Home made Yogurt, Sauerkraut, Kefir – Russian Roulette Anyone?

All of these products are prone to wild variation of bacteria in it. So if you are trying to do targeted changes, you are tossing a hand grenade into the room. If you are wanting to do random experiments because someone said it helped them (or often believe it may help and are still waiting– and believing), then that’s fine. Some people win the lottery…

Here’s a partial listing of the reported variety of bacteria found…

  • Home made yogurt may contain [2021]
    • Sphingomonas,
    • Burkholderia,
    • Lactobacillus,
    • Lactococcus,
    • Staphylococcus
  • Traditional fermented dairy products [2011]
    • Enterococcus faecalis,
    • Enterococcus durans,
    • Lactobacillus brevis,
    • Lactobacillus buchneri,
    • Lactobacillus casei,
    • Lactobacillus delbrueckii ssp. bulgaricus,
    • Lactobacillus diolivorans,
    • Lactobacillus fermentum,
    • Lactobacillus helveticus,
    • Lactobacillus kefiri,
    • Lactobacillus plantarum ssp. plantarum,
    • Lactococcus lactis ssp. lactis,
    • Leuconostoc lactis,
    • Leuconostoc mesenteroides,
    • Streptococcus thermophilus,
    • Weissella cibaria.
  • Turkish traditional fermented foods [2023]
    • Lactiplantibacillus pentosus,
    • Lactiplantibacillus plantarum,
    • Enterococcus lactis,
    • Enterococcus durans,
    • Enterococcus faecalis.
  • Traditional Iranian Fermented Food [2018]
    • Lactobacillus fermentum,
    • Lactobacillus plantarum,
    • Lactobacillus brevis
    • Weissella cibaria,
    • Enterococcus faecium
    • Enterococcus faecalis),
    • Leuconostoc citreum
    • Leuconostoc mesenteroides
    • Pediococcus pentosaceus. 
  • Spontaneous curly kale (Brassica oleracea var. sabellica) fermentation [2018]
    •  Lactobacillus plantarum
    • Lactobacillus paraplantarum
    • Lactobacillus brevis,
    • Lactobacillus curvatus
    • Weissella hellenica
    • Weissella cibaria
    • Pediococcus pentosaceus
    • Pediococcus acidilactici
    • Leuconostoc mesenteroides
    • Lactococcus lactis.
  • Fermented curly kale juice [2021]
    • Leuconostoc mesenteroides 
    • Lactobacillus plantarum,
    • Lactobacillus sakei,
    • Lactobacillus coryniformis
  • Brazilian kefir grains
    • Lactococcus,
    • Leuconostoc,
    • Lactobacillus
    • Oenococcus
  • Traditional milk kefir  [2022]
    • Kluyveromyces marxianus,
    • Saccharomyces cerevisiae,
    • Pichia fermentans
    • Pichia kudriavzevii.
    • From [2014]
    • Saccharomyces cerevisiae,
    • Saccharomyces unisporus,
    • Issatchenkia occidentalis
    • Kluyveromyces marxianus
  • Sauerkraut [2022]… “A total of 220 LAB strains, corresponding to 133 RAPD-PCR biotypes, were successfully isolated. “

Comments From Social Media

And a “true believer”

Back to work after ME/CFS for 10 years and Long COVID

This is a follow up post on ME/CFS x COVID :- Long COVID instead from June, 2022. The person on seeing the results stated “New Sample Looks Worse”. I am curious because so far all subsequent analysis showed objective improvements (and subjective improvements too!) for people with ME/CFS. I am prepared to be humbled. He used Ombre for the data processing.


  • Lifestyle-wise, I’ve definitely been experiencing a big increase in stress due to working a full-time job for the first time in almost a decade. The job is remote, I couldn’t do it otherwise, but it still requires some late nights and lots of childcare complications since my wife also works full-time and then some.
  • Diet-Wise: Not much has changed in terms of my diet. I remain 95% Gluten Free with the occasional slip-up or cheat. What’s interesting is a lot has flip-flopped in this sample, so maybe I was overdoing it on the last round of food suggestions, especially in terms of adding fiber to my smoothies, mostly resistant starch.
  • Supplementation-wise, I had been taking the recommended probiotics from my last sample at a pretty high and aggressive dose to very mixed reactions. I probably wasn’t rotating them often enough.
  • Paxlovid Experience: As I mentioned in a previous email, I had a pretty bad case of Covid around Christmas time. And to my surprise Paxlovid not only helped my acute Covid symptoms, but it overall made me feel much better than baseline. I was able to confirm this experiment in mid-January when another member of my family got COVID, but couldn’t tolerate their Paxlovid. So as an experiment, I took the remaining three-day course, and again almost immediately my brain fog, executive function, and most neurological symptoms lifted

Also, and this is where I think we might be able to confirm some of Dr. AI’s suggestions instead of you being humbled by them, I was getting desperate when starting the new job in January and coming off of my case of Covid. So I started throwing pretty much any “energy” and “anti-viral” supplement I had on hand or had short-term success in the past with, to try and get well enough to do a good job at my work. A lot of those herbs ended up on the avoid list on this sample, especially Baicalin, Oregano Oil, and Resveratrol (which is usually mostly Japanese Knotweed).

