At first that tinnitus was like something at the brain core activating and literally consuming consciousness.   That has improved over time and in the last year feels like it could become just a sound at some point soon.   Together with the tinnitus I have some irritation of my optical nerve that gets worse when the tinnitus gets worse.   These problems are severe and constant, and I cannot survive the stress of employment with such severe stressors on my brain.   

It is worth mentioning that I was an entrepreneur for more than 10 years, working 14+ hour days.  So I am the opposite of lazy.  I had lifelong IBS-D, which is now well controlled, and most of that IBS I attribute to gluten and to dairy.  I am gluten free, and now I get my only dairy exposure from carefully prepared yogurt.  

I have neurological symptoms of brain fog (along with tinnutus) tied to food.  About 60 to 90 minutes after eating the symptoms begin.   I have multiple SIBO tests, that show an ongoing set of issues.   My first SIBO test had enormous levels of background methane.   My practitioner treated this as a colonic issue and we corrected that completely.   My next SIBO test showed completely flat-line hydrogen and methane,  a hydrogen sulfide (H2S) SIBO. I went on a heavy yogurt diet and that improved my symptoms.   I stopped the yogurt, but when I repeated a SIBO test using the Trio test, it showed totally normally hydrogen, methane, and H2S.  So I cured the SIBO.   I have organic acid markers suggesting SIFO, and since I still have neurological issues after eating, we will start to treat for SIFO soon.

In terms of microbiome testing, the main trend I perceive here is:
💥 I started with 18% butyrate producers.  I corrected this by a heavy diet of prebiotics including GOS, PHGG, and Acacia.   Butyrate producers are now close to 40%.
💥 I have near extinction levels of Bifidobacterium and Lactobacillus.  This I attribute to both antibiotics as well as not eating dairy foods for 20 years.   I simply starved these genera out of existence.   I do not tolerate Lactobacillus supplements or foods.  They induce huge levels of brain fog.   I am currently making a Bifido only yogurt that I load with prebiotics, and this has been extremely helpful to my health.   The dairy is being completely tolerated, which I suspect is because I have thriving Bifido populations eating the lactose all the way through digestion.
💥 I have low and inconsistent levels of Faecalibacterium prausnitzii which is a key species I have identified that is almost universally associated with good health.   I am addressing this by taking stewed apples as my main breakfast meal each morning.   Pectin feeds this bacteria.
💥 I have low levels of Akkermansia, suggesting possible problems with my mucosal layer.  I test with very low secretory IgA and somewhat leaky gut, so these might confirm a mucosal layer issue.   I am trying to correct this with Bacillus coagulans, which gave me some success in earlier trials.
💥 I have slight elevations of methanogens and H2S producers.   Biomesight and Thryve disagree about which species are present.    I feel my best strategy for keeping these controlled is to focus on enhancing butyrate producers and trying to re-establish my acetate producers.
💥 My Proteobacteria are usually under 4% but I seem to have a large population of Sutterella wadsworthensis, always over 1%.   This bacteria is worth calling out because we are measuring levels in the colon with 16s testing, and the research says this bacteria gets more dense as you travel up the small intestine.   So I may have a fair amount of it in my duodenum.  Since it is a gram negative bacteria, there might be some LPS issues.   I plan to treat this with the yogurt.

As far as brain infection, this is unfortunately something for which good tests do not exist.  I do show significant volume loss in my brain, confirmed by a radiologist on the MRI as well as by software that analyzes brain volume from those images.  The severity of my neurological symptoms does seem consistent with some viral load that may have been pre-existing, and that simply exploited a number of years of low energy.   There is a really interesting study out of Japan in the last few years where they treated people who had certain viruses with anti-virals and followed them for years after that.   Treating with anti-virals effectively eradicated all risk of Alzheimer’s in later years, strongly suggesting a cause and effect between resident viral infection and neurodegeneration.  This establishes some credibility for the hypothesis.   At some point I may try to find a specialist and do some test around anti-virals to see if that affects symptoms.

My first impressions

One item stood out greatly in his story, the parallelism to what is seen with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Let me document out some of the parallelisms, I will cite just one publication for each:

He eliminated sufficient ME/CFS symptoms to likely not qualify for that diagnosis.