Other things on the avoid list now I had frequently been taking before this sample: salt (salty electrolyte packets added to water), fish oil, pulses (lots of beans, especially navy beans since those had been a strong Take for a few samples going), and Culturelle (lactobacillus rhamnosus gg) for diarrhea, because I’ve been experiencing more frequent and urgent loose stools since Covid.



First, I did a recalculated to make sure all samples used the same reference sets. The reference data is recomputed weekly so we have a living analysis system. The data has been processed thru both Ombre Labs (OL) and BiomeSight (BS), so the details are below. It is the same three FASTQ files processed by two different software packages (for backstory see: Different Microbiome Results from Different Providers on Same Sample, Same Raw Data via Ombre Labs(Thryve) and Biomesight, The taxonomy nightmare before Christmas…)

Lab Read Quality5.38.615.35.38.615.3
Bacteria Reported By Lab531567493671746687
Bacteria Over 99%ile3519816113
Bacteria Over 95%ile754637492422
Bacteria Over 90%ile1337157864750
Bacteria Under 10%ile5519328051161329
Bacteria Under 5%ile261732452350289
Bacteria Under 1%ile21451973583
Rarely Seen 1%7561578
Rarely Seen 5%243032615870
Outside Range from JasonH444555
Outside Range from Medivere121212151515
Outside Range from Metagenomics888777
Outside Range from MyBioma333888
Outside Range from Nirvana/CosmosId232323252525
Outside Range from XenoGene282828434343
Outside Lab Range (+/- 1.96SD)34219241417
Outside Box-Plot-Whiskers1358282976363
Outside Kaltoft-Moldrup150190223218290400
Condition Est. Over 99%ile707004
Condition Est. Over 95%ile211170318
Condition Est. Over 90%ile352342531
Enzymes Over 99%ile167171032420
Enzymes Over 95%ile338521021578540
Enzymes Over 90%ile481109134245183142
Enzymes Under 10%ile110311638126221441
Enzymes Under 5%ile1925843248112314
Enzymes Under 1%ile21881621264
Compounds Over 99%ile20824479516936
Compounds Over 95%ile46194128356502158
Compounds Over 90%ile639304235584694329
Compounds Under 10%ile598629691610663726
Compounds Under 5%ile592617668604628710
Compounds Under 1%ile584591628600606680

My initial impression is positive using Enzymes. The number of under production of Enzymes has been reduced significantly – between 50% reduction to 99% reduction. I am not that concerned with over production, typically the body discard surplus of chemicals (with a very few exceptions). I tend to be more concern over enzyme starvation. Special studies found that Compounds tend to have much weaker relationships than Enzymes. While included, I usually do not over-read the compound significance.

Outside of ranges were interesting — my preferred range Kaltoft-Moldrup, had less in the latest sample (with the numbers dropping with each sample). All of the 3rd party lab results were unchanged. To remind readers on the 3 suggested ranges assumptions:

  • Outside Lab Range (+/- 1.96SD) – forces on the bacteria analysis the belief that data is a bell curve — very false
  • Box-Plot-Whiskers – this method was created to deal better with data that is not a bell curve, but with the underlying assumptions of a skewed bell curve. – better but not ideal
  • Kaltoft-Moldrup – is the bacteria whisperer. It listens to the data and looks for atypical patterns. IMHO it produces the best identification of data of concern.

Conceptually the numbers under 10%ile and over 90%ile should be in the same ratio 1:1. What we see is shown below:

  • 2/2/2023 – 2.4 or 1.7 ratio
  • 11/1/2022 – 0.36 or 0.29
  • 4/11/2022 – 0.2 or 0.15

There was a major change, a flip in ratio with the latest sample. This was seen by the reader.

So is he better? IMHO — yes, the objective evidence is not as strong as I would like to see but:

  • None of the 3rd party ranges got worse (they remain unchanged)
  • His microbiome is producing a much richer amount of enzymes — which should cascade into a more balance system
  • The Kaltoft-Moldrup count continued to drop.

There are several events that adds noise to this analysis:

  • Going back to work (the fact that he continues to work must be viewed as solid evidence)
  • COVID and Paxlovid will alter the microbiome
    • I was unable to find any studies on the impact of Paxlovid on the microbiome 😞
  • Randomly tossing supplements into the mixture
  • There were huge differences in lab quality between samples (from 5.3 to 15.3)

Additional Analysis Points

Potential Medical Conditions Detected

The improvement seen in earlier post have persisted

  • 4/11/2022 : 26 items
  • 11/1/2022 : 5 items
  • 02/02/2023: 7 items

Bacteria Deemed Unhealthy

Nothing that is clear, randomness can explain the numbers well.

  • 4/11/2022 : 10 items
  • 11/1/2022 : 15 items
  • 02/02/2023: 12 items

Below we see the extreme %iles (0-9) improving over time. He went from a multitude of bacteria with token amounts to a more appropriate number with token amounts.

0 – 91040931859126
20 – 29342619552422
30 – 39342211383414
40 – 49141314192421
50 – 59161811212013
60 – 69241812282626
70 – 79172113233719
80 – 89211912312816
90 – 100261516432524

Going Forward

First thing is that my own experience in a significant ME/CFS flare was lots of swings in the microbiome results as I altered supplements and diet. The analogy that I often use is that the trip from the Port of ME/CFS to the Port of Health is not a straight flight like hoping on a plane (“as the crow flags”) but similar to travelling by a sailing ship that needs to make a lot of tacks (changes of boat directions) because of winds, shoals and reefs. A bad microbiome happens as a result of a long series of minor changes, we need to undo those.

Doing the usual trio of suggestions to build a consensus report.

In addition to that, we do some of the “has conditions and matches published literature” where the matching is over 95%ile

  • ME/CFS without IBS 12 of 18
  • Neuropathy (all types) 11 of 18
  • Hashimoto’s thyroiditis 8 of 17
  • Long COVID 68 of 167
  • COVID-19 39 of 96

This will produce a deep consensus report using 8 sets of suggestions. The downloads are attached

Looking at the consensus, we have 28 items that ALL of the suggestions agreed upon. These include:

A specific strain was recommended: Lactobacillus salivarius UCC118, but lactobacillus salivarius (probiotics) was a to-avoid — so I would skip that suggestion. The absence of any probiotics in this top list would inclined me to suggest skipping probiotics for the next round of doing and then testing.

The New Artificial Intelligence Diet (see More information) had #1 being … Cheerios!! — the reason was all of the fortifications added! Being gluten-free, then this is an easy to ignore.


I proceed to filter the diet suggestions by fruits first, and got the following:

When by Vegetables with this list:

Filtering by Roots, Tubers etc, had low values with only one nutrient contributing. Nuts etc had Sesame being the top item. Other items of note included: Avocado, Barleygrass, Coca (as in source of cocaine – have fun asking for that at your health food store!).

On the AVOID list, we have:

What I find interesting is that vegetable/fruit juice-based diets, is pretty broad. Using the AI Diet, we can likely isolate which food and vegetables are the best choices and may well explain the “why” for the general vague diet term.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

A Microbiome Trek Continues thru the land of ME/CFS

This is a continuation of prior blog posts. For other related posts see: Analysis Posts on Long COVID and ME/CFS. He used Ombre Labs for processing

The earlier posts for this person are:

Largely recovered. Post exertional malaise (PEM) persists selectively,

  • I am ramping up my workouts. Weights 10 -15 minutes even if I’m lifting close to max , I have no problems.
  • Basketball is the kicker. 

Using a fit bit watch to monitor found a threshold that impacts severity heart rate. generally tried to stay at 110.  Keeping it lower I think netted a less sever crash, but still happened.

  • My PEM crash starts exactly 5 hours post work out  for 24 hours. I played basketball till 5pm, these stats are reflective of me in a crashed state through the night till the following morning. 

Experiment: I got the Gammacore vagus nerve stimulator earlier this week and seeing some positive results. Body feel calmer overall. HRV looks better and balanced by fight or flight. My ANS almost always showed as sympathetic dominant . The Gammacore tvns device does seem to help push me to balance a little bit. I feel more social . Have to be careful not to overdo it, I feel like it flares up my allergies. If I hit the sweet spot, I feel more social/chatty and relaxed.   

PEM starting to think it could be MCAS. I feel flushed in the face at the 5 hour post workout. I crash , but also  get a stuffy nose and then disrupted sleep. Seems allergy like. 

  • Zyrtec and reservatol hasn’t stopped it.
  • Going to try the cromlyn spray you mentioned. I read it could take weeks  to work though?

Dec, 2022 Unfortunately I was unsuccessful and caught the stomache plague . Had to take a couple zofran. 

It took me 3 ombré tests to get one that didn’t get rejected. So strange!

Sample Comparison

The past 16 months of samples is shown below. Some general observations:

  • The number of different bacteria detected is reducing (while sample quality is improving — thus likely a true reduction)
  • Number of pathogenic bacteria is tending downwards
  • Bacteria patterns are starting to match some of the patterns from PubMed (i.e. moving into “normal” patterns)
  • Less Enzymes are at low levels
  • More Enzymes are at high levels
  • His catching “the stomach plague and taking Zofran (ondansetron hydrochloride,(prescription)  – 300 bacteria impacted listed)” is an hiccup for trending.

Remember that there is variability from time of day that the sample was taken (see Changing your Microbiome Results by when you take your sample!).