Ketogenic diet Literature

As usual, I go for gold-standard information from the US National Library of Medicine instead of internet rumor and snake-oil cure-alls. There are over 3800 studies. There medical cases when it is used with success (i.e. Epilepsy, Parkinson, Alzheimer’s diseases), but many of the studies have been with mice or with an apparent bias for positive results. Despite this, there was a good number of studies indicating general risks and complications. I have just cited a few studies from 2020 onwards, studies not available when this person made a regretted choice.

Clearly there are frequent downsides (beyond having DNA issues) that are not declared by advocates.

Testing Predictions

In my recent blog post, Predicted Symptoms – Performance Review, we found at least 50% of symptoms were correctly predicted using either KEGG Products or the Bacteria. This is a fresh test case sample. I forwarded the top 20 symptoms from these two predictors to the reader and he reported back. Results are below with the prediction engine reaching 60%.

SymptomZ-ScoreReader Comment
Comorbid-Mouth: Periradicular periodontitis inflammatory / chronic lesion around roots of teeth3.71I have now-well-controlled significant erosion of the gums.  It is not reversing but is hopefully not getting rapidly worse.
Comorbid: Small intestinal bacterial overgrowth (SIBO)3.35I apparently treated it and it no longer exists.
Post-exertional malaise: Worsening of symptoms after mild mental activity3.32Reading and focusing on written work quickly brings on fatigue and eye focus problems.
Age: 40-503.2150+
Autonomic Manifestations: Cortisol disorders or irregularity2.94Fasting brings on high cortisol and high fasting glucose.
DePaul University Fatigue Questionnaire : Tense muscles2.92Tense in general, not just muscles
30% hit rate
SymptomZ-ScoreReader Comment
Physical: Work-Sitting1.86☑️
Physical: Northern European1.82☑️
DePaul University Fatigue Questionnaire : Need to nap during each day1.77☑️
Neuroendocrine Manifestations: Poor gut motility1.653.5 hour transit time through small intestine
 Infection: Varicella Zoster Virus1.62I had chicken pox as a child.  Both parents had shingles.
Comorbid: Sugars cause sleep or cognitive issues1.53☑️
Physical: Long term (chronic) stress1.49☑️
DePaul University Fatigue Questionnaire : Ringing in the Ear1.4424×7 tinnitus that varies from bad to horrific
DePaul University Fatigue Questionnaire : Abnormal sensitivity to light1.41☑️
Neuroendocrine: Cold limbs (e.g. arms, legs hands)1.4Particularly in the feet, I have poor circulation
Post-exertional malaise: Worsening of symptoms after mild mental activity1.3Reading and focusing on written work quickly brings on fatigue and eye focus problems.
Immune Manifestations: Chronic Flatus / Flatulence / gas1.27☑️
60% hit rate

Some of the agreements were interesting, especially for Varicella Zoster Virus. The person does not have active, but we know because of Shingles that the virus persists. If the virus persists, then it will do some ‘taxonomy-forming’ of the gut to be friendly to it. I hope this person has gotten a Shingles Vaccinations.

My Approach

I will start by using some citizen science patterns from Microbiome Prescription. Specifically

As well as US Library of Medicine:

  • Irritable Bowel Syndrome 
  • ME/CFS with IBS 
  • Obsessive-compulsive disorder – while not diagnosed with it, it was a prediction that he agreed with

I will do a consensus report from the collection of suggestions for the 5 items cited above. I picked the largest cohort to get the best precision.

Second, I will clear the consensus report and do a naive consensus report with just the Kaltoft-Moltrup outliers and Dr. Jason Hawrelak. As with most of these educational reviews, I will often explore different paths for analysis.

Third, I will clear the consensus report and do all 15 prediction matches. I will leave it to the reader to do an uber consensus approach of everything together, including these items connected to vision:

First Pass

This is a revision based on the revised algorithms Suggestions from Symptoms are Changed.

Neurological-Audio: Tinnitus (ringing in ear) 

Neurocognitive: Unable to focus vision and/or attention 
IBD – PubMed using 6%
IBS – PubMed using 6%
ME/CFS with IBS
OBS – Pubmed 6%

We actually ended up with 100 “Safest Takes”

I downloaded the results and emailed to reader.