Lab Read Quality5.
Bacteria Reported By Lab434476472657506468
Bacteria Over 99%ile1071000
Bacteria Over 95%ile1829101562
Bacteria Over 90%ile255232382512
Bacteria Under 10%ile1086016016891187
Bacteria Under 5%ile442182844890
Bacteria Under 1%ile321824222
Rarely Seen 1%1723170
Rarely Seen 5%162712983116
Outside Range from JasonH554477
Outside Range from Medivere141415151818
Outside Range from Metagenomics998877
Outside Range from MyBioma444433
Outside Range from Nirvana/CosmosId151520202222
Outside Range from XenoGene343447473636
Outside Lab Range (+/- 1.96SD)81071672
Outside Box-Plot-Whiskers2758681356929
Outside Kaltoft-Moldrup9182791559870
Condition Est. Over 99%ile600000
Condition Est. Over 95%ile701210
Condition Est. Over 90%ile1013310
Enzymes Over 99%ile1000201
Enzymes Over 95%ile118929486116
Enzymes Over 90%ile6475111299193103
Enzymes Under 10%ile150119217175291354
Enzymes Under 5%ile7559125101151154
Enzymes Under 1%ile121527202129
Compounds Over 99%ile1003831313129
Compounds Over 95%ile463228227131244233
Compounds Over 90%ile606451365371358347
Compounds Under 10%ile59948286212259264
Compounds Under 5%ile5801915454154163
Compounds Under 1%ile569928234641

US National Library of Medicine Studies

As shown above, we now have some matches, he does not have any of the following

  • Liver Cirrhosis 100%ile 47 of 170
  • Atherosclerosis 100%ile 25 of 105
  • Hashimoto’s thyroiditis 92%ile 7 of 16
  • Ankylosing spondylitis 92%ile 26 of 133
  • hypertension (High Blood Pressure 85%ile 13 of 40 –clearly does not have
  • Obesity 57%ile 32 of 107 – clearly does not have

Looking at ME/CFS co-morbid, we find 17-20% overlap for Hashimoto’s thyroiditis [Src]; for Atherosclerosi see Effects of Post-Exertional Malaise on Markers of Arterial Stiffness in Individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome); Chronic Fatigue is associated with Ankylosing spondylitis (Src) as is Liver Cirrhosis. Both hypertension and Obesity are not associated with ME/CFS.

This type of matches is not intended to predict, rather, if you have these conditions then these are the bacteria shifts that may likely contribute to the severity of symptoms.

Going Forward

I am going to build a consensus using the 4 ME/CFS or Chronic Fatigue associated items above in addition to my usual ones – thus 7 sets of suggestions.

The top suggestions:

Going over to KEGG, we do NOT see Escherichia coli at the top of the list (often seen with ME/CFS). We see Bacillus subtilis near the top of the list, Lacticaseibacillus casei further down the list and Lactobacillus gasseri still further down. pediococcus acidilactic (probiotic) was not listed. Of special interest was the absence of B-Vitamins at the top of the list which is typical of people with ME/CFS.

My preference is usually to go with the most consensus (the best odds). So the short probiotic list would be:

He has started doing cited in Interesting Successful Clinical Trial for Long COVID and ?ME/CFS  which is available on Amazon for $40.

Speculation on ongoing Post Exertional Malaise issue

One of my dark fears with ME/CFS is that it will cause epigenetic changes. Epigenetic means that parts of your DNA is turned on or off. Reprogramming such a DNA change is outside of my focus.

There are some interesting studies on MCAS and epigenetics

  • “In TET2-deficient mast cells, chronic activation via the oncogenic KITD816V allele associated with mastocytosis, selects for a specific epigenetic signature characterized by hypermethylated DNA regions (HMR) at immune response genes” [2022]
  • Hypoxia modulates human mast cell adhesion to hyaluronic acid. [2022] – this is of special interest because cellular hypoxia is often seen with ME/CFS and FM
    • “Hypoxia-mediated regulation of mast cell adhesion to extracellular matrix components might be involved in the pathogenic accumulation of mast cells observed in the course of certain diseases including rheumatoid arthritis and cancer.”
  • “In this manuscript, we investigated the ability of mast cells primed with different stimuli to respond to a second stimulation with the same or different ligands, and determined the molecular and epigenetic drivers of these responses.” [2022]
  • “This study confirms that epigenetic changes are involved in mastocytosis, and suggests that allergy may be an important epigenetic modifier of the disease. ” [2021]

My current most probable hypothesis is that the higher level of activity with basketball than weight lifting results in more hypoxia (low oxygen levels) when then triggers a histamine cascade which takes some time to clear.

  • ” the secretion of the prestored mediator histamine was increased under hypoxia alone.” [2017]

The logical testing would be taking the following pre-basketball and then at hour 4, to see if they alter the pattern:

Going down the hypoxia path, my favorite experiment to suggest would be:

Using the New Artificial Intelligence Dietician on the Internet

No, it us not Chat_GPT — it uses a very different branch of AI. For more information see: Microbiome Menu Helper — The AI Dietician

In this case the top items look very reasonable (with 4,183 items listed):

The avoid items included:

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

Microbiome Menu Helper — The AI Dietician

This weekend I further integrated the microbiome prescription artificial intelligence with the Food and Nutrients database. A common problem is that many flavonoids and other stuff are not well known to most people. On the other hand, most dietician will be clueless on converting the suggestions to menus and food planning. This addition attempts to bridge this disconnect.

Video Walk Thru Below

How do I get to it?

It appears automatically on the usual suggestions page

Clicking will open a new tab so you do not loose your place

It is also on the consensus page

Clicking opens in a new tab

The Artificial Intelligence Dietician

This will open a page with often 2000+ food items

From single suggestions

Clicking on a food group button will filter it — for example, what would be a good fish to have?

Filtered Simple Suggestions

The Benefit Est is based on the estimates from suggestions. For consensus, the numbers will be higher because the consensus weight is not scaled.

Liver and Giblets looks like the best meats

Remember, clicking on the food will show what is in it. Nutrients that are used by the AI are marked with “Significant”

As a FYI on both Retinol and Vitamin A, being in the list. They are the same and yet different.

Vitamin A comes from two sources. One group, called retinoids, comes from animal sources and includes retinol. The other group, called carotenoids, comes from plants and includes beta-carotene


Foods To Avoid

As usual, clicking on column headers reverse the sort order. In this case, classic Norwegian Goat Cheese should be dropped from my diet (with tears)

Sorting to find items to avoid

Walk Thru

A special shout out to people who have been donating to running costs for the site. To get this working smoothly I had to get SSL Certificates for multiple parts of Microbiome Prescription, You should see 🔒 on all of the component sites now (if not, update the link to https://)

Microbiome Menu Helper The Artifical Intelligence Dietician – YouTube

Bottom Line

As always, having your plans reviewed by a human dietician and/or medical professional is always recommended.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

Problem with Doctored Data in Medical Studies

The article below from The Economist, Feb 24, 2023 hints that ChatGPT and similar machine learning programs being used in Medicine will likely kill people “By one estimate, this approach may have caused 10,000 deaths a year in Britain alone.”

Article is attached below.

Impact on Microbiome Prescription

Believe it or not, none is expected. Machine learning is not used. We use a very old school fuzzy logic expert system model. Additionally, we make microbiome samples available at http://citizenscience.microbiomeprescription.com/ and give the evidence trail as shown in this video.

Another ME/CFS after 19 years

This is actually a referral from a person from a previous blog, Another ME/CFS person has gone to Firmicutes!. He shared his experience (see that post) “ My energy levels are perhaps the best they’ve been in the last 5 years.  I’ve still got a very long way to go but the results thus far are promising!” and a friend decided to try

Back Story

I became symptomatic around 19, progressively got worse so that by the age of 23 I had fully crashed with classic CFS symptoms. Severe symptoms persisted for 5 years during which time I was unable to work due to unrelenting fatigue. Slowly got about 50% better through an extremely low stress lifestyle and dietary/food changes. I’ve tested positive for a few Lyme co infections, chronically low cortisol and pretty much anything else the chronic illness community tests for, I’ve tested and treated. I’m now 38 and my recovery seems to hang at about 40% of optimal capacity. I wake up unrefreshed and have lagging energy all day long. I have to live an extremely low stress life, if I don’t, my sympathetic system kick into high gear. I seem to have issues with histamines (though I can ingest them, I just get flushed, puffy and hot at times), and my fatigue gets profoundly worse around my cycle. I don’t have any significant digestive issues that I’m aware of.


The first thing that I should mention is that I recall a study finding that the duration of ME/CFS does not impact the probability of remission. So 19 years with ME/CFS is not a factor.

Dr. Jason Hawrelak Recommendations came in at 99.9%ile, so no pro-forma general issues. That is not unusual, most labs on ME/CFS patients report normal. Looking at the Potential Medical Conditions Detected list, nothing of concern.

Looking at specific bacteria, a few bacteria stand out:

Looking at bacteria distributions we see a good pattern except with the rare bacteria which appear under represented. An ideal microbiome would have the same count in this range. This suggests a well established microbiome, perhaps with a touch of “inbreeding”.

Diving into the species of Lactobacillus interests me. We find just one species dominates,  Lactobacillus rogosae. A 2018 review,”Occurrence and Dynamism of Lactic Acid Bacteria in Distinct Ecological Niches: A Multifaceted Functional Health Perspective“, states “All in all, no consistent marker for any pathology or a healthy state is simply defined by a specific proportion of Lactobacillus“. This strain being classified as a Lactobacillus has been challenged [1974] with the suggestion that they may belong to Propionibacterium, a family associated with Acne. We are back to the fuzziness of 16s lab software as well as challenge of RNA/DNA being exchanged between different bacteria.

We have irony here, because the friend was high in Firmicutes and we have 77% of the microbiome in this sample also being Firmicutes with heavy domination of several ones as shown in the Krona chart below

I am inclined to do the customary ones PLUS one just trying to reduce Lactobacillus to build the consensus.

Take Suggestions

The top items had one little surprise – Cadium! There is a source for this that is also known to be good for ME/CFS – dark chocolate! See Dark chocolate is high in cadmium and lead. Prior studies on ME/CFS found that dark chocolate/ cacao improves ME/CFS symptoms.