Second Pass

Bacteria NameAnalysis
AkkermansiaToo Low
  BacteroidesToo High
  BacteroidiaToo High
  BifidobacteriumToo Low
  Faecalibacterium prausnitziiToo Low
  MethanobrevibacterToo High
  ProteobacteriaToo High
Dr. Jason Hawrelak Targets
Bacteria NameAnalysis
  [Ruminococcus] torquesToo Low
  AggregatibacterToo High
  Blautia hydrogenotrophicaToo Low
  ChlorobaculumToo Low
Kaltoft-Moltrup Range

To confirm Kaltoft-Moltrup Ranges, I did a visual scan of his results, and there were no other extreme items. The default result was only 9 items on all lists, and they were only for Safest takes as shown below

Dropping the cut off point to 2 (from the default 3) increased the count to 18, with the items below added

It is interesting to note that we have 3 probiotics listed, none of them are Lactobacillus — the specific type that the reader reported severe issues with. Dropping the filter point to 1 (from default 3) we end up with 47 items and filtering to probiotics, we have the list below. A Lactobacillus showed up but only in combination with a prebiotic. Decreasing further, we see the following added next: bifidobacterium infantis,(probiotics), bifidobacterium longum bb536 (probiotics), bifidobacterium catenulatum,(probiotics)

Third Pass

This may seem to be a lot of work, but you can see that it may be done quickly from the YouTube video for this post. Note that we are doing only Bacteria (KEGG Products are too indirect to get suggestions). Remember that we did 12 sets of suggestions so the “Take Count” should be a matter of interest.

This is a revision based on the revised algorithms Suggestions from Symptoms are Changed. I only did the auto checked items. The second level suggestions were not checked.

Safest Takes
Safer
Likely Safe

I notices that many SAFEST items are in agreement with our first pass, i.e. Cacao, pediococcus acidilactic (probiotic), thyme (thymol, thyme oil). A few items, like clove are on the avoid list but the avoid came from a single suggestion.

KEGG Suggestions

This obtains suggestions using genes and is independent of the the above processes, all of suggestions had a very low weight of 2 (often we see numbers of 200-300), so these are likely weak suggestions, with the three best candidates below (any one of them is likely sufficient)

For supplements, only L-Cysteine appears (which was on the bottom of the list in our First Pass)

Putting it all together

In terms of probiotics, Bifidobacterium longum or bifidobacterium animalis lactis, symbioflor 2 e.coli probiotics, enterococcus faecium (probiotic),  bacillus subtilis (probiotics) and Akkermansia muciniphila (probiotic) appears to be preferred set — especially any that are not currently being taken.

I personally would advocate symbioflor 2 e.coli probiotics, (or Mutaflor) – because E.Coli probiotics appeared to make a major impact on reducing the time to recover for my own relapses/

I noticed what seems to be more than normal of polyphenols, herbs and spices. This is apparent on the third pass safest list and also second pass safest list but not on the first pass. I am inclined to ignore the first pass list for several: small number of bacteria in scope, symptoms and medical conditions were ignored.

Below you will find a YouTube of the analysis with additional commentary.

Reader feedback

“Your studies under Keto literature raise the possibility that a high fat diet may have exploded my levels of B wadsworthia during the active keto diet phase, and this alone may have promoted most of my brain fog.  That’s an interesting hypothesis I had not considered.   “

“On the various suggestions lists, do I understand that AI is not able to give us reasons for the suggestion, but rather it is just making associations between suggestions and reversal of symptoms that have been studied? ” INCORRECT, for the professional user, I detailed out the evidence as shown below

“It might help to define “Take Counts”.  Maybe you do that on the video.  What is also confusing is that on the Safer Takes list you have a “Take Net” and it is not clear how it is calculated.” The calculation something like this:

SUM( For each Bacteria (Magnitude of Shift desired + Function(Number of studies shifting in the right direction, Number of studies shifting in the wrong direction))

  • so substance with only a few (or just one) study for a bacteria will have a lower number
  • so substance with many studies for a bacteria will have a higher number
  • a bacteria that is slight off will have a lower number
  • a bacteria that is very off will have a higher number

The actual computational functions are proprietary and the results of 3 years of experiments.

“it is confusing because the same genus Bacteroides is alternately Too Low and then Too High.” There are THREE reference point the highs, the lows and the middle peaks (used for symptoms). People with a specific symptom may average at 35% of the median, so the goal is to shift you away from the 35% area. This it becomes a question of which direction? I made an “arbitrary” decision that if you are > 35% then we want to push you higher. If you are < 35%, we want to push you lower (ideally keeping you within the Kaltoft-Moltrup range of normal values). There is logic behind this “arbitrary” decision, but explaining it is complex.

Some Facebook comments