The full consensus and simple consensus (with some dosages) are below

Items to Reduce or Avoid

The avoid list containing many popular items claimed to help the microbiome (which it does in some cases). Some are obvious with high lactobacillus — i.e. avoid lactobacillus probiotics. lactulose is a key food for lactobacillus.

Vitamin A is omitted because one form helps and the other form hurts.


Only one probiotic had all 4 saying take: bacillus coagulans (probiotics). KEGG suggestions had #1 being Escherichia coli (Mutaflor or Symbioflor2) which was also on the take list with a variety of Bacillus probiotic on the list ( Bacillus amyloliquefaciens, Bacillus velezensis, Bacillus subtilis, Bacillus licheniformis, Bacillus subtilis subsp. natto) with all of the same ones on the take list (but not recommended by all 4 sets of suggestions).

In short, avoid Lactobacillus and Bifidobacterium probiotics. We want to greatly reduce the Lactobacillus from the 94%ile – it is very likely that d-lactic acid from lactobacillus is responsible for many symptoms. Bifidobacterium INCREASES Lactobacillus which is the opposite of what we want to do. We want to get lactobacillus down as a first priority, later we look at increasing bifidobacterium once it is down far enough.


Questions and Answers

  • Q: Is there any reason you chose to focus on the lowering Lactobacillus, as opposed to the Bifidobacterium being low?  Is it because the “bacteria deemed unhealthy” table is a more important focus to you than the jason hawrelak recommendations?
    • A: The medical condition of ME/CFS and Lactobacillus has a long connection to each other. Both “bacteria deemed unhealthy” and Hawrelak are general criteria. Being at the top of Hawrelak’s rating (99+% of people are worst), would imply no issues — you have issues.
  • Q:  Why not focus on the Mogibacterium that’s in the 100 percentile?
    • A: We could — it was a factor included in the bacteria picked to modify. The list of items is here.
      Some people go after a single bacteria with a “all other factors be ignored”. For example, Fennel reduces Mogibacterium BUT it also increases Lactobacillus!! The AI algorithm attempts to balance the dozens or substances impact on dozens (or thousands) of bacteria. You are welcome to do it by hand for the 123 bacteria flagged and the several thousand modifiers.
  • Q: the last question is about interpreting Krona charts. The Krona chart doesn’t seem to provide standard ranges
    • A: This form of visual display will get extremely busy and confusing with ranges. If you attempt to draw a high range line it will sit over a different bacteria.
  • Q:  So is there any easy way to know when a bacteria is high or low by looking at that chart?
    • A: Use the hierarchy chart. You can pick one set of ranges at the top of the page. Items that are high (by the selected ranges) are in blue, and low in pink.
      You can also hand pick bacteria to alter.
On My Profile Tab
Checking the checkboxes, then clicking Create… allows a hand picked set of targets.

Different Microbiome Results from Different Providers on Same Sample

This is part 2 of Unclassified Bacteria, Fungi and Virus and I will continue with this analogy

So doing a microbiome test is like collecting the DNA from a bunch of people at a major airport and then asking: Which country did this person originated from according to their DNA. You will find some people that are good matches to an ethnic origin and some people that are “Heinz 57” aka “Mutts”. These people are unclassified — just like some living components are unclassified.

Let us look at my own DNA to illustrate the issue better. The same DNA file was used for ALL of the following charts. Why do they not agree? Simple — thing are done by matching patterns. The reference library determines where matches are done. Every provider use difference reference libraries. There is no universal reference for Human DNA, nor for the microbiome.

From 23 and Me
From Ancestry

and one more site, this one almost causes whip lapse!


One site offers the choice of reference library to use and how to match


GEDMATCH show where your DNA matches historical samples

I look very Irish here

When we drill down to the next level, we see different “species names”

From 23 and me

Wait — we are talking about where

The above patterns are based on matching to current populations. We really would like older populations. There is a site that does that! My True Ancestry. The same DNA file suggests more UK or southern Germany. We could view this as an illustration between 16s and shotgun reports.

It is interesting to note that this seems closer to Family Tree DNA results shown above.

In some cases, it is not where the ancestors came from BUT where ancestor siblings settled, as in Iceland and the Shetland Island. The same can happen with bacteria identification. All that we know is that some components are shared.

Unclassified Bacteria, Fungi and Virus

Some people get concerned about finding unclassified stuff in their microbiome sample. This does not happen with some labs because they elected not to report what is not classified. Why? It leads to support calls asking for explanations (which means $$$$ spent for the company).

Labs could create synthetic proprietary names for the unclassified to make the issue disappear — that causes the issue to disappear but really does not help.

There is a part 2: Different Microbiome Results from Different Providers on Same Sample

An Analogy

Bacteria is like the population of a country or the world. They interbred to some extent

Genetic exchanges among bacteria occur by several mechanisms. In transformation, the recipient bacterium takes up extracellular donor DNA. In transduction, donor DNA packaged in a bacteriophage infects the recipient bacterium. In conjugation, the donor bacterium transfers DNA to the recipient by mating.

Medical Microbiology. 4th edition., Chapter 5

So doing a microbiome test is like collecting the DNA from a bunch of people at a major airport and then asking: Which country did this person originated from according to their DNA. You will find some people that are good matches to an ethnic origin and some people that are “Heinz 57” aka “Mutts”. These people are unclassified — just like some living components are unclassified.

If I was picked, the answer is pretty clear for the “genus” that fits me

Which agrees with paper records back to 1500

If we want to get a more precise name, the “species”, then some fuzziness appears

There is a possible misidentification, my father’s ancestors (all lines) records go back to 1600 on an island that is low probability.


The process of giving names to bacteria in the microbiome is extremely similar. For some bacteria (like me) we fit into a nice box. For the person below, the box is not so clear

Would this person be classified as Irish or English

Bottom Line

A wise man knows what he does not know, and what cannot be known.

Another Long COVID story

Back Story

COVID in February 2021. 37 y.o. Male at the time, athletic/fit. Crossfit x 3 a week, playing football weekly Only mild gastritis prior to Covid. No other health issues.

Moderate severity Covid, lots of symptoms.

And then Long COVID and CFS/ME type of symptoms mostly fatigue, PEM and GI problems (pain, food intolerance, bloating..etc) I’d say it’s a moderate/mild case of CFS/ME. But after 18 months still not back to previous levels, can’t walk too long otherwise i crash. I’d say i am around %75.

For other analysis of Long COVID see Analysis Posts on Long COVID and ME/CFS


We have two samples available, one early in Long COVID and one more recent

  • 2021-10-01
  • 2022-08-17

With this type of information, let us start by comparing them. We are fortunate that both samples are similar read quality which reduces fuzziness. Unfortunately, it appears that the microbiome dysfunction has increased in many aspects. One aspect that it has improved in terms of bacteria with very low counts. We went from 74% of bacteria with low counts down to 51% with low count. Ideally we would love to see the low count to drop to a modelled 15%.

I should note that the increase in some Outside Ranges is likely because many of the ranges are 0 to some amount, hence the older sample had less because it was full of different trace amounts. The same apply to many other criteria, the low abundance of many bacteria skewed the criteria to appear better.

CriteriaCurrent SampleOld Sample
Lab Read Quality5.85.9
Bacteria Reported By Lab393399
Bacteria Over 99%ile81
Bacteria Over 95%ile2621
Bacteria Over 90%ile4235
Bacteria Under 10%ile135160
Bacteria Under 5%ile56108
Bacteria Under 1%ile929
Lab: BiomeSight
Rarely Seen 1%10
Rarely Seen 5%98
Outside Range from JasonH1010
Outside Range from Medivere1515
Outside Range from Metagenomics1010
Outside Range from MyBioma88
Outside Range from Nirvana/CosmosId2222
Outside Range from XenoGene3434
Outside Lab Range (+/- 1.96SD)168
Outside Box-Plot-Whiskers4943
Outside Kaltoft-Moldrup147108
Condition Est. Over 99%ile180
Condition Est. Over 95%ile380
Condition Est. Over 90%ile500
Enzymes Over 99%ile561115
Enzymes Over 95%ile793282
Enzymes Over 90%ile866680
Enzymes Under 10%ile289294
Enzymes Under 5%ile149149
Enzymes Under 1%ile2923
Compounds Over 99%ile48050
Compounds Over 95%ile600214
Compounds Over 90%ile677298
Compounds Under 10%ile581315
Compounds Under 5%ile563242
Compounds Under 1%ile54891

I next went to the Krona Charts to try to understand the shifts better. We see a massive increase of unclassified Bacteroides

2021-10-01 Sample
2022-08-17 Sample

Going to sample comparison, we see that (genus) Bacteroides was at the 99%ile on both samples. Looking at members of this genus, we see several of the identified species at high levels

Going Forward

We have a good idea of the issue: lots of bacteria at token levels, lots of unidentified bacteria.

My approach is to try the following, looking ONLY at Restrict to Bacteria with Low Levels, do the following three

Then create a handpicked bacteria focused on Bacteroides and the high species under it. To express another way: Feed the weak and destitute, Bring down the mighty.

Doing the first step, we see at the top of the consensus:

The hand picked collection is below with percentiles

  • genus  Bacteroides 99
  • species  Bacteroides faecis 93
  • species  Bacteroides graminisolvens 86
  • species  Bacteroides ovatus 99
  • species  Bacteroides rodentium 95
  • species  Bacteroides stercorirosoris 91
  • species  Bacteroides thetaiotaomicron 93
  • species  Bacteroides uniformis 97
  • species  Bacteroides xylanisolvens 100

The suggestions for just these are shown below. The pattern is similar to other peoples suggestions with ME/CFS – lots of specific B-Vitamins, dark chocolate etc:

  • whole-grain barley
  • sucralose
  • Caffeine
  • Hesperidin (polyphenol)
  • polymannuronic acid
  • momordia charantia(bitter melon, karela, balsam pear, or bitter gourd)
  • walnuts
  • folic acid,(supplement Vitamin B9)
  • garlic (allium sativum)
  • vitamin a
  • lactobacillus casei (probiotics)
  • diosmin,(polyphenol)
  • Arbutin (polyphenol)
  • pyridoxine hydrochloride (vitamin B6)
  • retinoic acid,(Vitamin A derivative)
  • thiamine hydrochloride (vitamin B1)
  • Vitamin B-12
  • vitamin b3 (niacin)
  • vitamin b7 biotin (supplement) (vitamin B7)
  • Vitamin C (ascorbic acid)
  • melatonin supplement
  • luteolin (flavonoid)
  • lauric acid(fatty acid in coconut oil,in palm kernel oil,) – Monolaurin
  • Cacao

The avoid list (items that help bacteroides grow) included some items from our earlier to take list.

  • Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
  • inulin (prebiotic)
  • saccharin
  • resistant starch
  • red wine
  • stevia
  • xylan (prebiotic)
  • berberine
  • arabinoxylan oligosaccharides (prebiotic)
  • apple
  • high red meat
  • l-citrulline
  • low-fat diets
  • schisandra chinensis(magnolia berry or five-flavor-fruit)
  • lactobacillus plantarum (probiotics)
  • Slippery Elm
  • triphala
  • Pulses
  • wheat bran

This is not unexpected, every substance/modifier has multiple impact.

Looking at the resulting consensus and items that are agreed to by both analysis, we have (in descending order):

  • bacillus subtilis (probiotics)
  • walnuts
  • high fiber diet
  • fruit/legume fibre
  • lactobacillus reuteri (probiotics)
  • saccharomyces cerevisiae (probiotics)
  • Nicotine
  • glycine
  • oregano (origanum vulgare, oil) |
  • pediococcus acidilactic (probiotic)
  • lactobacillus rhamnosus gg (probiotics)
  • rhubarb
  • bifidobacterium pseudocatenulatum,(probiotics)

Going over the KEGG based suggestions we see Escherichia coli at the top (typical for ME/CFS) and the next regular probiotic being Bacillus subtilis (in agreement with the above), followed by other Bacillus. Pediococcus acidilactici was listed. Further down the list we see Clostridium butyricum, Lacticaseibacillus casei (i.e. L. Casei above), Lacticaseibacillus rhamnosus. These were a pleasant surprise to see the same probiotics suggested from different models.

As a FYI, clostridium butyricum (probiotics) was on the consensus list, but mutaflor escherichia coli nissle 1917 (probiotics) was on the avoid for the consensus.

Bottom line for probiotics to try (add just one new one a week so you can see the response to each). See Simple Suggestions download below for suggested dosages or look them up on 📏🍽️ Dosages for Supplements. Using too small (almost homeopathic) dosages is a common error – the dosages on bottles are determined for profit margin (repeat business) and not from effective dosages from clinical studies.

The two downloads of the final consensus are attached below.

I would suggest getting another sample 6 weeks after implementing the above to see what the progress is.

As always, review with your medical professional before implementing.

Bottom Line

This patient history and their microbiome are in agreement. The antibiotics suggestions (off label usage) matches the history.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

Contribute your time to Citizen Science to find Gut Changers!

On Microbiome Prescription we have Look up a modifier of bacteria with many entries. It would be good for everyone if we increase the number of entries especially for atypical items.

YOU can help make it happen!

  • Find a herb, spice, food, drug of special interest to you
  • Go to https://pubmed.ncbi.nlm.nih.gov/
  • Type in the name with ” 16s microbiome “, for example, with “Vitamin K”

Click Search. you will hopefully get a few dozen (or hundreds) of studies.

Next is the long haul part. Go thru them (Example URL).

Ruminococcus ASV, a Lachnospiraceae Anerostipes ASV, two Lachnospiraceae NK4A136 group ASVs, and a Muribaculaceae ASV were enriched in the vitamin K deficient group, whereas a Bacteroides ASV was enriched in the MK4 group, and a Lactobacillus ASV in the MK9 group. 

  • Then comes the thinking part. Trying to describe the results.
    • Can you write a sentence such as “Vitamin K may increase Bacteroides  and Lactobacillus and a reduce Ruminococcus,  Anerostipes ,Muribaculaceae ” 
    • If so, include that in the file sent. (I will verify and it will serve as a double check the reading)
  • If the study was done on a person (or mouse) with a specific condition, we still include it.
  • Sometimes you will find that a substance with a common name may have multiple breakdowns.
  • Ideally, find other names of the substance and search each one.

Sending the Information to Me

I would suggest putting the links (i.e. like https://pubmed.ncbi.nlm.nih.gov/36471554/ ) in a text file or excel and sending to me. Where practical, include a sentence on the impact.

The information sent (when added to database) is available to everyone! This is citizen science! As I remarked to someone earlier today “I do not have a business model, I have a pro bono model“.

Clicking on 📚 PubMed Citations  will show the citations

The encoded data can be used to evaluate yours and others microbiome against the patterns reported.

The data is extended on entry to it’s children and it’s parent (with reduced confidence)

That is it!!! You spend the time so others do not have to and can act on their challenges with better information!

Email the lists to Research@microbiomeprescription.com